Philippa Kaur

AbstractThe Haemaphysalis longicornis tick, an important arthropod vector native to East Asia, has expanded its range to Oceania and the American continents, where populations are exclusively parthenogenetic females. While the parthenogenetic population possesses a stable triploid genetic system, the absence of a triploid genome assembly has prohibited us from understanding their genetic evolution and biological traits. Here, we present a chromosome-level, haplotype-resolved triploid genome assembly of the parthenogenetic population by integrating ultra-deep long-read high-fidelity, short-read high-throughput chromosome conformation capture, and isoform sequencing technologies. The phylogenetic analysis suggested that triploid genome originated from an autopolyploid event rather than interspecific hybridization. We identified functional genes that enable parthenogenetic ticks to complete embryogenesis without fertilization. Parthenogenetic ticks showed higher tolerance to Rickettsia and virus infections, with more differential expressed genes compared to bisexual ticks. They exhibited lower levels of deleterious mutations across the genome and a broader global distribution predicted by ecological niche modeling.

Similar content being viewed by others

A chromosome-scale reference genome assembly for Triplophysa lixianensis

Article
Open access
19 December 2024

Unzipped chromosome-level genomes reveal allopolyploid nematode origin pattern as unreduced gamete hybridization

Article
Open access
07 November 2023

Chromosome-level genome assembly of the Stoliczka’s Asian trident bat (Aselliscus stoliczkanus)

