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. 2023 Aug 7;12:e85422. doi: 10.7554/eLife.85422

Megafaunal extinctions, not climate change, may explain Holocene genetic diversity declines in Numenius shorebirds

Hui Zhen Tan 1, Justin JFJ Jansen 2, Gary A Allport 3, Kritika M Garg 1,†,‡, Balaji Chattopadhyay 1,§, Martin Irestedt 4, Sean EH Pang 1, Glen Chilton 5, Chyi Yin Gwee 1,#, Frank E Rheindt 1,✉
Editors: Irby Lovette6, Christian Rutz7
PMCID: PMC10406428  PMID: 37549057

Abstract

Understanding the relative contributions of historical and anthropogenic factors to declines in genetic diversity is important for informing conservation action. Using genome-wide DNA of fresh and historic specimens, including that of two species widely thought to be extinct, we investigated fluctuations in genetic diversity and present the first complete phylogenomic tree for all nine species of the threatened shorebird genus Numenius, known as whimbrels and curlews. Most species faced sharp declines in effective population size, a proxy for genetic diversity, soon after the Last Glacial Maximum (around 20,000 years ago). These declines occurred prior to the Anthropocene and in spite of an increase in the breeding area predicted by environmental niche modeling, suggesting that they were not caused by climatic or recent anthropogenic factors. Crucially, these genetic diversity declines coincide with mass extinctions of mammalian megafauna in the Northern Hemisphere. Among other factors, the demise of ecosystem-engineering megafauna which maintained open habitats may have been detrimental for grassland and tundra-breeding Numenius shorebirds. Our work suggests that the impact of historical factors such as megafaunal extinction may have had wider repercussions on present-day population dynamics of open habitat biota than previously appreciated.

Research organism: Other

eLife digest

About 20,000 years ago, the Earth was a much colder world roamed by giant mastodons, gigantic elks, woolly mammoths and sabre-tooth tigers. Yet these imposing creatures were living on borrowed time: by the start of the Holocene, around 10,000 years later, many animals over 45kg had vanished across the Northern Hemisphere, closing the book on what is known as the Quaternary extinction event. As large grazers disappeared, the landscape likely changed too. Where open tundra and grasslands may have once dominated, woodlands and shrubs probably took over, creating ripple effects for surviving species.

These extinction events took place in a changing world, with glaciers starting to retreat about 20,000 years ago and human populations colonizing an increasing share of this planet’s land area. In fact, since the end of this last glacial maximum, ecosystems have been reshaped by a succession and a combination of climatic, historical and human-driven forces. This makes it difficult for scientists to disentangle the relative contribution of these factors on the lives of animals.

Tan et al. decided to explore this question by reconstructing how effective population sizes changed over the past 20,000 years for nine species of curlews and whimbrels. These shorebirds, which together comprise the genus Numenius, breed slowly and nest in open environments such as moorlands or tundra. Many are currently under threat.

Fluctuations in the numbers of breeding individuals affect the genetic diversity of a species, and these events leave tell-tale genetic signatures that can be uncovered through DNA analyses. Tan et al. had enough fresh and museum samples to infer these changes for five Numenius species, revealing that genetic diversity brutally dropped soon after the last glacial period ended.

At the time, humans were yet to make significant changes on their environment and a warming world should have supported population growth. Tan et al. suggest that, instead, this sharp decline is linked to the late Quaternary extinctions of large mammals: with the demise of grazing animals which could keep woodlands at bay, the shorebirds lost their open nesting grounds. This event has left its mark in the genome of existing species, with these birds still exhibiting a low level of genetic diversity that may put them at further risk for extinction.

Introduction

Rates of population decline and extinction have risen sharply during the ongoing sixth mass extinction crisis (Ceballos et al., 2020; Dirzo and Raven, 2003; Sánchez-Bayo and Wyckhuys, 2019; Stuart et al., 2004). Species distribution models based on future climate scenarios forecast that rates of endangerment will further accelerate, underscoring the need for conservation action (Thomas et al., 2004). In this era of increasing biodiversity loss, the maintenance of genetic diversity within species has become a focus of conservation as it is thought to predict evolutionary adaptability and extinction risk (Frankham, 2005; Hoban et al., 2020; Jetz et al., 2014). Modern declines in genetic diversity have been documented for a handful of species (Allentoft and O’Brien, 2010; Chattopadhyay et al., 2019; Evans and Sheldon, 2008; Garner et al., 2005), but we continue to know little about the global mechanisms of genetic diversity loss.

Anthropogenic climate change is widely recognized for its pervasive impact on biodiversity and genetic diversity (Johnson et al., 2017; Miraldo et al., 2016; Turvey and Crees, 2019). However, historical events have equally left their signature in the genetic profiles of present-day species (Hewitt, 2000). Comparative genomics of extinct versus extant species could add an important perspective to elucidating such trends in faunal endangerment (Frankham, 2005).

We used a museomic approach to investigate fluctuations in effective population size in all nine species of the migratory shorebird genus Numenius, known as whimbrels and curlews, including two species, the slender-billed curlew (N. tenuirostris) and Eskimo curlew (N. borealis), that are presumed to be extinct (Buchanan et al., 2018; Butchart et al., 2018; Kirwan et al., 2015; Pearce-higgins et al., 2017; Roberts et al., 2010; Roberts and Jarić, 2016). Members of the genus Numenius breed across the Northern Hemisphere’s tundras and temperate grasslands, and are particularly vulnerable to endangerment due to comparatively long generation times (Pearce-higgins et al., 2017).

Our objective was to characterize genetic diversity fluctuations in Numenius shorebirds, assess the relative impact of historical and anthropogenic factors on these fluctuations, and determine the mechanisms that may have had the biggest impact on their populations. Because of their dependence on open habitats, we expected the genetic diversity trends of whimbrels and curlews to track the availability of such habitats across the Late Quaternary. We also expected significant declines in genetic diversity during the late Holocene when global human activity intensified, not least because the demise of the two extinct species has been attributed to habitat loss and hunting (Committee on the Status of Endangered Wildlife in Canada, 2009; Gallo-Orsi and Boere, 2001). By testing the timing of genetic diversity fluctuations against that of important ecological events, we homed in on the factors that influenced the evolutionary trajectory of this threatened shorebird lineage over the last ~20,000 years.

Results and discussion

We sequenced 67 ancient and fresh samples across all nine Numenius species for target enrichment (Figure 1A; Supplementary file 1). After filtering for quality, a final alignment of 514,771 bp across 524 sequence loci was retained for each of the 62 remaining samples at a mean coverage of 118 X. Phylogenomic analyses using MP-EST (Liu et al., 2010) revealed two separate groups with high support, here called the ‘whimbrel clade’ and the ‘curlew clade,’ that diverged approximately 5 million years ago (Figure 1B; Figure 1—figure supplement 1A). This is the first phylogenomic tree to include all members of the genus Numenius. The use of degraded DNA from toepads of museum specimens allowed us to include the two presumably extinct taxa. Of these, the slender-billed curlew emerged as sister to the Eurasian curlew (N. arquata), a phenotypically similar species that occurs in sympatry in Central Asia (Sharko et al., 2019). On the other hand, the Eskimo curlew emerged as a distinct member of the curlew clade with no close relatives (Figure 1B). Our phylogenomic dating analyses demonstrated that 40.6% of the evolutionary distinctness (Jetz et al., 2014) of the curlew clade has been lost with the presumable extinction of the two species, and that another 15% is endangered (Figure 1B; Supplementary file 2).

Figure 1. Numenius phylogenomic relationships and Quaternary population trajectories.

(A) Breeding distribution map and sampling localities of each Numenius species (BirdLife International and Handbook of the Birds of the World, 2017; Lappo et al., 2012); wintering and migratory ranges are not shown. Colors correspond to species identities in (B). Diagonal lines denote regions with co-distributed species. Each circle represents one sample unless otherwise specified by an adjacent number. The only known breeding records of N. tenuirostris were from near the village of Krasnoperova c.10 km south of Tara, Omsk (Russia), which is denoted by a black star (★), although this might not have been the core breeding area. (B) Phylogenomic tree constructed from an alignment of 514,771 bp across 524 sequence loci. Tree topology (including bootstrap support values) and divergence times were estimated with MP-EST and MCMCTree, respectively. Only bootstrap <100 is displayed. Sample sizes for each species are given in brackets. IUCN Red List status of critically endangered (CR) and endangered (EN) species is indicated. (C) Results of demographic history reconstruction using stairway plot for selected species displayed with key climatic, biotic, and anthropogenic events. Effective population size: Line colors correspond to species identities in the tree in (B) and numbers at present time represent present-day effective population sizes. Thick lines represent the median effective population size while thin lines represent the 2.5 and 97.5 percentile estimations. The vertical gray line denotes the Last Glacial Maximum (LGM) and panels are shaded to aid reference to the time axis. Suitable breeding area: predicted suitable breeding area at LGM (22,000 years ago), mid-Holocene (6,000 years ago), and present-day (1960–1990) using Maxent. Dot colors correspond to species identities in the tree in (B). Dotted lines connecting the dots are for visualization purposes and do not represent fluctuations in the breeding area. The following panels display the timings of key climatic, biotic and anthropogenic events, including megafaunal extinction (in terms of the number of extinct genera with dotted shading denoting uncertainty in estimates; Koch and Barnosky, 2006), agricultural land use, and human population size (HYDE 3.2; Klein Goldewijk et al., 2017; Klein Goldewijk et al., 2010). Line type corresponds to geographical area (Nearctic versus Palaearctic) as denoted in the ‘Human population size’ panel.

© 2023, Lynx Edicions

Illustrations of Numenius birds in Figure 1B were reproduced with permission from Lynx Edicions. The illustrations are not covered by a CC-BY 4.0 license and further reproduction of this panel would need permission from the copyright holder.

Figure 1.

Figure 1—figure supplement 1. Principal component (PC) analysis of Numenius samples, with the percentage of variation of the two most important PCs displayed.

Figure 1—figure supplement 1.

Colors correspond to species identities in Figure 1. The four plots display PC analyses of (A) all Numenius species, (B) Eurasian whimbrel N. phaeopus (broken down by subspecies), (C) Eurasian curlew N. arquata (broken down by subspecies), and (D) Palaearctic curlews N. tenuirostris, N. madagascariensis, and N. arquata.

Figure 1—figure supplement 2. Demographic history reconstruction using stairway plot for N. borealis, showing results for two datasets, one containing all five samples and the other a subset of three samples with low missingness.

Figure 1—figure supplement 2.

Present-day effective population size of each dataset is indicated above the lines.

Figure 1—figure supplement 3. Visualization of the ecological niche model results in green corresponding to a higher probability of presence and brown corresponding to a lower probability.

Figure 1—figure supplement 3.

Gray areas show the area for which Maxent analyses were performed. Numbers below each plot represent the total suitable breeding area for each study species at the respective time periods.
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To characterize the differential impacts of extinction pressures, we reconstructed the demographic history of Numenius shorebirds. For five species with a sufficiently high sample size, we employed stairway plots (Liu and Fu, 2020) to infer fluctuations in effective population size (Ne), a proxy for genetic diversity, given that this method works well for reduced representation genomic datasets such as ours, and has a relatively high accuracy for reconstructions of diversity change in the Late Quaternary (Liu and Fu, 2020). Fluctuations in Ne were compared against key biotic and anthropogenic events of the Late Quaternary. We also accounted for climatic changes by modeling the extent of suitable breeding areas of each species under climate conditions prevalent during the present-day (1960–1990), mid-Holocene (6,000 years ago), and Last Glacial Maximum (LGM; 22,000 years ago) using the Maxent algorithm (Phillips et al., 2006).