Article
Open access
15 December 2023

IntroductionThe native Asian tick1, Haemaphysalis longicornis, has expanded its range into novel territories, including Australia, New Zealand, and the USA2, where it primarily reproduces through parthenogenesis. The parthenogenetic mode of reproduction enables the establishment of large populations from a single individual, thereby enhancing their invasive potential. Since its initial detection in the United States in 2017, parthenogenetic populations has rapidly spread to 19 states within a few years3, showcasing robust ecological adaptation and population expansion potential4. Haemaphysalis longicornis is known to transmit various pathogens that affect both humans and animals, including severe fever with thrombocytopenia syndrome virus (SFTSV)5 as well as Rickettsia, Anaplasma, Ehrlichia, Borrelia and Babesia6. Given the wide range of animal hosts associated with H. longicornis4, there is an elevated risk of pathogen acquisition and transmission. Therefore, intensified research and surveillance efforts on parthenogenetic H. longicornis are imperative for comprehending their biological traits, population distribution, and for preventing and controlling their threats to human and animal health.Haemaphysalis longicornis is the only reported tick species with widely distributed parthenogenetic populations. It is generally believed that parthenogenetic species lack genetic diversity, leading to the continuous accumulation of deleterious mutations, which ultimately results in a decline of parthenogenetic populations until extinction7. However, the geographic distribution of parthenogenetic H. longicornis population continues to expand3,8, contradicting the traditional theory of parthenogenetic extinction. The parthenogenetic H. longicornis has a stable triploid genetic system9, which may result from abnormal gamete fusion mechanisms10. Triploids are generally unstable and often sterile due to the challenges of pairing and equal segregation of three homologous chromosomes during meiosis and gametogenesis10. Understanding how the triploid tick successfully overcomes cellular, genetic, and embryonic developmental constraints could provide insights into the genetic basis of both sexual and asexual reproductive modes. Additionally, genome polyploidization can significantly affect gene expression regulation11, impacting not only reproductive modes but also environmental and immune adaptation capabilities12,13, which are closely related to the vector competence of H. longicornis. The genomic and transcriptomic data are needed to understand these evolutionary adaptions.In this study, we performed genome sequencing of the parthenogenetic population reared in the laboratory using ultra-deep long-read high-fidelity (HiFi), short-read high-throughput chromosome conformation capture (Hi-C), and long-read isoform sequencing (Iso-Seq) technologies. This approach enabled the assembly of the three homologous chromosome sets (subgenomes), followed by phylogenetic analysis to determine their evolutionary origin. A comprehensive analysis of the genomic features revealed functional gene groups linked to parthenogenetic reproduction. Transcriptome analyses of bacterial and viral infections provided insights into pathogen-vector interactions and the enhanced reservoir capacity of parthenogenetic Haemaphysalis longicornis. Additionally, we evaluated population dispersion, reproductive fitness, and predicted the global suitable habitats for parthenogenetic H. longicornis (Fig. 1a).Fig. 1: Genome assembly of parthenogenetic Haemaphysalis longicornis.a The workflow of this study: ① The laboratory-reared parthenogenetic and bisexual H. longicornis populations used in this study; ② Ploidy analysis of the parthenogenetic and bisexual populations using flow cytometry; ③ Genome sequencing and assembling of the parthenogenetic population using ultra-deep high-fidelity (HiFi) sequencing, chromosome conformation capture (Hi-C), isoform sequencing (Iso-Seq), and RNA sequencing (RNA-seq) technologies; ④ Functional gene analysis related to parthenogenesis; ⑤ Transcriptome analyses after bacterial and viral infections in both parthenogenetic and bisexual H. longicornis populations; ⑥ Population dispersion and predicted the global suitable habitats of both populations. b Polyploid analysis results of parthenogenetic and bisexual ticks using flow cytometry. The parthenogenetic population shows a peak at the 112 position, while the diploid bisexual population peaks at the 74 position, indicating the triploid karotype of parthenogenetic population. c K-mer spectra and fitted models generated by GenomeScope software. The triploid genome has three major coverage peaks. “abb” indicates two nucleotides are the same in a given position of the triploid genome, and the third is different, while “abc” indicates all three nucleotides are different. d Haplotype-resolved Hi-C interaction heatmap for the 33 chromosomes across the three subgenomes (A, B, and C). Using the numbered chromosomes as coordinates, each dot’s color indicates the logarithmic value of the interaction intensity between the corresponding genomic bin pairs. The interaction intensity ranges from white (low) to red (high). “Chr” denotes chromosomes. e Example of a gene (DDX5) with multiple alternative splicing events. The Iso-Seq long reads (light red) are mapped to the annotated gene model to identify distinct alternative splicing events, indicating the improvement in the annotation of alternative splicing events. A Sashimi plot is shown to summarize the splice junctions of the gene from aligned Iso-Seq data.Full size imageResultsHigh-quality triploid genomeWe established a laboratory parthenogenetic H. longicornis population using field-collected ticks. Adults from the tenth generation of this in-bred population were used for genome assembly and annotation. Flow cytometry was employed to compare the ploidy levels between the parthenogenetic and bisexual female ticks. The results showed fluorescence intensity peaks at positions 112 and 74 for the parthenogenetic and bisexual populations, respectively, indicating that the parthenogenetic tick had a ploidy level 1.5 times higher than that of the bisexual individual, confirming the triploid karyotype of parthenogenetic H. longicornis (Fig. 1b). A genome survey of the parthenogenetic population, conducted using second-generation sequencing, revealed three distinct k-mer peaks at different depths (Fig. 1c), indicating its triploid genome. Subsequently, we tripled the sequencing depth by increasing the HiFi coverage from 30× to 95× and the Hi-C coverage from 100× to 300×, and finally yielded 210.42 Gb of HiFi data (Supplementary Table 1) and 660 Gb of Hi-C data (Supplementary Table 2).Using a haplotype-resolved de novo assembler, we obtained contig sequences for the parthenogenetic H. longicornis genome, resulting in a total contig length of 7936.5 Mb, a contig N50 of 11.06 Mb. Genome completeness was evaluated using BUSCO (v5.4.2) with the arthropoda_odb10 dataset, yielding a completeness score of 98.6% (Table 1). Compared to the previously assembled bisexual H. longicornis genome, which measured 2554.5 Mb with a contig N50 of 0.74 Mb and a BUSCO completeness of 91.3%14, the genome of the parthenogenetic population obtained in this study showed substantial improvements in continuity and completeness. Leveraging Hi-C sequencing data from the same population, we assembled three chromosomal haplotypes totaling 7378.94 Mb, which were mapped to 33 chromosomes, covering 98.04% of the total assembled genome sequence (Fig. 1d). Based on this assembly, we used the genome of the bisexual population as a reference to phase the chromosomal haplotypes into three subgenomes according to evolutionary relationships (Supplementary Fig. 1). The sizes of these subgenomes were 2454.2 Mb, 2438.4 Mb, and 2486.5 Mb, respectively (GenBank accession number: PRJNA1133666).Table. 1 Genome assembly and annotation statistics of parthenogenetic H. longicornisFull size tableTo achieve comprehensive and precise gene annotation, we employed long-read Iso-Seq on the same parthenogenetic population, generating 147,239 consensus isoforms with an average length of 1834 bp (Supplementary Table 3). By integrating these long-read transcripts into the genome annotation, we identified 28,591, 28,584 and 28,735 genes across the three subgenomes, respectively. The average coding sequence (CDS) length of parthenogenetic genes reached 1415 bp, with a 59.0% increase over the bisexual annotation. Iso-Seq analysis of the parthenogenetic tick genome significantly improved the gene models, resulting in a 5.3-fold increase in 5’ UTR elements (n = 19,851), 7.1-fold increase in 3’ UTR elements (n = 22,015), and 5.5-fold increase in alternative splicing events (n = 6430) over the annotation without Iso-Seq data (Supplementary Fig. 2), as illustrated in the example of DDX5 gene (Fig. 1e). Major alternative splicing events included exon skipping (39.1%), intron retention (31.7%), alternative 3’ splice sites (14.9%), alternative 5’ splice sites (11.4%) and mutually exclusive exon (2.9%, Supplementary Fig. 3). Furthermore, 54.9% of the parthenogenetic genome sequence was annotated as repetitive elements, including Class I/LINE (7.8%), Class I/LTR (21.0%), and Class II/DNA transposons (26.2%), with similar proportions across the three subgenomes (Supplementary Fig. 4). Compared to existing genome assemblies of bisexual H. longicornis, our assembly provided three phased subgenomes with significantly enhanced quality, supporting the future exploration of the triploid genome.Likely origin from an autopolyploid eventTo investigate the origin of parthenogenetic triploids, we constructed a phylogenetic tree based on orthologous gene families using genome data from published tick species14,15. The three subgenomes of the parthenogenetic tick formed a monophyletic clade, showing the highest similarity among themselves, while the diploid bisexual H. longicornis appeared as the closest outgroup (Fig. 2a). The evolutionary placement of bisexual H. longicornis outside of the parthenogenetic triploids suggests that the formation of the parthenogenetic triploids resulted from an autopolyploid event or involvement of closely related species16. The estimated divergence time between parthenogenetic and bisexual H. longicornis ticks is approximately 9.8 million years ago (MYA). In a triploid genome, two forms of heterozygosity were observed across the three subgenomes: one where one allele differed from the other two (abb), and another where all three alleles were different (abc). K-mer analysis revealed that the “abb” form (4.01%) in parthenogenetic H. longicornis was 3.4 times more prevalent than the “abc” form (1.18%) (Fig. 2b), and comparable to the “ab” form (3.60–4.18%) observed in the diploid genome of bisexual H. longicornis (Supplementary Fig. 5). This finding further supports the hypothesis that the parthenogenetic triploid genome arose from the duplication of a single haploid, possibly due to the fusion of unreduced gametes generated under environmental stress with haploid sperm17.Fig. 2: Phylogenetic and genomic characteristics of parthenogenetic H. longicornis in comparison to bisexual H. longicornis.a The phylogenetic tree based on protein sequences of single-copy gene families using maximum-likelihood method. The three subgenomes of the parthenogenetic tick form a monophyletic clade, exhibiting the greatest similarity to one another. The number to the right of each node indicates the estimated divergence time (in million years ago). PHl denotes parthenogenetic H. longicornis. b Structure of heterozygous sites in the triploid genome and a hypothesis model for the origin of parthenogenetic ticks. The solid lines in the homologous chromosomes correspond to mutations, and colors correspond to distinct origins. The percentages in parentheses represent the proportion of each heterozygous form (e.g., “ab” in diploid genomes, and “abb” or “abc” in triploid genomes), estimated using Genomescope 2.0. c Chromosome synteny between the three subgenomes of the parthenogenetic ticks and the bisexual ticks. The chromosome numbers are indicated on the top. Light coral and steel blue lines represent synteny blocks involved in chromosome fission and fusion events. d Gene ontology (GO) enrichment analysis. Gene families that are the significantly expanded at the ancestral node of the three parthenogenetic subgenomes are included. The size of each circle represents the number of the enriched GO term in the database. The color denotes the p-value of the enriched GO term using Fisher’s exact test.Full size imageTo evaluate the selection pressures on the three subgenomes, we calculated the nonsynonymous substitution rate (Ka), synonymous substitution rate (Ks), and the Ka/Ks ratio for each homologous gene in the subgenome compared to its homolog in the bisexual tick genome. The Ka/Ks ratio for genes in subgenome A (median Ka/Ks = 0.254) was significantly higher than those in subgenome B (median Ka/Ks = 0.243, Wilcoxon signed rank test, p = 0.002) and subgenome C (median Ka/Ks = 0.239, Wilcoxon signed rank test, p = 1.9 × 10−5, Supplementary Fig. 6), indicating that genes in subgenome A were under more relaxed purifying selection compared to their homologs in subgenomes B and C. This pattern is similar to the divergent selection pressures observed among subgenomes in allopolyploid species like plants18. Chromosome chr01 exhibited the highest Ka/Ks ratio (median Ka/Ks = 0.34, Supplementary Fig. 7) compared to other chromosomes (median Ka/Ks = 0.23, Wilcoxon rank sum test, p < 2.2 × 10⁻¹⁶). Notably, chr01 was identified as a sex chromosome (Supplementary Fig. 8), suggesting a faster evolutionary rate for the sex chromosome in the parthenogenetic population. Similarly, in studies on parthenogenetic species with typical X0 systems, such as aphids19,20, the dN/dS ratio of the X chromosome is also significantly higher than that of autosomes, confirming the phenomenon of accelerated evolution of the X chromosome.Synteny analysis between the parthenogenetic and bisexual genomes revealed that the parthenogenetic subgenomes shared 12,738–14,344 syntenic genes, whereas they shared 7750–9962 syntenic genes with the bisexual genome (Supplementary Fig. 9), indicating a high degree of homology among the three parthenogenetic subgenomes. Remarkably, we identified chromosome fission and fusion events between the genomes of parthenogenetic and bisexual ticks. Specifically, the parthenogenetic chromosome 11 originated entirely from the fission of bisexual chromosome 2, while the parthenogenetic chromosome 7 resulted from the complete fusion of bisexual chromosomes 7 and 11 (Fig. 2c). Gene function enrichment analysis showed that genes associated with chromosome fission and fusion were primarily involved in pathways such as apoptosis, fatty acid biosynthesis, and Hedgehog signaling (Supplementary Fig. 10). These findings suggest potential roles in the development, metabolism, and unique reproductive mode of the parthenogenetic population, warranting further investigation.To investigate the evolution of gene families following whole-genome duplication, we conducted a comparative genomic analysis between the parthenogenetic triploid and the bisexual diploid. The birth-death model of gene families revealed that the three subgenomes of the parthenogenetic tick respectively harbored 131, 123, and 151 significantly expanded gene families, as well as 138, 98, and 98 significantly contracted gene families. In contrast, the bisexual genome exhibited only 71 significantly expanded and 76 significantly contracted gene families (Supplementary Fig. 11), suggesting that the rates of gene family expansion and contraction accelerated following polyploidization, potentially enhancing the ecological adaptability21. To pinpoint major alteration of gene families during the differentiation between parthenogenetic and bisexual ticks, we further analyzed the functions of 72 expanded gene families at the ancestral nodes of the three subgenomes in the parthenogenetic tick. Gene Ontology (GO) analysis revealed that these genes were significantly enriched in the xenobiotic catabolic process, cell cycle checkpoint signaling, and positive regulation of the reproductive process (Fig. 2d), indicating that triploidization led to functional enhancements in environmental adaptation, cell cycle regulation, and reproduction.To assess gene loss and retention among the three subgenomes (A, B, and C), we analyzed their gene content using multiple approaches. Overall, the three subgenomes exhibit strong functional complementarity. For example, KEGG Orthology (KO) annotation identified 5117 KO families across the three subgenomes, of which 74.6% were shared by all three. When considering any two subgenomes together, the coverage increased to 98.3–98.8%, suggesting that genes absent in one subgenome are often retained in another. We further analyzed the functional categories of the missing KO families. Subgenome A exhibited greater gene loss in Genetic Information Processing (n = 69), particularly in pathways related to translation and DNA repair, compared to subgenomes B (n = 44) and C (n = 47). In contrast, subgenome B showed more extensive loss in Organismal Systems (n = 55), especially in categories related to the immune and nervous systems, compared to A (n = 28) and C (n = 45). These patterns likely reflect differential retention and pseudogenization following triploidization. Similar gene loss patterns were observed in BUSCO and OrthoFinder-based gene family analyses (Supplementary Fig. 12), consistently indicating that gene loss in individual subgenomes is largely compensated by retention in the others.Genes linked to parthenogenesisTo explore the mechanisms related to triploid reproduction and embryonic development in parthenogenetic H. longicornis, we compared the ovary transcriptional profiles from ‌our previously published study‌ induced by blood-feeding between parthenogenetic and bisexual H. longicornis (GenBank accession numbers: SRX11972413-SRX11972430)22, taking advantage of the parthenogenetic triploid genome assembled in this study. We firstly identified 7154 1:1:1:1 homologous gene pair between the three subgenomes and the bisexual genome. Comparative analysis revealed that the expression levels of homologous genes within each parthenogenetic subgenome were significantly lower than those in bisexual ticks at all stages of blood-feeding—early, partially engorged, and fully engorged (Fig. 3a, Supplementary Fig. 13). Among the 7154 homologous genes, the expression patterns of the three parthenogenetic subgenomes were highly similar to those of the bisexual ticks (Supplementary Fig. 14; Spearman’s rank correlation test, all P < 0.001). The reduced but similar expression profiles in the triploid parthenogenetic ticks compared to the diploid bisexual ticks suggest the presence of a dosage compensation mechanism in parthenogenetic ticks, similar to observations in tetraploid fish11.Fig. 3: Functional genes associated with parthenogenetic H. longicornis reproduction.a Expression of homologous genes of three subgenomes and bisexual H. longicornis. Each cell represents the expression level of a homolog gene in terms of transcripts per million reads (TPM). Transcriptome data are obtained from ovaries dissected at early blood-feeding early stage (48 h), partially engorged stage (72 h), and engorged stage. b Histograms of the genome-wide expression divergence of homologous genes in ovarian tissues of parthenogenetic H. longicornis. The x axis indicates the ratio of the gene expression between homologous gene pairs. N values indicate the number of dominant genes (fold change >2) in each subgenome. The p-values indicate the significance of subgenome dominance by comparing the expression levels of homologous gene pairs from the two subgenomes based on Wilcoxon rank-sum test. c Ka/Ks ratios of genes in dominant and balanced groups. The gray boxes represent the genes in balanced group. The line in the middle of each box plot represents the median of the dataset; the upper and lower edges of box plot indicate the third quartile and first quartile, respectively; and the line extending from the edge is 1.5 times the interquartile range. The p-values are estimated using the Wilcoxon rank-sum test. d Genes related to parthenogenesis in the cell cycle and oocyte meiosis pathway. TPM values of genes that are differentially expressed in parthenogenetic tick ovary but not expressed in bisexual tick during blood feeding were plotted. e Functional genes that promote stable reproductive development in the parthenogenetic H. longicornis. Genes downregulated during the transition from the early blood-feeding stage to the partially engorged stage are highlighted in blue, while those with no significant expression changes are shown in gray. G1, S, G2, and M represent the four phases of the cell cycle.Full size imageWe further analyzed the expression differences among the three subgenomes at various blood-feeding stages. In both the early blood-feeding and engorged stages of ovarian tissues, the expression levels of homologous genes in subgenome A were significantly higher than those in subgenomes B and C (Fig. 3b; Wilcoxon signed-rank test, all P < 0.001), while no significant difference in expression levels was observed between subgenomes B and C. During the partially engorged stage, no significant differences were detected in pairwise comparisons (Supplementary Fig. 15). Notably, the Ka/Ks ratios of differentially expressed genes between subgenomes were significantly higher than those of non-differentially expressed genes (Fig. 3c; Wilcoxon signed-rank test, all P < 0.001). These findings suggest that differential expression and accumulation of genetic mutations among these homologous genes may facilitate neo- or sub-functionalization, ultimately driving functional innovation in gene evolution23,24.Next, we focused on analyzing gene expression related to embryonic development in parthenogenetic and bisexual ticks. Within two key reproduction-related pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG), we discovered 27 genes in the cell cycle pathway and 13 genes in the oocyte meiosis pathway that were differentially expressed across various blood-feeding stages in parthenogenetic H. longicornis, but were not expressed in the bisexual ticks (Fig. 3d). The ovarian tissue of H. longicornis undergoes rapid development during the blood-feeding stage, and these genes may play a critical role in regulating meiosis and promoting the transition of oocytes into embryos. Based on the functional research background of these 40 genes, we primarily categorized them into the cell cycle involved to cell cycle regulation, DNA replication, chromosomal segregation or dislocation, and the oocyte meiosis involved to meiotic exit or arrest (Supplementary Fig. 16). Notably, five genes (CCNA, CCNB3, CDC20, APC12, APC13) that promote cell cycle progression showed a specifically downregulated expression from the early blood-feeding to partially engorged stage (Fig. 3e). During this stage, the ovarian cells rapidly proliferate, requiring a substantial synthesis of these proteins25. This regulatory change in expression may represent an adaptive mechanism shaped by long-term parthenogenesis, contributing to the reproductive development and stable inheritance of triploid lineages26,27. While the evolutionary origin of triploidy may involve historical chromosomal events, the dynamic expression of these cell cycle and meiosis-related genes likely reflects ongoing adaptations to maintain genomic stability and embryonic viability in the exclusively parthenogenetic reproduction of present-day H. longicornis.Higher vector capacityTo investigate the impact of polyploid evolution on the potential transmission capability for pathogens, we inoculated Rickettsia heilongjiangensis and severe fever with thrombocytopenia syndrome virus (SFTSV) into parthenogenetic and bisexual H. longicornis, respectively. R. heilongjiangensis exhibited significant higher proliferation levels in parthenogenetic than bisexual ticks throughout the observation period (Fig. 4a, repeated measures ANOVA, P < 0.001). SFTSV proliferation was significantly higher in parthenogenetic ticks during the first 20 days (repeated measures ANOVA, P < 0.001), although there was no significant difference in SFTSV loads between parthenogenetic and bisexual ticks 25 days post-infection. This indicates that Rickettsia and SFTSV have a stronger replication capacity in parthenogenetic H. longicornis.Fig. 4: Differences in pathogen replication and transcriptome profile after infections between parthenogenetic and bisexual ticks.a Replication curves of Rickettsia heilongjiangensis and severe fever with thrombocytopenia syndrome virus (SFTSV) in the parthenogenetic and bisexual ticks. The replications of both pathogens are measured at 5d, 10d, 15d, 20d and 25d post-injection, with three biological replicates at each time point. Shaded region indicates mean ± standard deviation. b Histograms of the genome-wide expression divergence of homologous genes in whole body of parthenogenetic H. longicornis 15 days after pathogen infections. The x axis indicates the ratio of the gene expression between homologous gene pairs. N values indicate the number of dominant genes (fold change > 2) in each subgenome. The p-values indicate the significance of subgenome dominance by comparing the expression levels of homologous gene pairs from the two subgenomes based on Wilcoxon rank-sum test. c The heatmap of differentially expressed genes after Rickettsia and SFTSV infection. Each cell in the heatmap represents the fold-change of the genes with same Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation, with upregulated genes in red on the left and downregulated genes in blue on the right. d Differentially expressed genes in the Toll and Imd pathways after R. heilongjiangensis infection. The upregulated genes are marked in rose pink, and the downregulated genes are marked in light blue. The rounded rectangles highlight genes with significant transcriptional changes, and dashed lines indicate genes that are not annotated by KEGG.Full size imageWe analyzed the expression differences of homologous gene pairs across the three subgenomes after infection with two pathogens. In both the uninfected control group and the groups infected with Rickettsia or SFTSV, the expression levels of homologous genes in subgenomes B and C were significantly higher than in subgenome A (Fig. 4b; Wilcoxon signed-rank test, all P < 0.05). This suggests that subgenomes B and C may play a more prominent role in response to pathogen infections. Following infection with R. heilongjiangensis and SFTSV, parthenogenetic H. longicornis exhibited 1048 (459 upregulated and 589 downregulated) and 1120 (387 upregulated and 733 downregulated) differentially expressed genes (DEGs), respectively. In contrast, the bisexual ticks had fewer DEGs, with 797 (359 upregulated and 438 downregulated) and 746 (323 upregulated and 423 downregulated) for the respective pathogens (Fig. 4c). These findings suggest that parthenogenetic ticks may have a more complex gene regulatory response to pathogen infections.Subsequently, to explore the immunological evolutionary characteristics of parthenogenetic H. longicornis, we compared the differentially expressed genes in the Toll and Imd pathways between parthenogenetic and bisexual populations. After Rickettsia infection, the expression levels of extracellular cytokines Spatzle (SPZ), transmembrane cytokine receptor (Toll), interleukin-1 receptor-associated kinase 1 (Pelle), and dual oxidase (DUOX) were upregulated in both parthenogenetic and bisexual ticks (Fig. 4d), emphasizing the important roles of these genes in immune signaling and antibacterial defense28. Notably, this observation aligns with prior studies documenting the profound reduction of the IMD pathway in ticks29, which may explain the limited number of differentially expressed genes (DETs) detected in this pathway. However, our findings suggest that remnant components of the IMD pathway, such as Tube and Bendless, retain pathogen-responsive functionality: parthenogenetic ticks specifically upregulated interleukin-1 receptor-associated kinase 4 (Tube) and E2 ubiquitin-conjugating enzyme (Bendless) post-infection, which are linked to pathogen defense and intracellular signaling regulation30,31,32. At the same time, it specifically downregulated c-Jun N-terminal kinase (JNKK), which may have contributed to the increased rickettsia infection33,34. Following infection with the SFTSV, the parthenogenetic H. longicornis specifically downregulated X-linked inhibitor of apoptosis protein (XIAP) and transforming growth factor-β activated kinase 1 (TAK1) (Supplementary Fig. 17), which may contribute to immune evasion35,36. As parthenogenetic H. longicornis exhibited a higher Rickettsia load and faster replication of SFTSV, it is speculated that its complex immune regulatory mechanisms enhance pathogen tolerance rather than eliminating pathogens37, thereby increasing its capacity to serve as a pathogen vector38.To further investigate potential microbial factors contributing to the differences in vector competence, we compared the composition and activity of Coxiella symbionts in parthenogenetic and bisexual H. longicornis. Metagenomic analysis based on whole-genome sequencing of five individuals from each group revealed that Coxiella burnetii was the predominant symbiont in both populations, with strain-level similarity (average ANI: 99.64%). Notably, Candidatus Coxiella mudrowiae was exclusively detected in several bisexual individuals but absent in the parthenogenetic group. Furthermore, mapping ovary transcriptomes from three feeding stages (early, partially engorged, and fully engorged) to the Coxiella genome revealed significantly higher transcript abundance in parthenogenetic ovaries, especially during the fully engorged stage (Student’s t test, P < 0.05) (Supplementary Fig. 18). These results suggest that Coxiella symbionts may play a more active role in parthenogenetic ticks during blood feeding, potentially contributing to their enhanced tolerance to pathogens and higher vector capacity.Greater dispersal potentialWe further investigated the role of parthenogenetic population structure in the global dissemination of H. longicornis. Parthenogenetic H. longicornis is morphologically similar to the bisexual ticks, leading to a lack of distinction in most public records. To differentiate between the two, we conducted next-generation sequencing on five parthenogenetic and five bisexual laboratory ticks. Utilizing the GATK4 pipeline (Supplementary Fig. 19), we identified 192 single nucleotide polymorphisms (SNPs) in the mitochondrial genome, demonstrating that parthenogenetic and bisexual ticks can be distinguished based on these mitochondrial SNPs. Subsequently, we analyzed 465 mitochondrial genomes from H. longicornis samples collected throughout China, including our published39 and unpublished data, and 87 full-length mitochondrial sequences obtained from GenBank (Supplementary Table 4). A total of 127 sequences were identified as belonging to parthenogenetic ticks, including 119 from China, three from the USA, two from Australia, and one each from New Zealand, Japan, and South Korea (Fig. 5a). Phylogenetic analysis revealed that the parthenogenetic population was divided into three clades: clade 1, primarily from southwestern China (Chongqing, Sichuan, Guizhou provinces); clade 2, from central and eastern China; and clade 3 from eastern China and other countries (Supplementary Fig. 20). This suggests that clade 3 is crucial in the global geographic expansion of H. longicornis. Bisexual H. longicornis sequences from China and Japan formed five clades, with the closest branches to the parthenogenetic ticks found in Japan and various regions of China. Further geographical genetic distance analysis revealed that the geographic distribution range of parthenogenetic population was 4.3 times wider than that of bisexual population within the same genetic distance range (Fig. 5b). The mean dispersal index for the parthenogenetic population was 1.8 × 10⁷, significantly higher than 9.5 × 10⁵ for the bisexual population (Student’s t test, P < 0.001), indicating a greater dispersal capacity of parthenogenetic H. longicornis.Fig. 5: Phylogeographic characteristics and ecological adaptability of parthenogenetic and bisexual Haemaphysalis longicornis.a Phylogenetic tree of parthenogenetic and bisexual H. longicornis across the world. Maximum-likelihood method are used to build the tree using mitochondrial genomes of ticks collected in China and published mitochondrial genomes from GenBank. Parthenogenetic H. longicornis is indicated in red, and bisexual H. longicornis in blue. b Phylogeographic analysis of parthenogenetic and bisexual H. longicornis. Each point represents a pairwise comparison between two tick samples in terms of geographic distance (x axis) and genetic distance based on mitochondrial sequence (y axis). c Differences in genome-wide mutations between parthenogenetic and bisexual H. longicornis. Each point represents the average number of mutations per subgenome of parthenogenetic H. longicornis (n = 12) and bisexual H. longicornis (n = 12) deep sequencing samples. Wilcoxon rank-sum test is used to test the significance of the difference between two populations for each type of mutations. The line in the middle of each box plot represents the median of the dataset; the upper and lower edges of box plot indicate the third quartile and first quartile, respectively; and the line extending from the edge is 1.5 times the interquartile range. d Morphological and reproductive characteristics of parthenogenetic and bisexual H. longicornis. Wilcoxon rank-sum test is used to test the significant differences between the two populations. The p-values < 0.01 are summarized with three asterisks, and p-values < 0.0001 are summarized with four asterisks. e Potential global distribution map of parthenogenetic H. longicornis and bisexual H. longicornis. The probabilities of habitat suitability are shown for parthenogenetic H. longicornis (in red) and bisexual H. longicornis (in blue) predicted by ecological niche modeling.Full size imageIt is generally believed that parthenogenetic species face challenges in overcoming deleterious mutation accumulation due to reduced genetic diversity, leading to decreased environmental adaptability40. This perspective appears contradictory to the observed widespread expansion of parthenogenetic H. longicornis. To investigate this, we compared deleterious (loss-of-function) mutations between field-collected parthenogenetic and bisexual ticks. The number of deleterious mutations per subgenome in parthenogenetic ticks was 49 on average, significantly lower than the mean of 905 in bisexual ticks (Wilcoxon rank-sum test, P < 0.0001), challenging the conventional understanding that deleterious mutation accumulation reduces the adaptability of parthenogenetic species17. Moreover, non-synonymous mutations in parthenogenetic ticks constituted only 5.6%, and synonymous mutations were 7.1% of those found in bisexual populations, both with significant differences (Wilcoxon rank-sum test, both P < 0.001, Fig. 5c). These findings suggest a lower mutation rate in the parthenogenetic population, which might be attributed to the lack of genetic recombination in parthenogenesis17. However, without the capacity for genetic recombination, these ticks face significant constraints in incorporating new beneficial mutations. An alternative explanation may involve concerted evolution within these genomes, which could promote sequence homogenization across subgenomes and facilitate error correction of deleterious mutations41. Additionally, the number of positively selected genes in the three subgenomes of the parthenogenetic population was 63, 47, and 57, respectively, all lower than the 314 identified in the bisexual population (Supplementary Fig. 21), further supporting the lower mutation rate in the parthenogenetic population. Collectively, both the reduced mutation rate and lower accumulation of deleterious mutations underpin the current adaptive advantage of parthenogenetic H. longicornis.To assess the phenotypic impact of the long-term accumulation of deleterious mutations, we compared the morphological and reproductive characteristics between parthenogenetic and bisexual populations (Fig. 5d, Supplementary Fig. 22). Compared to bisexual population, parthenogenetic ticks exhibited significantly larger dorsal shield (4.16 ± 0.16 vs 3.68 ± 0.25 mm²), genital pores (357.21 ± 25.49 vs 321.21 ± 27.52 μm), and eggs (76.60 ± 3.30 vs 72.35 ± 3.56 μg). Additionally, because mating is not required, the blood-feeding and egg incubation periods of parthenogenetic H. longicornis were significantly shorter than those of bisexual population. Polyploidization brings unique physiological changes to parthenogenetic populations and is crucial for cell growth and yolk biosynthesis. It may lead to larger cell volumes and faster growth rates42. The reduced hatching rate of parthenogenetic H. longicornis eggs (80.85 ± 7.72% vs 89.01 ± 7.72%) might reflect the instability of the triploidy on embryonic cell development. These phenotype results further indicate that parthenogenetic H. longicornis might have successfully overcome the reproductive and developmental issues imposed by the triploid genome.To further support this view, we conducted ecological model prediction for the two populations (Fig. 5e). The model predicted that the suitable habitats of bisexual H. longicornis were confined within East Asia (Supplementary Figs. 23, 24). In contrast, ecological niche modeling predicted that parthenogenetic H. longicornis could inhabit a broad range of suitable habitats across multiple continents (Supplementary Figs. 25, 26), including East Asia (China, North Korea, South Korea, Japan), Oceania (Australia, New Zealand), and North America (e.g., the United States). Notably, South American countries such as Uruguay, Chile, and Brazil were also identified as ecologically suitable, despite limited or no current records. These findings highlight the potential for future spread into new regions, particularly in South America, if accidental introductions occur.DiscussionWe assembled and annotated a high-quality genome of parthenogenetic H. longicornis, achieving superior genome assembly continuity and completeness using ultra-depth HiFi long-read sequencing data. Subsequently, a chromosome-level assembly of the initial haplotypes was completed through Hi-C data, resulting in three high-quality subgenomes. Additionally, Iso-Seq was performed on parthenogenetic H. longicornis, enabling a comprehensive annotation of alternative splicing events. Most polyploid genome assemblies come from allopolyploid species, such as hexaploid gibel carp (AAABBB) and a tetraploid frog (LLSS)43,44, which typically include a single representative genome for multiple haplotypes of the same origin but rarely differentiate between them. In this study, we differentiated the three haplotypes of the same origin and obtained a fully-phased genome assembly, providing critical data investigate the complex genetic and evolutionary mechanisms underlying parthenogenesis in H. longicornis.In the allopolyploid genomes, subgenomes can experience varying degree of selection pressures, biased gene retentions, and biased gene expression (i.e. subgenome dominance)11,18,45. Subgenome dominance has not been frequently reported in young autopolyploid species, likely due to lack of phased polyploid genome. By integrating the phased triploid genome and transcriptome data, it was observed that homologous genes in subgenome A of parthenogenetic H. longicornis exhibit higher expression levels during key stages of embryonic development, while subgenomes B and C show higher expression levels following pathogen infection, demonstrating functional divergence among the subgenomes in H. longicornis. Further analysis of gene expression data revealed the unique expression of multiple genes involved in the cell cycle and oocyte meiosis pathways within the ovary of blood-feeding parthenogenetic H. longicornis. These genes may play crucial roles in overcoming constraints on embryonic development and promoting genomic stability in parthenogenetic populations25. Additionally, parthenogenetic H. longicornis exhibited higher pathogen loads and more sophisticated immune manipulation, suggesting a heightened pathogen tolerance and an enhanced capacity to serve as a vector.To assess the dispersal potential of parthenogenetic H. longicornis, mitochondrial genome data were used to distinguish parthenogenetic from bisexual H. longicornis populations around the world, which had not been systematically categorized in previous distribution studies5. It was found that bisexual populations are mainly confined to East Asia, while parthenogenetic populations have spread to the Americas and Australia, occupying a wider geographic range. A major challenge faced by parthenogenetic species is the accumulation of deleterious mutations in the absence of sexual recombination, which could lead to the extinction of parthenogenetic population40. However, we found that the genome of parthenogenetic H. longicornis exhibited lower levels of deleterious mutations compared to bisexual populations and did not show signs of mutational degradation, as seen in clonally reproducing fish46. Furthermore, the comparison of the morphological and reproductive characteristics of the two populations further demonstrated that parthenogenetic populations have successfully overcome the reproductive degeneration pressures caused by asexuality and triploid instability17.In conclusion, our study suggests that the evolutionary origin of parthenogenetic H. longicornis likely stems from an autopolyploid event. The triploid genome and parthenogenetic mode of reproduction have provided several advantages to H. longicornis, including increased reproductive efficiency, heightened pathogen tolerance, enhanced vector capacity, and greater dispersal potential. Future studies should focus on the global surveilence and transmitted pathogens of parthenogenetic H. longicornis to better understand the public health implications of this unique tick population.MethodsTicks and pathogensThe parthenogenetic and bisexual Haemaphysalis longicornis were collected by flag-dragging and 50 ml ventilated centrifuge tube from Cangxi County (31◦ 44’ 35” N, 105◦ 49’ 04” E), Sichuan Province, and Yu County (40◦ 03’ 03” N, 115◦ 23’ 15” E), Hebei Province, China. The collection of wild ticks was carried out with the permission and assistance of the local forestry protection agency. The parthenogenetic population have undergone more than ten generations of in-bred laboratory culture, were maintained in an incubator at 26 ± 1°C, 85 ± 5% relative humidity, with a 12:12 h light-dark cycle during the non-blood-feeding phase. The strains of Rickettsia heilongjiangensis and severe fever with thrombocytopenia syndrome virus (SFTSV) used in this study were isolated from field-collected H. longicornis ticks and previously characterized in our laboratory. Ticks were placed on experimental rabbits’ ears during blood-feeding. The rabbits used for tick feeding were New Zealand White Closed Colony, female, 8 weeks old (about 2.2 kg) at the start of the experiment. They were individually housed in 60 × 50 × 45 cm stainless steel cages (wood shavings changed every 2 days), with the housing room maintained at 22–24 °C, 50–60% relative humidity, and a 12 h light/12 h dark cycle. All experimental procedures adhered to the protocols approved by the Institutional Animal Care and Use Committee (IACUC) of the Beijing Institute of Microbiology and Epidemiology. The animal ethics approval number is IACUC-DWZX-027-20. We have complied with all relevant ethical regulations for animal use.Genome ploidy analysisThe differentiation between parthenogenetic and bisexual H. longicornis was achieved through karyotype analysis using Sysmex Partec CyStain DNA 1 Step Kit (Sysmex Partec, Germany)9. The basis capitulum of adult ticks from parthenogenetic and bisexual populations were carefully severed, and the visceral tissues were dissected using fine forceps. The tissues were placed in a plastic dish containing 500 μl of CyStain UV Ploidy reagent and finely minced using a sharp blade to fully release the nuclei, and soaked for 1 min. The resulting suspension was filtered through a 50 μm CellTrics filter into a sample tube. Subsequently, 2 ml of CyStain UV Ploidy reagent was added and incubated in the dark for 2 min. The nuclei suspension was then analyzed using the CyFlow Space Flow Cytometer (Sysmex Partec, Germany) and the corresponding FloMax software.Genome size estimationPrior to de novo assembling, the genome size of the parthenogenetic H. longicornis was estimated using an Illumina short-read DNA sequencing dataset. Jellyfish (v2.2.10)47 was employed to determine the k-mer frequency, and GenomeScope 2.048 was utilized to estimate the genome size and heterozygosity rate.Genome sequencingA total of 17.730 μg of genomic DNA was extracted from the offspring of adult ticks (n = 90) from a single mother for Pacific Biosciences CCS library construction. The DNA quality was verified using agarose gel electrophoresis, confirming the high integrity of the DNA molecules49. For Pacific Biosciences sequencing, the genomic DNA was sheared into ~15 Kb fragments to construct a long-read library according to the manufacturer’s instructions, and then the library was sequenced on the Pacific Biosciences Sequel II system utilizing an 8 M SMRT Cell with the Sequel II Sequencing Kit (Pacific Biosciences, USA).Transcriptome sequencingTranscriptome samples were categorized into three groups: parthenogenetic female (n = 5), bisexual female (n = 5), and bisexual male (n = 5). Each group had one Iso-Seq sample and three RNA-seq samples sequenced to optimize sequence accuracy and completeness. Total RNA was extracted and purified from 5 ticks whole body for each sample using miRNeasy Mini Kit (QIAGEN, Germany). Transcripts Per Million (TPM) was employed as the normalization unit for the quantitative analysis of transcriptomic data. TPM values for samples in each group have been organized into a table (Supplementary Data 1), encompassing gene expression data from all experimental groups and their biological replicate samples (n = 3). Ovarian transcriptome data were derived from our previously published study22. Ovaries were dissected from parthenogenetic and bisexually populations at three distinct blood-feeding stages: early (48 h post-feeding), partially (96 h), and engorged. For each experimental group, 30 ovarian tissues were collected, homogenized, and pooled into three RNA-seq samples, which were then subjected to paired-end RNA sequencing using the Illumina Hiseq platform (Illumina, USA).Hi-C sequencingChromatin conformation capture data were obtained from adult ticks (n = 15) ‌issued from the same mother‌ as those used in genome sequencing. The ticks were dissected into several sections using scissors, and 2% formaldehyde was used to crosslink proteins with DNA and DNA with DNA fragments within the cells. The DNA was then digested with DpnII enzyme (New England Biolabs, USA), creating sticky ends on either side of the crosslinked fragments. A suitable labeling system was prepared to biotin-label the DNA ends. The blunt ends were ligated using T4 DNA ligase. The DNA was de-crosslinked and purified, followed by fragmentation of 1–4 µg samples using the Covaris S220 (Covaris, USA), with 300–700 bp fragments recovered. Streptavidin magnetic beads were used to capture the DNA fragments that retained interaction signals, followed by library construction. The concentration and insert size of the library were assessed using the Qubit 3.0 and GX platforms. Sequencing was performed with the Illumina platform using paired-end (PE) 150 base pair reads.De novo assemblyThe genome of H. longicornis was assembled using hifiasm (v0.19) with the obtained HiFi reads and parameters “l = 0, n = 4”. Genome assembly completeness was evaluated using BUSCO (v5.4.2)50 in protein mode (-m prot) with the arthropoda_odb10 dataset. The accuracy of the assembly was verified by mapping all Illumina paired-end reads to the assembled genome using BWA (v0.7.17)51 and HiFi reads using Minimap2 (v2.26)52. The mapping rate and genome coverage of sequencing reads were assessed using samtools (v1.15).Genome annotationProtein-coding genes were annotated using three approaches: ab initio prediction, homology prediction, and transcriptome prediction. Ab initio prediction was performed with Augustus (v3.1.0)53 and SNAP (2006-07-28)54. Homology-based prediction used GeMoMa (v1.7)55, referencing genes from H. longicornis (GCA_013339765), Ixodes scapularis (GCF_016920785), Rhipicephalus microplus (GCF_013339725), Dermacentor silvarum (GCF_013339745), and Drosophila melanogaster (GCF_000001215). For transcriptome prediction, three methods were used to assemble transcripts. The first method utilized Hisat (v2.1.0)56 and Stringtie (v2.1.4)57 for transcript assembly, followed by gene prediction with GeneMarkS-T (v5.1)57. The second method assembled transcripts using Trinity (v2.11)58 and then performed gene prediction with PASA (v2.4.1)59. The third method aligned Iso-Seq transcripts with gmap (2020-06-30) and predicted genes with PASA (v2.4.1)59. All the prediction results were integrated using EVM (v1.1.1)60 and refined with PASA (v2.4.1)59. Gene sequences were annotated against databases including NR, eggNOG, KEGG, SWISS-PROT, and Pfam61,62,63,64.GO and KEGG enrichment analysisGene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the topGO and clusterProfiler65 packages in R. GO term enrichment was conducted using Fisher’s exact test in the topGO package, while KEGG pathway enrichment was performed with the enricher function in clusterProfiler. For both analyses, p values were adjusted for multiple testing using the Benjamini–Hochberg method implemented via the p.adjust function in R (method = “BH”). Terms with adjusted p values (FDR) < 0.05 were considered significantly enriched.Chromosome assembly using Hi-C dataFollowing Hi-C library sequencing, paired-end reads were mapped to the draft assembly using BWA (v0.7.17). Valid interaction pairs were identified using HiC-Pro (v2.8.1)66, and the LACHESIS pipeline generated chromosome-level scaffolds. For the synteny analysis, synteny blocks were identified using MCScanX (version not specified)67 and JCVI (v0.9.13)68 with a cscore threshold of >0.5.Repeat annotationTransposable elements (TEs) were initially predicted using RepeatModeler2 (v2.0.1)69, which incorporated RECON (v1.0.8)70 and RepeatScout (v1.0.6)71. The predicted results were classified with RepeatClassifier using the known Dfam (v3.5) database. Subsequently, de novo prediction of LTRs was specifically performed with LTR_retriever (v2.9.0)72, relying on prediction results from LTRharvest (v1.5.10)73 and LTR_FINDER (v1.07)74. The de novo prediction results were then merged and deduplicated with known databases to create a species-specific repeat sequence database. Finally, RepeatMasker (v4.1.2)75 was employed to predict transposable elements in the genome based on the constructed repeat sequence database. Tandem repeat sequences were primarily predicted using the MIcroSAtellite identification tool (v2.1)76 and Tandem Repeat Finder (version 4.09, parameters: 2 7 7 80 10 50 500 -d -h)77.Non-coding RNA annotationFive types of non-coding RNA were annotated. tRNAs were identified using tRNAscan-SE (v1.3.1)78, rRNAs with barrnap (v0.9)79. miRNAs, snoRNAs, and snRNAs were predicted using the Rfam (v14.5)80 database via Infernal (v1.1)81.Pathogen infectionBoth R. heilongjiangensis and SFTSV were cultured in Vero81 cells using DMEM medium supplemented with 5% fetal bovine serum (FBS), maintained at 37 °C in a 5% CO₂ incubator. Pathogen injection was performed on both parthenogenetic and bisexual populations using the Eppendorf FemtoJet 4× microinjector (Eppendorf, Germany). Each tick was injected with 0.5 μL of purified pathogen culture via the lateral margin ventral of spiracular plate, with three adult ticks per group. After injection, ticks were not offered blood meals and were maintained in a thermostatic incubator (26 ± 1°C, relative humidity 75%, L: D = 12 h: 12 h). Pathogen proliferation in ticks was monitored at 5d, 10d, 15d, 20d and 25d post-infection using the TB Green® Premix Ex Taq™ kit (Takara, Japan) to extract RNA from whole ticks. The loads of R. heilongjiangensis and SFTSV were detected using quantitative real-time PCR with specific primers (R. heilongjiangensis, targeting Sca1 gene: Forward 5’-GTTTGTGGATGCGTGGTA-3’, Reverse 5’-AACCCGATAGTAGCAC-3’; SFTSV, targeting L segment: Forward 5’-ACATTCCATGCCATCTCAGG-3’, Reverse 5’-GTCCTCTTCCATCTCCACRATC-3’). Upon establishing the pathogen proliferation curves within H. longicornis, RNA-seq sequencing was performed on both infected parthenogenetic and bisexual populations at 20 d post-infection. Uninfected adult ticks served as the control group.Differential expression analysisWe collected samples of parthenogenetic and bisexual H. longicornis both before and after infection for RNA extraction and transcriptome sequencing. The RNA-Seq reads were aligned to the reference genome using HISAT2 (v2.1.0)56. We used featureCounts82 to count the number of mapped reads on each gene and transcript, and estimated gene expression levels using transcripts per kilobase million mapped reads (TPM). Statistically differentially expressed genes were identified using edgeR (v3.28.1)83 with a false discovery rate (FDR) ≤ 0.05 (Benjamini–Hochberg method) and |log2(fold change)| ≥ 1. Enriched KEGG pathways and GO terms were identified using the clusterProfiler (v3.6.1)84 package (FDR < 0.05).Iso-Seq analysis pipelineThe adult female samples of parthenogenetic was used for RNA extraction and library construction following standard protocol of Iso-Seq from Pacific Bioscience. Each library was sequenced on the PacBio Sequel-II Systems (Pacific Bioscience, CA). The isoseq3 pipeline (v3.8.2) (https://github.com/PacificBiosciences/IsoSeq) characterized the full-length transcripts through three major steps: generating CCS reads, classifying full-length (FL) reads, and clustering full-length non-chimeric (FLnc) reads. First, CCS reads were generated using CCS (v6.4.0) from the subreads BAM files with a minimum quality of 0.9 (–min-rq 0.9) and minimum number of FL subreads (n = 3). Second, Lima (v2.7.1) was used to remove the cDNA primers. The polyA tail and artificial concatemers were removed to produce FLnc reads. Third, FLnc reads were clustered by Isoseq3 to generate high quality FL consensus sequences. The high-quality FL reads were then aligned to the reference genome using pbmm2, and redundant transcripts were collapsed based on exonic structures. Finally, pigeon (v1.0.0) was used to classify transcripts against annotations and filter out potential artifacts.Gene family and phylogenetic analysisTo elucidate the evolutionary history of H. longicornis, a maximum-likelihood phylogenetic tree was constructed using protein sequences from the three subgenomes of parthenogenetic H. longicornis, bisexual H. longicornis (China National Center for Bioinformation: PRJCA040748) and seven other tick species14,15,85 and two mites (GCF_002443255.1 and GCF_000255335.2). OrthoFinder (v2.5.4)86 clustered protein sequences into orthogroups, alignments were built using MUSCLE (v3.6)87, and a maximum-likelihood tree was constructed with IQ-TREE (v2.2.0-beta)88. Divergence time of each node was estimated using MCMCTREE (v4.9) in PAML (v4.10.6)89 using an independent-rates model (clock = 2). The calibration points obtained from the TimeTree database (http://timetree.org/)90 included: (i) 336.0–453.4 million years ago for Parasitiformes; (i) the divergence of Ixodidae (62.1–316.5 Mya); (ii) and 55.9–134.0 million years ago for Metastriata; (ii) the split between Rhipicephalus sanguineus and Rhipicephalus microplus (37.0–52.0 Mya), and (iii) the split between R. sanguineus and Dermacentor silvarum (46.6–118.3 Mya). Additionally, we applied a soft maximum bound of 33.0 Mya for the divergence between BHL and PHL, based on the estimated divergence time between Haemaphysalis longicornis and its closest relative, Haemaphysalis flava. As there is no direct divergence estimate between Ixodes scapularis and Ixodes persulcatus in the TimeTree database—and given that I. scapularis is more distantly related to Ixodes persulcatus than I. ricinus—we set a minimum bound of 47 Mya for the divergence between I. persulcatus and I. scapularis, based on the estimated divergence time between I. persulcatus and I. ricinus.The CAFÉ (v5.0)91 was used to analyze gene family expansion and contraction by comparing family sizes between parent and child nodes in the phylogenetic tree, using a p-value threshold of 0.05 to identify significant changes. For gene presence/absence analyses based on KO annotation, particularly for functionally important gene families, we adopted a conservative strategy. In addition to the HLB genome from Jia et al.14, we incorporated a second independently annotated bisexual genome65, which has a BUSCO completeness of 87.1%. A gene family was considered truly absent only if it was missing from both assemblies.Positive selection analysisFor the genes in each orthogroup identified in the “Gene Family and Phylogenetic Analysis” section, protein sequences were aligned using MAFFT (v7.505)92 and gene trees were constructed with FastTree (v2.1.11)93. Paralogous genes were pruned from these alignments using PhyloPYPruner (v1.2.4)94, retaining only one-to-one orthologs present in at least seven species or subgenomes. The corresponding CDS sequences were then aligned using Prank (v.170427)95 based on the protein sequence alignments. CODEML from the PAML package90 was used to detect signals of positive selection under branch-site models along the predefined branch. A likelihood ratio test was performed to assess the significance of the branch-site model against the null model. The p-values were calculated using Chi-square statistics, and an FDR < 0.05 (BH method) was applied to eliminate potential false positives. Pairwise Ka/Ks ratio between the orthologous sequence pairs from each parthenogenetic subgenome and bisexual H. longicornis genome were estimated using “wgd ksd –one_v_one” command (v1.1)96.Mitochondrial phylogenetic analysisBased on the second-generation sequencing data from laboratory-reared parthenogenetic and bisexual populations, the full-length mitogenomes were de novo assembled using the “all” module from MitoZ (v2.3)97. We then identified the variants in the full-length mitochondrial genomes of both populations using Geneious Prime (version 2023.2)98, confirming that the mitochondrial genome can differentiate between the two populations. Next, we analyzed 465 mitochondrial genomes from our sequencing database of H. longicornis collected in the field for SNP calling. Variants in the mitochondrial genome were called using the GATK4 (v4.1.2.0) joint genotype pipeline99. Variants were filtered to include those with a read depth of ≥3 and a quality score of ≥30. Additionally, individuals with a genotype rate of >0.9 were included to generate the mitochondrial consensus sequence for each sample using bcftools (v1.9)100. We then aligned the sequences using MAFFT (v7.505)92 and constructed the phylogenetic tree with IQ-TREE (v2.2.0-beta)88, utilizing the best-fit substitution model and ultrafast bootstrap with 1000 replicates.Geographical genetic distance analysisIn this study, nucleotide sequences were processed to remove redundancy using the CD-HIT-EST program with a threshold of 0.99100. The genetic distances for each sequence were calculated using MEGA11101. Geographic distances were determined based on latitude and longitude using the distm function from the geosphere R package. We utilized the dispersal index (I) to indicate dispersal velocity and performed an independent sample t-test to compare the dispersal rates of bisexual and parthenogenetic ticks. The dispersal index and t-test calculations were performed using custom scripts written in R version 4.4.Variant calling and mutation impact predictionThe whole genome Illumina reads of the parthenogenetic H. longicornis and bisexual H. longicornis individuals were mapped to their respective genomes using BWA (Version 0.7.17-r1188)51. Duplicate reads were marked using Picard tools (v2.18.23, http://broadinstitute.github.io/picard/). SNPs were called using FreeBayes (v1.3.6)102 and filtered based on the following thresholds: 1) minimum read coverage ≥7 for parthenogenetic H. longicornis and ≥10 for bisexual H. longicornis; 2) for heterozygous variant calls, the allele frequency of alternative alleles ≥2/7; 3) for homozygous variant calls, the allele frequency of alternative alleles ≥6/7; 4) variant quality score ≥20, genotype quality ≥20, mapping quality of reads supporting the variant ≥20, and fraction of properly paired reads ≥0.9. The potential impact of called variants, including loss of function, synonymous, and non-synonymous mutations, were evaluated using SnpEff (v5.2a)103 with default parameters.Phenotype analysisPhysiological and reproductive characteristics were recorded for both parthenogenetic and bisexual females104. Measurements included dorsal area (n = 30), genital pore length (n = 30), unfed weight (n = 20), engorged quantity (n = 20), egg weight (n = 20), egg mass (n = 20), blood-feeding weight (n = 20), egg perimeter (n = 60), blood-feeding stage (n = 20), preoviposition stage (n = 20), oviposition stage (n = 20), incubation stage (n = 20), hatchability (n = 20), blood-sucking index (n = 20) and reproductive index (n = 20). Statistical comparisons between the two populations were performed using Wilcoxon rank-sum test.Modeling the distribution of parthenogenetic H. longicornis
To model the distribution of parthenogenetic and bisexual H. longicornis, the exact locations (longitude and latitude coordinates) of the collection points for these two populations were extracted from previous reports and our field survey data. In our field survey data, the identification of parthenogenetic H. longicornis or bisexual H. longicornis was based on the assembled mitochondrial sequences. All reported occurrences of H. longicornis in Australia, New Zealand, and the USA were treated as parthenogenetic H. longicornis since parthenogenetic H. longicornis are the dominant population in these areas105,106. The reported occurrences of male H. longicornis were treated as bisexual H. longicornis.The environmental and meteorological factors were collected to predict the potential distributions of H. longicornis. The temperature and precipitation data from the WorldClim database (https://worldclim.org). The elevation, vegetation (Percent Tree Cover) and the global land cover (GLCNMO) data were downloaded from the Global Map data archives website (https://globalmaps.github.io/). Slope aspect and slope degree data was extracted based on elevation data using ArcMap v10.7. Highly correlated factors and duplicated distribution points were removed using ENMTools (v1.4.4)107. Then an ecological niche model was applied to predict potential distribution of each population, and Maxent (v3.4.4)108 was applied to train the models. Parameter calibration, model evaluation, and selection of candidate models were accomplished using kuenm package in R (version 3.6.3)109.Statistics and reproducibilityAll statistical analyses were performed using R software (v4.4.0). The statistical methods and corresponding p-values for each test are provided in the main text or figure legends. Statistical significance is indicated as follows: p < 0.05 (*), p < 0.01 (**), p < 0.001 (***), and p < 0.0001 (****). Data are presented as the mean ± standard deviation (SD). The number of biological replicates (n) for each experiment is specified in the figure legends.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The genome assemblies and annotations generated in this study are available at GenBank with project ID: PRJNA1133666. We have deposited the raw sequencing data in the China National Center for Bioinformation (CNCB) under accession number CRA031217. This repository includes all raw HiFi, Iso-seq, and Hi-C data as required. Additionally, the numerical source data for all figures have been deposited in Figshare (https://doi.org/10.6084/m9.figshare.30373816). The RNA-seq data of uninfected ticks and ticks infected with two pathogens at GenBank with accession numbers SRR30735266-SRR30735286. The metagenomic sequencing data of tick whole body are available at GenBank with accession numbers SRR30788678-SRR30788687.
ReferencesHoogstraal, H. Review of Haemaphysalis (Kaiseriana) longicornis Neumann of Australia, New Zealand, New Caledonia, Fiji, Japan, Korea, and northeastern China and USSR, and its parthenogenetic and bisexual populations (Ixodoidea, Ixodidae). J. Parasitol. 54, 1197–1213 (1968).Article 
PubMed 
CAS 