The Last Glacial Period preceding the LGM saw ice sheets at their maximum extent (Hughes et al., 2013). During this time, tundra habitats dominated the northern latitudes and an increase in Ne in the tundra-inhabiting Eurasian whimbrel (N. phaeopus) was observed (Binney et al., 2017; Wang et al., 2021; Zimov et al., 1995; Figure 1C). Soon after, during the Pleistocene-Holocene transition, our stairway plots revealed generally sharp declines of Ne in most species despite an increase in the area of suitable breeding habitat predicted (Figure 1C). The extent of breeding habitat predicted by our ecological niche models relied on bioclimatic variables, suggesting that – paradoxically – favorable conditions for Numenius shorebirds in the lead-up to the Holocene did not trigger an increase in genetic diversity, but instead coincided with precipitous declines in Ne. A decrease in Ne could be expected during the period when most species underwent rapid range expansion shortly after the LGM (Braasch et al., 2019). However, Ne declines in all species persisted beyond the mid-Holocene up until a period when habitat availability started to resemble the levels that were prevalent just before the Anthropocene (Figure 1C; Figure 1—figure supplement 3). Therefore, the Holocene collapse of genetic diversity in Numenius shorebirds cannot be explained purely by range expansions. To understand the drivers of Ne declines in Numenius shorebirds, factors other than climate change would need to be considered.

During the Pleistocene-Holocene transition (starting at roughly 20,000 years ago), a mass extinction of megafaunal mammals (≥44 kg) was underway, known as the Late Quaternary Extinctions (Hedberg et al., 2022; Johnson, 2009), with most becoming extinct by 10kya (Figure 1C; Koch and Barnosky, 2006; Stuart, 2015). Megafaunal mammals are ecosystem-engineers that maintain open landscapes such as temperate grasslands and steppes through grazing, browsing, and physical impacts (Bakker et al., 2016; Goheen et al., 2018). During the intervening period between their extinction and the spread of ungulate domestication, there would have been no functional replacements for these ecosystem services (Hedberg et al., 2022; Lundgren et al., 2020). Open grasslands would have been subject to increasing forest succession (Johnson, 2009) and the amount of suitable habitat for Numenius shorebirds might have been less than predicted by forecasts relying only on bioclimatic variables. Therefore, genetic diversity fluctuations in Numenius shorebirds run counter to expectations based on natural climate change and seem to be better explained by the demise of the ecosystem-engineers that would have helped maintain shorebird breeding habitats.

By the late Holocene, the genetic diversity of most Numenius shorebirds stabilized at a time when anthropogenic impact was only starting to expand across the Northern Hemisphere with a steep rise in human population and land conversion for agriculture (Figure 1C). The timing of these events is inconsistent with the hypothesis that direct anthropogenic activity has been the main cause of genetic diversity declines in Numenius (Crisp et al., 2011). Events unrelated to modern anthropogenic pressure seem to have played a bigger role in the diversity declines observed in Numenius shorebirds (Lucena-Perez et al., 2020; Nadachowska-Brzyska et al., 2015). It is possible that additional adverse effects caused by more recent anthropogenic impacts are not yet reflected in the genomes investigated, perhaps exacerbated by shorebirds’ long generation times.

At present, members of the curlew clade, which predominantly breed in temperate grasslands at lower latitudes, generally exhibit levels of Ne that are lower than those of the higher latitude-breeding whimbrels (Figure 1C). Temperate grasslands face far greater anthropogenic pressures from land use than the northerly tundra (Pimm et al., 2014), contributing to further declines in curlews more so than in whimbrels. Strong differences in the demographic histories uncovered within the whimbrel clade (specifically between N. phaeopus and N. hudsonicus) probably reflect the uneven distribution of glacial extent and impact across the northern hemisphere, with North America being covered by extensive ice sheets during the LGM while most of Siberia remained ice-free, allowing for a disproportionate increase of Ne in N. phaeopus. Genetic diversity estimates were lowest in the presumably extinct slender-billed curlew N. tenuirostris (Figure 1C). Low genetic diversity may contribute to a species’ extinction risk (Frankham, 2005; Spielman et al., 2004), although such links must be examined for each species independently and could possibly be conflated with other factors such as total population size (Evans and Sheldon, 2008; Teixeira and Huber, 2021).

Our study uncovers substantial declines in genetic diversity in curlews and whimbrels across the Late Quaternary. Analysing Ne fluctuations over time allowed us to test which factors may have coincided with genetic diversity declines. Of the factors investigated, megafaunal extinctions—not natural climate change in the post-glacial period—best explain these declines and may have had cascading effects on species’ evolutionary trajectories that continue to impact them to the present-day. Future work should examine additional factors such as non-breeding habitat availability, although this factor is unlikely to account for post-LGM diversity declines in Old World shorebirds as the total length of coastlines would have increased in areas such as Southeast Asia where rising sea levels have led to the inundation of large shelf areas and created complex archipelagos such as Indonesia (De Groeve et al., 2022; Sarr et al., 2019). Our results underscore that grassland biomes and their biota face unique challenges that warrant more conservation attention (Ceballos et al., 2010; Chan et al., 2005; Helm et al., 2009; Nakahama et al., 2018; Török et al., 2016; Wesche et al., 2016). Our work demonstrates that relatively brief evolutionary events, such as the Late Quaternary Extinctions of megafauna, may have long-lasting evolutionary effects on populations, in our case for roughly ~10,000 years. The plight of Numenius shorebirds is a sobering reminder of the importance of conserving remaining genetic diversity to ensure the resilience of our planet’s biota.

Materials and methods

Taxon sampling

We acquired samples for all nine species in the genus Numenius, encompassing most of the known subspecies. Species and subspecies identities are as provided by the source museum or institution (Supplementary file 1) or assigned in reference to known breeding and wintering locations (Birds of the World, 2022). We also included one common redshank Tringa totanus as an outgroup for phylogenetic rooting. All samples were acquired through museum loans except for an individual of the endangered subspecies N. phaeopus alboaxillaris that was sampled during fieldwork by GAA (Supplementary file 1). Where possible, we acquired fresh samples (tissue or blood) because of their higher DNA quality. To represent rarely-sampled or presently-rare taxa for which no fresh samples were available, we acquired toepad material from historic museum specimens and applied ancient DNA methods.

Baits design for target capture

We used the Calidris pugnax genome (accession no. GCA_001458055.1) (Küpper et al., 2015) to design baits to capture selected exons. We used EvolMarkers (Li et al., 2012) to identify single-copy exons conserved between C. pugnax, Taeniopygia guttata (accession no. GCF_003957565.1; released by the Vertebrate Genomes Project) and Ficedula albicollis (accession no. GCA_000247815.1). Exons longer than 500 bp with a minimum identity of 55% and an e-value 10e-15 were isolated with bedtools 2.28.0 (Quinlan and Hall, 2010), forming our target loci. Only target loci with 40–60% GC content were retained and any overlapping loci were merged (Quinlan and Hall, 2010). Target loci with repeat elements were then filtered out in RepeatMasker 4.0.6 (Smit et al., 2015). We arrived at a final set of 565 unique target loci with a mean length of 970 bp. These target loci were used to design 19,003 100 bp-long biotinylated RNA baits at 4 X tiling density (MYcoarray/Arbor Biosciences, USA).

Laboratory methods

Both fresh and historic samples were subjected to DNA extraction, followed by library preparation and target enrichment, with slight modifications for various sample types to optimize yield. DNA extractions of fresh samples were performed using the DNEasy Blood & Tissue Kit (Qiagen, Germany) with an additional incubation step with heat-treated RNase. Extractions for historic samples were performed using the same kit but with modifications (Chattopadhyay et al., 2019). Historic samples were washed with nuclease-free molecular grade water before extraction and dithiothreitol was added to the digestion mix. DNA precipitation was performed for at least 12 hr and MinElute Spin Columns were used for elution (Qiagen, Germany). Historic samples were processed in a dedicated facility for highly degraded specimens.

DNA extracted from fresh samples was sheared via sonification using Bioruptor Pico (Diagenode, Belgium) to a target size of 250 bp. DNA extracts from historic samples were generally smaller than the target size; hence no further shearing was performed. Whole-genome libraries were prepared using the NEBNext Ultra II DNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, USA) with modifications for subsequent target enrichment. For fresh samples, adaptor concentrations were kept constant regardless of the DNA input amount. Size selection with AMPure XP beads (Beckman Coulter, USA) was performed for 250 bp insert sizes. The reaction was split into two equal parts before polymerase chain reaction (PCR) amplification and combined afterward for subsequent steps. For historic samples, a formalin-fixed, paraffin-embedded (FFPE) DNA repair step was first performed using NEBNext FFPE DNA Repair Mix (New England BioLabs). A 10-fold dilution of adaptors was used, and no size selection was performed. For both types of samples, twelve cycles of PCR amplification were performed.

Target enrichment was carried out following the MYbaits manual (Arbor Biosciences, USA) with modifications (Chattopadhyay et al., 2019). We used 1.1 uL of baits per fresh sample (~5 X dilution) and 2.46 uL of baits per ancient sample (~2 X dilution). For fresh samples, hybridization of baits and target loci was performed at 65 °C for 20 hr and 15 cycles of amplification were performed. For historic samples, hybridization was performed at 60 °C for 40 hr, and 20 cycles of amplification were performed. For both fresh and historic samples, one negative control sample was added for each batch of extraction, library preparation, and target enrichment. Extracts, whole-genome libraries, final enriched libraries, and all negatives were checked for DNA concentration on a Qubit 2.0 Fluorometer using the Qubit dsDNA HS assay kit (Thermo Fisher Scientific, USA), and for fragment size on a Fragment Analyzer using the HS NGS Fragment kit (1–6000 bp) (Agilent Technologies Inc, USA). Final enriched libraries were pooled at equimolar quantities. A total of 67 enriched libraries were sequenced, with fresh and historic samples sequenced separately on two Illumina HiSeq 150 bp paired-end lanes (NovogeneAIT, Singapore).

Reference genome assembly

We obtained a sample of N. phaeopus (ZMUC 112728) from the Natural History Museum of Denmark, Copenhagen, for reference genome assembly. Its genomic DNA was extracted using the KingFisher Duo Prime Magnetic Particle Processor (Thermo Fisher Scientific, USA) and the KingFisher Cell and Tissue DNA Kit (Thermo Fisher Scientific). A linked-read sequencing library was prepared using the Chromium Genome library kits (10 X Genomics) and sequenced on one Illumina Hiseq X lane at SciLifeLab Stockholm (Sweden). The de novo assembly analysis was performed using 10 X Chromium Supernova (v. 2.1.1). Reads were filtered for low quality and duplication, while assemblies were checked for accuracy and coverage and the best assembly was selected based on the highest genome coverage with the fewest errors. The final genome had a size of 1.12 Gb at a coverage of 50 X with N50=3504.2 kbp.

Raw reads processing

Raw reads were checked for sequence quality in FastQC 0.11.8 (Babraham Bioinformatics) and trimmed to remove low-quality termini and adaptors in fastp 0.20.0 (Chen et al., 2018). We retained reads with a minimum length of 36 bp and set a phred quality threshold of 20. Retained reads started at the first base satisfying minimum quality criteria at the 5’-end and were truncated wherever the average quality fell below the threshold in a sliding window of 5 bp. Duplicates were removed using FastUniq 1.1 (Xu et al., 2012) before sequence quality, duplication rate, and adaptor content were checked again in FastQC. We employed FastQ Screen 0.14.0 (Wingett and Andrews, 2018) to assign the source of DNA against a list of potential contaminants. We aligned reads to our assembled Numenius phaeopus genome, Homo sapiens (accession no. GCF_000001405.39), and a concatenated database of all bacterial genomes available on GenBank (National Center for Biotechnology Information (NCBI), 1988). Only reads that mapped uniquely to the N. phaeopus genome were retained. Reads were sorted and re-paired using BBtools 37.96 (Bushnell, 2014). Downstream bioinformatic procedures were split into single nucleotide polymorphism (SNP)-based and sequence-based analyses.