Google Scholar 
Rainey, T., Occi, J. L., Robbins, R. G. & Egizi, A. Discovery of Haemaphysalis longicornis (Ixodida: Ixodidae) parasitizing a sheep in New Jersey, United States. J. Med. Entomol. 55, 757–759 (2018).Article 
PubMed 

Google Scholar 
United States Department of Agriculture. National Haemaphysalis longicornis (Asian longhorned tick) situation report. USDA Animal Plant Health Inspection Service (United States Department of Agriculture, 2023).Zhao, L. et al. Distribution of Haemaphysalis longicornis and associated pathogens: analysis of pooled data from a China field survey and global published data. Lancet Planet. Health 4, e320–e329 (2020).Article 
PubMed 

Google Scholar 
Zhuang, L. et al. Transmission of severe fever with thrombocytopenia syndrome virus by Haemaphysalis longicornis Ticks, China. Emerg. Infect. Dis. 24, 868–871 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Fang, L. Q. et al. Emerging tick-borne infections in mainland China: an increasing public health threat. Lancet Infect. Dis. 15, 1467–1479 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Butlin, R. Evolution of sex: the costs and benefits of sex: New insights from old asexual lineages. Nat. Rev. Genet. 3, 311–317 (2002).Article 
PubMed 
CAS 

Google Scholar 
Zhang, X. et al. Rapid spread of severe fever with thrombocytopenia syndrome virus by parthenogenetic Asian longhorned ticks. Emerg. Infect. Dis. 28, 363–372 (2022).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Chen, X. et al. Multiple lines of evidence on the genetic relatedness of the parthenogenetic and bisexual Haemaphysalis longicornis (Acari: Ixodidae). Infect. Genet. Evol. 21, 308–314 (2014).Article 
PubMed 
CAS 

Google Scholar 
Comai, L. The advantages and disadvantages of being polyploid. Nat. Rev. Genet. 6, 836–846 (2005).Article 
PubMed 
CAS 

Google Scholar 
Li, J. T. et al. Parallel subgenome structure and divergent expression evolution of allo-tetraploid common carp and goldfish. Nat. Genet. 53, 1493–1503 (2021).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Anatskaya, O. V. & Vinogradov, A. E. Polyploidy and Myc proto-oncogenes promote stress adaptation via epigenetic plasticity and gene regulatory network rewiring. Int. J. Mol. Sci. 23, 9691 (2022).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
David, K. T. Global gradients in the distribution of animal polyploids. Proc. Natl. Acad. Sci. USA 119, e2214070119 (2022).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Jia, N. et al. Large-scale comparative analyses of tick genomes elucidate their genetic diversity and vector capacities. Cell 182, 1328–1340.e13 (2020).Article 
PubMed 
CAS 

Google Scholar 
De, S. et al. A high-quality Ixodes scapularis genome advances tick science. Nat. Genet. 55, 301–311 (2023).Article 
PubMed 
CAS 

Google Scholar 
Lv, Z., Nyarko, C. A., Ramtekey, V., Behn, H. & Mason, A. S. Defining autopolyploidy: cytology, genetics, and taxonomy. Am. J. Bot. 111, e16292 (2024).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Jaron, K. S. et al. Genomic features of parthenogenetic animals. J. Hered. 112, 19–33 (2021).Article 
PubMed 
CAS 

Google Scholar 
Cheng, F. et al. Gene retention, fractionation and subgenome differences in polyploid plants. Nat. Plants 4, 258–268 (2018).Article 
PubMed 
CAS 

Google Scholar 
Jaquiéry, J. et al. Disentangling the causes for Faster-X evolution in aphids. Genome Biol. Evol. 10, 507–520 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Li, Z. et al. Phylloxera and aphids show distinct features of genome evolution despite similar reproductive modes. Mol. Biol. Evol. 40, msad271 (2023).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Van de Peer, Y., Mizrachi, E. & Marchal, K. The evolutionary significance of polyploidy. Nat. Rev. Genet. 18, 411–424 (2017).Article 
PubMed 

Google Scholar 
Wang, T. H. et al. The ovarian development genes of bisexual and parthenogenetic Haemaphysalis longicornis evaluated by transcriptomics and proteomics. Front. Vet. Sci. 8, 783404 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Schnable, J. C., Springer, N. M. & Freeling, M. Differentiation of the maize subgenomes by genome dominance and both ancient and ongoing gene loss. Proc. Natl. Acad. Sci. USA 108, 4069–4074 (2011).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Ramírez-González, R. H. et al. The transcriptional landscape of polyploid wheat. Science 361, eaar6089 (2018).Article 
PubMed 

Google Scholar 
Vardy, L., Pesin, J. A. & Orr-Weaver, T. L. Regulation of Cyclin A protein in meiosis and early embryogenesis. Proc. Natl. Acad. Sci. USA 106, 1838–1843 (2009).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Chen, J. et al. Comparative proteomic analysis provides new insights into the molecular basis of thermal-induced parthenogenesis in silkworm (Bombyx mori). Insects 14, 134 (2023).Article 
PubMed 
PubMed Central 

Google Scholar 
Snyman, M. & Xu, S. Transcriptomics and the origin of obligate parthenogenesis. Heredity 131, 119–129 (2023).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Shaw, D. K. et al. Vector immunity and evolutionary ecology: the harmonious dissonance. Trends Immunol. 39, 862–873 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Jalovecka, M. et al. Activation of the tick Toll pathway to control infection of Ixodes ricinus by the apicomplexan parasite Babesia microti. PLoS Pathog. 20, e1012743 (2024).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Shaw, D. K. et al. Infection-derived lipids elicit an immune deficiency circuit in arthropods. Nat. Commun. 8, 14401 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Rosa, R. D. et al. Exploring the immune signalling pathway-related genes of the cattle tick Rhipicephalus microplus: from molecular characterization to transcriptional profile upon microbial challenge. Dev. Comp. Immunol. 59, 1–14 (2016).Article 
PubMed 
CAS 

Google Scholar 
Fogaça, A. C. et al. Tick immune system: what is known, the interconnections, the gaps, and the challenges. Front. Immunol. 12, 628054 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Rana, V. S., Kitsou, C., Dumler, J. S. & Pal, U. Immune evasion strategies of major tick-transmitted bacterial pathogens. Trends Microbiol 31, 62–75 (2023).Article 
PubMed 
CAS 

Google Scholar 
Garver, L. S., de Almeida Oliveira, G. & Barillas-Mury, C. The JNK pathway is a key mediator of Anopheles gambiae antiplasmodial immunity. PLoS Pathog. 9, e1003622 (2013).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Severo, M. S. et al. The E3 ubiquitin ligase XIAP restricts Anaplasma phagocytophilum colonization of Ixodes scapularis ticks. J. Infect. Dis. 208, 1830–1840 (2013).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Paquette, N. et al. Serine/threonine acetylation of TGFβ-activated kinase (TAK1) by Yersinia pestis YopJ inhibits innate immune signaling. Proc. Natl. Acad. Sci. USA 109, 12710–12715 (2012).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Schneider, D. S. & Ayres, J. S. Two ways to survive infection: what resistance and tolerance can teach us about treating infectious diseases. Nat. Rev. Immunol. 8, 889–895 (2008).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Dharmarajan, G., Walker, K. D. & Lehmann, T. Variation in tolerance to parasites affects vectorial capacity of natural Asian tiger mosquito populations. Curr. Biol. 29, 3946–3952.e5 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Ye, R. Z. et al. Virome diversity shaped by genetic evolution and ecological landscape of Haemaphysalis longicornis. Microbiome 12, 35 (2024).Article 
PubMed 
PubMed Central 

Google Scholar 
Kearney, M. R. et al. Parthenogenesis without costs in a grasshopper with hybrid origins. Science 376, 1110–1114 (2022).Article 
PubMed 
CAS 

Google Scholar 
Jiang, X., Song, Q., Ye, W. & Chen, Z. J. Concerted genomic and epigenomic changes accompany stabilization of Arabidopsis allopolyploids. Nat. Ecol. Evol. 5, 1382–1393 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Chen, Q. et al. Every gain comes with loss: ecological and physiological shifts associated with polyploidization in a pygmy frog. Mol. Biol. Evol. 42, msaf037 (2025).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Wang, Y. et al. Comparative genome anatomy reveals evolutionary insights into a unique amphitriploid fish. Nat. Ecol. Evol. 6, 1354–1366 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Session, A. M. et al. Genome evolution in the allotetraploid frog Xenopus laevis. Nature 538, 336–343 (2016).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Renny-Byfield, S. et al. Ancient gene duplicates in Gossypium (cotton) exhibit near-complete expression divergence. Genome Biol. Evol. 6, 559–571 (2014).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Kočí, J. et al. No evidence for accumulation of deleterious mutations and fitness degradation in clonal fish hybrids: Abandoning sex without regrets. Mol. Ecol. 29, 3038–3055 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Marçais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27, 764–770 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Ranallo-Benavidez, T. R., Jaron, K. S. & Schatz, M. C. GenomeScope 2.0 and Smudgeplot for reference-free profiling of polyploid genomes. Nat. Commun. 11, 1432 (2020).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Abu Almakarem, A. S. et al. Extraction of DNA from plant and fungus tissues in situ. BMC Res. Notes 5, 59 (2012).Article 

Google Scholar 
Burton, J. N. et al. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nat. Biotechnol. 31, 1119–1125 (2013).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Stanke, M., Diekhans, M., Baertsch, R. & Haussler, D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics 24, 637–644 (2008).Article 
PubMed 
CAS 

Google Scholar 
Korf, I. Gene finding in novel genomes. BMC Bioinforma. 5, 59 (2004).Article 

Google Scholar 
Keilwagen, J., Wenk, M., Erickson, J. L., Schattat, M. H. & Grosse, I. Using intron position conservation for homology-based gene prediction. Nucleic Acids Res. 44, e89 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Grabherr, M. G., Haas, B. J., Yassour, M., Levin, J. Z. & Amit, I. Trinity: reconstructing a full-length transcriptome without a genome from RNA-seq data. Nat. Biotechnol. 29, 644 (2013).Article 

Google Scholar 
Haas, B. J. et al. Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies. Nucleic Acids Res. 31, 5654–5666 (2003).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol. 9, R7 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 46, D308–D314 (2018).
Google Scholar 
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).Article 
PubMed 
CAS 

Google Scholar 
Boeckmann, B. et al. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res. 31, 365–370 (2003).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Finn, R. D. et al. Pfam: Clans, web tools and services. Nucleic Acids Res. 34, D247–D251 (2006).Article 
PubMed 
CAS 

Google Scholar 
Yu, Z. et al. The new Haemaphysalis longicornis genome provides insights into its requisite biological traits. Genomics 114, 110317 (2022).Article 
PubMed 
CAS 

Google Scholar 
Simao, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).Article 
PubMed 
CAS 

Google Scholar 
Wang, Y. P. et al. MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 40, e49 (2012).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Tang, H. B. et al. jcvi: JCVI Utility Libraries (Zenodo, 2015).Flynn, J. M. et al. RepeatModeler2 for automated genomic discovery of transposable element families. Proc. Natl. Acad. Sci. USA 117, 9451–9457 (2020).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Bao, Z. & Eddy, S. R. Automated de novo identification of repeat sequence families in sequenced genomes. Genome Res. 12, 1269–1276 (2002).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Price, A. L., Jones, N. C. & Pevzner, P. A. De novo identification of repeat families in large genomes. Bioinformatics 21, i351–i358 (2005).Article 
PubMed 
CAS 

Google Scholar 
Ou, S. J. & Jiang, N. LTR_retriever: a highly accurate and sensitive program for identification of long terminal-repeat retrotransposons. Plant Physiol. 176, 1411–1422 (2017).
Google Scholar 
Ellinghaus, D., Kurtz, S. & Willhoeft, U. LTRharvest, an efficient and flexible software for de novo detection of LTR retrotransposons. BMC Bioinforma. 9, 18 (2008).Article 

Google Scholar 
Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 35, W265–W268 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Tarailo-Graovac, M. & Chen, N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr. Protoc. Bioinformatics 25, 10.11–4.10.14 (2009).Beier, S., Thiel, T., Münch, T., Scholz, U. & Mascher, M. MISA-web: a web server for microsatellite prediction. Bioinformatics 33, 2583–2585 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 27, 573–580 (1999).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Lowe, T. M. & Eddy, S. R. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 25, 0955–0964 (1997).Article 
CAS 

Google Scholar 
Loman, T. A novel method for predicting ribosomal RNA genes in prokaryotic genomes. BINP30 20161, 8914064 (2017).
Google Scholar 
Griffiths-Jones, S. et al. Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res. 33, D121–D124 (2005).Article 
PubMed 
CAS 

Google Scholar 
Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29, 2933–2935 (2013).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).Article 
PubMed 
CAS 

Google Scholar 
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).Article 
PubMed 
CAS 

Google Scholar 
Yu, G. C., Wang, L. G., Han, Y. Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284–287 (2012).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Huo, S. M. et al. Comparative genome and transcriptome analyses reveal innate differences in response to host plants by two color forms of the two-spotted spider mite Tetranychus urticae. BMC Genomics 22, 569 (2021).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Nguyen, L. T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).Article 
PubMed 
CAS 

Google Scholar 
Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).Article 
PubMed 
CAS 

Google Scholar 
Kumar, S., Stecher, G., Suleski, M. & Hedges, S. B. TimeTree: a resource for timelines, timetrees, and divergence times. Mol. Biol. Evol. 34, 1812–1819 (2017).Article 
PubMed 
CAS 

Google Scholar 
Mendes, F. K., Vanderpool, D., Fulton, B. & Hahn, M. W. CAFE 5 models variation in evolutionary rates among gene families. Bioinformatics 36, 5516–5518 (2021).Article 
PubMed 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS One 5, e9490 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Kocot, K. M. et al. PhyloTreePruner: a phylogenetic tree-based approach for selection of orthologous sequences for phylogenomics. Evol. Bioinform. 9, 411–420 (2013).Article 

Google Scholar 
Löytynoja, A. Phylogeny-aware alignment with PRANK. Methods Mol. Biol. 1079, 155–170 (2014).Article 
PubMed 

Google Scholar 
Zwaenepoel, A. & Van de Peer, Y. wgd-simple command line tools for the analysis of ancient whole-genome duplications. Bioinformatics 35, 2153–2155 (2019).Article 
PubMed 
CAS 

Google Scholar 
Meng, G., Li, Y., Yang, C. & Liu, S. MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization. Nucleic Acids Res. 47, e63 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Kearse, M. et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Van der Auwera, G. A. et al. From FastQ data to high confidence variant calls: The Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinform. 43, 11.10.1–11.10.33 (2013).Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).Article 
PubMed 
CAS 

Google Scholar 
Tamura, K., Stecher, G. & Kumar, S. MEGA11: molecular evolutionary genetics analysis version 11. Mol. Biol. Evol. 38, 3022–3027 (2021).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. arXiv https://arxiv.org/abs/1207.3907 (2012).Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Chen, Z., Yang, X. J., Bu, F. J., Yang, X. L. & Liu, J. Z. Morphological, biological and molecular characteristics of bisexual and parthenogenetic Haemaphysalis longicornis. Vet. Parasitol. 189, 344–352 (2012).Article 
PubMed 
CAS 

Google Scholar 
Greay, T. L. et al. A survey of ticks (Acari: Ixodidae) of companion animals in Australia. Parasit. Vectors 9, 207 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Egizi, A. et al. First glimpse into the origin and spread of the Asian longhorned tick, Haemaphysalis longicornis, in the United States. Zoonoses Public Health 67, 637–650 (2020).Article 
PubMed 
CAS 

Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. ENMTools: a toolbox for comparative studies of environmental niche models. Ecography 33, 607–611 (2010).Article 

Google Scholar 
Phillips, S., Anderson, R., Dudík, M., Schapire, R., & Blair, M. E. Opening the black box: an open-source release of Maxent. Ecography 40, 887–893 (2017).Cobos, M. E., Peterson, A. T., Barve, N. & Osorio-Olvera, L. kuenm: an R package for detailed development of ecological niche models using Maxent. PeerJ 7, e6281 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Download referencesAcknowledgementsThis work was supported by grants from the Natural Science Foundation of China (U24A20363, 32201274), the National Key Research and Development Program of China (2023YFC2305901), the Hebei Province Higher Education Science and Technology Research Project (BJK2023017).Author informationAuthor notesThese authors contributed equally: Tianhong Wang, Wenqiang Shi, Yi-Fei Wang.Authors and AffiliationsState Key Laboratory of Pathogen and Biosecurity, Beijing, PR ChinaTianhong Wang, Wenqiang Shi, Xiao-Ming Cui, Wen-Jie Zhu, Jia-Jing Zheng, Xiao-Yu Shi, Dai-Yun Zhu, Na Jia, Jia-Fu Jiang & Wu-Chun CaoDepartment of Biochemistry and Molecular Biology, College of Basic Medicine, Hebei University of Chinese Medicine, Shijiazhuang, Hebei, PR ChinaTianhong WangInstitute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, PR ChinaYi-Fei Wang, Lian-Feng Li, Di Zhao, Shi-Jing Shen, Wan-Ying Gao, Hua Wei, Lin Zhao & Wu-Chun CaoHebei Key Laboratory of Animal Physiology, Biochemistry and Molecular Biology, College of Life Sciences, Hebei Normal University, Shijiazhuang, Hebei, PR ChinaZhijun Yu & Jingze LiuAuthorsTianhong WangView author publicationsSearch author on:PubMed Google ScholarWenqiang ShiView author publicationsSearch author on:PubMed Google ScholarYi-Fei WangView author publicationsSearch author on:PubMed Google ScholarLian-Feng LiView author publicationsSearch author on:PubMed Google ScholarXiao-Ming CuiView author publicationsSearch author on:PubMed Google ScholarDi ZhaoView author publicationsSearch author on:PubMed Google ScholarShi-Jing ShenView author publicationsSearch author on:PubMed Google ScholarWen-Jie ZhuView author publicationsSearch author on:PubMed Google ScholarWan-Ying GaoView author publicationsSearch author on:PubMed Google ScholarHua WeiView author publicationsSearch author on:PubMed Google ScholarJia-Jing ZhengView author publicationsSearch author on:PubMed Google ScholarZhijun YuView author publicationsSearch author on:PubMed Google ScholarXiao-Yu ShiView author publicationsSearch author on:PubMed Google ScholarDai-Yun ZhuView author publicationsSearch author on:PubMed Google ScholarLin ZhaoView author publicationsSearch author on:PubMed Google ScholarNa JiaView author publicationsSearch author on:PubMed Google ScholarJia-Fu JiangView author publicationsSearch author on:PubMed Google ScholarJingze LiuView author publicationsSearch author on:PubMed Google ScholarWu-Chun CaoView author publicationsSearch author on:PubMed Google ScholarContributionsW.C.C., J.L. and T.W. conceptualized the project and designed the study. W.S., Y.F.W. X.M.C. and L.Z. collected the data and conducted the data analysis. L.F.L., S.J.S., W.J.Z. and W.Y.G. collected samples and carried out experiments. Y.F.W., D.Y.Z. and X.Y.S. prepared materials for sequencing. W.S., J.J.Z., H.W., Z.Y. and performed genome analysis and interpretation. T.W. and W.S. wrote the original draft of the manuscript. W.C.C., N.J. and J.F.J. revised the manuscript.Corresponding authorCorrespondence to
Wu-Chun Cao.Ethics declarations

Competing interests
The authors declare no competing interests.

Peer review

Peer review information
Communications Biology thanks Jan Perner and the other, anonymous, reviewers for their contribution to the peer review of this work. Primary Handling Editors: John Mulley and Johannes Stortz.

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary informationSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Reporting-summaryRights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleWang, T., Shi, W., Wang, YF. et al. Triploidy drives vector capacity and expansion of the parthenogenetic tick Haemaphysalis longicornis.
Commun Biol 8, 1775 (2025). https://doi.org/10.1038/s42003-025-09153-xDownload citationReceived: 07 January 2025Accepted: 30 October 2025Published: 16 December 2025Version of record: 16 December 2025DOI: https://doi.org/10.1038/s42003-025-09153-xShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative More

AbstractPersistent and above-average warming has advanced the start of spring permafrost thawing, intensifying climate warming through carbon feedbacks. However, the extent to which variations in spring permafrost thawing contribute to greening trends in permafrost-affected areas (i.e., increases in vegetation greenness) over time remains unclear, limiting our understanding of the ecological consequences of permafrost degradation. Analyzing 40-year freeze/thaw data and multiple satellite-derived greenness indicators, we identify widespread increases in the sensitivity of spring greenness to spring permafrost thawing based on moving-window analyses, indicating that advances in spring permafrost thawing have played a progressively stronger role in promoting spring greening, particularly in boreal forests and tundra regions underlain by continuous permafrost. In addition to the region-specific climate and permafrost conditions, we uncover biogeophysical pathways accounting for the increase in sensitivity of spring greenness to spring permafrost thawing, including reduced albedo, earlier vegetation phenology, and enhanced soil moisture infiltration. Notably, state-of-the-art Dynamic Global Vegetation Models consistently underestimate both the magnitude and variability of sensitivity of spring greenness to spring permafrost thawing. These findings highlight the temporal changes in vegetation responses to freeze-thaw dynamics, necessitating improved model projections concurrent with climate change.

Similar content being viewed by others

Tundra vegetation change and impacts on permafrost

Article

11 January 2022

Vegetation greening amplifies shallow soil temperature warming on the Tibetan Plateau

Article
Open access
04 June 2024

Warming-independent shortened snow cover duration enhances vegetation greening across northern permafrost region

Article
Open access
01 April 2025

Data availability

All data used in this study are freely available from public repositories. Freeze-thaw state data are provided by FT-ESDR (https://nsidc.org/data/nsidc-0477/versions/5). Satellite-based greenness indicators include kNDVI (https://www.environment.snu.ac.kr/data/longterm-vi), LCSPP v3.2 (https://zenodo.org/records/14568024), BESS v2.0 GPP (https://www.environment.snu.ac.kr/bessv2), GOSIF, CSIF, and VODCA v2 (https://researchdata.tuwien.at/records/t74ty-tcx62). Meteorological data are available from TerraClimate (https://www.climatologylab.org/). Global monthly CO2 records are available from NOAA. ERA5-Land datasets are available from Google Earth Engine and Copernicus Climate Data Store (https://cds.climate.copernicus.eu/). In situ surface temperature records are available from GTN-P (https://gtnp.arcticportal.org/). Active layer thickness data are provided by ESA. Soil organic carbon and nitrogen data are available from SoilGrids (https://www.isric.org/explore/soilgrids). Maximum root depth is available from EartH2Observe (http://www.earth2observe.eu/). Global canopy height data are available from ETH Global Canopy Height 2020 (https://code.earthengine.google.com/126c172d63e7ce780596c8d26f06d384). Land cover datasets include TEOW biomes (https://www.worldwildlife.org/publications/terrestrial-ecoregions-of-the-world), MODIS MCD12C1 IGBP (https://lpdaac.usgs.gov/), MOSEV dNBR (https://zenodo.org/records/4265209), and permafrost type data from NSIDC. Source data are provided with this paper.
Code availability

All data analyses were performed using R v.4.3.1. The codes used in this study are available at Zenodo [https://doi.org/10.5281/zenodo.17543943]95.
ReferencesHeijmans, M. M. P. D. Tundra vegetation change and impacts on permafrost. Nat. Rev. Earth Environ. 3, 68–84 (2022).Myneni, R. B., Keeling, C. D., Tucker, C. J., Asrar, G. & Nemani, R. R. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).
Google Scholar 
Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).
Google Scholar 
Pearson, R. G. et al. Shifts in Arctic vegetation and associated feedbacks under climate change. Nat. Clim. Change 3, 673–677 (2013).
Google Scholar 
Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).
Google Scholar 
Xu, X., Riley, W. J., Koven, C. D., Jia, G. & Zhang, X. Earlier leaf-out warms air in the north. Nat. Clim. Change 10, 370–375 (2020).
Google Scholar 
Schuur, E. A. G. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).
Google Scholar 
Walvoord, M. A. & Kurylyk, B. L. Hydrologic impacts of thawing permafrost—a review. Vadose Zone J. 15, 1–20 (2016).
Google Scholar 
Webb, E. E. et al. Permafrost thaw drives surface water decline across lake-rich regions of the Arctic. Nat. Clim. Change 12, 841–846 (2022).
Google Scholar 
Biskaborn, B. K. et al. Permafrost is warming at a global scale. Nat. Commun. 10, 264 (2019).
Google Scholar 
Smith, S. L., O’Neill, H. B., Isaksen, K., Noetzli, J. & Romanovsky, V. E. The changing thermal state of permafrost. Nat. Rev. Earth Environ. 3, 10–23 (2022).
Google Scholar 
Natali, S. M., Schuur, E. A. G. & Rubin, R. L. Increased plant productivity in Alaskan tundra as a result of experimental warming of soil and permafrost. J. Ecol. 100, 488–498 (2012).
Google Scholar 
Schuur, E. A. G. & Mack, M. C. Ecological response to permafrost Thaw and consequences for local and global ecosystem services. Annu. Rev. Ecol. Evol. Syst. 49, 279–301 (2018).
Google Scholar 
Luo, S., Wang, J., Pomeroy, J. W. & Lyu, S. Freeze–Thaw changes of seasonally frozen ground on the Tibetan Plateau from 1960 to 2014. J. Clim. 33, 9427–9446 (2020).
Google Scholar 
Li, X., Jin, R., Pan, X., Zhang, T. & Guo, J. Changes in the near-surface soil freeze–thaw cycle on the Qinghai-Tibetan Plateau. Int. J. Appl. Earth Observ. Geoinf. 17, 33–42 (2012).
Google Scholar 
Wang, J. & Liu, D. Vegetation green-up date is more sensitive to permafrost degradation than climate change in spring across the northern permafrost region. Glob. Change Biol. 28, 1569–1582 (2022).
Google Scholar 
Jin, X.-Y. et al. Impacts of climate-induced permafrost degradation on vegetation: a review. Adv. Clim. Change Res. 12, 29–47 (2021).
Google Scholar 
Wrona, F. J. et al. Transitions in Arctic ecosystems: Ecological implications of a changing hydrological regime. JGR Biogeosci. 121, 650–674 (2016).
Google Scholar 
Yang, M., Nelson, F. E., Shiklomanov, N. I., Guo, D. & Wan, G. Permafrost degradation and its environmental effects on the Tibetan Plateau: a review of recent research. Earth-Sci. Rev. 103, 31–44 (2010).
Google Scholar 
Buermann, W. et al. Widespread seasonal compensation effects of spring warming on northern plant productivity. Nature 562, 110–114 (2018).
Google Scholar 
Piao, S. et al. Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity. Nat. Commun. 5, 5018 (2014).
Google Scholar 
Jorgenson, M. T., Shur, Y. L. & Pullman, E. R. Abrupt increase in permafrost degradation in Arctic Alaska. Geophys. Res. Lett. 33, 2005GL024960 (2006).
Google Scholar 
Berner, L. T. et al. Summer warming explains widespread but not uniform greening in the Arctic tundra biome. Nat. Commun. 11, 4621 (2020).
Google Scholar 
Grünberg, I., Wilcox, E. J., Zwieback, S., Marsh, P. & Boike, J. Linking tundra vegetation, snow, soil temperature, and permafrost. Biogeosciences 17, 4261–4279 (2020).
Google Scholar 
Miller, P. A. & Smith, B. Modelling tundra vegetation response to recent Arctic warming. AMBIO 41, 281–291 (2012).
Google Scholar 
Tang, S. et al. Resonance between projected Tibetan Plateau surface darkening and Arctic climate change. Sci. Bull. 69, 367–374 (2024).
Google Scholar 
Wang, J. & Liu, D. Deciphering the biophysical impact of permafrost greening on summer surface offset. Earth’s. Future 12, e2023EF004077 (2024).
Google Scholar 
Pistone, K., Eisenman, I. & Ramanathan, V. Observational determination of albedo decrease caused by vanishing Arctic sea ice. Proc. Natl. Acad. Sci. USA 111, 3322–3326 (2014).
Google Scholar 
Dragoni, D. et al. Evidence of increased net ecosystem productivity associated with a longer vegetated season in a deciduous forest in south-central Indiana, USA. Glob. Change Biol. 17, 886–897 (2011).
Google Scholar 
Richardson, A. D. et al. Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests. Tree Physiol. 29, 321–331 (2009).
Google Scholar 
Li, J. et al. Weakening warming on spring freeze–thaw cycle caused greening Earth’s third pole. Proc. Natl. Acad. Sci. USA 121, e2319581121 (2024).
Google Scholar 
Bui, M. T., Lu, J. & Nie, L. A review of hydrological models applied in the permafrost-dominated Arctic region. Geosciences 10, 401 (2020).
Google Scholar 
Shu, S., Jain, A. K., Koven, C. D. & Mishra, U. Estimation of permafrost SOC stock and turnover time using a land surface model with vertical heterogeneity of permafrost soils. Glob. Biogeochem. Cycles 34, e2020GB006585 (2020).
Google Scholar 
Wang, T. et al. Permafrost thawing puts the frozen carbon at risk over the Tibetan Plateau. Sci. Adv. 6, eaaz3513 (2020).
Google Scholar 
Hjort, J. et al. Impacts of permafrost degradation on infrastructure. Nat. Rev. Earth Environ. 3, 24–38 (2022).
Google Scholar 
Loranty, M. M. et al. Reviews and syntheses: changing ecosystem influences on soil thermal regimes in northern high-latitude permafrost regions. Biogeosciences 15, 5287–5313 (2018).
Google Scholar 
Turetsky, M. R. et al. Permafrost collapse is accelerating carbon release. Nature 569, 32–34 (2019).
Google Scholar 
Strauss, J. et al. Deep Yedoma permafrost: a synthesis of depositional characteristics and carbon vulnerability. Earth-Sci. Rev. 172, 75–86 (2017).
Google Scholar 
Bintanja, R. & Andry, O. Towards a rain-dominated Arctic. Nat. Clim. Change 7, 263–267 (2017).
Google Scholar 
Liljedahl, A. K. et al. Pan-Arctic ice-wedge degradation in warming permafrost and its influence on tundra hydrology. Nat. Geosci. 9, 312–318 (2016).
Google Scholar 
Neumann, R. B. et al. Warming effects of spring rainfall increase methane emissions from thawing permafrost. Geophys. Res. Lett. 46, 1393–1401 (2019).
Google Scholar 
Pilyugina, P. et al. A physics-informed machine learning framework for permafrost stability assessment. IEEE Access 13, 96423–96433 (2025).
Google Scholar 
Kim, Y., Kimball, J. S., Glassy, J. & Du, J. An extended global Earth system data record on daily landscape freeze–thaw status determined from satellite passive microwave remote sensing. Earth Syst. Sci. Data 9, 133–147 (2017).
Google Scholar 
Muñoz-Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 13, 4349–4383 (2021).
Google Scholar 
Chen, X. et al. Different responses of surface freeze and thaw phenology changes to warming among Arctic permafrost types. Remote Sens. Environ. 272, 112956 (2022).
Google Scholar 
Kim, Y., Kimball, J. S., Zhang, K. & McDonald, K. C. Satellite detection of increasing Northern Hemisphere non-frozen seasons from 1979 to 2008: implications for regional vegetation growth. Remote Sens. Environ. 121, 472–487 (2012).
Google Scholar 
Li, J., Wu, C., Peñuelas, J., Ran, Y. & Zhang, Y. The start of frozen dates over northern permafrost regions with the changing climate. Glob. Change Biol. 29, 4556–4568 (2023).
Google Scholar 
Camps-Valls, G. et al. A unified vegetation index for quantifying the terrestrial biosphere. Sci. Adv. 7, eabc7447 (2021).
Google Scholar 
Chen, J. M. et al. Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink. Nat. Commun. 10, 4259 (2019).
Google Scholar 
Porcar-Castell, A. et al. Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science. Nat. Plants 7, 998–1009 (2021).
Google Scholar 
Hu, Z. et al. Decoupling of greenness and gross primary productivity as aridity decreases. Remote Sens. Environ. 279, 113120 (2022).
Google Scholar 
Ding, Z., Peng, J., Qiu, S. & Zhao, Y. Nearly half of global vegetated area experienced inconsistent vegetation growth in terms of greenness, cover, and productivity. Earth’s. Future 8, e2020EF001618 (2020).
Google Scholar 
Jeong, S. et al. Persistent global greening over the last four decades using novel long-term vegetation index data with enhanced temporal consistency. Remote Sens. Environ. 311, 114282 (2024).
Google Scholar 
Fang, J. et al. A long-term reconstruction of a global photosynthesis proxy over 1982–2023. Sci. Data 12, 372 (2025).
Google Scholar 
Lian, X. et al. Diminishing carryover benefits of earlier spring vegetation growth. Nat. Ecol. Evol. 8, 218–228 (2024).
Google Scholar 
Li, B. et al. BESSv2.0: A satellite-based and coupled-process model for quantifying long-term global land–atmosphere fluxes. Remote Sens. Environ. 295, 113696 (2023).
Google Scholar 
Li, X. & Xiao, J. A global, 0.05-degree product of solar-induced chlorophyll fluorescence derived from OCO-2, MODIS, and reanalysis data. Remote Sens. 11, 517 (2019).
Google Scholar 
Zhang, Y., Joiner, J., Alemohammad, S. H., Zhou, S. & Gentine, P. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences 15, 5779–5800 (2018).
Google Scholar 
Zotta, R.-M. et al. VODCA v2: multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring. Earth Syst. Sci. Data 16, 4573–4617 (2024).
Google Scholar 
Friedlingstein, P. et al. Global Carbon Budget 2023. Earth Syst. Sci. Data 15, 5301–5369 (2023).
Google Scholar 
Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).
Google Scholar 
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).
Google Scholar 
Li, W. et al. Widespread increasing vegetation sensitivity to soil moisture. Nat. Commun. 13, 3959 (2022).
Google Scholar 
Pinzon, J. & Tucker, C. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014).
Google Scholar 
Shen, M. et al. Increasing altitudinal gradient of spring vegetation phenology during the last decade on the Qinghai–Tibetan Plateau. Agric. For. Meteorol. 189–190, 71–80 (2014).
Google Scholar 
Chen, J. et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens. Environ. 91, 332–344 (2004).
Google Scholar 
Wang, J., Liu, D., Ciais, P. & Peñuelas, J. Decreasing rainfall frequency contributes to earlier leaf onset in northern ecosystems. Nat. Clim. Change 12, 386–392 (2022).
Google Scholar 
Wu, C. et al. Increased drought effects on the phenology of autumn leaf senescence. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01464-9 (2022).
Google Scholar 
Wang, X. & Wu, C. Estimating the peak of growing season (POS) of China’s terrestrial ecosystems. Agric. For. Meteorol. 278, 107639 (2019).
Google Scholar 
Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl. Acad. Sci. USA 114, 10572–10577 (2017).
Google Scholar 
Lang, N., Jetz, W., Schindler, K. & Wegner, J. D. A high-resolution canopy height model of the Earth. Nat. Ecol. Evol. 7, 1778–1789 (2023).
Google Scholar 
Obu, J. et al. Northern Hemisphere permafrost map based on TTOP modelling for 2000–2016 at 1 km2 scale. Earth-Sci. Rev. 193, 299–316 (2019).
Google Scholar 
Poggio, L. et al. SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. Soil 7, 217–240 (2021).
Google Scholar 
Alonso-González, E. & Fernández-García, V. MOSEV: a global burn severity database from MODIS (2000–2020). Earth Syst. Sci. Data 13, 1925–1938 (2021).
Google Scholar 
Brown, J., Ferrians, O., Heginbottom, J. A. & Melnikov, E. Circum-Arctic map of permafrost and ground-ice conditions. Version 2. 10.7265/skbg-kf16 (2002).Chen, L. et al. Leaf senescence exhibits stronger climatic responses during warm than during cold autumns. Nat. Clim. Change 10, 777–780 (2020).
Google Scholar 
He, L. et al. Lagged precipitation effects on plant production across terrestrial biomes. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-025-02806-4 (2025).
Google Scholar 
Wang, S. et al. Recent global decline of CO 2 fertilization effects on vegetation photosynthesis. Science 370, 1295–1300 (2020).
Google Scholar 
Fu, Y. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).
Google Scholar 
Yang, G., Crowther, T. W., Lauber, T., Zohner, C. M. & Smith, G. R. A globally consistent negative effect of edge on aboveground forest biomass. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-025-02840-2 (2025).
Google Scholar 
Wolkovich, E. M. et al. A simple explanation for declining temperature sensitivity with warming. Glob. Change Biol. 27, 4947–4949 (2021).
Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Google Scholar 
Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T. & Zeileis, A. Conditional variable importance for random forests. BMC Bioinform. 9, 307 (2008).
Google Scholar 
Wang, X. et al. Enhanced habitat loss of the Himalayan endemic flora driven by warming-forced upslope tree expansion. Nat. Ecol. Evol. 6, 890–899 (2022).
Google Scholar 
Berdugo, M., Gaitán, J. J., Delgado-Baquerizo, M., Crowther, T. W. & Dakos, V. Prevalence and drivers of abrupt vegetation shifts in global drylands. Proc. Natl. Acad. Sci. USA 119, e2123393119 (2022).
Google Scholar 
Wright, M. N. & Ziegler, A. ranger: a fast implementation of random forests for high dimensional data in C++ and R. J. Stat. Soft. 77, 1–17 (2017).Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020).Keenan, T. F. et al. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Change 4, 598–604 (2014).
Google Scholar 
Sugihara, G. et al. Detecting causality in complex ecosystems. Science 338, 496–500 (2012).
Google Scholar 
Shen, M. et al. Can changes in autumn phenology facilitate earlier green-up date of northern vegetation?. Agric. For. Meteorol. 291, 108077 (2020).
Google Scholar 
Wu, C. et al. Contrasting responses of autumn-leaf senescence to daytime and night-time warming. Nat. Clim. Change 8, 1092–1096 (2018).
Google Scholar 
Bagozzi, R. P. & Yi, Y. Specification, evaluation, and interpretation of structural equation models. J. Acad. Mark. Sci. 40, 8–34 (2012).
Google Scholar 
Gao, S. et al. An earlier start of the thermal growing season enhances tree growth in cold humid areas but not in dry areas. Nat. Ecol. Evol. 6, 397–404 (2022).
Google Scholar 
Rosseel, Y. lavaan: an R package for structural equation modeling. J. Stat. Soft. 48, 1–36 (2012).Hua, H., Wang, J. & Wu, C. Accelerated land surface greening caused by earlier permafrost thawing. Zenodo 10.5281/ZENODO.17543942 (2025).Download referencesAcknowledgementsThis work was funded by the National Natural Science Foundation of China (42125101, W2412014). J.W. was funded by the National Natural Science Foundation of China (42571037) and “Kezhen and Bingwei” Young Scientist Program of IGSNRR. C.M.Z. was funded by SNF Ambizione grant PZ00P3_193646. J.P. was funded by the TED2021-132627B-I00 grant funded by the Spanish MCIN, AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR, the Fundación Ramón Areces project CIVP20A6621 and the Catalan government grants SGR221-1333 and AGAUR2023 CLIMA 00118. We also appreciate the funding from the Science and Technology Program of Guangdong (No. 2024B1212070012).Author informationAuthor notesThese authors contributed equally: Hao Hua, Jian Wang.Authors and AffiliationsThe Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, ChinaHao Hua, Jian Wang & Chaoyang WuUniversity of the Chinese Academy of Sciences, Beijing, ChinaHao Hua, Jian Wang & Chaoyang WuDepartment of Environmental Systems Science, Institute of Integrative Biology, ETH, Zurich, Zurich, SwitzerlandConstantin M. ZohnerCSIC, Global Ecology Unit CREAF-CSIC-UAB, Barcelona, Catalonia, SpainJosep PeñuelasCREAF, Barcelona, Catalonia, SpainJosep PeñuelasNorthwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, ChinaYouhua RanAuthorsHao HuaView author publicationsSearch author on:PubMed Google ScholarJian WangView author publicationsSearch author on:PubMed Google ScholarConstantin M. ZohnerView author publicationsSearch author on:PubMed Google ScholarJosep PeñuelasView author publicationsSearch author on:PubMed Google ScholarYouhua RanView author publicationsSearch author on:PubMed Google ScholarChaoyang WuView author publicationsSearch author on:PubMed Google ScholarContributionsC.W. designed the research. H.H. and J.W. wrote the first draft of the manuscript and performed the analyses. C.M.Z. and J.P. discussed the research design, interpretation, and manuscript revision. Y.R. provided methodological support. All authors assessed the analyses and contributed to improving the manuscript.Corresponding authorCorrespondence to
Chaoyang Wu.Ethics declarations

Competing interests
The authors declare no competing interests.