SNP calling

For SNP-based analyses, reads were aligned to the target sequences used for bait design with bwa-mem 0.7.17 (Li, 2013). The output alignment files were converted to bam files (view) and sorted by coordinates (sort) using SAMtools 1.9 (Li et al., 2009). Alignments were processed in Picard 2.20.0 (Picard tools, Broad Institute, Massachusetts, USA) to add read group information (AddOrReplaceReadGroups), and another round of duplicate identification was performed (MarkDuplicates) before alignment files were indexed (BuildBamIndex). The reference file of target sequences was indexed in SAMtools (faidx) and a sequence dictionary was created in Picard (CreateSequenceDictionary). To improve SNP calling accuracy, indel realignment was performed in GATK 3.8 (McKenna et al., 2010) (RealignerTargetCreator, IndelRealigner). We inspected historic DNA alignments in mapDamage 2.0.9 (Jónsson et al., 2013) and trimmed up to 5 bp from the 3’ ends of both read to minimize frequencies of G to A misincorporation (<0.1) and soft clipping (<0.2). Finally, alignments were checked for quality and coverage in QualiMap 2.2.1 (Okonechnikov et al., 2016).

We first generated likelihoods for alignment files in BCFtools 1.9 (Li, 2011) (mpileup), skipping indels. Using the same program, we then called SNPs (call) for all Numenius samples using the multiallelic and rare-variant calling model. Called SNPs were filtered in VCFtools 0.1.16 (Danecek et al., 2011) to retain sites with quality values >30, mean depth 30–150, minor allele frequency ≥0.02, and missing data <5%, in this order. Missingness and depth of sites and individuals, respectively, were quantified for SNPs called. We removed eight individuals from downstream analyses due to a combination of high missing data (>0.4%) and low coverage (<36 X), yielding a SNP set representing 58 samples. A Perl script (rand_var_per_chr.pl) was used to call one SNP per locus to avoid calling linked SNPs (Caballero, 2018). SNPs were further screened for linkage disequilibrium in PLINK 1.9 (Purcell et al., 2007) using a sliding window of 50 SNPs with a step size of 10 and an r2 correlation threshold of 0.9. We also screened for the neutrality of SNPs in BayeScan 2.1 (Foll and Gaggiotti, 2008) using default settings. We additionally created a dedicated SNP set per species for input into demographic history reconstruction using the method described above, but without minor allele frequency cut-offs and with all SNPs at each locus retained.

Population genomic analyses

We conducted principal component analysis (PCA) for all Numenius samples using the R package SNPRelate 1.16.0 (R Development Core Team, 2022; Zheng et al., 2012; Figure 1—figure supplement 1A). We did not detect any considerable genomic differentiation along subspecific delimitations within N. phaeopus and N. arquata, whose population-genetic structure had been resolved with thousands of genome-wide markers in a previous study (Tan et al., 2019; Figure 1—figure supplement 1B, C). Samples of N. p. alboaxillaris and N. a. suschkini, two Central Asian taxa that are described in the literature as phenotypically differentiated (Allport, 2017; Engelmoer and Roselaar, 1998a; Engelmoer and Roselaar, 1998b; Morozov, 2000), did not emerge as genomically distinct from other conspecific populations and are likely to represent ecomorphological adaptations controlled by few genes. Sample NBME 1039630, which had been labeled as N. tenuirostris, and sample MCZR 15733, which was initially identified as an N. arquata that shares many morphological features with N. tenuirostris, clustered with N. arquata samples (Figure 1—figure supplement 1D; Supplementary file 1). Both samples were assigned to N. arquata in subsequent phylogenetic analyses.

Sequence assembly

For sequence-based analyses, reads were assembled using HybPiper 1.3.1 (Johnson et al., 2016) (reads_first) to yield sequence loci. Firstly, reads were mapped to the target sequences using BWA 0.7.17 (Li and Durbin, 2009) and sorted by gene. Contigs were then assembled from the reads mapped to respective loci using SPAdes 3.13 (Bankevich et al., 2012) with a coverage cutoff value of 20. Using Exonerate 2.4.0 (Slater and Birney, 2005), these contigs were then aligned to the target sequences and sorted before one contig per locus was chosen to yield the final sequences. We inspected locus lengths (get_seq_lengths) and recovery efficiency (hybpiper_stats) across all loci. We then investigated potentially paralogous loci (paralog_investigator) by building gene trees using FastTree 2.1.11 (Price et al., 2010) (paralog_retriever), leading to the removal of 10 loci. Finally, sequences from the same loci were retrieved from all samples to generate a multisequence alignment for each locus (retrieve_sequences.py). All loci retained were present in at least 80% of individuals and constituted at least 60% of the length of total target loci. In summary, a total of 525 loci with a mean length of 969 bp (492–6,054 bp) were recovered from 62 samples.

Phylogenomic analyses using sequence data

Multisequence alignment was performed for each locus using MAFFT 7.470 (Katoh and Standley, 2013), allowing for reverse complement sequences as necessary. Alignments were checked for gaps using a custom script, and loci with >35% gaps were removed from downstream analyses. A total alignment length of 514,771 bp was obtained.

Phylogenomic analyses were performed on a concatenated dataset as well as on individual gene trees. Concatenation was performed with abioscript 0.9.4 (Larsson, 2010) (seqConCat). For the concatenated dataset, we constructed maximum-likelihood (ML) trees using RAxML 8.2.12 (Stamatakis, 2014) with 100 alternative runs on distinct starting trees. We applied the general time reversible substitution model with gamma-distributed rate variation among sites and with the estimation of the proportion of invariable sites (GTR + I + G) (Abadi et al., 2019; Arenas, 2015).

For individual gene trees, the best substitution model for each locus was determined using jModelTest 2.1.10 (Darriba et al., 2012) by virtue of the corrected Akaike information criterion value. We then constructed ML trees in PhyML 3.1 with the subtree pruning and regrafting algorithm, using 20 initial random trees. We performed 100 bootstrap replicates with ML estimates for both proportions of invariable sites and the value of the gamma shape parameter. Individual gene trees were then rooted with Newick Utilities 1.3.0 (Junier and Zdobnov, 2010). We removed one locus from downstream analyses due to the absence of an outgroup sequence such that 524 loci were retained across 62 samples.

Species tree analyses were performed using the rooted gene trees in MP-EST 1.6 (Liu et al., 2010), without calculation of triple distance among trees. We grouped samples by species and performed three runs of 10 independent tree searches per dataset (Cloutier et al., 2019). To calculate the bootstrap values of the species tree, we performed multi-locus, site-only resampling (Mirarab, 2014) from the bootstrap trees’ (100 per gene) output from PhyML. The resulting 100 files, each with 100 bootstrap trees, were rooted and species tree analyses were performed in the same manner for each file in MP-EST. The best tree from each run was identified by the best ML score and compiled. Finally, we used the majority rule in PHYLIP 3.695 (Felsenstein, 2009) to count the number of times a group descending from each node occurred so as to derive the bootstrap value (consense).

For the estimation of divergence times, we applied MCMCtree and BASEML (dos Reis and Yang, 2011), a package in PAML 4.9e (Yang, 2007). To prepare the molecular data from 62 samples and 524 loci, we compiled the DNA sequence of each sample and combined all samples onto separate rows of the same file. We then obtained consensus sequences for each species using Geneious Prime 2020.2 (Kearse et al., 2012), with a majority support threshold of 50% and ignoring gaps. We visually checked the resulting consensus sequences to ensure that ambiguous bases remained infrequent. Consensus sequences were organized by loci as per the input format for MCMCtree. We then prepared the input phylogenetic tree using the topology estimated in MP-EST with calibrations of the two most basal nodes, namely between our outgroup (Tringa totanus) and all Numenius species, as well as that between the whimbrel and curlew clades within Numenius. Due to a lack of known fossils within the genus Numenius, we were unable to perform fossil node calibrations. Instead, we utilized p-distance values calculated from the COI sequences of Numenius species. Specifically, we applied the bird COI mutation rate of 1.8% per million years (Lavinia et al., 2016) and converted mean, maximum, and minimum p-distance values of both nodes to time (100 million years ago (MYA)). We maintained a conservative position and scaled the COI-based timings by a factor of two to obtain the final lower and upper bounds of node timings. We used the default probability of 0.025 that the true node age is outside the calibration provided.

To run MCMCtree, we first calculated the gradient and Hessian matrix of the branch lengths with the GTR substitution model applied, using default values of gamma rates and numbers of categories (mcmctree-outBV.ctl). We then performed two independent Markov chain Monte Carlo (MCMC) samplings of the posterior distribution of divergence times and rates (mcmctree.ctl). All default values were used except that a constraint on the root age was set to <0.3 (100 MYA). We also varied the prior for the birth-death process with species sampling and ensured that time estimates are not affected by the priors applied Dos and Yang, 2019. We then performed convergence diagnostics for both runs in R to ensure that posterior means are similar among multiple runs, while checking that the parameter space has been explored thoroughly by the MCMC chain. Finally, we conducted MCMC sampling from the prior with no data to check the validity of priors used by comparing them with the posterior times estimated. Again, two independent MCMC samplings were performed with convergence diagnostics.

Phylogenetic trees were visualized in FigTree 1.4.4 (Rambaut, 2018) with bootstrap values and node ages (MYA) including the 95% credibility intervals. Evolutionary distinctness and phylogenetic diversity were calculated for each branch (Jetz et al., 2014) using the divergence times estimated in MCMCTree.

Demographic history reconstruction

We derived trends in effective population size using stairway plot 2.1.1, which uses the SNP frequency spectrum and is suitable for reduced representation datasets (Liu and Fu, 2020; Patton et al., 2019). From the dedicated SNP sets that were created without minor allele frequency cut-off, we calculated a folded site frequency spectrum using vcf2sfs.py 1.1 (Marques et al., 2019). We assumed a mutation rate per site per generation of 8.11 e-8 , as estimated for shorebirds in the same order as Numenius (Charadriiformes) (Wang et al., 2019), and applied the following generation times respectively: N. americanus 7 years, N. arquata 10 years, N. hudsonicus 6 years, N. phaeopus 6 years, N. tenuirostris 5 years (Bird et al., 2020; IUCN, 2020). We ran a stairway plot on all species, applying the recommended parameters.

Stairway plot is expected to perform at its highest accuracy in the reconstruction of demographic history in the recent rather than distant past. However, the definition of the recent past varies from anywhere between 30 generations to ~40,000 generations before the present (Liu and Fu, 2015; Patton et al., 2019). We did not set a cutoff for the time period investigated but let it be determined by the program itself. Additionally, we omitted reconstructions of the last 10 steps to avoid overinterpretation of the distant past (Liu and Fu, 2015). We only displayed the results from the time period for which there was data across all species, and only for four species represented by five or more samples (stairway_plot_es Stairbuilder), as recommended for accurate inference (X. Liu, personal communication, October 14, 2020). We later also included N. americanus, for which we had four samples, as its sample size did not appear to affect the reliability of the results (Figure 1). We were unable to include the remaining species (N. borealis, N. tahitiensis, and N. minutus) as their demographic history reconstructions were clearly affected by a lack of sufficient sample size. For N. borealis, two out of the five samples showed high missingness, with adverse effects on stairway plot analyses, both in runs including all five samples and those that excluded the two samples of high missingness (Figure 1—figure supplement 2). Our ability to trial a large number of samples for laboratory work was also limited by the availability of target enrichment baits.

We attempted to infer demographic history using sequentially Markovian coalescent-based methods, which are more reliable for older timescales, to corroborate our stairway plot results (Patton et al., 2019). In particular, we used the Pairwise Sequentially Markovian Coalescent (PSMC) model (Li and Durbin, 2011) as it has been successfully applied to reduced-representation datasets (Liu and Hansen, 2017). This method allows for analyses of all species as only one sample per species is required as input. However, given the constraints created by the sampling density of our target enrichment dataset, we were unable to run PSMC successfully.