Peer review

Peer review information
Nature Communications thanks Andreas Dietz, who co-reviewed with Martina Wenzl; Steven Cumming, and Tingting Xu for their contribution to the peer review of this work. A peer review file is available.

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary informationSupplementary InformationReporting SummaryPeer Review fileSource dataSource DataRights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleHua, H., Wang, J., Zohner, C.M. et al. Accelerated land surface greening caused by earlier permafrost thawing.
Nat Commun (2025). https://doi.org/10.1038/s41467-025-67644-1Download citationReceived: 13 January 2025Accepted: 04 December 2025Published: 16 December 2025DOI: https://doi.org/10.1038/s41467-025-67644-1Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative More

AbstractWetlands serve as vital carbon sinks; however, compared with the surface carbon pool, the relative stock and stability of organic carbon (OC) buried in deep sediment layers remain uncertain, particularly in alpine regions. Based on 42 sediment across seven wetlands on the Qinghai-Xizang Plateau, this study disentangled the OC accumulation process and its drivers since the Holocene. Our results highlighted that deep sediments ( >1 m) stored ~70% of the total OC in alpine wetlands, much of which was labile. Historical warming facilitated the accumulation of such labile OC. Moreover, an intercalated sedimentary structure, formed by silt and fine-grained clay both below and above the OC-rich layer, prevented them from rapid decomposition. Given that elevated groundwater temperatures and intensified hydrological processes in alpine regions may stimulate the decomposition of these massive labile OC pools, releasing carbon into surface water or the atmosphere, future climate change assessments should take this long-overlooked carbon pool into account.

Similar content being viewed by others

Warming drives dissolved organic carbon export from pristine alpine soils

Article
Open access
25 April 2024

Substantial increase of organic carbon storage in Chinese lakes

Article
Open access
14 September 2024

The land–ocean Arctic carbon cycle

Article

06 February 2025

Data availability

The data supporting the findings of this study were deposited on figshare (https://doi.org/10.6084/m9.figshare.30655583).
Code availability

The R packages utilized in this study are publicly available, and no custom code was used.
ReferencesWhiting, G. J. & Chanton, J. P. Greenhouse carbon balance of wetlands: methane emission versus carbon sequestration. Tellus Ser. B-Chem. Phys. Meteorol. 53, 521–528 (2001).
Google Scholar 
Stewart, A. J. et al. Revealing the hidden carbon in forested wetland soils. Nat. Commun. 15, 726 (2024).
Google Scholar 
Jackson, R. B. et al. The ecology of soil carbon: pools, vulnerabilities, and biotic and abiotic controls. Annu. Rev. Ecol. Evol. Syst. 48, 419–445 (2017).
Google Scholar 
Li, Y. et al. Factors controlling peat soil thickness and carbon storage in temperate peatlands based on UAV high-resolution remote sensing. Geoderma 449, 117009 (2024).
Google Scholar 
Hinson, A. L. et al. The spatial distribution of soil organic carbon in tidal wetland soils of the continental United States. Glob. Change Biol. 23, 5468–5480 (2017).
Google Scholar 
Ding, J. Z. et al. The permafrost carbon inventory on the Tibetan Plateau: a new evaluation using deep sediment cores. Glob. Change Biol. 22, 2688–2701 (2016).
Google Scholar 
Post, W. M., Emanuel, W. R., Zinke, P. J. & Stangenberger, A. G. Soil carbon pools and world life zones. Nature 298, 156–159 (1982).
Google Scholar 
Gitay, H. et al. in Contribution of Working Group II to the IPCC Third Assessment Report: Impacts, Adaptation and Vulnerability (eds J. J. McCarthy et al.) 235-342 (Cambridge University Press, 2001).Vasilevich, R., Lodygin, E., Beznosikov, V. & Abakumov, E. Molecular composition of raw peat and humic substances from permafrost peat soils of European Northeast Russia as climate change markers. Sci. Total Environ. 615, 1229–1238 (2018).
Google Scholar 
Routh, J. et al. Multi-proxy study of soil organic matter dynamics in permafrost peat deposits reveal vulnerability to climate change in the European Russian Arctic. Chem. Geol. 368, 104–117 (2014).
Google Scholar 
Kalisz, B., Urbanowicz, P., Smólczyński, S. & Orzechowski, M. Impact of siltation on the stability of organic matter in drained peatlands. Ecol. Indic. 130, 108149 (2021).
Google Scholar 
Negassa, W., Acksel, A., Eckhardt, K.-U., Regier, T. & Leinweber, P. Soil organic matter characteristics in drained and rewetted peatlands of northern Germany: Chemical and spectroscopic analyses. Geoderma 353, 468–481 (2019).
Google Scholar 
Davies, M. A., Blewett, J., Naafs, B. D. A. & Finkelstein, S. A. Ecohydrological controls on apparent rates of peat carbon accumulation in a boreal bog record from the Hudson Bay Lowlands, northern Ontario, Canada. Quat. Res. 104, 14–27 (2021).
Google Scholar 
Gao, C. et al. High intensity fire accelerates accumulation of a stable carbon pool in permafrost peatlands under climate warming. Catena 227, 107108 (2023).
Google Scholar 
Zhang, T. et al. Warming-driven erosion and sediment transport in cold regions. Nat. Rev. Earth Environ. 3, 832–851 (2022).
Google Scholar 
García Lino, M. C. et al. Carbon dynamics in high-Andean tropical cushion peatlands: A review of geographic patterns and potential drivers. Ecol. Monogr. 94, e1614 (2024).
Google Scholar 
Heger, A., Becker, J. N., Vásconez, L. K. & Eschenbach, A. Drivers for soil organic carbon stabilization in Elbe River floodplains. J. Plant Nutr. Soil Sci. 187, 346–355 (2024).
Google Scholar 
Deiss, L. et al. Soil carbon fractions from an alluvial soil texture gradient in North Carolina. Soil Sci. Soc. Am. J. 81, 1096–1106 (2017).
Google Scholar 
Chen, H. et al. Carbon and nitrogen cycling on the Qinghai–Tibetan Plateau. Nat. Rev. Earth Environ. 3, 701–716 (2022).
Google Scholar 
Jones, M. C. & Yu, Z. Rapid deglacial and early Holocene expansion of peatlands in Alaska. Proc. Natl. Acad. Sci. USA 107, 7347–7352 (2010).
Google Scholar 
Loisel, J. et al. Insights and issues with estimating northern peatland carbon stocks and fluxes since the Last Glacial Maximum. Earth-Sci. Rev. 165, 59–80 (2017).
Google Scholar 
Juselius-Rajamäki, T., Vaeliranta, M. & Korhola, A. The ongoing lateral expansion of peatlands in Finland. Glob. Change Biol. 29, 7173–7191 (2023).
Google Scholar 
Gao, J., Yao, T. D., Masson-Delmotte, V., Steen-Larsen, H. C. & Wang, W. C. Collapsing glaciers threaten Asia’s water supplies. Nature 565, 19–21 (2019).
Google Scholar 
Yao, T. et al. The imbalance of the Asian water tower. Nat. Rev. Earth Environ. 3, 618–632 (2022).
Google Scholar 
Feldman, A. F. et al. Plant responses to changing rainfall frequency and intensity. Nat. Rev. Earth Environ. 5, 276–294 (2024).
Google Scholar 
Shi, P. J. et al. Earthquakes have accelerated the carbon dioxide emission rate of soils on the Qinghai-Tibet Plateau. Glob. Change Biol. 31, 10 (2025).
Google Scholar 
Jobbágy, E. G. & Jackson, R. B. The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecol. Appl. 10, 423–436 (2000).
Google Scholar 
Chen, H. et al. The carbon stock of alpine peatlands on the Qinghai–Tibetan Plateau during the Holocene and their future fate. Quat. Sci. Rev. 95, 151–158 (2014).
Google Scholar 
Krauss, K. W. et al. The role of the upper Tidal Estuary in Wetland blue carbon storage and flux. Glob. Biogeochem. Cycles 32, 817–839 (2018).
Google Scholar 
Wang, Q. F. et al. The vertical distribution of soil organic carbon and nitrogen in a permafrost-affected wetland on the Qinghai-Tibet Plateau: implications for Holocene development and environmental change. Permafr. Periglac. Process 33, 286–297 (2022).
Google Scholar 
Batjes, N. H. Total carbon and nitrogen in the soils of the world. Eur. J. Soil Sci. 65, 10–21 (2014).
Google Scholar 
Şenkul, Ç, Gürboğa, Ş, Doğan, M. & Doğan, T. High-resolution geochemical (μXRF) and palynological analyses for climatic and environmental changes in lake sediments from Sultansazlığı Marsh (Central Anatolia) during the last 14.5 kyr. Quat. Int. 613, 24–38 (2022).
Google Scholar 
Sun, Z. et al. Changes in atmospheric circulation and glacier melting since the last deglaciation revealed by a lacustrine dD record at Ngamring Co, the upper-middle Yarlung Tsangpo watershed. Paleogeogr. Paleoclimatol. Paleoecol. 598, 11 (2022).
Google Scholar 
Gu, Z., Liu, J., Yuan, B. & Liu, D. Monsoon variations of the Qinghai-Xizang Plateau during the last 12,000 years——geochemical evidence from the sediments in the Siling lake. Chin. Sci. Bull. 38, 577–581 (1993).
Google Scholar 
Wang, Y., Xia, A. & Xue, K. Cold and humid climates enrich soil carbon stock in the Third Pole grasslands. Innovation 5, 100545 (2024).
Google Scholar 
Liu, L. N. et al. Spatial and temporal variations of vegetation cover on the central and eastern Tibetan Plateau since the Last Glacial Period. Glob. Planet. Change 240, 14 (2024).
Google Scholar 
Bond, G. et al. Persistent solar influence on North Atlantic climate during the Holocene. Science 294, 2130–2136 (2001).
Google Scholar 
Peltre, C., Bruun, S., Du, C. W., Thomsen, I. K. & Jensen, L. S. Assessing soil constituents and labile soil organic carbon by mid-infrared photoacoustic spectroscopy. Soil Biol. Biochem. 77, 41–50 (2014).
Google Scholar 
Harris, L. I. et al. Permafrost thaw causes large carbon loss in boreal peatlands while changes to peat quality are limited. Glob. Chang Biol. 29, 5720–5735 (2023).
Google Scholar 
Martínez Cortizas, A. et al. 9000 years of changes in peat organic matter composition in Store Mosse (Sweden) traced using FTIR-ATR. Boreas 50, 1161–1178 (2021).
Google Scholar 
Oades, J. M. The retention of organic-matter in soils. Biogeochemistry 5, 35–70 (1988).
Google Scholar 
Jacobs, P. M. & Mason, J. A. Impact of Holocene dust aggradation on A horizon characteristics and carbon storage in loess-derived Mollisols of the Great Plains, USA. Geoderma 125, 95–106 (2005).
Google Scholar 
Yamamoto, K. et al. Tropical peat debris storage in the tidal flat in northern part of the Bengkalis island, Indonesia. MATEC Web Conf. 276, 06002 (2019).
Google Scholar 
Yang, S. et al. Burial of organic carbon in Holocene sediments of the Zhujiang (Pearl River) and Changjiang (Yangtze River) estuaries. Mar. Chem. 123, 1–10 (2011).
Google Scholar 
Zhou, G. Y. et al. Climate and litter C/N ratio constrain soil organic carbon accumulation. Natl. Sci. Rev. 6, 746–757 (2019).
Google Scholar 
Li, Q.-W. et al. Precipitation patterns impact soil aggregates and organic carbon of an alpine wetland on the Qinghai-Tibetan Plateau. Catena 244, 108249 (2024).Zhang, Y., Huang, X., Zhang, Z., Blewett, J. & Naafs, B. D. A. Spatiotemporal dynamics of dissolved organic carbon in a subtropical wetland and their implications for methane emissions. Geoderma 419, 115876 (2022).
Google Scholar 
Zhao, W. et al. Combination of mineral protection and molecular characteristics rather than alone to govern soil organic carbon stability in Qinghai-Tibetan plateau wetlands. J. Environ. Manag. 344, 118757 (2023).
Google Scholar 
Ding, Y. et al. The contribution of wetland plant litter to soil carbon pool: Decomposition rates and priming effects. Environ. Res. 224, 115575 (2023).
Google Scholar 
Xu, H. W. et al. Variation in soil organic carbon stability and driving factors after vegetation restoration in different vegetation zones on the Loess Plateau, China. Soil Tillage Res. 204, 12 (2020).
Google Scholar 
Malmer, N. & Wallén, B. Input rates, decay losses and accumulation rates of carbon in bogs during the last millennium: internal processes and environmental changes. Holocene 14, 111–117 (2004).
Google Scholar 
Ofiti, N. O. E. et al. Climate warming and elevated CO2 alter peatland soil carbon sources and stability. Nat. Commun. 14, 7533 (2023).
Google Scholar 
Liu, S. et al. Sedimentary ancient DNA reveals a threat of warming-induced alpine habitat loss to Tibetan Plateau plant diversity. Nat. Commun. 12, 2995 (2021).
Google Scholar 
Zimmermann, H. H. et al. Sedimentary ancient DNA and pollen reveal the composition of plant organic matter in Late Quaternary permafrost sediments of the Buor Khaya Peninsula (north-eastern Siberia). Biogeosciences 14, 575–596 (2017).
Google Scholar 
Fowler, H. J., Blenkinsop, S., Green, A. & Davies, P. A. Precipitation extremes in 2023. Nat. Rev. Earth Environ. 5, 250–252 (2024).
Google Scholar 
Sims, J. R. & Haby, V. A. Simplified colorimetric determination of soil organic matter. Soil Sci. 112, 137–141 (1971).
Google Scholar 
Folk, R. L. & Ward, W. C. Brazos River bar: a study in the significance of grain size parameters. J. Sediment. Res. 27, 3–26 (1957).
Google Scholar 
Ezcurra, E. Precision and bias of carbon storage estimations in wetland and mangrove sediments. Sci. Adv. 10, eadl1079 (2024).
Google Scholar 
Reimer, P. J. et al. The IntCal20 Northern Hemisphere radiocarbon age calibration curve (0–55 cal kBP). Radiocarbon 62, 725–757 (2020).
Google Scholar 
Blaauw, M. & Christen, J. A. Flexible paleoclimate age-depth models using an autoregressive gamma process. Bayesian Anal. 6, 457–474 (2011).
Google Scholar 
Hodgkins, S. B. et al. Tropical peatland carbon storage linked to global latitudinal trends in peat recalcitrance. Nat. Commun. 9, 3640 (2018).
Google Scholar 
Kylander, M. E., Ampel, L., Wohlfarth, B. & Veres, D. High-resolution X-ray fluorescence core scanning analysis of Les Echets (France) sedimentary sequence: new insights from chemical proxies. J. Quat. Sci. 26, 109–117 (2011).
Google Scholar 
Glass, J. B. et al. Molybdenum geochemistry in a seasonally dysoxic Mo-limited lacustrine ecosystem. Geochim. et. Cosmochim. Acta 114, 204–219 (2013).
Google Scholar 
Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M. & Lauro, C. PLS path modeling. Comput. Stat. Data Anal. 48, 159–205 (2005).
Google Scholar 
Wu, B. Medium resolution land cover data of Qinghai-Tibet Plateau (1980–2020). National Tibetan Plateau Data Center. https://doi.org/10.11888/Terre.tpdc.300593 (2023).China Geological Survey. Regional geological map of Xizang (1:250,000). National Geological Data Center. https://www.ngac.cn/ (2005).Download referencesAcknowledgementsThis work was supported by National Key Research and Development Program of China (Grant No. 2024YFF0808700), the National Natural Science Foundation of China (42407285), the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) (Grant No. 2019QZKK0304), and the Key Science and Technology Program of Xizang (Grant No. XZ202101ZD0011G). We thank the Geothermal Geological Brigade (Tibet Bureau of Geology and Mineral Resources) for drilling and sampling, and Dr. Gao Shaopeng (Institute of Tibetan Plateau Research, CAS) for his supervision of the XRF scanning. No specific permissions were required for geological sampling. We sincerely thank the editor and the three anonymous reviewers for their constructive comments and insightful suggestions, which greatly improved the quality of this manuscript.Author informationAuthor notesThese authors contributed equally: Youqing Yang, Xiaoping Wang.These authors jointly supervised this work: Jianqing Du, Xiaoyong Cui.Authors and AffiliationsCollege of Life Sciences, University of Chinese Academy of Sciences, Beijing, ChinaYouqing Yang, Zeyuan Wang, Danhong Chen, Chenhao Gao, Yanbin Hao & Xiaoyong CuiBeijing Yanshan Earth Critical Zone National Research Station, University of Chinese Academy of Sciences, Beijing, ChinaYouqing Yang, Jianqing Du, Zhixiang Niu, Yanbin Hao, Haishan Niu, Kai Xue, Xiaoyong Cui & Yanfen WangInternational Joint Research Laboratory for Global Change Ecology, School of Life Sciences, Henan University, Kaifeng, ChinaXiaoping WangNational Key Laboratory of Earth System Numerical Modeling and Application, Chinese Academy of Sciences, Beijing, ChinaJianqing Du, Yanbin Hao & Kai XueCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing, ChinaJianqing Du, Zhixiang Niu, Yitao Zhang, Liyuan Ma, Haijun Zhang, Qiang Liu, Yu Wu, Haishan Niu, Kai Xue & Yanfen WangState Key Laboratory of Lithospheric and Environmental Coevolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, ChinaHui ZhangState Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Chinese Academy of Sciences, Beijing, ChinaYanfen WangAuthorsYouqing YangView author publicationsSearch author on:PubMed Google ScholarXiaoping WangView author publicationsSearch author on:PubMed Google ScholarJianqing DuView author publicationsSearch author on:PubMed Google ScholarHui ZhangView author publicationsSearch author on:PubMed Google ScholarZhixiang NiuView author publicationsSearch author on:PubMed Google ScholarYitao ZhangView author publicationsSearch author on:PubMed Google ScholarZeyuan WangView author publicationsSearch author on:PubMed Google ScholarLiyuan MaView author publicationsSearch author on:PubMed Google ScholarHaijun ZhangView author publicationsSearch author on:PubMed Google ScholarDanhong ChenView author publicationsSearch author on:PubMed Google ScholarChenhao GaoView author publicationsSearch author on:PubMed Google ScholarQiang LiuView author publicationsSearch author on:PubMed Google ScholarYu WuView author publicationsSearch author on:PubMed Google ScholarYanbin HaoView author publicationsSearch author on:PubMed Google ScholarHaishan NiuView author publicationsSearch author on:PubMed Google ScholarKai XueView author publicationsSearch author on:PubMed Google ScholarXiaoyong CuiView author publicationsSearch author on:PubMed Google ScholarYanfen WangView author publicationsSearch author on:PubMed Google ScholarContributionsJ.D. and Yanfen Wang conceived the study and designed the research protocol. Y.Y., Z.N., Y.Z., Z.W., L.M., Q.L., and Yu Wu carried out the field investigation and sample collection. Y.Y., X.W., Haijun Zhang, D.C., and C.G. performed data processing and statistical analyses. Y.Y. and X.W. drafted the initial manuscript. J.D., Hui Zhang, Y.H., H.N., K.X., and X.C. contributed to data interpretation and critically revised the manuscript. Yanfen Wang acquired funding for the project.Corresponding authorsCorrespondence to
Jianqing Du or Xiaoyong Cui.Ethics declarations

Competing interests
The authors declare no competing interests.

Peer review

Peer review information
Communications Earth and Environment thanks Alberto Araneda and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Somaparna Ghosh [A peer review file is available].

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary informationTransparent Peer Review fileSupplementary InformationReporting SummaryRights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleYang, Y., Wang, X., Du, J. et al. Climate and sedimentary structure drive deep labile carbon accumulation in alpine wetlands.
Commun Earth Environ (2025). https://doi.org/10.1038/s43247-025-03081-8Download citationReceived: 25 April 2025Accepted: 01 December 2025Published: 16 December 2025DOI: https://doi.org/10.1038/s43247-025-03081-8Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative More

AbstractSince the Industrial Revolution, human activities have altered atmospheric nitrogen (N) deposition to global forests, affecting carbon dioxide emissions from soils (soil respiration or SR) – one of the largest land-atmosphere carbon fluxes. However, experimental studies have demonstrated both positive and negative effects of N deposition on SR in global forests, leading to debates on how N deposition increases or decreases SR. We developed a framework for generalizing SR responses to N deposition using synthesized data from 168 N addition experiments worldwide and observed SR across the global natural N deposition gradient. The findings indicate that N deposition decreased SR in 2.9% of global forested areas, particularly in eastern China, western Europe, and the eastern USA. However, the net effect of N deposition increased the global forest SR by ~5% (1.7 ± 0.1 PgC yr–1). If N pollution could be effectively controlled, global forest SR would decrease, potentially contributing to a reduction in the terrestrial carbon emissions.

Similar content being viewed by others

Temporal patterns of soil carbon emission in tropical forests under long-term nitrogen deposition

Article

01 December 2022

Unexpected sustained soil carbon flux in response to simultaneous warming and nitrogen enrichment compared with single factors alone

Article

24 September 2024

Nitrogen deposition contributed to a global increase in nitrous oxide emissions from forest soils

Article
Open access
28 September 2024

Data availability

Data supporting the findings of this study (including CO2_exp and CO2_obs datasets) are available in Zenodo (https://doi.org/10.5281/zenodo.17670031).
Code availability

R code file supporting the findings of this study is available in Zenodo (https://doi.org/10.5281/zenodo.17670031).
ReferencesFriedlingstein, P. et al. Global Carbon Budget 2022. Earth Syst. Sci. Data 14, 4811–4900 (2022).
Google Scholar 
Lei, J. et al. Temporal changes in global soil respiration since 1987. Nat. Commun. 12, 403 (2021).
Google Scholar 
Lu, H. B. et al. Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models. Environ. Res Lett. 16, 14 (2021).
Google Scholar 
Raich, J. W. & Schlesinger, W. H. The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate. Tellus B 44, 81–99 (1992).
Google Scholar 
Cai, W. X. et al. Carbon sequestration of Chinese forests from 2010 to 2060 spatiotemporal dynamics and its regulatory strategies. Sci. Bull. 67, 836–843 (2022).
Google Scholar 
Mo, L. et al. Integrated global assessment of the natural forest carbon potential. Nature 624, 92–101 (2023).
Google Scholar 
Griscom, B. W. et al. Natural climate solutions. Proc. Natl. Acad. Sci. USA 114, 11645–11650 (2017).
Google Scholar 
Galloway, J. N. et al. Nitrogen cycles: Past, present, and future. Biogeochemistry 70, 153–226 (2004).
Google Scholar 
Ackerman, D., Millet, D. B. & Chen, X. Global estimates of inorganic nitrogen deposition across four decades. Glob. Biogeochem. Cycles 33, 100–107 (2019).
Google Scholar 
Liu, L., Wen, Z., Liu, S., Zhang, X. & Liu, X. Decline in atmospheric nitrogen deposition in China between 2010 and 2020. Nat. Geosci. 17, 733–736 (2024).
Google Scholar 
Aber, J. D. et al. Nitrogen saturation in temperate forest ecosystems: hypotheses revisited. BioScience 48, 921–934 (1998).
Google Scholar 
Fenn, M. E. et al. Nitrogen excess in North American ecosystems: predisposing factors, ecosystem responses, and management strategies. Ecol. Appl 8, 706–733 (1998).
Google Scholar 
Cen, X. et al. Global patterns of nitrogen saturation in forests. One Earth 8, 101132 (2025).
Google Scholar 
Du, E. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. 13, 221–226 (2020).
Google Scholar 
Cen, X. et al. Suppression of nitrogen deposition on global forest soil CH4 uptake depends on nitrogen status. Glob. Biogeochem. Cycles 38, e2024GB008098 (2024).
Google Scholar 
Cleveland, C. C. & Townsend, A. R. Nutrient additions to a tropical rain forest drive substantial soil carbon dioxide losses to the atmosphere. Proc. Natl. Acad. Sci. USA 103, 10316–10321 (2006).
Google Scholar 
Mo, J. et al. Nitrogen addition reduces soil respiration in a mature tropical forest in southern China. Glob. Change Biol. 14, 403–412 (2008).
Google Scholar 
Bowden, R. D., Davidson, E., Savage, K., Arabia, C. & Steudler, P. Chronic nitrogen additions reduce total soil respiration and microbial respiration in temperate forest soils at the Harvard Forest. For. Ecol. Manag. 196, 43–56 (2004).
Google Scholar 
Janssens, I. A. et al. Reduction of forest soil respiration in response to nitrogen deposition. Nat. Geosci. 3, 315–322 (2010).
Google Scholar 
Liu, Y. et al. Spatially explicit estimate of nitrogen effects on soil respiration across the globe. Glob. Change Biol. 29, 3591 (2023).
Google Scholar 
Chen, C. & Chen, H. Y. H. Mapping global nitrogen deposition impacts on soil respiration. Sci. Total Environ. 871, 161986 (2023).
Google Scholar 
Bond-Lamberty, B. et al. Twenty Years of Progress, Challenges, and Opportunities in Measuring and Understanding Soil Respiration. J. Geophys. Res.: Biogeosciences 129, e2023JG007637 (2024).
Google Scholar 
Zhou, L. Y. et al. Different responses of soil respiration and its components to nitrogen addition among biomes: a meta-analysis. Glob. Change Biol. 20, 2332–2343 (2014).
Google Scholar 
de Vries, W., Du, E. Z. & Butterbach-Bahl, K. Short and long-term impacts of nitrogen deposition on carbon sequestration by forest ecosystems. Curr. Opin. Environ. Sustainability 9-10, 90–104 (2014).
Google Scholar 
Li, X. Y. et al. The contrasting effects of deposited NH4+ and NO3- on soil CO2, CH4 and N2O fluxes in a subtropical plantation, southern China. Ecol. Eng. 85, 317–327 (2015).
Google Scholar 
Bai, Y. et al. Tradeoffs and thresholds in the effects of nitrogen addition on biodiversity and ecosystem functioning: evidence from inner Mongolia Grasslands. Glob. Change Biol. 16, 358–372 (2010).
Google Scholar 
Reich, P. B., Tjoelker, M. G., Machado, J.-L. & Oleksyn, J. Universal scaling of respiratory metabolism, size and nitrogen in plants. Nature 439, 457–461 (2006).
Google Scholar 
Egidi, E., Coleine, C., Delgado-Baquerizo, M. & Singh, B. K. Assessing critical thresholds in terrestrial microbiomes. Nat. Microbiol. 8, 2230–2233 (2023).
Google Scholar 
Michaelis, L. & Menten, M. L. Die kinetik der invertinwirkung. Biochem. z. 49, 352 (1913).
Google Scholar 
Zhou, Z., Wang, C., Zheng, M., Jiang, L. & Luo, Y. Patterns and mechanisms of responses by soil microbial communities to nitrogen addition. Soil Biol. Biochem. 115, 433–441 (2017).
Google Scholar 
LeBauer, D. S. & Treseder, K. K. Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed. Ecology 89, 371–379 (2008).
Google Scholar 
Geisseler, D. & Horwath, W. R. Relationship between carbon and nitrogen availability and extracellular enzyme activities in soil. Pedobiologia 53, 87–98 (2009).
Google Scholar 
Allison, S. D. & Vitousek, P. M. Responses of extracellular enzymes to simple and complex nutrient inputs. Soil Biol. Biochem. 37, 937–944 (2005).
Google Scholar 
Goyal, S. S. & Huffaker, R. C. In Nitrogen in Crop Production (ed. Hauck, R. D.) (ASA, CSSA, SSSA;) 97–118 (1984).Kreutzer, K., Butterbach-Bahl, K., Rennenberg, H. & Papen, H. The complete nitrogen cycle of an N-saturated spruce forest ecosystem. Plant Biol. 11, 643–649 (2009).
Google Scholar 
Costantini, D., Metcalfe, N. B. & Monaghan, P. Ecological processes in a hormetic framework. Ecol. Lett. 13, 1435–1447 (2010).
Google Scholar 
Morgan Ernest, S. K. & Brown, J. H. Homeostasis and compensation: The role of species and resources in ecosystem stability. Ecology 82, 2118–2132 (2001).
Google Scholar 
Gilliam, F. S. Response of the herbaceous layer of forest ecosystems to excess nitrogen deposition. J. Ecol. 94, 1176–1191 (2006).
Google Scholar 
Bobbink, R. et al. Global assessment of nitrogen deposition effects on terrestrial plant diversity: a synthesis. Ecol. Appl. 20, 30–59 (2010).
Google Scholar 
Zheng, M. et al. Temporal patterns of soil carbon emission in tropical forests under long-term nitrogen deposition. Nat. Geosci. 15, 1002–1010 (2022).
Google Scholar 
Jian J. et al. A restructured and updated global soil respiration database (SRDB-V5). Earth Syst. Sci. Data 13, 255–267 (2021).Fog, K. The effect of added nitrogen on the rate of decomposition of organic matter. Biol. Rev. 63, 433–462 (1988).
Google Scholar 
Erofeeva, E. A. Environmental hormesis: from cell to ecosystem. Curr. Opin. Environ. Sci. Health 29, 100378 (2022).
Google Scholar 
Amyntas, A. et al. Niche complementarity among plants and animals can alter the biodiversity–ecosystem functioning relationship. Funct. Ecol. 37, 2652–2665 (2023).
Google Scholar 
Band, N., Kadmon, R., Mandel, M. & DeMalach, N. Assessing the roles of nitrogen, biomass, and niche dimensionality as drivers of species loss in grassland communities. Proc. Natl. Acad. Sci. 119, e2112010119 (2022).
Google Scholar 
Wang, C. et al. Long-term nitrogen input reduces soil bacterial network complexity by shifts in life history strategy in temperate grassland. iMeta 3, e194 (2024).
Google Scholar 
Liu, J. et al. Nitrogen addition reduced ecosystem stability regardless of its impacts on plant diversity. J. Ecol. 107, 2427–2435 (2019).
Google Scholar 
Melillo, J. M. et al. Long-term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science 358, 101–104 (2017).
Google Scholar 
Levin, S. A. The problem of pattern and scale in ecology: the Robert H. MacArthur award lecture. Ecology 73, 1943–1967 (1992).
Google Scholar 
Bae, K., Fahey, T. J., Yanai, R. D. & Fisk, M. Soil nitrogen availability affects belowground carbon allocation and soil respiration in northern hardwood forests of new hampshire. Ecosystems 18, 1179–1191 (2015).
Google Scholar 
Haynes, B. E. & Gower, S. T. Belowground carbon allocation in unfertilized and fertilized red pine plantations in Northern Wisconsin. Tree Physiol. 15, 317–325 (1995).
Google Scholar 
Wallenstein, M. D., McNulty, S., Fernandez, I. J., Boggs, J. & Schlesinger, W. H. Nitrogen fertilization decreases forest soil fungal and bacterial biomass in three long-term experiments. For. Ecol. Manag. 222, 459–468 (2006).
Google Scholar 
Treseder, K. K. Nitrogen additions and microbial biomass: a meta-analysis of ecosystem studies. Ecol. Lett. 11, 1111–1120 (2008).
Google Scholar 
Zhang, T. A., Chen, H. Y. H. & Ruan, H. Global negative effects of nitrogen deposition on soil microbes. ISME J. 12, 1817–1825 (2018).
Google Scholar 
Xing, A. J. et al. Nonlinear responses of ecosystem carbon fluxes to nitrogen deposition in an old-growth boreal forest. Ecol. Lett. 25, 77–88 (2022).
Google Scholar 
Zhang, D. et al. Changes in above-/below-ground biodiversity and plant functional composition mediate soil respiration response to nitrogen input. Funct. Ecol. 35, 1171–1182 (2021).
Google Scholar 
Lamanna, C. et al. Functional trait space and the latitudinal diversity gradient. Proc. Natl Acad. Sci. 111, 13745–13750 (2014).
Google Scholar 
Hillebrand, H. On the Generality of the Latitudinal Diversity Gradient. Am. Naturalist 163, 192–211 (2004).
Google Scholar 
Du, E. & de Vries, W. Links between nitrogen limitation and saturation in terrestrial ecosystems. Glob. Change Biol. 31, e70271 (2025).
Google Scholar 
Bond-Lamberty, B., Bronson, D., Bladyka, E. & Gower, S. T. A comparison of trenched plot techniques for partitioning soil respiration. Soil Biol. Biochem. 43, 2108–2114 (2011).
Google Scholar 
Wang, W., Chen, W. & Wang, S. Forest soil respiration and its heterotrophic and autotrophic components: Global patterns and responses to temperature and precipitation. Soil Biol. Biochem. 42, 1236–1244 (2010).
Google Scholar 
Zhao, Z., Ding, X., Wang, G. & Li, Y. 30 m Resolution Global Maps of Forest Soil Respiration and Its Changes From 2000 to 2020. Earth’s Future 12, e2023EF004007 (2024).
Google Scholar 
Cottingham, K. L., Lennon, J. T. & Brown, B. L. Knowing when to draw the line: designing more informative ecological experiments. Front. Ecol. Environ. 3, 145–152 (2005).
Google Scholar 
Yu, G. et al. Stabilization of atmospheric nitrogen deposition in China over the past decade. Nat. Geosci. 12, 424–429 (2019).
Google Scholar 
Liu, Y. et al. Spatially explicit estimate of nitrogen effects on soil respiration across the globe. Glob. Change Biol. 29, 3591–3600 (2023).
Google Scholar 
Yan, W., Zhong, Y., Yang, J., Shangguan, Z. & Torn, M. S. Response of soil greenhouse gas fluxes to warming: A global meta-analysis of field studies. Geoderma 419, 115865 (2022).
Google Scholar 
Shangguan, W., Dai, Y., Duan, Q., Liu, B. & Yuan, H. A global soil data set for earth system modeling. J. Adv. Modeling Earth Syst. 6, 249–263 (2014).
Google Scholar 
Liu, H. et al. Annual dynamics of global land cover and its long-term changes from 1982 to 2015. Earth Syst. Sci. Data 12, 1217–1243 (2020).
Google Scholar 
Hansen, M. C., Stehman, S. V. & Potapov, P. V. Quantification of global gross forest cover loss. Proc. Natl. Acad. Sci. 107, 8650–8655 (2010).
Google Scholar 
Wang, C. & Tang, Y. Responses of plant phenology to nitrogen addition: a meta-analysis. Oikos 128, 1243–1253 (2019).
Google Scholar 
Xu, C. et al. Long-term, amplified responses of soil organic carbon to nitrogen addition worldwide. Glob. Change Biol. 27, 1170–1180 (2021).
Google Scholar 
Wang, Z., Xing, A. & Shen, H. Effects of nitrogen addition on the combined global warming potential of three major soil greenhouse gases: A global meta-analysis. Environ. Pollut. 334, 121848 (2023).
Google Scholar 
R Core Team. R: A Language and Environment For Statistical Computing (R Foundation for Statistical Computing, 2020).Breiman, L. Random Forests. Mach. Learn. 45, 5–32 (2001).
Google Scholar 
Bond-Lamberty, B. & Thomson, A. Temperature-associated increases in the global soil respiration record. Nature 464, 579–U132 (2010).
Google Scholar 
Reay, D. S., Dentener, F., Smith, P., Grace, J. & Feely, R. A. Global nitrogen deposition and carbon sinks. Nat. Geosci. 1, 430–437 (2008).
Google Scholar 
Wang, W. J., Dalal, R. C., Moody, P. W. & Smith, C. J. Relationships of soil respiration to microbial biomass, substrate availability and clay content. Soil Biol. Biochem. 35, 273–284 (2003).
Google Scholar 
Chen, S., Zou, J., Hu, Z., Chen, H. & Lu, Y. Global annual soil respiration in relation to climate, soil properties and vegetation characteristics: Summary of available data. Agr. For. Meteorol. 198-199, 335–346 (2014).
Google Scholar 
Huang, N. et al. Spatial and temporal variations in global soil respiration and their relationships with climate and land cover. Sci. Adv. 6, 11 (2020).
Google Scholar 
Liaw, A. & Wiener, M. Classification and regression by randomForest. R. N. 2, 18–22 (2002).
Google Scholar 
Muggeo, V. M. Segmented: an R package to fit regression models with broken-line relationships. R. N. 8, 20–25 (2008).
Google Scholar 
Ritz, C., Baty, F., Streibig, J. C. & Gerhard, D. Dose-response analysis using R. PLoS ONE 10, e0146021 (2016).
Google Scholar 
ESRI. ArcGIS Desktop: Release 10 (ESRI, 2011).Mason, R. E. et al. Evidence, causes, and consequences of declining nitrogen availability in terrestrial ecosystems. Science 376, eabh3767 (2022).
Google Scholar 
Download referencesAcknowledgementsThe authors thank Prof. James Raich (Iowa State University) for his inputs on early version of this paper. We are grateful to all the researchers who spent time and effort on manipulative experiments and observations and made the data publicly accessible. This work was financially supported by the National Natural Science Foundation of China (32430067, 32588202, 42141004) and the National Key R&D Program of China (2023YFF1305900, 2022YFF080210102) received by N.H., and the Pioneer Center for Landscape Research in Sustainable Agricultural Futures (Land-CRAFT), DNRF grant number P2 received by K.B.B.Author informationAuthors and AffiliationsKey Laboratory of Boreal Forest Ecosystem Conservation and Restoration, National Forestry and Grassland Administration, Harbin, ChinaXiaoyu Cen & Nianpeng HeKey Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, ChinaXiaoyu Cen, Nianpeng He, Shuli Niu, Kailiang Yu, Li Xu & Mingxu LiDepartment of Earth System Science, Stanford University, Stanford, CA, USAXiaoyu Cen, Peter Vitousek & Elizabeth L. PaulusPioneer Center Land-CRAFT, Department of Agroecology, Aarhus University, Aarhus C, DenmarkXiaoyu Cen & Klaus Butterbach-BahlInstitute of Carbon Neutrality, School of Ecology, Northeast Forestry University, Harbin, ChinaNianpeng HeJoint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, USABen Bond-LambertyState Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaEnzai DuHigh Meadows Environmental Institute, Princeton University, Princeton, NJ, USAKailiang YuKey Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, ChinaMianhai ZhengDepartment of System Earth Science, Maastricht University, Innovalaan 1, Venlo, the NetherlandsKevin Van SundertGeochemistry and Biogeochemistry Group, SLAC National Accelerator Laboratory, Menlo Park, CA, USAElizabeth L. PaulusNicholas School of the Environment, Duke University, Durham, NC, USALiyin HeInstitute for Meteorology and Climate Research, Atmospheric Environmental Research, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, GermanyKlaus Butterbach-BahlAuthorsXiaoyu CenView author publicationsSearch author on:PubMed Google ScholarPeter VitousekView author publicationsSearch author on:PubMed Google ScholarNianpeng HeView author publicationsSearch author on:PubMed Google ScholarBen Bond-LambertyView author publicationsSearch author on:PubMed Google ScholarShuli NiuView author publicationsSearch author on:PubMed Google ScholarEnzai DuView author publicationsSearch author on:PubMed Google ScholarKailiang YuView author publicationsSearch author on:PubMed Google ScholarMianhai ZhengView author publicationsSearch author on:PubMed Google ScholarKevin Van SundertView author publicationsSearch author on:PubMed Google ScholarElizabeth L. PaulusView author publicationsSearch author on:PubMed Google ScholarLiyin HeView author publicationsSearch author on:PubMed Google ScholarLi XuView author publicationsSearch author on:PubMed Google ScholarMingxu LiView author publicationsSearch author on:PubMed Google ScholarKlaus Butterbach-BahlView author publicationsSearch author on:PubMed Google ScholarContributionsX.C. collected data and carried out the analysis based on feedback from N.H., P.V., and K.B.B. X.C. drafted the initial manuscript, P.V., N.H., B.B.L., S.N., E.D., K.Y., M.Z., K.V.S., E.L.P., L.H., L.X., M.L., and K.B.B. reviewed and edited the manuscript.Corresponding authorCorrespondence to
Nianpeng He.Ethics declarations

Competing interests
The authors declare no competing interests.

Peer review

Peer review information
Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary informationSupplementary InformationTransparent Peer Review fileRights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and permissionsAbout this articleCite this articleCen, X., Vitousek, P., He, N. et al. A general framework for nitrogen deposition effects on soil respiration in global forests.
Nat Commun (2025). https://doi.org/10.1038/s41467-025-67203-8Download citationReceived: 07 February 2025Accepted: 25 November 2025Published: 16 December 2025DOI: https://doi.org/10.1038/s41467-025-67203-8Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative More

AbstractIndividual recognition is fundamental to the social behaviour of many animals. In the context of territorial behaviour, animals in high-density populations encounter conspecific rivals and potential mates more frequently, which should enhance the individuality of territorial signals to facilitate recognition among conspecifics. We investigated vocal individuality in male territorial calls of two populations of little owls (Athene noctua) with different densities. Further, to explore the potential influence of local population distribution on individuality, we also examined isolated males without neighbours and clumped males with neighbours. Our findings indicate higher individuality at higher densities across both scenarios, measured using two individuality metrics: Beecher’s information statistic and Discrimination score. Clumped males exhibited significantly lower acoustic niche overlaps (i.e. higher vocal individuality) compared to isolated males. However, only a non-significant trend for lower acoustic niche overlaps (i.e. higher vocal individuality) was found for males from high density compared to low density populations. This suggests that the immediate social environment might be more influential than larger-scale population density patterns. This study suggests that vocal individuality in a territorial species is influenced by conspecific density, similar to findings in group-living and colonial species.

Similar content being viewed by others

Species-independent analysis and identification of emotional animal vocalizations