Ecological niche modeling

We performed ecological niche modeling (Anderson et al., 2011) to predict the extent of suitable breeding areas for species across the duration of our demographic history reconstruction. We were able to do so for each species in the stairway plot except Numenius tenuirostris due to the paucity of confirmed breeding records. We obtained species occurrence data from eBird, 2021 and the Global Biodiversity Information Facility (GBIF; using only records with coordinate uncertainty <1,000 m) (GBIF.org, 2022a; GBIF.org, 2022b; GBIF.org, 2022c; GBIF.org, 2022d; GBIF.org, 2022e; GBIF.org, 2022f). For N. phaeopus, we also included confirmed breeding localities from Lappo et al., 2012 to improve the sample size. Species occurrence data from various sources were combined and further filtered (Supplementary file 3A). Occurrence points were filtered by month to retain only records in peak breeding months of respective species (Birds of the World, 2022). For species with sufficient occurrence points, occurrence points were also filtered by year to match the time range of the climatic variables, i.e., 1960–1990. Otherwise, occurrence records from all years were used to maximize sample size. For species that span the entire Palaearctic (N. phaeopus and N. arquata), sampling density was much higher in Europe. To account for the extreme sampling bias, in addition to generating a kernel density estimate (see next paragraph), occurrence records within Europe for these two species were randomly down-sampled to match sampling density across the rest of the Palearctic. Occurrence records outside of the known breeding area of each species were removed (BirdLife International and Handbook of the Birds of the World, 2017; Lappo et al., 2012). Finally, to reduce spatial autocorrelation, occurrence records were thinned using a 50 km buffer (Aiello-Lammens et al., 2015).

To account for sampling bias specific to shorebirds, such as those of this study, we generated a kernel density estimate using the R package spatialEco 1.3–7 (Evans, 2021) based on the occurrences of species within Scolopacidae. The kernel density estimates were then used to inform background point selection (i.e. matching sampling bias) (Kramer-Schadt et al., 2013). For each species, we further limited the sampling of background points to areas outside a 10 km buffer around occurrence points and within a 500 km buffer around the known breeding area using the R packages terra 1.5–21 and raster 3.5–15 (Hijmans, 2022b; Hijmans, 2022a). A total of 10,000 background points were then sampled without replacement for each species.

All 19 bioclimatic variables (raster; 2.5 arcmin resolution of ~4.5 km) from WorldClim 1.4 (Hijmans et al., 2005) were obtained for the present-day (1960–1990), mid-Holocene (6,000 years ago), and LGM (22,000 years ago). Bioclimatic variables were then prepared for input into Maxent 3.4.4 using QGIS 3.4 QGIS.org, 2022 following De Alban, 2022. Polygon shapefiles were first created for each species, which included the present-day breeding distribution as well as areas south of that to accommodate for potential shifts in distribution around the LGM. These polygons were then used to crop the bioclimatic variable raster for each respective species (Conrad et al., 2015).

We applied Maxent 3.4.4, which makes use of presence-only data and environmental data to model species’ geographical distributions (Phillips et al., 2006). Species-specific Maxent analyses were performed using the respective breeding occurrence records, background points, and present-day bioclimatic variables of each species. To reduce collinearity among predictors, we removed predictors with a high variance inflation factor (>3) for each species. To facilitate parameter tuning, 20 candidate models were built for each species and evaluated using the R package ENMeval 2.0.3, testing combinations of feature classes (L, LQ, LQH, LQPH) and regularisation multipliers (0.5, 1, 2, 3, 4) (Kass et al., 2021; Merow et al., 2013). To test for model overfitting and transferability, candidate models were cross-validated using the ‘block’ partitioning technique (i.e. occurrences and background points were partitioned into four spatial blocks, where occurrence numbers among partitions are equal) (Fourcade et al., 2018; Muscarella et al., 2014). Candidate models with omission rates (minimum training presence threshold) exceeding 0.2 were rejected. The candidate model with the highest area under the receiver-operator curve (AUC) was selected as the final model (Supplementary file 3B) and used to predict suitable breeding areas under present-day, mid-Holocene, and LGM climate conditions (Figure 1—figure supplement 3).

Predicted species distributions were visualised in R (R Development Core Team, 2022). We performed a binary classification of predicted occurrence probability using the maximum sum of sensitivity plus specificity threshold (Liu et al., 2013) and calculated suitable breeding area using the R package raster 3.5–15.

Acknowledgements

We thank the following personnel and institutions for their generous contribution of samples (Supplementary file 1): Paul Sweet and Thomas Trombone at the American Museum of Natural History (AMNH, New York); Robert Palmer and Leo Joseph at the Australian National Wildlife Collection (ANWC, Canberra); Molly Hagemann at the Bernice Pauahi Bishop Museum (BPBM, Hawaii); David Allan at the Durban Natural Science Museum (DNSM, Durban) and Celine Santillan who assisted in sample transport; Ben Marks at the Field Museum of Natural History (FMNH, Chicago); Foo Maosheng at the Lee Kong Chian Natural History Museum (LKCNHM, Singapore); Carla Marangoni and Gloria Svampa at the Museo Civico di Zoologia (MCZR, Rome); Henry McGhie at the University of Manchester, Manchester Museum (MMUM, Manchester); Robert Prŷs-Jones, Mark Adams, Alex Bond, Ari Benucci and Douglas Russell at the Natural History Museum, London (NHMUK, Tring); Manuel Schweizer at the Naturhistorisches Museum der Bürgergemeinde Bern (NMBE, Bern); Bob McGowan at the Natural Museum of Scotland (NMS, Edinburgh); Joanna Sumner at Museums Victoria (NMV, Melbourne); Angela Ross at the National Museums NI (NMNI, Northern Ireland) and David Allen and Graeme Buchanan who assisted in sample transport; Jan Bolding Kristensen at the Natural History Museum of Denmark (SNM, Copenhagen); José Alves and Camilo Carneiro at the University of Iceland (UOI, Reykjavik); Sharon Birks at the Burke Museum, University of Washington (UWBM, Seattle); Pavel S Tomkovich, Dmitry Shitikov and Vladimir Sotnikov at the Zoological Museum of Moscow State University (ZMMU, Moscow); and Fyodor Kondrashov and Lisa Chilton who assisted in sample transport. Fletcher Smith assisted in Mozambique (with permission from Lucilia Chuquela, Museu de História Natural and Universidade Eduardo Mondlane, Maputo) with further assistance from Rebecca and Cyril Kormos, Patricia Zurita and Vinayagan Dhamarajah. HZT acknowledges Elize Ying Xin Ng, Pratibha Baveja, Yong Chee Keita Sin, Shivaram Rasu, Dominic Yong Jie Ng, Meng Yue Wu, Liu Xiaoming, and Jose Don De Alban for assistance with laboratory procedures and analyses. 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.

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Contributor Information

Frank E Rheindt, Email: dbsrfe@nus.edu.sg.

Irby Lovette, , United States.

Christian Rutz, University of St Andrews, United Kingdom.

Funding Information

This paper was supported by the following grants:

  • Swedish Research Council 2019-03900 to Martin Irestedt.

  • DBT-Ramalingaswami Fellowship BT/HRD/35/02/2006 to Kritika M Garg.

  • South East Asian Biodiversity Genomics (SEABIG) Grant WBS R-154-000-648-646 to Balaji Chattopadhyay.

  • South East Asian Biodiversity Genomics (SEABIG) Grant WBS R-154-000-648-733 to Balaji Chattopadhyay.

  • Trivedi School of Biosciences, Ashoka University to Balaji Chattopadhyay.

  • Singapore Ministry of Education Tier 2 grant WBS R-154-000-C41-112 to Frank E Rheindt.

Additional information

Competing interests

No competing interests declared.

Author contributions

Conceptualization, Resources, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing – review and editing.

Conceptualization, Resources, Writing – review and editing.

Conceptualization, Resources, Writing – review and editing.

Resources, Supervision, Methodology, Writing – review and editing.

Resources, Supervision, Methodology, Writing – review and editing.

Resources, Funding acquisition, Writing – review and editing.

Formal analysis, Methodology, Writing – review and editing.

Resources, Writing – review and editing.

Supervision, Methodology, Writing – review and editing.

Conceptualization, Resources, Supervision, Funding acquisition, Visualization, Methodology, Writing - original draft, Project administration, Writing – review and editing.

Additional files

Supplementary file 1. Sampling information.

Legend: Details of samples collected for this study.

elife-85422-supp1.docx (56.2KB, docx)
Supplementary file 2. Evolutionary distinctness of Numenius species.

Legend: Evolutionary distinctness, phylogenetic diversity, and evolutionarily distinct and globally endangered (EDGE) scores of Numenius species.

elife-85422-supp2.docx (44.6KB, docx)
Supplementary file 3. Ecological niche modeling information.

Legend: Details of occurrence points, parameters, and results of ecological niche modeling using Maxent.

elife-85422-supp3.docx (46.5KB, docx)
MDAR checklist

Data availability

DNA reads generated in this study are available on Sequence Read Archive under BioProject PRJNA742889. The reference genome generated in this study is available at DDBJ/ENA/GenBank as a Whole Genome Shotgun project under the accession JARKVS000000000. The version described in this paper is version JARKVS010000000. Pipelines and analysis codes are available on GitHub: https://github.com/tanhuizhen/Numenius_Target-enrichment_Analyses (copy archived at Tan, 2023).

The following datasets were generated:

Tan HZ, Jansen JFJ, Allport GA, Garg KM, Chattopadhyay B, Irestedt M, Pang SEH, Chilton G, Gwee CY, Rheindt FE. 2023. Numenius target enrichment libraries. NCBI BioProject. PRJNA742889

Tan HZ, Jansen JFJ, Allport GA, Garg KM, Chattopadhyay B, Irestedt M, Pang SEH, Chilton G, Gwee CY, Rheindt FE. 2023. Numenius phaeopus reference genome. NCBI GenBank. JARKVS000000000

The following previously published datasets were used:

Project Vertebrate Genomes 2019. Taeniopygia guttata (zebra finch) genome sequencing and assembly, primary haplotype. NCBI Assembly. GCA_003957565.1

University Uppsala 2013. Ficedula albicollis Genome sequencing and assembly. NCBI Assembly. GCA_000247815.2

Küpper et al. 2015. Genome assembly of the ruff (Philomachus pugnax) NCBI Assembly. GCA_001458055.1/

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Editor's evaluation

Irby Lovette 1

This study uses genomic inferences to reconstruct past population sizes of whimbrel and curlew shorebirds, along with niche modeling approaches, to explore changes in those populations over millenia. Steppe-dependent breeding species appear to have declined more prominently than species that breed in other habitats. The coincident timing of these declines of steppe-dependent breeding shorebirds, and the extinction of the mammalian megafauna that likely maintained that habitat, raises the intriguing possibility that those mammalian extinctions had broad effects on these shorebirds and the entire community of steppe-dependent organisms.

Decision letter

Editor: Irby Lovette1

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]

Thank you for submitting the article "Historical climate change and megafaunal extinctions linked to genetic diversity declines in shorebirds" for consideration by eLife.

Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Irby Lovette as the Reviewing Editor, and a Senior Editor. The reviewers have opted to remain anonymous.

Comments to the Authors:

We are sorry to say that, after consultation with the reviewers, we have decided that your article is not suitable in its present form for publication in eLife. That said, we would be willing to consider a fresh submission of a thoroughly revised version that includes additional data and carefully addresses the reviewers' other concerns.

All reviewers were intrigued by your study and found much to admire in it. The main limitations are those that are clearly identified in the reports below, namely: (1) the fact that the Ne analyses are available for only a subset of the species of interest; and (2) concerns that the presentation of these results extrapolates beyond the available data in assigning causality to megafaunal effects on broadscale habitat changes. Our sense is that these two critiques are related, in that having a small sample size of demographic reconstructions makes it hard to test rigorously for a temporal association of the type predicted by the megafaunal hypothesis.

The Guest Reviewing Editor notes that there are three N. borealis specimens at Cornell, and that they would be happy to sample them for you and facilitate a loan, should you want to use them to augment your sample size of that extinct taxon.

Reviewer #1:

Overall I am very enthusiastic about this study, which has a high degree of novelty. In particular I have never before heard of the potential link between megafaunal extinction around the time of the LGM, broadscale habitat change as a direct consequence, and population declines in steppe-breeding shorebird species. Some of the links in this chain of causality will be hard to test rigorously, but the hypothesis is fascinating and the demographic data summarized in this submission are consistent with this pattern.

The data and analysis methods seem generally robust and appropriate. The phylogenetic analyses could have been done in many ways (and those of us from this discipline love to quibble about these nuances), but the resulting tree here seems robust in terms of topology and underlying data depth. The phylogeny includes some interesting new information on the affinities of these species to one another.

For me, the real meat of this paper is in the stairway plots of historical Ne, as summarized in Figure 1C. The four species included there each have a distinctively different pattern of Ne over time. Since these analyses and estimates are so integral to the paper and its inferential conclusions, it may be worthwhile to explore/address whether they have relevant biases or limitations. For example, Patton et al. (2019) (Contemporary Demographic Reconstruction Methods Are Robust to Genome Assembly Quality: A Case Study in Tasmanian Devils) report that these particular methods are most reliable at estimating only fairly recent (30 generations) Ne's.

My primary critique of this submission is that the historical demographic estimates are made for less than half (4 of 9) of these shorebird species. The within-species sample size cutoff of n=5 for these estimates means that several additional species could have been included if just one more sample was available. I understand the difficulty of sourcing material for some of these species, but Ne stairway plots for more taxa would substantially elevate the inferential power of this cross-taxon comparison in which it seems that tundra-breeding species might have a different pattern than temperate-breeding species.

With a greater sample of species to work with, it might then be possible to formally test whether species with the largest predicted shifts in breeding habitat show corresponding changes in estimated Ne. Such a result would substantially bolster the inferential argument presented here about that potential relationship.

In terms of presentation, the paper jumps between historical climate change at recent geological time scales, and current anthropogenic climate change. The two extinct curlew species in this clade almost certainly went extinct owing to primary causes other than anthropogenic climate change. Past effects of climate and broad-scale habitat change are certainly relevant, but the chain of causality presented here could be improved.

Some statements about past processes are presented as facts, whereas my understanding is that they are still subject to debate. In particular the causality of human hunting>megafaunal extinction>widespread habitat change from grasslands to forest is more of a hypothesis. Similarly, the evidence for lower genetic diversity creating higher extinction risk is fairly nuanced. In presenting their own results, the authors may want to be slightly less declarative about causality, such as in saying "Our study revealed that climatic and environmental changes impacted the genetic diversity of curlews" when the data are corelative and based on only a couple of taxa.

In general I recommend focusing more tightly on the inferences about the potential relationship between past Ne and broadscale habitat change during the Pleistocene and thereafter. These are fascinating and intriguing enough on their own, but they are indeed highly inferential. In terms of present and future risk, anthropogenic climate change is probably not among the greatest potential drivers of population declines and possible extinction in this group of shorebirds.

Recommendations for the authors:

I recommend substantially reworking the abstract to make it less general and to highlight even more the truly novel components of this study.

If you decide to add more genomic data, we have two borealis specimens in the Cornell Museum of Vertebrates that I would be happy to sample for you (though I do understand the challenges of adding data, doing the reanalyses, and arranging all of the permits to get samples from here to there…). I really do wish that you could add at least borealis and americanus to the stairway plots, since they each typify one of the different habitats in your general hypothesis.

I'm not sure that this is really one of the MOST 'extinction prone' groups of birds, especially in comparison to various island groups.

Personally I think that the timing of human spread around the globe and megafaunal extinction is too close to be spurious, but that debate goes back decades and I'm not sure there is consensus among the experts on whether it was causal?

Reviewer #2:

In this paper, the authors use genomic data to explore how historical processes may have helped shape the current conservation statuses of a group of threatened (and in some cases extinct) migratory birds. They conclude that nearly all of the species in the group have exhibited declines in their effective population size since the Last Glacial Maxima, but that these declines have been particularly large in more temperate breeding species. The authors then attribute the more substantial declines of the temperate breeding species to the larger overlap these species have had with historic anthropogenic activities.

This paper has a number of strengths: (1) It is clearly written and easy to follow. (2) It provides the first species-level phylogeny of this group of species. (3) It focuses on a group of species that has already experienced two extinctions and includes a number of other threatened and endangered taxa; as such insights that may contribute to their conservation are sorely needed.

The paper also has a number of significant weaknesses. Most importantly: (1) No testable hypotheses or predictions are set forth. (2) While sampling across taxon is extensive, sampling within each taxa is more limited and precludes the inclusion of most in the core analyses. (3) No effort is made to reconstruct the historic ranges of any of the species or, therefore, to robustly analyze the historical processes that may have been most likely to affect their effective population sizes.

In the end, I find it difficult to know what conclusions to walk away with. With only five taxa meeting the threshold of five individuals for inclusion in the generation of effective population size 'histories' it's really difficult to run any formal analyses that might allow for robust statistical tests. This then limits the ability of the authors to draw strong conclusions about which groups of species have experienced the strongest declines in genetic diversity.

I also urge the authors to read and digest Crisp et al. 2011 in Trends in Ecology and Evolution. That paper provides a framework for how biogeographical studies can generate testable hypotheses, something that this manuscript lacks. Besides 'eyeballing' Figure 1C, it's impossible to assess whether any of the historical processes discussed actually were likely to play a role in the observed declines in effective population size. What alternative hypotheses might there be that could also be tested and refuted?

Reviewer #3:

Overall, a study such as the one presented is important for understanding our current biodiversity crisis and I appreciate the work. The methods are thorough and very nicely written. At this stage based on the framework presented the authors do not satisfactorily present evidence that megafauna decline and climate change drove the reduced population sizes in the focal species. To strengthen the work there need to be testable hypotheses presented and I would recommend moving away from the idea that only the glacial-interglacial end of Pleistocene climate change event is responsible for extinction while ignoring long term human impacts on biodiversity across the entirety of the Holocene (past 11,000 years).

The authors use genetic data to evaluate the evolutionary relationships and the population size of species of Numenius (shorebird) species. They found a decline in population sizes starting at the end of the last glacial of the Pleistocene. These declines are comparable to the declines found in megafauna which generally (on continents) occurred at toward the end of the Pleistocene and the end of the last glacial. However, there is not a direct link demonstrated that the declines of these shorebirds were caused by the declines of megafauna (which is what the title states). While many large mammals were ecosystem engineers, and the loss of these species could possibly impact the breeding habitats of these species leading to a reduction of breeding grounds resulting in declines this conjecture there is no data supporting these linkages herein. For example, one could also argue that increased human population size could also be driving these patterns or just the loss of wintering habitat due to higher sea levels and reduced coastline in some areas may be driving these patterns as well. I would argue that you have a more evidence that (as stated in the abstract): "Species breeding in temperate regions, where they widely overlap with human populations, have been most strongly affected". This falls in line with the fact that Eskimo Curlew was decimated by human hunting in the 19th century and was already in decline from the conversion of breeding ground habitat to agricultural land. Human activities since the late Pleistocene (which includes landscape modifications) have led to the current extinction crisis that has occurred since the late Pleistocene. It would be more effective to highlight this more than megafaunal losses driving the population declines in these species. Trophic cascades driving extinction/population reductions are a possibility but without direct evidence it is best left for discussion points.

To significantly strengthen the argument that climate change is the driving factor of declines I recommend investigating how effective population sizes changed across the Quaternary (past 2.6 million years). The last Pleistocene glacial- modern (Holocene) interglacial climate change event is not a unique and these dynamic conditions have occurred 20+ times across the Quaternary. Therefore, if you find that multiple times across the past 2.6 million years there has been a reduction in effective population size that corresponds to interglacial intervals then perhaps you can state that climate change has led to the decline of Numenius populations. Therefore, only looking at a single time point where there were much more destructive processing taking place i.e., human direct and indirect impacts, I find it is hard to draw this firm conclusion that it is climate change driving these patterns in your data. To better sort out various processes and patterns I would recommend restructuring the introduction to provide the background regarding the history of the Quaternary from a climate, extinction, and human perspective and provide testable hypotheses e.g., 1. Across the past 2.6 million years during interglacial intervals, when, presumably, breeding habitats and shoreline (wintering habitats) were less extensive, effective population sizes were greatly reduced due to limited resources. If this finding is not the case, then you would need another factor to have driven these species to have reduced population sizes at the end of the Pleistocene-Holocene and throughout the Holocene.

Overall, human impacts especially highlighting what happened to the Eskimo Curlew as hunting (loss of millions of individuals), breeding habitat destruction for agriculture, and the extinction of its main food item on the breeding ground (Rocky Mountain locust) should be included to strengthen the work. You can even provide human population size estimates, as available, in various parts of the breeding ranges across the Holocene.

I appreciate this project and manuscript and the acknowledgement that we need to understand declines and extinction of species considering the current diversity crisis.

Recommendations for the authors:

1. Title is too general for the study and the results.

2. The climate change event that occurred at the end of the Pleistocene and beginning of the Holocene was not a unique event. These glacial-interglacial intervals occurred 20+ times across the past 2.6 million years. Accordingly, it is important to evaluate whether there were multiple reductions of effective population size in these species across this time. This would strengthen the argument that climate change is impactful on these shorebirds' distributions and population size. If not then another factors need to be evaluated.

Line 107 – Is the decline seen in differential hunting of various species after 700 years ago?

Figure 1 caption – It looks like the demographic modeling wasn't performed for more than half of your focal species. This should be explicitly stated in the methods/results/discussion for transparency.

Line 150 in the figure caption there is a missing space

For the historical specimen DNA extraction methods please briefly elaborate and provide details on how you modified the Qiagen kit for the historical specimen extractions. Currently there is a citation but please briefly include some extra details so folks can easily access them.

Line 263 – is the perl script for removing potential linked SNPs available somewhere or upon request? Might be worthwhile mentioning that it is to strengthen the methods.

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Megafaunal extinctions-not climate change-seem to explain Holocene genetic diversity declines in Numenius shorebirds" for further consideration by eLife. Your revised article has been evaluated by Christian Rutz (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

Essential revisions:

As the reviewing editor, I am supportive of the publication of this paper in its current form. However, Reviewer 1 still has reservations about the power of inference here for inferring a causal effect of the megafaunal extinctions. The way eLife works, we have had a dialogue about these disconnects, and even after that back-and-forth we are in different places about the inferential power of this study. To reconcile these different levels of comfort with that inference, please consider the following small changes:

1) in the title, change "seem to" to "may"

2) add an additional qualifier to the abstract to indicate that the megafaunal scenario is one of multiple potential explanations.

I can't speak for the reviewer, but I come from this partly from a place of thinking that the megafaunal hypothesis is really cool and that it is indeed consistent with your available data. I think that highlighting it here might well spark other investigations of this general type and that this novelty is worth something of its own. I also come from a comparative phylogeography background which helps me understand the limitations of the N that evolution has provided to test any particular hypothesis, along with the awareness that it is very rare to have all historical data points converge on a simple scenario.

Reviewer #1 (Recommendations for the authors):

I commend the authors on their revised effort and greatly appreciate the addition of the ecological niche modeling, as well as explicit hypotheses to be tested. To my eyes the modeling appears robust. The paper is also clearly written and flows nicely from idea to idea.