Article
Open access
06 August 2025

Density-dependent network structuring within and across wild animal systems

Article

04 September 2025

Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method

Article
Open access
31 March 2021

Data availability

Data available on figshare: [doi.org/10.6084/m9.figshare.29958992](http:/doi.org/10.6084/m9.figshare.29958992).
ReferencesJudge, P. G. & de Waal, F. B. Rhesus monkey behaviour under diverse population densities: coping with long-term crowding. Anim. Behav. 54, 643–662 (1997).
Google Scholar 
Jirotkul, M. Population density influences male-male competition in guppies. Anim. Behav. 58, 1169–1175 (1999).
Google Scholar 
Knell, R. J. Population density and the evolution of male aggression. J. Zool. 278, 83–90 (2009).
Google Scholar 
Cooper, W. E., Dimopoulos, I., Pafilis, P. & Sex Age, and population density affect aggressive behaviors in island lizards promoting cannibalism. Ethology 121, 260–269 (2015).
Google Scholar 
Luna, Á., Palma, A., Sanz-Aguilar, A., Tella, J. L. & Carrete, M. Personality-dependent breeding dispersal in rural but not urban Burrowing owls. Sci. Rep. 9, 2886 (2019).
Google Scholar 
Fedy, B. C. & Stutchbury, B. J. Territory switching and floating in White-bellied antbird (Myrmeciza longipes), a resident tropical passerine in Panama. Auk 121, 486–496 (2004).
Google Scholar 
Schoepf, I., Schmohl, G., König, B., Pillay, N. & Schradin, C. Manipulation of population density and food availability affects home range sizes of African striped mouse females. Anim. Behav. 99, 53–60 (2015).
Google Scholar 
Arcese, P. & Smith, J. N. Effects of population density and supplemental food on reproduction in Song sparrows. J. Anim. Ecol. 57, 119–136 (1988).
Google Scholar 
Sofaer, H. R., Sillett, T. S., Langin, K. M., Morrison, S. A. & Ghalambor, C. K. Partitioning the sources of demographic variation reveals density-dependent nest predation in an Island bird population. Ecol. Evol. 4, 2738–2748 (2014).
Google Scholar 
Emlen, S. T. & Oring, L. W. Ecology, sexual selection, and the evolution of mating systems. Science 197, 215–223 (1977).
Google Scholar 
Lukas, D. & Clutton-Brock, T. H. The evolution of social monogamy in mammals. Science 341, 526–530 (2013).
Google Scholar 
Morales, M. B. et al. Density dependence and habitat quality modulate the intensity of display territory defence in an exploded lekking species. Behav. Ecol. Sociobiol. 68, 1493–1504 (2014).
Google Scholar 
Beecher, M. D. Signalling systems for individual recognition: an information theory approach. Anim. Behav. 38, 248–261 (1989).
Google Scholar 
Pollard, K. A., Blumstein, D. T. & Griffin, S. C. Pre-screening acoustic and other natural signatures for use in noninvasive individual identification. J. Appl. Ecol. 47, 1103–1109 (2010).
Google Scholar 
Freeberg, T. M. Social complexity can drive vocal complexity: group size influences vocal information in Carolina chickadees. Psychol. Sci. 17, 557–561 (2006).
Google Scholar 
Freeberg, T. M., Dunbar, R. I. & Ord, T. J. Social complexity as a proximate and ultimate factor in communicative complexity. Philosophical Trans. Royal Soc. B: Biol. Sci. 367, 1785–1801 (2012).
Google Scholar 
Peckre, L., Kappeler, P. M. & Fichtel, C. Clarifying and expanding the social complexity hypothesis for communicative complexity. Behav. Ecol. Sociobiol. 73, 1–19 (2019).
Google Scholar 
Arnold, B. D. & Wilkinson, G. S. Individual specific contact calls of Pallid bats (Antrozous pallidus) attract conspecifics at roosting sites. Behav. Ecol. Sociobiol. 65, 1581–1593 (2011).
Google Scholar 
Charrier, I., Aubin, T. & Mathevon, N. Mother-calf vocal communication in Atlantic walrus: a first field experimental study. Anim. Cogn. 13, 471–482 (2010).
Google Scholar 
Pitcher, B. J., Harcourt, R. G. & Charrier, I. Rapid onset of maternal vocal recognition in a colonially breeding mammal, the Australian sea lion. PLoS One 5(8), e12195 (2010).
Google Scholar 
Sheehan, M. J. et al. Selection on coding and regulatory variation maintains individuality in major urinary protein scent marks in wild mice. PLoS Genet. 12(1), e1005891 (2016).
Google Scholar 
Tibbetts, E. A. Complex social behaviour can select for variability in visual features: a case study in Polistes wasps. Proc. R Soc. Lond. B Biol. Sci. 271, 1955–1960 (2004).
Google Scholar 
Beecher, M. D., Medvin, M. B., Stoddard, P. K. & Loesche, P. Acoustic adaptations for parent-offspring recognition in swallows. Exp. Biol. 45, 179–193 (1986).
Google Scholar 
Loesche, P., Stoddard, P. K., Higgins, B. J. & Beecher, M. D. Signature versus perceptual adaptations for individual vocal recognition in swallows. Behaviour 118, 15–25 (1991).
Google Scholar 
Medvin, M. B., Stoddard, P. K. & Beecher, M. D. Signals for parent-offspring recognition: a comparative analysis of the begging calls of Cliff swallows and Barn swallows. Anim. Behav. 45, 841–850 (1993).
Google Scholar 
Martin, M., Gridley, T., Elwen, S. H. & Charrier, I. Extreme ecological constraints lead to high degree of individual stereotypy in the vocal repertoire of the Cape fur seal (Arctocephalus pusillus pusillus). Behav. Ecol. Sociobiol. 75, 1–16 (2021).
Google Scholar 
Pollard, K. A. & Blumstein, D. T. Social group size predicts the evolution of individuality. Curr. Biol. 21, 413–417 (2011).
Google Scholar 
Wilkinson, G. S. Social and vocal complexity in bats. In Animal Social Complexity: Intelligence, Culture, and Individualized Societies (Harvard University Press, 2003). 
Google Scholar 
Brooks, R. J. & Falls, J. B. Individual recognition by song in White-throated sparrows. I. Discrimination of songs of neighbors and strangers. Can. J. Zool. 53, 879–888 (1975).
Google Scholar 
Godard, R. Long-term memory of individual neighbours in a migratory songbird. Nature 350, 228–229 (1991).
Google Scholar 
Hyman, J. & Hughes, M. Territory owners discriminate between aggressive and nonaggressive neighbours. Anim. Behav. 72, 209–215 (2006).
Google Scholar 
Jaška, P., Linhart, P. & Fuchs, R. Neighbour recognition in two sister songbird species with a simple and complex repertoire-a playback study. J. Avian Biol. 46, 151–158 (2015).
Google Scholar 
Temeles, E. J. The role of neighbours in territorial systems: when are they ‘dear enemies’? Anim. Behav. 47, 339–350 (1994).
Google Scholar 
Fisher, J. B. Evolution and bird sociality. Evol. As Process., 71–83. (1954).Blumstein, D. T., Mcclain, D. R. & Jesus, C. Alarcón-Nieto, G. Breeding bird density does not drive vocal individuality. Curr. Zool. 58, 765–772 (2012).
Google Scholar 
Delgado, M. D. M. et al. Population characteristics may reduce the levels of individual call identity. PLoS One 8(10), e77557 (2013).
Google Scholar 
Chen, Z. & Wiens, J. J. The origins of acoustic communication in vertebrates. Nat. Commun. 11, 1–8 (2020).
Google Scholar 
Ramanankirahina, R., Joly, M., Scheumann, M. & Zimmermann, E. The role of acoustic signaling for spacing and group coordination in a nocturnal, pair-living primate, the Western woolly Lemur (Avahi occidentalis. Am. J. Phys. Anthropol. 159, 466–477 (2016).
Google Scholar 
Zhang, C. M., Sun, C. N. & Lucas, F. Acoustic signal dominance in the multimodal communication of a nocturnal mammal. Curr. Zool. (2021).
Google Scholar 
Linhart, P. & Šálek, M. The assessment of biases in the acoustic discrimination of individuals. PLoS One 12(5), e0177206 (2017).
Google Scholar 
Odom, K. J., Slaght, J. C. & Gutiérrez, R. J. Distinctiveness in the territorial calls of Great horned owls within and among years. J. Raptor Res. 47, 21–30 (2013).
Google Scholar 
Yee, S. A., Puan, C. L., Chang, P. K. & Azhar, B. Vocal individuality of Sunda scops-owl (Otus lempiji) in Peninsular Malaysia. J. Raptor Res. 50, 379–390 (2016).
Google Scholar 
Madhavan, M. & Linhart, P. Vocal individuality in owls: a taxon-wide review in the context of Tinbergen’s four questions. J. Ornithol. 166, 307–319 (2025).
Google Scholar 
Cavanagh, P. M. & Ritchison, G. Variation in the bounce and whinny songs of the Eastern Screech-Owl. Wilson Bull. 99, 620–627 (1987).
Google Scholar 
Grieco, F. Aggregation of Eurasian scops owls Otus scops breeding in Magpie Pica pica nests. Ardea 106, 177–191 (2018).
Google Scholar 
Nagy, C. M. & Rockwell, R. F. Identification of individual Eastern Screech-Owls megascops Asio via vocalization analysis. Bioacoustics 21, 127–140 (2012).
Google Scholar 
Dreiss, A. N., Ruppli, C. A. & Roulin, A. Individual vocal signatures in barn Owl nestlings: does individual recognition have an adaptive role in sibling vocal competition? J. Evol. Biol. 27, 63–75 (2014).
Google Scholar 
Van Nieuwenhuyse, D., Van Harxen, R., Johnson, D. H. & De Raedt, J. The Little Owl: Population Dynamics, Behavior and Management of Athene noctua (Cambridge University Press, 2023). 
Google Scholar 
Chrenková, M., Dobrý, M. & Šálek, M. Further evidence of large-scale population decline and range contraction of the Little Owl Athene noctua in central Europe. Folia Zool. 66, 106–116 (2017).
Google Scholar 
Šálek, M. et al. Scale-dependent habitat associations of a rapidly declining farmland predator, the Little Owl Athene noctua, in contrasting agricultural landscapes. Agric. Ecosyst. Environ. 224, 56–66 (2016).
Google Scholar 
Le Gouar, P. J. et al. Long-term trends in survival of a declining population: the case of the Little Owl (Athene noctua) in the Netherlands. Oecologia 166, 369–379 (2011).
Google Scholar 
Żmihorski, M., Altenburg, D., Romanowski, J., Kowalski, M. & Osojca, G. Long term decline of the Little Owl (Athene noctua Scop., 1769) in central Poland. Pol. J. Ecol. 54, 321–324 (2006).
Google Scholar 
Mayer, M. et al. Fine-scale movement patterns and habitat selection of Little owls (Athene noctua) from two declining populations. PLoS One 16(9), e0256608 (2021).
Google Scholar 
Šálek, M. & Lövy, M. Spatial ecology and habitat selection of Little Owl Athene noctua during the breeding season in central European farmland. Bird. Conserv. Int. 22, 328–338 (2012).
Google Scholar 
Šálek, M. & Mayer, M. Farmstead modernization adversely affects farmland birds. J. Appl. Ecol. 60, 101–110 (2023).
Google Scholar 
Šálek, M. & Schröpfer, L. Population decline of the Little Owl (Athene noctua Scop.) in the Czech Republic. Pol. J. Ecol. 56, 527–534 (2008).
Google Scholar 
Šálek, M., Chrenkova, M. & Kipson, M. High population density of Little Owl (Athene noctua) in hortobagy National park, Hungary, central Europe. Pol. J. Ecol. 61, 165–169 (2013).
Google Scholar 
Šálek, M. et al. In Owl’s Paradise: Little Owl Population Densities in Traditional Human Settlements Represent One of the Highest Densities Reported among Owls. J. Raptor Res. 59(1), 1–11 (2025).
Google Scholar 
Jacobsen, L. B., Sunde, P., Rahbek, C., Dablesteen, T. & Thorup, K. Territorial calls in the Little Owl (Athene noctua): spatial dispersion and social interplay of mates and neighbours. Ornis Fenn. 90(1), 41–49 (2013).
Google Scholar 
Orlando, G., Varesio, A. & Chamberlain, D. Field evaluation for playback surveys: species-specific detection probabilities and distance estimation errors in a nocturnal bird community. Bird. Study. 68, 78–87 (2021).
Google Scholar 
Hardouin, L. A., Tabel, P. & Bretagnolle, V. Neighbour-stranger discrimination in the Little owl, Athene noctua. Anim. Behav. 72, 105–112 (2006).
Google Scholar 
Siracusa, E. R. et al. Familiar Neighbors, but not relatives, enhance fitness in a territorial mammal. Curr. Biol. 31, 438–445 (2021).
Google Scholar 
Falls, J. B. Individual recognition by sounds in birds. In Acoustic Communication in Birds Vol. 2 (eds Kroodsma, D. E. & Miller, E. H.) 237–278 (Academic, 1982).
Google Scholar 
Exo, K. M. Annual cycle and ecological adaptions in the Little Owl (Athene noctua). J. Ornithol. 129, 393–415 (1988).
Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km Spatial resolution climate surfaces for global land areas. Intl J. Climatology. 37, 4302–4315 (2017).
Google Scholar 
Průchová, A., Šálek, M. & Linhart, P. Social factors affect vocal activity patterns of two common call types in Little Owl males. J. Ornithol. 166, 235–246 (2025).
Google Scholar 
Šálek, M. Dlouhodobý pokles početnosti sýčka obecného (Athene noctua) v jádrové oblasti Jeho rozšíření v Čechách. Sylvia 50, 2–12 (2014).
Google Scholar 
Exo. Population ecology of Little Owls Athene noctua in Central Europe: a review. The ecology and conservation of European owls, 64–75. (1992).Charif, R. A., Waack, A. M. & Strickman, L. M. Raven Pro 1.4 User’s Manual (Cornell Lab of Ornithology, 2010).Araya-Salas, M. Rraven: connecting R and Raven bioacoustic software. R package version 1.0.9. (2020).Linhart, P. et al. Measuring individual identity information in animal signals: overview and performance of available identity metrics. Methods Ecol. Evol. 10, 1558–1570 (2019).
Google Scholar 
Blumstein, D. T. & Munos, O. Individual, age and sex-specific information is contained in Yellow-bellied marmot alarm calls. Anim. Behav. 69, 353–361 (2005).
Google Scholar 
Tumulty, J. P., Lange, Z. K. & Bee, M. A. Identity signaling, identity reception, and the evolution of social recognition in a Neotropical frog. Evolution 76, 158–170 (2022).
Google Scholar 
Favaro, L., Gamba, M., Alfieri, C., Pessani, D. & McElligott, A. G. Vocal individuality cues in the African penguin (Spheniscus demersus): a source-filter theory approach. Sci. Rep. 5(1), 17255 (2015).
Google Scholar 
Galeotti, P., Paladin, M. & Pavan, G. Individually distinct hooting in male Pygmy owls Glaucidium passerinum: a multivariate approach. Ornis Scand. 1993, 15–20 (1993).
Google Scholar 
Li, Y., Xia, C., Lloyd, H., Li, D. & Zhang, Y. Identification of vocal individuality in male cuckoos using different analytical techniques. Avian Res. 8, 1–7 (2017).
Google Scholar 
Hutchinson, G. E. Concluding remarks. Cold Spring Harb Symp. Quant. Biol. 22, 415–427 (1957).
Google Scholar 
Alvarado-Serrano, D. F. & Knowles, L. L. Ecological niche models in phylogeographic studies: applications, advances and precautions. Mol. Ecol. Resour. 14, 233–248 (2014).
Google Scholar 
Bearhop, S., Adams, C. E., Waldron, S., Fuller, R. A. & MacLeod, H. Determining trophic niche width: a novel approach using stable isotope analysis. J. Anim. Ecol. 73, 1007–1012 (2004).
Google Scholar 
Raxworthy, C. J., Ingram, C. M., Rabibisoa, N. & Pearson, R. G. Applications of ecological niche modeling for species delimitation: a review and empirical evaluation using day geckos (Phelsuma) from Madagascar. Syst. Biol. 56, 907–923 (2007).
Google Scholar 
Hart, P. J., Ibanez, T., Paxton, K., Tredinnick, G., Sebastián-González, E., & Tanimoto-Johnson, A. Timing is everything: acoustic niche partitioning in two tropical wet forest bird communities. Front. Ecol. Evol. 9, 753363 (2021).Henry, C. S. & Wells, M. M. Acoustic niche partitioning in two cryptic sibling species of Chrysoperla green lacewings that must duet before mating. Anim. Behav. 80, 991–1003 (2010).
Google Scholar 
Sinsch, U., Lümkemann, K., Rosar, K., Schwarz, C. & Dehling, M. Acoustic niche partitioning in an Anuran community inhabiting an Afromontane wetland (Butare, Rwanda). Afr. Zool. 47, 60–73 (2012).
Google Scholar 
Swanson, H. K. et al. A new probabilistic method for quantifying n-dimensional ecological niches and niche overlap. Ecology 96, 318–324 (2015).
Google Scholar 
Core Team, R. R. C. R: A language and environment for statistical computing. (2022).Budka, M., Matyjasiak, P., Typiak, J., Okołowski, M. & Zagalska-Neubauer, M. Experienced males modify their behaviour during playback: the case of the Chaffinch. J. Ornithol. 160, 673–684 (2019).
Google Scholar 
Linhart, P., Fuchs, R., Poláková, S. & Slabbekoorn, H. Once bitten twice shy: long-term behavioural changes caused by trapping experience in Willow warblers Phylloscopus trochilus. J. Avian Biol. 43, 186–192 (2012).
Google Scholar 
Oñate-Casado, J., Porteš, M., Beran, V., Petrusek, A. & Petrusková, T. An experience to remember: lifelong effects of playback-based trapping on behaviour of a migratory passerine bird. Anim. Behav. 182, 19–29 (2021).
Google Scholar 
Sexton, K., Redmond, L., Murphy, M. & Dolan, A. Dawn song of Eastern kingbirds: intrapopulation variability and sociobiological correlates. Behav 144, 1273–1295 (2007).
Google Scholar 
Tobias, J. A., Gamarra-Toledo, V., García-Olaechea, D., Pulgarín, P. C. & Seddon, N. Year-round resource defence and the evolution of male and female song in suboscine birds: social armaments are mutual ornaments: evolution of mutual ornaments in birds. J. Evol. Biol. 24, 2118–2138 (2011).
Google Scholar 
Xia, C. et al. Dawn singing intensity of the male Brownish-Flanked Bush warbler: effects of territorial insertions and number of neighbors. Ethology 120, 324–330 (2014).
Google Scholar 
Ripmeester, E. A. P., Kok, J. S., Van Rijssel, J. C. & Slabbekoorn, H. Habitat-related birdsong divergence: a multi-level study on the influence of territory density and ambient noise in European Blackbirds. Behav. Ecol. Sociobiol. 64, 409–418 (2010).
Google Scholar 
Stuart, C. J., Grabarczyk, E. E., Vonhof, M. J. & Gill, S. A. Social factors, not anthropogenic noise or artificial light, influence onset of dawn singing in a common songbird. Auk 136, ukz045 (2019).
Google Scholar 
Owen, K. C. & Mennill, D. J. Singing in a fragmented landscape: Wrens in a tropical dry forest show sex differences in the effects of neighbours, time of day, and time of year. J. Ornithol. 162, 881–893 (2021).
Google Scholar 
Sánchez, N. V. & Mennill, D. J. Behavioural consequences of conspecific neighbours: a systematic literature review of the effects of local density on avian vocal communication. J. Ornithol. 165, 847–859 (2024).
Google Scholar 
Gokcekus, S., Firth, J. A., Regan, C. & Sheldon, B. C. Recognising the key role of individual recognition in social networks. Trends Ecol. Evol. 36, 1024–1035 (2021).
Google Scholar 
Tibbetts, E. A., Mullen, S. P. & Dale, J. Signal function drives phenotypic and genetic diversity: the effects of signalling individual identity, quality or behavioural strategy. Phil Trans. R Soc. B. 372, 20160347 (2017).
Google Scholar 
Osiecka, A. N., Briefer, E. F., Kidawa, D. & Wojczulanis-Jakubas, K. Strong individual distinctiveness across the vocal repertoire of a colonial seabird, the Little auk, Alle alle. Anim. Behav. 210, 199–211 (2024).
Google Scholar 
Charrier, I. Mother-offspring vocal recognition and social system in pinnipeds. Coding strategies in vertebrate acoustic communication 231–246 Cham: Springer International Publishing. (2020). Elfström, S. T. Responses of territorial Meadow pipits to strange and familiar song phrases in playback experiments. Anim. Behav. 40, 786–788 (1990).
Google Scholar 
Wegrzyn, E., Leniowski, K. & Osiejuk, T. S. Introduce yourself at the beginning-possible identification function of the initial part of the song in the Great reed warbler Acrocephalus arundinaceus. Ornis 86 (2), 61–70 (2009). (2009).
Google Scholar 
Mathevon, N. et al. Singing in the rain forest: how a tropical bird song transfers information. PLoS ONE. 3, e1580 (2008).
Google Scholar 
Wheeldon, A., Kwiatkowska, K., Szymański, P. & Osiejuk, T. S. Male and female songs propagation in a duetting tropical bird species in its preferred and secondary habitat. PLoS ONE. 17, e0275434 (2022).
Google Scholar 
Lengagne, T. Temporal stability in the individual features in the calls of Eagle owls (Bubo bubo). Behaviour 138, 1407–1419 (2001).
Google Scholar 
Garland, T. & Adolph, S. C. Why not to do two-Species comparative studies: limitations on inferring adaptation. Physiological Zool. 67, 797–828 (1994).
Google Scholar 
Goymann, W. & Schwabl, H. The tyranny of phylogeny—A plea for a less dogmatic stance on two-species comparisons: Funding bodies, journals and referees discourage two‐ or few‐species comparisons, but such studies provide essential insights complementary to phylogenetic comparative studies. BioEssays 43(8), 2100071 (2021).
Google Scholar 
Kidawa, D., Wojczulanis-Jakubas, K., Jakubas, D., Palme, R. & Barcikowski, M. Mine or my neighbours’ offspring: an experimental study on parental discrimination of offspring in a colonial seabird, the Little auk Alle alle. Sci. Rep. 13, 15088 (2023).
Google Scholar 
Klenova, A. V., Volodin, I. A. & Volodina, E. V. The variation in reliability of individual vocal signature throughout ontogenesis in the Red-crowned crane Grus japonensis. Acta ethol. 12, 29–36 (2009).
Google Scholar 
Lefevre, K., Montgomerie, R. & Gaston, A. J. Parent–offspring recognition in Thick-billed murres (Aves: Alcidae). Anim. Behav. 55, 925–938 (1998).
Google Scholar 
Osiecka, A. N., Oliva, M. Q., Kouřil, J., Petrusková, T. & Burchardt, L. S. Yellowhammer (Emberiza citrinella) males sing using individual isochronous rhythms and maximise rhythmic dissimilarity with neighbours. bioRxiv. Preprint at. https://doi.org/10.1101/2025.06.17.660106 (2025).
Google Scholar 
Geberzahn, N. & Aubin, T. How a songbird with a continuous singing style modulates its song when territorially challenged. Behav. Ecol. Sociobiol. 68, 1–12 (2014).
Google Scholar 
Hardouin, L. A., Reby, D., Bavoux, C., Burneleau, G. & Bretagnolle, V. Communication of male quality in owl hoots. Am. Nat. 169, 552–562 (2007).
Google Scholar 
Linhart, P., Jaška, P., Petrusková, T., Petrusek, A. & Fuchs, R. Being angry, singing fast? Signalling of aggressive motivation by syllable rate in a songbird with slow song. Behav. Processes. 100, 139–145 (2013).
Google Scholar 
McGregor, P. K., Dabelsteen, T., Shepherd, M. & Pedersen, S. B. The signal value of matched singing in Great tits: evidence from interactive playback experiments. Anim. Behav. 43, 987–998 (1992).
Google Scholar 
Thomsen, H. M., Balsby, T. J. & Dabelsteen, T. The imitation dilemma: can parrots maintain their vocal individuality when imitating conspecifics? Behaviour 156, 787–814 (2019).
Google Scholar 
Delport, W., Kemp, A. C. & Ferguson, J. W. H. Vocal identification of individual African wood owls Strix woodfordii: A technique to monitor long-term adult turnover and residency. Ibis 144, 30–39 (2002).
Google Scholar 
Rognan, C. B., Szewczak, J. M. & Morrison, M. L. Vocal individuality of Great gray owls in the Sierra Nevada. J. Wildl. Manage. 73, 755–760 (2009).
Google Scholar 
Takagi, M. Vocalizations of the Ryukyu scops owl Otus elegans: individually recognizable and stable. Bioacoustics 29, 28–44 (2020).
Google Scholar 
Tripp, T. M. & Otter, K. A. Vocal individuality as a potential long-term monitoring tool for Western screech-owls, Megascops kennicottii. Can. J. Zool. 84, 744–753 (2006).
Google Scholar 
Rivera-Gutierrez, H. F., Pinxten, R. & Eens, M. Songs differing in consistency elicit differential aggressive response in territorial birds. Biol. Lett. 7, 339–342 (2011).
Google Scholar 
Sierro, J., de Kort, S.R., Hartley, I.R. Sexual selection for both diversity and repetition in birdsong. Nat. Commun. 14(1), 3600 (2023).
Google Scholar 
Miles, L. S., Rivkin, L. R., Johnson, M. T., Munshi-South, J. & Verrelli, B. C. Gene flow and genetic drift in urban environments. Mol. Ecol. 28, 4138–4151 (2019).
Google Scholar 
Evans, K. L. Individual species and urbanisation. Urban ecology, 53-87 (2010).Hill, S. D., Aryal, A., Pawley, M. D. M. & Ji, W. So much for the city: Urban-rural song variation in a widespread Asiatic songbird. Integr. Zool. 13, 194–205 (2018).
Google Scholar 
Narango, D. L. & Rodewald, A. D. Urban-associated drivers of song variation along a rural–urban gradient. Behav. Ecol. 27, 608–616 (2016).
Google Scholar 
Halfwerk, W. et al. Adaptive changes in sexual signalling in response to urbanization. Nat. Ecol. Evol. 3, 374–380 (2018).
Google Scholar 
Brenowitz, E. A. Evolution of the vocal control system in the avian brain. Semin Neurosci. 3, 399–407 (1991).
Google Scholar 
Ten Cate, C. Re-evaluating vocal production learning in non-oscine birds. Phil Trans. R Soc. B 376(1836), 20200249 (2021).
Google Scholar 
Brumm, H. & Zollinger, S. A. The evolution of the Lombard effect: 100 years of psychoacoustic research. Behaviour 148, 1173–1198 (2011).
Google Scholar 
Dunlop, R. A., Cato, D. H. & Noad, M. J. Evidence of a Lombard response in migrating Humpback whales (Megaptera novaeangliae). J. Acoust. Soc. Am. 136, 430–437 (2014).
Google Scholar 
Halfwerk, W., Lea, A. M., Guerra, M. A., Page, R. A. & Ryan, M. J. Vocal responses to noise reveal the presence of the Lombard effect in a frog. Behav. Ecol. 27, 669–676 (2016).
Google Scholar 
Janik, V. M. & Slater, P. J. The different roles of social learning in vocal communication. Anim. Behav. 60, 1–11 (2000).
Google Scholar 
Arellano, C. M., Canelón, N. V., Delgado, S. & Berg, K. S. Allo-preening is linked to vocal signature development in a wild parrot. Behav. Ecol. 33, 202–212 (2022).
Google Scholar 
Beecher, M. & Brenowitz, E. Functional aspects of song learning in songbirds. Trends Ecol. Evol. 20, 143–149 (2005).
Google Scholar 
Palmero, A. M., Illera, J. C. & Laiolo, P. Song characterization in the Spectacled warbler (Sylvia conspicillata): a circum-Mediterranean species with a complex song structure. Bioacoustics 21, 175–191 (2012).
Google Scholar 
Slater, P. J. B. & Lachlan, R. F. Is Innovation in Bird Song Adaptive? In Animal Innovation (eds Reader, S. M. & Laland, K. N.) 117–136 (Oxford University Press, 2003).
Google Scholar 
Walcott, C., Mager, J. N. & Piper, W. Changing territories, changing tunes: male loons, Gavia immer, change their vocalizations when they change territories. Anim. Behav. 71, 673–683 (2006).
Google Scholar 
Groothuis, T. The influence of social experience on the development and fixation of the form of displays in the Black-headed gull. Anim. Behav. 43, 1–14 (1992).
Google Scholar 
Download referencesAcknowledgementsWe are grateful to all four anonymous reviewers whose feedback on a previous version of the manuscript helped to improve its quality and clarity.FundingThis study was supported by the Czech Science Foundation (GACR 21–04023 K). Martin Šálek was partly supported by the Czech Academy of Sciences in the framework of the program Strategy AV 21 and the research aim of the Czech Academy of Sciences (RVO 68081766). Malavika Madhavan was partly supported by the University of South Bohemia (GAJU No. 047/2025/P). Martin Šálek and Malavika Madhavan were also supported by One Nature Project (LIFE17 IPE/CZ/000005, LIFE IP: N2K Revisited), supported by the EU´s Financial Instrument LIFE.Author informationAuthors and AffiliationsDepartment of Zoology, Faculty of Science, University of South Bohemia, České Budějovice, Czech RepublicMalavika Madhavan, Lucie Hornátová, Alexandra Průchová & Pavel LinhartInstitute of Vertebrate Biology, Czech Academy of Sciences, Brno, Czech RepublicMartin ŠálekFaculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague-Suchdol, Czech RepublicMartin ŠálekCenter for Sustainable Landscapes under Global Change (SustainScapes), Department of Biology, Aarhus University, Aarhus, DenmarkPavel LinhartAuthorsMalavika MadhavanView author publicationsSearch author on:PubMed Google ScholarLucie HornátováView author publicationsSearch author on:PubMed Google ScholarMartin ŠálekView author publicationsSearch author on:PubMed Google ScholarAlexandra PrůchováView author publicationsSearch author on:PubMed Google ScholarPavel LinhartView author publicationsSearch author on:PubMed Google ScholarContributionsPL and MŠ conceived the study and acquired funding for the project. MŠ and AP collected the data, and AP, PL, LH, and MM curated the data. MM, LH, and PL investigated the data and performed the analyses. MM and PL drafted the manuscript, and all authors contributed to its editing and final approval.Corresponding authorCorrespondence to
Malavika Madhavan.Ethics declarations

Competing interests
The authors declare no competing interests.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationBelow is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and permissionsAbout this articleCite this articleMadhavan, M., Hornátová, L., Šálek, M. et al. Social environment affects vocal individuality in a non-learning species.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-29387-3Download citationReceived: 23 July 2025Accepted: 17 November 2025Published: 15 December 2025DOI: https://doi.org/10.1038/s41598-025-29387-3Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative
KeywordsAcoustic nicheBioacousticsOwlPopulation densitySignal evolutionVocal learningVocal plasticity More

AbstractAgricultural systems in rural Nepal face significant transformation due to climate change and shifting household labour dynamics. Male out-migration, a key but underexplored driver of this change, disrupts traditional gendered labor divisions and reshapes agricultural decision-making. In smallholder farms, traditionally, men are more responsible for tasks like ploughing and harvesting, whereas women take on planting, weeding, and winnowing roles. In the context of male out-migration, women must take primary responsibility for managing farms and households. However, persistent social and structural inequalities continue to constrain their decision-making authority. The feminisation of agriculture has important implications for adopting sustainable agricultural practices (SAPs). Using survey data from 400 households and Poisson regression analysis, this study examines the effects of migration, remittances, and female-managed farms on SAP adoption. Our results highlight that household farms having migrated member(s), receiving remittance and female-managed farms are more likely to adopt SAPs. In contrast, a higher number of out-migrating females negatively affects adoption, reflecting women’s critical role in sustainable farming adoption. Their participation in women’s groups, which provide training and financial resources and their management of tasks such as seed selection, winnowing, and organic pest control, are essential to SAP implementation. As such, our study provides a deeper understanding of the positive role of females in SAP adoption. We advocate for policies that recognising intersectional vulnerabilities, supporting women’s groups, lead to increased adoption of sustainable agricultural practices.