Where I continue to struggle, however, is with the inferential power of the study. I apologize, but I cannot quite move past the fact that, with only five species, it is impossible to run any statistical analyses linking changes in Ne with changes in habitat availability or any other historical factor.

– For instance, Reviewer #3 brought up in the previous round of comments the possibility that rising sea levels reduced stopover/nonbreeding habitat simultaneous with the megafaunal extinction. That possibility is not addressed with any analyses, and quite possibly can't be addressed given the limited sample sizes involved, but it should nonetheless be mentioned.

– What is more, the apparent disconnect between the ecological niche modeling results and Ne results, gives me pause. While the reason offered is plausible, it's hard not to get stuck there without some sort of actual analysis to help explain the difference.

– Finally, I am struck by the dramatic differences in Ne patterns between N. phaeopus and N. hudsonicus given their biological and ecological similarity. Is this an artifact of only having 5 N. hudsonicus? (I could provide you with dozens of samples if you would like!) In either case, some mention of this discrepancy in patterns should at least be mentioned in the main text.

Thus, all told, I just feel uncomfortable pinning it all on the megafaunal extinction and urge caution with framing the paper (including the title) that way. I know how hard it is to pin contemporary declines on any single factor and, so, it seems that it is likely even harder to do so when looking into the past thousands of years.

eLife. 2023 Aug 7;12:e85422. doi: 10.7554/eLife.85422.sa2

Author response

[Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]

Reviewer #1:

Overall I am very enthusiastic about this study, which has a high degree of novelty. In particular I have never before heard of the potential link between megafaunal extinction around the time of the LGM, broadscale habitat change as a direct consequence, and population declines in steppe-breeding shorebird species. Some of the links in this chain of causality will be hard to test rigorously, but the hypothesis is fascinating and the demographic data summarized in this submission are consistent with this pattern.

The data and analysis methods seem generally robust and appropriate. The phylogenetic analyses could have been done in many ways (and those of us from this discipline love to quibble about these nuances), but the resulting tree here seems robust in terms of topology and underlying data depth. The phylogeny includes some interesting new information on the affinities of these species to one another.

We thank the reviewer for the validation of our novel research questions and robust methods.

For me, the real meat of this paper is in the stairway plots of historical Ne, as summarized in Figure 1C. The four species included there each have a distinctively different pattern of Ne over time. Since these analyses and estimates are so integral to the paper and its inferential conclusions, it may be worthwhile to explore/address whether they have relevant biases or limitations. For example, Patton et al. (2019) (Contemporary Demographic Reconstruction Methods Are Robust to Genome Assembly Quality: A Case Study in Tasmanian Devils) report that these particular methods are most reliable at estimating only fairly recent (30 generations) Ne's.

We appreciate the reviewer’s feedback regarding our stairway plot analyses. In “Materials and methods: Demographic history reconstruction”, we have now provided detailed elaborations on the rationale for stairway plot being our method of choice for demographic history reconstructions. Specifically, the new version of our manuscript acknowledges the utility of stairway plots for reduced representation datasets and recent time scales. As the reviewer mentioned, Patton et al. (2019) found that stairway plot is most reliable within 30 generations before present (<100 years). On the other hand, Liu and Fu (2015) found that despite higher dispersion, the mean results of stairway plot reliably track known demographic histories even up to 1 mya (~40,000 Homo sapiens generations). The time period for which stairway plot is informative seems to vary across papers (Nadachowska-Brzyska, Konczal, and Babik, 2022). When running stairway plot, we did not restrict the time range for reconstructions but allowed the program to determine the suitable upper limit. We also refrained from discussions of results from the last ten steps of the resulting stairway plot, as recommended by Liu and Fu (2015). All these details can now be found in the Methods section of the revised manuscript.

My primary critique of this submission is that the historical demographic estimates are made for less than half (4 of 9) of these shorebird species. The within-species sample size cutoff of n=5 for these estimates means that several additional species could have been included if just one more sample was available. I understand the difficulty of sourcing material for some of these species, but Ne stairway plots for more taxa would substantially elevate the inferential power of this cross-taxon comparison in which it seems that tundra-breeding species might have a different pattern than temperate-breeding species.

We agree with the reviewer that it is unfortunate that the loss of historic museum samples due to low DNA quality has prevented us from including even more species in our demographic reconstruction. We have pursued three avenues to address the reviewer’s concerns:

1) Following the reviewer’s encouragement, we were eventually able to perform stairway plot analyses for one additional species (N. americanus), raising the total number of species analysed to 5 out of 9 species. With this addition, our stairway plot analysis now features representatives of tundra and temperate-breeding species from both North America and Northern Eurasia, strengthening our cross-taxon comparisons.

2) We additionally ran multiple stairway plots for one more crucial species, the extinct Eskimo Curlew, under different sampling regimes (see new Figure S2). While our total sample size for Eskimo Curlews was n=5, theoretically allowing us to include this species, two of the samples were characterized by extremely poor DNA quality. We ran stairway plot for all 5 individuals, including the two degraded samples, as well as for 3 out of 5 (minus the two degraded samples). In both cases, the resultant stairway plot is clearly biased, either by low sample size or low input data quality. Similar attempts have also been made for all other species that were excluded from stairway plot analyses due to low sample size. While not adding considerably to our power of inference, we present these results in the Supplement.

3) Beyond stairway plot, we have also implemented the Pairwise Sequentially Markovian Coalescent (PSMC) model, a complementary analysis that is suitable for low sample sizes, in an attempt to include the species excluded from stairway plots. While the method has been applied successfully on reduced-representation datasets before, the sampling density of our target enrichment dataset falls below the threshold for successful analysis. We have included these trials in our methods for readers to find out about our total attempts at species inclusion.

With a greater sample of species to work with, it might then be possible to formally test whether species with the largest predicted shifts in breeding habitat show corresponding changes in estimated Ne. Such a result would substantially bolster the inferential argument presented here about that potential relationship.

We thank the reviewer for the suggestion to quantify and compare the extent of potential breeding areas across the duration of Ne fluctuations provided by stairway plot. We have undertaken substantial additional analysis to perform ecological niche modelling to reconstruct the potential past breeding range of Numenius shorebirds (see Materials and methods: Ecological niche modelling). We acquired bioclimatic variables for the present day (1960-1990), mid-Holocene (6,000 years ago) and Last Glacial Maximum (22,000 years ago), with present-day species occurrence data as model input. Model parameters were systematically tested and the model with the highest continuous Boyce index coupled with a low omission rate was selected as the best model. The best model was then projected onto paleo-climatic datasets to reconstruct past breeding distributions for 4 species with sufficient input on present-day breeding occurrence. Total breeding area was then quantified and compared across the three time points (see new Figure 1C). Most importantly, we found that bioclimatic models predict an increase in potential breeding area for the late Pleistocene (~20,000 – 10,000 years ago) when the main decline in population-genetic diversity occurred in most Numenius species. This finding considerably strengthens our hypothesis that factors other than historic climate change, especially megafaunal extinction, may have constituted the main driver of Numenius shorebird diversity decline.

In terms of presentation, the paper jumps between historical climate change at recent geological time scales, and current anthropogenic climate change. The two extinct curlew species in this clade almost certainly went extinct owing to primary causes other than anthropogenic climate change. Past effects of climate and broad-scale habitat change are certainly relevant, but the chain of causality presented here could be improved.

Some statements about past processes are presented as facts, whereas my understanding is that they are still subject to debate. In particular the causality of human hunting>megafaunal extinction>widespread habitat change from grasslands to forest is more of a hypothesis. Similarly, the evidence for lower genetic diversity creating higher extinction risk is fairly nuanced. In presenting their own results, the authors may want to be slightly less declarative about causality, such as in saying "Our study revealed that climatic and environmental changes impacted the genetic diversity of curlews" when the data are corelative and based on only a couple of taxa.

In general I recommend focusing more tightly on the inferences about the potential relationship between past Ne and broadscale habitat change during the Pleistocene and thereafter. These are fascinating and intriguing enough on their own, but they are indeed highly inferential. In terms of present and future risk, anthropogenic climate change is probably not among the greatest potential drivers of population declines and possible extinction in this group of shorebirds.

We are grateful for the reviewer’s reminder to improve our presentation of potential chains of causality in our manuscript. We have addressed the reviewer’s concerns in the following ways:

1) We have conducted a thorough review of our manuscript to modify or remove statements that are excessively declarative in their claims. We have revised certain statements (e.g., those suggesting that climatic and environmental change have impacted the genetic diversity of Numenius) to be more accurate and less definitive in portraying the findings of our study. We have also removed statements of causative links that are not essential to our discussion (e.g. widespread megafaunal extinction was underway due to … the arrival of early Homo sapiens that carried out hunts). We agree with the reviewer that a discussion of the drivers of megafaunal extinction goes beyond the scope of our study, which is more thoroughly focused on the drivers of Numenius endangerment, so we have ensured that our manuscript no longer engages in a discussion of such tangential subjects.

2) We have shifted our discussion away from extinction risk and focused it on the observed trends in genetic diversity. Links between genetic diversity loss and extinction risk are supported by an increasing body of literature but – nevertheless – remain controversial, and our discussion of these links is now restricted to a few carefully-worded sentences. In the revised discussion, we have acknowledged that such links are more relevant to small populations and we have provided additional citations.

3) To strengthen our inferences regarding factors that affect demographic history, we have added more climatic, biotic and anthropogenic factors to our hypothesis testing (Figure 1C). For example, in the revised main figure, we can now infer with greater confidence that recent anthropogenic factors do not play a large role in genetic diversity declines as these declines took place before the modern human footprint became pervasive. Of additional significance is that our ecological niche modelling now shows that climatic conditions favoured an increase in breeding distribution of Numenius shorebirds after the Last Glacial Maximum at a time when genetic diversity in most species precipitously declined. This is a strong indicator that factors other than natural climate change or direct anthropogenic impacts played a role in genetic diversity declines among Numenius species.

Recommendations for the authors:

I recommend substantially reworking the abstract to make it less general and to highlight even more the truly novel components of this study.

We have completely rewritten the abstract to focus on our findings surrounding Numenius shorebirds. Our abstract highlights the important components of our study, namely that we provide the first complete phylogenomic tree including two presumably extinct species, and that we reveal a generally sharp decline in genetic diversity of Numenius shorebirds soon after the Last Glacial Maximum. We have added new ecological niche modelling analyses and a range of biotic and anthropogenic factors that allow us to home in on the potential drivers of genetic diversity declines. In the abstract, we also now discuss with greater clarity that megafaunal extinctions, more so than direct anthropogenic or natural climatic factors, seem to be able to account for genetic diversity declines in Numenius shorebirds.

If you decide to add more genomic data, we have two borealis specimens in the Cornell Museum of Vertebrates that I would be happy to sample for you (though I do understand the challenges of adding data, doing the reanalyses, and arranging all of the permits to get samples from here to there…). I really do wish that you could add at least borealis and americanus to the stairway plots, since they each typify one of the different habitats in your general hypothesis.

We are very grateful to the reviewer for the generous offer of precious ancient samples. Despite having sufficient sampling of Numenius borealis (n=5) for the application of stairway plot, two samples turned out to be quite degraded and showed high missingness when bioinformatic diagnostics were performed (Materials and methods: Demographic history reconstruction), negatively affecting our inferences when including these samples in stairway plot analysis (new Figure S2). When we ran stairway plots only on samples with low missingness, results were clearly affected by a lack of sufficient sample size (new Figure S2). While we strongly considered adding new samples (including perhaps the ones generously offered by the reviewer), we decided against it in the end due to external constraints that would not allow us to expand the scope of this project in such a fundamental way. We are also cognizant that any historic sample has a chance of being unusable in bioinformatic steps due to DNA degradation. On a positive note, we were able to include the extant species Numenius americanus in stairway plot analyses, thereby increasing the representation of North American species and strengthening our comparisons and discussions.

I'm not sure that this is really one of the MOST 'extinction prone' groups of birds, especially in comparison to various island groups.

We have removed the statement as suggested by the reviewer.

Personally I think that the timing of human spread around the globe and megafaunal extinction is too close to be spurious, but that debate goes back decades and I'm not sure there is consensus among the experts on whether it was causal?

We agree with the reviewer that there have been many studies investigating the relative interactions between human spread and megafaunal extinction, with differing conclusions at different geographical scales. As the cause of megafaunal extinction is not crucial to our study and we did not generate data for this question, we have removed this part of the discussion.

Reviewer #2:

In this paper, the authors use genomic data to explore how historical processes may have helped shape the current conservation statuses of a group of threatened (and in some cases extinct) migratory birds. They conclude that nearly all of the species in the group have exhibited declines in their effective population size since the Last Glacial Maxima, but that these declines have been particularly large in more temperate breeding species. The authors then attribute the more substantial declines of the temperate breeding species to the larger overlap these species have had with historic anthropogenic activities.

This paper has a number of strengths: (1) It is clearly written and easy to follow. (2) It provides the first species-level phylogeny of this group of species. (3) It focuses on a group of species that has already experienced two extinctions and includes a number of other threatened and endangered taxa; as such insights that may contribute to their conservation are sorely needed.

We thank the reviewer for the positive reflections on our study.

The paper also has a number of significant weaknesses. Most importantly: (1) No testable hypotheses or predictions are set forth.

We are grateful for the reviewer’s suggestion to improve the framing of our manuscript. In our revised manuscript, we have made our hypothesis more explicit in the “Introduction”. Our null hypothesis is that diversity declines in Numenius shorebirds are largely attributable to direct anthropogenic factors, considering that habitat loss and hunting are major threats that are known to have caused extinction events in this group of migratory birds. At the same time, we also hypothesise that their genetic diversity should closely track the availability of breeding habitat throughout the Late Quaternary. Therefore, we expect to observe that genetic diversity declines would have been most significant during the late Holocene, and would intensify with increasing anthropogenic activity. Coincidentally, our data contradict this null hypothesis and point to other factors being more important. We whole-heartedly agree with the reviewer that a hypothesis framing substantially improves our manuscript.

(2) While sampling across taxon is extensive, sampling within each taxa is more limited and precludes the inclusion of most in the core analyses.

We thank the reviewer for recognising our extensive species sampling of the shorebird genus Numenius, allowing us to produce the first complete phylogenomic tree of this genus, including two presumably extinct species. At the same time, we aimed to acquire comprehensive sampling within each taxon through an exhaustive search of suitable samples from museums and collaborators. We recognise that Reviewer #1 had also reflected similar concerns, which we have responded to in detail in Reviewer #1 response #3. In summary, we have now added N. americanus to the stairway plot, allowing for the inclusion of 5 out of 9 species. Importantly, the species featured represent extinct and extant species, as well as tundra or temperate-breeding species of both continents, allowing for adequate comparisons.

(3) No effort is made to reconstruct the historic ranges of any of the species or, therefore, to robustly analyze the historical processes that may have been most likely to affect their effective population sizes.

We have taken the reviewer’s suggestion to heart and conducted a substantial volume of additional ecological niche modelling on the species included in the stairway plot analysis for which sufficient occurrence points were available (n=4; Materials and methods: Ecological niche modelling). We acquired bioclimatic variables and species occurrence points in the breeding ranges of Numenius shorebirds and input them into Maxent to reconstruct potential breeding distributions. We also quantified the total suitable breeding area at present (1960-1990), mid-Holocene (6,000 years ago) and Last Glacial Maximum (22,000 years ago) (new Figure 1C). With these additional analyses, we were able to directly test which processes may have played a role in impacting the effective population sizes of Numenius shorebirds. Effective population sizes generally declined across Numenius species in the aftermath of the Last Glacial Maximum, even though this period was characterized by increases in the extent of suitable breeding area, suggesting that climate-induced habitat changes are unlikely to have affected fluctuations in Ne, and hinting at other causes instead.

In the end, I find it difficult to know what conclusions to walk away with. With only five taxa meeting the threshold of five individuals for inclusion in the generation of effective population size 'histories' it's really difficult to run any formal analyses that might allow for robust statistical tests. This then limits the ability of the authors to draw strong conclusions about which groups of species have experienced the strongest declines in genetic diversity.

I also urge the authors to read and digest Crisp et al. 2011 in Trends in Ecology and Evolution. That paper provides a framework for how biogeographical studies can generate testable hypotheses, something that this manuscript lacks. Besides 'eyeballing' Figure 1C, it's impossible to assess whether any of the historical processes discussed actually were likely to play a role in the observed declines in effective population size. What alternative hypotheses might there be that could also be tested and refuted?

We appreciate the reviewer’s critical comment and have taken their suggestion on board. The Introduction of our revised manuscript now clearly frames the hypothesis of our study, which is then addressed again in the Discussion. In Crisp et al. (2011), which we have added as a citation, timings of events are used to test hypotheses of unobserved processes. To strengthen our inferences about the factors that may have played a role in Numenius Ne fluctuations, we have adopted the same approach as in Crisp et al. (2011) by including more climatic, biotic, and anthropogenic factors and relating the timing of their occurrence directly to the diversity fluctuations of the stairway plot results (Figure 1C). In brief, the timing of the steep declines of genetic diversity in most Numenius species considerably pre-dates the expansion of the modern human footprint on the planet, rendering direct anthropogenic impacts an unlikely culprit of diversity declines. By the same token, natural climate change would have predicted an increase rather than decline in post-LGM genetic diversity. Instead, our reconstructions of Ne fluctuations are consistent with the impact of megafaunal extinctions on habitat maintenance. We have carried out revisions to the entire manuscript to reflect this clear thread of hypothesis testing in response to the reviewer’s concern, and we hope the new manuscript achieves this well.

Reviewer #3:

Overall, a study such as the one presented is important for understanding our current biodiversity crisis and I appreciate the work. The methods are thorough and very nicely written.

We thank the reviewer for the positive review of our manuscript.

At this stage based on the framework presented the authors do not satisfactorily present evidence that megafauna decline and climate change drove the reduced population sizes in the focal species. To strengthen the work there need to be testable hypotheses presented and I would recommend moving away from the idea that only the glacial-interglacial end of Pleistocene climate change event is responsible for extinction while ignoring long term human impacts on biodiversity across the entirety of the Holocene (past 11,000 years).

The authors use genetic data to evaluate the evolutionary relationships and the population size of species of Numenius (shorebird) species. They found a decline in population sizes starting at the end of the last glacial of the Pleistocene. These declines are comparable to the declines found in megafauna which generally (on continents) occurred at toward the end of the Pleistocene and the end of the last glacial. However, there is not a direct link demonstrated that the declines of these shorebirds were caused by the declines of megafauna (which is what the title states). While many large mammals were ecosystem engineers, and the loss of these species could possibly impact the breeding habitats of these species leading to a reduction of breeding grounds resulting in declines this conjecture there is no data supporting these linkages herein. For example, one could also argue that increased human population size could also be driving these patterns or just the loss of wintering habitat due to higher sea levels and reduced coastline in some areas may be driving these patterns as well. I would argue that you have a more evidence that (as stated in the abstract): "Species breeding in temperate regions, where they widely overlap with human populations, have been most strongly affected". This falls in line with the fact that Eskimo Curlew was decimated by human hunting in the 19th century and was already in decline from the conversion of breeding ground habitat to agricultural land. Human activities since the late Pleistocene (which includes landscape modifications) have led to the current extinction crisis that has occurred since the late Pleistocene. It would be more effective to highlight this more than megafaunal losses driving the population declines in these species. Trophic cascades driving extinction/population reductions are a possibility but without direct evidence it is best left for discussion points.

We acknowledge the reviewer’s important comments, most of which coincide with comments made by previous reviewers (and addressed therein). We reiterate the most important actions that we have taken in response to the reviewer’s points, but we would also like to refer the reviewer to our previous responses to the other two referees (see above). In brief, we have carried out the following revisions in response to the points raised by this reviewer:

1) Firstly, we have defined a clear hypothesis in the “Introduction” and adjusted the entire manuscript around a hypothesis-testing framework (also see Reviewer #2 response #2, Reviewer #2 response #5).

2) Secondly, we have now included more climatic, biotic, and anthropogenic factors throughout the time range of our stairway plot reconstructions. We have also performed additional analyses to quantify the potential breeding area of Numenius shorebirds at three points in time to verify if habitat availability correlates with Ne fluctuations (also see Reviewer #1 response #5).

3) Thirdly, we are grateful for the reviewer’s reminder to be mindful when making statements about causality and have reviewed our manuscript to remove broad claims (also see Reviewer #1 response #5, Reviewer #2 response #5).

Based on our enhanced dataset, we are now able to show that declines in Ne considerably pre-date the expansion of the human footprint on the planet, ruling out a major direct impact of recent anthropogenic activities on the Ne decline documented in our study.

Contrary to expectations, anthropogenic factors such as increasing human population size and habitat conversion only rose in significance after the Ne of most Numenius species had already declined and stabilised. We cannot rule out that recent anthropogenic activity has had adverse effects on genetic diversity in Numenius, but these effects do not yet seem to be captured by our data, possibly on account of shorebirds’ long generation times. These points are now discussed in the revised manuscript version.

To significantly strengthen the argument that climate change is the driving factor of declines I recommend investigating how effective population sizes changed across the Quaternary (past 2.6 million years). The last Pleistocene glacial- modern (Holocene) interglacial climate change event is not a unique and these dynamic conditions have occurred 20+ times across the Quaternary. Therefore, if you find that multiple times across the past 2.6 million years there has been a reduction in effective population size that corresponds to interglacial intervals then perhaps you can state that climate change has led to the decline of Numenius populations. Therefore, only looking at a single time point where there were much more destructive processing taking place i.e., human direct and indirect impacts, I find it is hard to draw this firm conclusion that it is climate change driving these patterns in your data. To better sort out various processes and patterns I would recommend restructuring the introduction to provide the background regarding the history of the Quaternary from a climate, extinction, and human perspective and provide testable hypotheses e.g., 1. Across the past 2.6 million years during interglacial intervals, when, presumably, breeding habitats and shoreline (wintering habitats) were less extensive, effective population sizes were greatly reduced due to limited resources. If this finding is not the case, then you would need another factor to have driven these species to have reduced population sizes at the end of the Pleistocene-Holocene and throughout the Holocene.

We understand the reviewer’s suggestion to compare Ne fluctuations across multiple cycles of Quaternary cooling and warming to conclusively pin down the role of climate change in affecting genetic diversity. However, we are afraid that testing for such cyclical changes in genetic diversity is beyond what we would be able to achieve with our dataset of genome-wide loci, especially considering the computational complexity in accounting for multiple glacial cycles. Additionally, there is also very scant bioclimatic data for cycles that pre-date the LGM.

We echo the reviewer’s sentiment that natural climate change should not be credited too readily with having exerted such a strong impact on genetic diversity in shorebirds, and we would like to point out that our manuscript’s main conclusions actually agree with this notion. After adding novel analyses using Maxent to model the extent of suitable breeding area of multiple species at present (1960-1990), mid-Holocene (6,000 years ago) and Last Glacial Maximum (22,000 years ago), we found that bioclimatic data predict a post-LGM increase in breeding area for most species (even though these same species experienced a decline in genetic diversity). Therefore, we are in agreement with the reviewer that natural climate change is unlikely to have resulted in the genetic decline, and that other factors must be responsible. We hope the revised version of the manuscript satisfactorily addresses these reviewer criticisms.

Overall, human impacts especially highlighting what happened to the Eskimo Curlew as hunting (loss of millions of individuals), breeding habitat destruction for agriculture, and the extinction of its main food item on the breeding ground (Rocky Mountain locust) should be included to strengthen the work. You can even provide human population size estimates, as available, in various parts of the breeding ranges across the Holocene.

As suggested by the reviewer, we have now added more anthropogenic factors and provide this data in greater detail (new Figure 1C). We have now incorporated human population size estimates for North America and Eurasia from HYDE 3.2. We have also added the corresponding agricultural land use estimates from HYDE 3.2. Both measures of anthropogenic impact showed great increases only in the most recent millennium, while genetic diversity declines substantially pre-date this period of human impact. We are able to infer with confidence that the main factors causing the steep genetic diversity declines documented in our study are unlikely to be due to direct anthropogenic impacts, although such impacts may have exacerbated the situation for temperate-breeding species (see new Discussion).

I appreciate this project and manuscript and the acknowledgement that we need to understand declines and extinction of species considering the current diversity crisis.

We thank the reviewer for affirming the value of our manuscript.

Recommendations for the authors:

1. Title is too general for the study and the results.

We have amended the title to focus on genetic diversity declines in Numenius shorebirds.

2. The climate change event that occurred at the end of the Pleistocene and beginning of the Holocene was not a unique event. These glacial-interglacial intervals occurred 20+ times across the past 2.6 million years. Accordingly, it is important to evaluate whether there were multiple reductions of effective population size in these species across this time. This would strengthen the argument that climate change is impactful on these shorebirds' distributions and population size. If not then another factors need to be evaluated.

We agree with the reviewer that climate oscillations have occurred many times throughout the Pleistocene. The reviewer had raised this point in the introductory remarks, and we have addressed it there (see Reviewer #3 response #3). In summary:

1) While we agree that a demonstration of correlation across multiple glacial cycles would be valuable, we are not certain that our dataset (and many other datasets available) would allow for such complex demonstrations.

2) Our data refute the notion that natural climate change has impacted Numenius shorebird trajectories. Given that this correlation could not be established even for the time after the Last Glacial Maximum, perhaps showing such a correlation for previous glacial maxima becomes moot.

Line 107 – Is the decline seen in differential hunting of various species after 700 years ago?

We have considered the reviewer’s suggestion that differential hunting pressures may play a role in the differential declines of curlews versus whimbrels in recent years. However, we will not be including this discussion point as there is too little information regarding differential hunting pressures (Pearce-Higgins et al., 2017) and cooccurring Numenius species may be easily confused with one another by hunters (Jiguet et al., 2021). Moreover, we have since removed our discussion of their differential declines to be more conservative in our inferences.

Figure 1 caption – It looks like the demographic modeling wasn't performed for more than half of your focal species. This should be explicitly stated in the methods/results/discussion for transparency.

With the inclusion of N. americanus, we now have more than half of our focal species represented in our demographic modeling. Nonetheless, we thank the reviewer for the reminder to state this more explicitly in our manuscript. We have added a detailed discussion of our criteria for a species’ inclusion in our demographic modeling (see Materials and methods: Demographic history reconstruction).

Line 150 in the figure caption there is a missing space

We have amended the figure caption to include the necessary space.

For the historical specimen DNA extraction methods please briefly elaborate and provide details on how you modified the Qiagen kit for the historical specimen extractions. Currently there is a citation but please briefly include some extra details so folks can easily access them.

We have included descriptions of the modifications made to the Qiagen kit for DNA extraction of historic samples (Materials and methods: Laboratory methods).

Line 263 – is the perl script for removing potential linked SNPs available somewhere or upon request? Might be worthwhile mentioning that it is to strengthen the methods.

We have included the name of the perl script, alongside the citation and link to its github repository (Materials and methods: SNP calling).

[Editors’ note: what follows is the authors’ response to the second round of review.]

Essential revisions:

As the reviewing editor, I am supportive of the publication of this paper in its current form. However, Reviewer 1 still has reservations about the power of inference here for inferring a causal effect of the megafaunal extinctions. The way eLife works, we have had a dialogue about these disconnects, and even after that back-and-forth we are in different places about the inferential power of this study. To reconcile these different levels of comfort with that inference, please consider the following small changes:

1) in the title, change "seem to" to "may"

We have made the suggested change to the title of our manuscript.

2) add an additional qualifier to the abstract to indicate that the megafaunal scenario is one of multiple potential explanations.

In our abstract, we have added the phrase “…among other factors…” before our discussion of the potential impact of megafaunal extinctions, and made additional smaller wording changes to indicate that there are other factors that can explain genetic diversity loss in Numenius shorebirds (Abstract, line 37).

Reviewer #1 (Recommendations for the authors):

I commend the authors on their revised effort and greatly appreciate the addition of the ecological niche modeling, as well as explicit hypotheses to be tested. To my eyes the modeling appears robust. The paper is also clearly written and flows nicely from idea to idea.

Where I continue to struggle, however, is with the inferential power of the study. I apologize, but I cannot quite move past the fact that, with only five species, it is impossible to run any statistical analyses linking changes in Ne with changes in habitat availability or any other historical factor.

We thank the reviewer for their thoughtful feedback and we acknowledge that the sample size of our study precludes statistical analyses that may increase our inferential power. We have screened our manuscript to remove any remaining instances where we may have used strong language to build a causal connection between megafaunal extinctions and shorebird diversity loss (for example, Results and discussion, line 164 “…may have had cascading effects …”). Our manuscript now consistently employs cautious wording in drawing on any such potential associations.

– For instance, Reviewer #3 brought up in the previous round of comments the possibility that rising sea levels reduced stopover/nonbreeding habitat simultaneous with the megafaunal extinction. That possibility is not addressed with any analyses, and quite possibly can't be addressed given the limited sample sizes involved, but it should nonetheless be mentioned.

We have taken the reviewer’s suggestion on board and added a sentence discussing that the impact of rising sea levels on the availability of non-breeding habitats for Numenius remains to be investigated (Results and discussion, lines 165–169). However, we have also added our perspective that rising sea levels after the LGM changing non-breeding habitat availability are unlikely to have explained post-LGM declines in genetic diversity in Numenius shorebirds. Sea level rises have increased the total length of shorelines, at least in the Old World, but quite possibly also across the planet, through the generation of complex archipelagos (e.g. Indonesia) via sea water immersion in shelf areas (De Groeve et al., 2022; Sarr et al. 2019). Therefore, rising sea levels are likely to have increased rather than reduced the availability of non-breeding habitat and we consider it unlikely that its incorporation would have changed any of our conclusions in this study. Nonetheless, our discussion now briefly touches upon this subject and calls for this factor to be taken into account in future studies.

– What is more, the apparent disconnect between the ecological niche modeling results and Ne results, gives me pause. While the reason offered is plausible, it's hard not to get stuck there without some sort of actual analysis to help explain the difference.

We have expanded our discussion to further address the discrepancy between our ecological niche modelling and Ne results (Results and discussion, lines 115–121). While we initially shared the reviewer’s consternation, we do believe that this disconnect forms the very heart piece of our conclusions, indicating that climate change alone does not explain Numenius diversity declines, and instead offering alternative explanations such as megafaunal extinctions. Our expanded discussion now also offers an alternative explanation that at least part of the discrepancy could be explained by rapid range expansion, as predicted by an increase in suitable habitat between LGM and midHolocene, resulting in decreases in Ne. However, Ne continued to decrease when predicted suitable habitat remained relatively constant after the mid-Holocene, suggesting that other factors (such as megafaunal extinction) would need to be considered to understand this discrepancy.

– Finally, I am struck by the dramatic differences in Ne patterns between N. phaeopus and N. hudsonicus given their biological and ecological similarity. Is this an artifact of only having 5 N. hudsonicus? (I could provide you with dozens of samples if you would like!) In either case, some mention of this discrepancy in patterns should at least be mentioned in the main text.

We are thankful to the reviewer for specifically pointing out the discrepancy in demographic history between the two sister species N. phaeopus and N. hudsonicus, which is indeed pronounced and requires an explanation. Our previous manuscript version did not offer any explanation as we feared this may be seen as too taxon-specific, but we have been happy to add a likely explanation of this pattern in the present draft version (Results and discussion, lines 150–154). We also thank the reviewer for generously offering us additional samples for N. hudsonicus, but we do not believe that additional samples are necessary this late in the process, as our sample sizes should be sufficient for the conclusions we drew. Rather than attributing these differences to sample size, we believe the discrepancy in pattern between the two whimbrels can easily be explained by the differences in glacial extent and impact between North America and Asia. North America was covered by vast ice sheets during the LGM while most of Siberia remained ice-free, allowing for disproportionately high levels of N(e) in N. phaeopus in comparison with N. hudsonicus.

Associated Data

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

    Data Citations

    1. Tan HZ, Jansen JFJ, Allport GA, Garg KM, Chattopadhyay B, Irestedt M, Pang SEH, Chilton G, Gwee CY, Rheindt FE. 2023. Numenius target enrichment libraries. NCBI BioProject. PRJNA742889 [DOI] [PMC free article] [PubMed]
    2. Tan HZ, Jansen JFJ, Allport GA, Garg KM, Chattopadhyay B, Irestedt M, Pang SEH, Chilton G, Gwee CY, Rheindt FE. 2023. Numenius phaeopus reference genome. NCBI GenBank. JARKVS000000000 [DOI] [PMC free article] [PubMed]
    3. Project Vertebrate Genomes 2019. Taeniopygia guttata (zebra finch) genome sequencing and assembly, primary haplotype. NCBI Assembly. GCA_003957565.1
    4. University Uppsala 2013. Ficedula albicollis Genome sequencing and assembly. NCBI Assembly. GCA_000247815.2
    5. Küpper et al. 2015. Genome assembly of the ruff (Philomachus pugnax) NCBI Assembly. GCA_001458055.1/

    Supplementary Materials

    Supplementary file 1. Sampling information.

    Legend: Details of samples collected for this study.

    elife-85422-supp1.docx (56.2KB, docx)
    Supplementary file 2. Evolutionary distinctness of Numenius species.

    Legend: Evolutionary distinctness, phylogenetic diversity, and evolutionarily distinct and globally endangered (EDGE) scores of Numenius species.

    elife-85422-supp2.docx (44.6KB, docx)
    Supplementary file 3. Ecological niche modeling information.

    Legend: Details of occurrence points, parameters, and results of ecological niche modeling using Maxent.

    elife-85422-supp3.docx (46.5KB, docx)
    MDAR checklist
    elife-85422-mdarchecklist1.pdf (183.8KB, pdf)

    Data Availability Statement

    DNA reads generated in this study are available on Sequence Read Archive under BioProject PRJNA742889. The reference genome generated in this study is available at DDBJ/ENA/GenBank as a Whole Genome Shotgun project under the accession JARKVS000000000. The version described in this paper is version JARKVS010000000. Pipelines and analysis codes are available on GitHub: https://github.com/tanhuizhen/Numenius_Target-enrichment_Analyses (copy archived at Tan, 2023).

    The following datasets were generated:

    Tan HZ, Jansen JFJ, Allport GA, Garg KM, Chattopadhyay B, Irestedt M, Pang SEH, Chilton G, Gwee CY, Rheindt FE. 2023. Numenius target enrichment libraries. NCBI BioProject. PRJNA742889

    Tan HZ, Jansen JFJ, Allport GA, Garg KM, Chattopadhyay B, Irestedt M, Pang SEH, Chilton G, Gwee CY, Rheindt FE. 2023. Numenius phaeopus reference genome. NCBI GenBank. JARKVS000000000

    The following previously published datasets were used:

    Project Vertebrate Genomes 2019. Taeniopygia guttata (zebra finch) genome sequencing and assembly, primary haplotype. NCBI Assembly. GCA_003957565.1

    University Uppsala 2013. Ficedula albicollis Genome sequencing and assembly. NCBI Assembly. GCA_000247815.2

    Küpper et al. 2015. Genome assembly of the ruff (Philomachus pugnax) NCBI Assembly. GCA_001458055.1/

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