Similar content being viewed by others

Climate risks and adaptation strategies of farmers in East Africa and South Asia

Article
Open access
18 May 2021

Gender-based differences in eco-efficient farming

Article
Open access
07 May 2025

Empowering women in sustainable agriculture

Article
Open access
26 March 2024

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request. Due to ethical considerations and to protect participants’ privacy, the raw survey data cannot be made openly accessible.
ReferencesLeder, S. Beyond the ‘Feminization of agriculture’: rural out-migration, shifting gender relations and emerging spaces in natural resource management. J. Rural Stud. 91, 157–169. https://doi.org/10.1016/j.jrurstud.2022.02.009 (2022).
Google Scholar 
Sunam, R., Sugden, F., Kharel, A., Sunuwar, T. R. & Ito, T. Unpacking youth engagement in agriculture: Land, labour mobility and youth livelihoods in rural Nepal. J. Agrarian Change. 25 (1), e12611. https://doi.org/10.1111/joac.12611 (2025).
Google Scholar 
Rayamajhee, V., Guo, W. & Bohara, A. K. The impact of climate change on rice production in Nepal. Econ. Disasters Clim. Change. 5, 111–134. https://doi.org/10.1007/s41885-020-00079-8 (2021).
Google Scholar 
CIAT; World Bank Climate-Smart agriculture in Nepal. CSA country profiles for Asia series. In International Centerfor Tropical Agriculture (CIAT); the World Bank; CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS); Local Initiatives for Biodiversity Research and Development 26 (LI-BIRD), 2017).
Google Scholar 
Kandel, G. P., Bavorova, M., Ullah, A., Kaechele, H. & Pradhan, P. Building resilience to climate change: examining the impact of agro-ecological zones and social groups on sustainable development. Sustain. Dev. 31 (5), 3796–3810 (2023).
Google Scholar 
Gomis, R., Kapsos, S. & Kuhn, S. World employment and social outlook: trends 2020. ILO: Geneva Switzerland, 127. (2020).The World Bank. Employment in agriculture (% of total employment). (2025). https://data.worldbank.org/indicator/SL.AGR.EMPL.ZS?locations=NPWorld Bank. Unlocking Nepal’s Growth Potential: Nepal Country Economic Memorandum 2025. (2025b). Retrieved from https://www.worldbank.org/en/country/nepal/publication/unlocking-nepal-s-growth-potentialJaquet, S., Shrestha, G., Kohler, T. & Schwilch, G. The effects of migration on livelihoods, land management, and vulnerability to natural disasters in the Harpan watershed in Western Nepal. Mt. Res. Dev. 36 (4), 494–505. https://doi.org/10.1659/MRD-JOURNAL-D-16-00034.1 (2016).
Google Scholar 
Bhawana, K. C. & Race, D. Women’s approach to farming in the context of feminization of agriculture: A case study from the middle hills of Nepal. World Dev. Perspect. 20, 100260. https://doi.org/10.1016/j.wdp.2020.100260 (2020).
Google Scholar 
Adhikari, J. & Hobley, M. Everyone is leaving. Who will Sow our fields? The livelihood effects on women of male migration from Khotang and Udaypur districts, Nepal, to the Gulf countries and Malaysia. Himalaya 35 (1), 11–23 (2015).
Google Scholar 
National Statistics Office (NSO). National Population and Housing Census 2021: National Report (NSO, 2022).Maharjan, A., Bauer, S. & Knerr, B. Do rural women who stay behind benefit from male out-migration? A case study in the hills of Nepal. Gend. Technol. Dev. 16 (1), 95–123. https://doi.org/10.1177/097185241101600105 (2012).
Google Scholar 
Maharjan, A., Bauer, S. & Knerr, B. International migration, remittances and subsistence farming: evidence from Nepal. Int. Migration. 51, e249–e263. https://doi.org/10.1111/j.1468-2435.2012.00767.x (2013).
Google Scholar 
Upreti, B. R., Ghale, Y., Shivakoti, S. & Acharya, S. Feminization of agriculture in the Eastern hills of nepal: A study of women in cardamom and ginger farming. SAGE Open. 8 (4), 2158244018817124. https://doi.org/10.1177/2158244018817124 (2018).
Google Scholar 
Koirala, S. & Bashyal, S. The land left behind: a systematic review of transnational migration-induced change and its implication for rural sustainability in Nepal. Humanit. Social Sci. Commun. 12 (1), 1–12. https://doi.org/10.1057/s41599-024-04180-1 (2025).
Google Scholar 
Atreya, K. & Gartaula, H. Changing gender role declines maize yield, but remittances offset: findings from migrant households in the central Himalayas. Nepal. Outlook Agric. 51 (2), 247–259. https://doi.org/10.1177/00307270221097984 (2022).
Google Scholar 
Koirala, S. Empowering absence? Assessing the impact of transnational male Out-Migration on left behind wives. Social Sci. 12 (2), 80. https://doi.org/10.3390/socsci12020080 (2023).
Google Scholar 
Leder, S., Upadhyaya, R., van der Geest, K., Adhikari, Y. & Büttner, M. Rural out-migration and water governance: gender and social relations mediate and sustain irrigation systems in Nepal. World Dev. 177, 106544. https://doi.org/10.1016/j.worlddev.2024.106544 (2024).
Google Scholar 
Buisson, M. C., Clement, F. & Leder, S. Women’s empowerment and the will to change: evidence from Nepal. J. Rural Stud. 94, 128–139. https://doi.org/10.1016/j.jrurstud.2022.06.005 (2022).
Google Scholar 
Pandey, R. Farmers’ perception on agro-ecological implications of climate change in the Middle-Mountains of nepal: a case of Lumle village. Kaski Environ. Dev. Sustain. 21 (1), 221–247. https://doi.org/10.1007/s10668-017-0031-9 (2019).
Google Scholar 
Pandey, R. Male out-migration from the himalaya: implications in gender roles and household food (in) security in the Kaligandaki Basin, Nepal. Migration Dev. 10 (3), 313–341. https://doi.org/10.1080/21632324.2019.1634 (2021).
Google Scholar 
Kellogg, N. J., Andersen, A. B., Biraschi, R. & Puri, S. Beyond the (dis) empowerment binary: inevitability and the feminization of agriculture in Chitwan, Nepal. HIMALAYA-The J. Association Nepal. Himal. Stud. 40 (2), 108–118 (2021).
Google Scholar 
Yokying, P., Saksena, S. & Fox, J. Impacts of migration on time allocation of those who remain at home in rural Nepal. J. Int. Dev. 35 (7), 2067–2106. https://doi.org/10.1002/jid.3765 (2023).
Google Scholar 
Maharjan, A., Kochhar, I., Chitale, V. S., Hussain, A. & Gioli, G. Understanding rural outmigration and agricultural land use change in the Gandaki basin. Nepal. Appl. Geogr. 124, 102278. https://doi.org/10.1016/j.apgeog.2020.102278 (2020).
Google Scholar 
Maharjan, K. L. & Gonzalvo, C. M. Examining farmers’ willingness to learn environmental conservation agriculture: implications for women farmer empowerment in Bagmati Province. Nepal. Agric. 15 (7), 726. https://doi.org/10.3390/agriculture15070726 (2025).
Google Scholar 
Sugden, F. et al. Agrarian stress and climate change in the Eastern gangetic plains: gendered vulnerability in a stratified social formation. Glob. Environ. Change. 29, 258–269. https://doi.org/10.1016/j.gloenvcha.2014.10.008 (2014).
Google Scholar 
Gartaula, H. N., Niehof, A. & Visser, L. Feminisation of agriculture as an effect of male Out-migration: unexpected outcomes from Jhapa District, Eastern Nepal. Int. J. Interdisciplinary Social Sci. 5 (2). https://doi.org/10.18848/1833-1882/CGP/v05i02/51588 (2010).Kaspar, H. I Am the Household Head Now! Gender Aspects of Out-migration for Labour in Nepal (Nepal Institute of Development Studies, 2005).Kim, J. J., Stites, E., Webb, P., Constas, M. A. & Maxwell, D. The effects of male out-migration on household food security in rural Nepal. Food Secur. 11, 719–732. https://doi.org/10.1007/s12571-019-00919-w (2019).
Google Scholar 
Doss, C. R., Meinzen-Dick, R., Pereira, A. & Pradhan, R. Women’s empowerment, extended families and male migration in nepal: insights from mixed methods analysis. J. Rural Stud. 90, 13–25. https://doi.org/10.1016/j.jrurstud.2022.01.003 (2022).
Google Scholar 
Nadeem, M., Wang, Z., Shahbaz, P., Haq, S. U. & Boz, I. The nexus of women’s empowerment and sustainable agricultural practices in developing countries: a case of Pakistani women farmers. J. Environ. Planning Manage. 68 (4), 820–842. https://doi.org/10.1080/09640568.2023.2273198 (2025).
Google Scholar 
Shahbaz, P. et al. Adoption of climate smart agricultural practices through women involvement in decision making process: exploring the role of empowerment and innovativeness. Agriculture 12 (8), 1161. https://doi.org/10.3390/agriculture12081161 (2022).
Google Scholar 
Aryal, J. P. et al. Does women’s participation in agricultural technology adoption decisions affect the adoption of climate-smart agriculture? Insights from Indo‐Gangetic plains of India. Rev. Dev. Econ. 24 (3), 973–990. https://doi.org/10.1111/rode.12670 (2020).
Google Scholar 
Dar, M. H. et al. Gender focused training and knowledge enhances the adoption of climate resilient seeds. Technol. Soc. 63, 101388. https://doi.org/10.1016/j.techsoc.2020.101388 (2020).
Google Scholar 
Agarwal, T. et al. Gendered impacts of climate-smart agriculture on household food security and labor migration: insights from Bihar, India. Int. J. Clim. Change Strateg. Manag. 14 (1), 1–19. https://doi.org/10.1108/IJCCSM-01-2020-0004 (2022).
Google Scholar 
Glazebrook, T. & Opoku, E. Gender and sustainability: learning from women’s farming in Africa. Sustainability 12 (24), 10483. https://doi.org/10.3390/su122410483 (2020).
Google Scholar 
Theriault, V., Smale, M. & Haider, H. How does gender affect sustainable intensification of cereal production in the West African sahel? Evidence from Burkina Faso. World Dev. 92, 177–191. https://doi.org/10.1016/j.worlddev.2016.12.003 (2017).
Google Scholar 
DFID. Key sheets for sustainable development: overview. (1999).Natarajan, N., Newsham, A., Rigg, J. & Suhardiman, D. A sustainable livelihoods framework for the 21st century. World Dev. 155, 105898. https://doi.org/10.1016/j.worlddev.2022.105898 (2022).
Google Scholar 
UNDP. Guidance Note: Application of the Sustainable Livelihoods Framework in Development Projects (United Nations Development Programme, Regional Centre for Latin America and the Caribbean, 2017).Crenshaw, K. Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stanford Law Rev. 43 (6), 1241–1299. https://doi.org/10.2307/1229039 (1991).
Google Scholar 
Collins, P. H. Black Feminist Thought, 2nd edition, New York: Routledge. (2000).Collins, P. H. Black Feminist Thought: Knowledge, consciousness, and the Politics of Empowerment (routledge, 2022).Kimberly, C. Demarginalizing the Intersection of Race and Sex: A Black Feminist Critique of Anti-Discrimination Doctrine, Feminist Theory and Anti‐Racist Politics. In The University of Chicago Legal Forum (Vol. 140, p. 139). (1989).Naess, L. O., Wangari-Muneri, E., Nightingale, A. J. & Mehta, L. Climate change and the operation of power: intersectionality, dispossession, and knowledge politics in pastoral communities. J. Peasant Stud. 1–26. https://doi.org/10.1080/03066150.2025.2451288 (2025).Tiwari, P. C. & Joshi, B. Gender processes in rural out-migration and socio-economic development in the himalaya. Migration Dev. 5 (2), 230–250. https://doi.org/10.1080/21632324.2015.1022970 (2016).
Google Scholar 
Islam, M. S., Sarker, M. F. H., Ehsan, S. M. A. & Sohel, M. S. Rethinking women empowerment in rural bangladesh: male out-migration, left-behind wives, and changing gender roles. Social Sci. Humanit. Open. 11, 101425. https://doi.org/10.1016/j.ssaho.2025.101425 (2025).
Google Scholar 
Pattnaik, I., Lahiri-Dutt, K., Lockie, S. & Pritchard, B. The feminization of agriculture or the feminization of agrarian distress? Tracking the trajectory of women in agriculture in India. J. Asia Pac. Econ. 23 (1), 138–155. https://doi.org/10.1080/13547860.2017.1394569 (2018).
Google Scholar 
Collins, A. Saying all the right things? Gendered discourse in climate-smart agriculture. J. Peasant Stud. 45 (1), 175–191. https://doi.org/10.1080/03066150.2017.1377187 (2017).
Google Scholar 
Mehta, D., Pandey, R., Gupta, A. K. & Juhola, S. Nature-based solutions in Hindu Kush himalayas: IUCN global standard based synthesis. Ecol. Ind. 154, 110875. https://doi.org/10.1016/j.ecolind.2023.110875 (2023).
Google Scholar 
Thapa, S. & Bhattarai, K. Farming Systems, Food Security and Contemporary Climate Issues in Nepal. In The Routledge International Handbook of Himalayan Environments, Development and Wellbeing (pp. 177–189). Routledge. (2025). https://doi.org/10.4324/9781003450894Sujakhu, N. M., Ranjitkar, S., Su, Y., He, J. & Xu, J. A gendered perspective on climate change adaptation strategies: a case study from Yunnan, China. Local Environ. 28 (1), 117–133. https://doi.org/10.1080/13549839.2022.2130883 (2023).
Google Scholar 
Raj, R., Ravula, P., Bhanjdeo, C. M. P., Sogani, A., Rao, N. & R., & Male migration and the transformation of gendered agriculture work: a comparative exploration of heterogeneity across selected Indian States. Gend. Place Cult. 1–27. https://doi.org/10.1080/0966369X.2025.2468178 (2025).Southard, E. M. & Randell, H. Climate change, agrarian distress, and the feminization of agriculture in South Asia. Rural Sociol. 87 (3), 873–900. https://doi.org/10.1111/ruso.12439 (2022).
Google Scholar 
Coxe, S., West, S. G. & Aiken, L. S. The analysis of count data: A gentle introduction to Poisson regression and its alternatives. J. Pers. Assess. 91 (2), 121–136. https://doi.org/10.1080/00223890802634175 (2009).
Google Scholar 
Hilbe, J. M. Poisson regression. In Modeling Count Data (35–73). Cambridge University Press. (2014).Kroll, C. N. & Song, P. Impact of multicollinearity on small sample hydrologic regression models. Water Resour. Res. 49 (6), 3756–3769. https://doi.org/10.1002/wrcr.20315 (2013).
Google Scholar 
García García, C., Gomez, S., García Pérez, J. & R., & A review of ridge parameter selection: minimization of the mean squared error vs. mitigation of multicollinearity. Commun. Statistics-Simulation Comput. 53 (8), 3686–3698. https://doi.org/10.1080/03610918.2022.2110594 (2024).
Google Scholar 
Long, J. S. & Freese, J. Regression Models for Categorical Dependent Variables Using Stata 3rd edn (Stata, 2014).Cameron, A. C., Trivedi, P. K. & Rev Microeconometrics using Stata (.) Stata. (2010).Meinzen-Dick, R., Quisumbing, A., Behrman, J., Biermayr-Jenzano, P., Wilde, V., Noordeloos,M., … Beintema, N. (2019). Engendering agricultural research. Food Security, 11(3),565–574. https://doi.org/10.1007/s12571-019-00917-6.Kandel, G. P., Bavorova, M., Ullah, A. & Pradhan, P. Food security and sustainability through adaptation to climate change: lessons learned from Nepal. Int. J. Disaster Risk Reduct. 101, 104279. https://doi.org/10.1016/j.ijdrr.2024.104279 (2024).
Google Scholar 
Kilic, T., Palacios-Lopez, A. & Goldstein, M. Caught in the productivity trap: A gendered perspective on the commercialization of agriculture in Africa. World Bank. Economic Rev. 36 (1), 75–104. https://doi.org/10.1093/wber/lhab006 (2022).
Google Scholar 
Nanda, M. & Ghosh, S. How does male out-migration impact the lives of left-behind women? Trade-off between feminization of agriculture and empowerment of farm women. Eval. Program Plan. 102603. https://doi.org/10.1016/j.evalprogplan.2025.102603 (2025).Fertő, I. & Bojnec, Š. Empowering women in sustainable agriculture. Sci. Rep. 14 (1), 7110. https://doi.org/10.1038/s41598-024-57933-y (2024).
Google Scholar 
Tourtelier, C., Gorman, M. & Tracy, S. Influence of gender on the development of sustainable agriculture in France. J. Rural Stud. 101, 103068. https://doi.org/10.1016/j.jrurstud.2023.103068 (2023).
Google Scholar 
Zhang, Y., Hu, N., Yao, L., Zhu, Y. & Ma, Y. The role of social network embeddedness and collective efficacy in encouraging farmers’ participation in water environmental management. J. Environ. Manage. 340, 117959. https://doi.org/10.1016/j.jenvman.2023.117959 (2023).
Google Scholar 
Beaman, L. & Dillon, A. Do female agricultural extension agents affect female farmers’ outcomes? Evidence from Ethiopia. Am. Economic Journal: Appl. Econ. 14 (1), 66–95. https://doi.org/10.1257/app.20200265 (2022).
Google Scholar 
Doss, C., Meinzen-Dick, R., Quisumbing, A. & Theis, S. Women in agriculture: four Myths. Global Food Secur. 16, 69–74. https://doi.org/10.1016/j.gfs.2017.10.001 (2018).
Google Scholar 
Ragasa, C., Aberman, N. L. & Alvarez-Mingote, C. Does providing agricultural and nutrition information to both men and women improve household food security? J. Agric. Econ. 70 (3), 940–963. https://doi.org/10.1111/1477-9552.12314 (2019).
Google Scholar 
Spangler, K. & Christie, M. E. Renegotiating gender roles and cultivation practices in the Nepali mid-hills: unpacking the feminization of agriculture. Agric. Hum. Values. 37, 415–432. https://doi.org/10.1007/s10460-019-09997-0 (2020).
Google Scholar 
Holmelin, N. B. Competing gender norms and social practice in Himalayan farm management. World Dev. 122, 85–95. https://doi.org/10.1016/j.worlddev.2019.05.018 (2019).
Google Scholar 
Fry, C. et al. Migrants as sustainability actors: contrasting nation, City and migrant discourses and actions. Glob. Environ. Change. 87, Article 102860. https://doi.org/10.1016/j.gloenvcha.2024.102860 (2024).Head, L., Klocker, N., Dun, O., Waitt, G., Goodall, H., Aguirre-Bielschowsky, I.,… Toole, S. (2021). Barriers to and enablers of sustainable practices: insights from ethnic minority migrants. Local Environment, 26(5), 595–614. https://doi.org/10.1080/13549839.2021.1904856.Batista, C., Han, D., Haushofer, J., Khanna, G., McKenzie, D., Mobarak, A. M., … Yang,D. (2025). Brain drain or brain gain? Effects of high-skilled international emigration on origin countries. Science, 388(6749), eadr8861. https://www.science.org/doi/full/10.1126/science.adr8861.Tambo, J. A. & Wünscher, T. Migration and conservation agriculture in Northern ghana: exploring the role of social networks and environmental perceptions. Food Secur. 9 (4), 685–698. https://doi.org/10.1007/s12571-017-0682-1 (2017).
Google Scholar 
De Brauw, A. et al. Biofortification, crop adoption and health information: impact pathways in Mozambique and Uganda. Am. J. Agric. Econ. 100 (3), 906–930. https://doi.org/10.1093/ajae/aay005 (2018).
Google Scholar 
Ram Mohan, R., Puskur, R., Chandrasekar, D. & Valera, H. G. A. Do gender dynamics in intra-household decision making shift with male migration? Evidence from rice-farming households in Eastern India. Gend. Technol. Dev. 27 (2), 157–183. https://doi.org/10.1080/09718524.2022.2140381 (2022).
Google Scholar 
Aryal, J. P., Thapa, G. & Simtowe, F. Mechanisation of small-scale farms in South asia: empirical evidence derived from farm households survey. Technol. Soc. 65, 101591. https://doi.org/10.1016/j.techsoc.2021.101591 (2021).
Google Scholar 
Khatri, N. D., Paudel, D., Bhusal, P., Ghimire, S. & Bhandari, B. Determinants of farmers’ decisions to adopt agroforestry practices: insights from the Mid-hills of Western Nepal. Agroforest Syst. 97 (5), 833–845. https://doi.org/10.1007/s10457-023-00830-6 (2023).
Google Scholar 
Bhatta, L. D., Shrestha, A., Neupane, N., Jodha, N. S. & Wu, N. Shifting dynamics of nature, society and agriculture in the Hindu Kush himalayas: perspectives for future mountain development. J. Mt. Sci. 16 (5), 1133–1149. https://doi.org/10.1007/s11629-018-5146-4 (2019).
Google Scholar 
Stark, O. & Bloom, D. E. The new economics of labor migration. Am. Econ. Rev. 75 (2), 173–178 (1985). https://www.jstor.org/stable/1805591
Google Scholar 
Yu, Z., Zhang, K., Wang, Z. & Liu, C. Immigration remittances, agricultural investment, and household wealth accumulation. Finance Res. Lett. 68, 105991. https://doi.org/10.1016/j.frl.2024.105991 (2024).
Google Scholar 
Mutai, N. C., Ibeh, L., Nguyen, M. C., Kiarie, J. W. & Ikamari, C. Sustainable economic development in kenya: influence of diaspora remittances, foreign direct investment and imports. Afr. J. Economic Manage. Stud. 16 (1), 61–78. https://doi.org/10.1108/AJEMS-01-2024-0059 (2025).
Google Scholar 
Wu, X., Chen, L., Ma, L., Cai, L. & Li, X. Return migration, rural household investment decision, and poverty alleviation: evidence from rural Guangdong, China. Growth Change. 54 (1), 304–325. https://doi.org/10.1111/grow.12656 (2023).
Google Scholar 
Cui, H., Wang, Y. & Zheng, L. Livelihood sustainability of rural households in response to external shocks, internal stressors and geographical disadvantages: empirical evidence from rural China. Environ. Dev. Sustain. 1–30. https://doi.org/10.1007/s10668-024-04666-7 (2024).Jijin, P. Remittances and labour force heterogeneity: can the disincentive effect vary? Int. Rev. Appl. Econ. 1–23. https://doi.org/10.1080/02692171.2024.2404947 (2024).Vemireddy, V. & Choudhary, A. A systematic review of labor-saving technologies: implications for women in agriculture. Global Food Secur. 29, 100541. https://doi.org/10.1016/j.gfs.2021.100541 (2021).
Google Scholar 
Arslan, A., McCarthy, N., Lipper, L., Asfaw, S. & Cattaneo, A. Adoption and intensity of adoption of conservation farming practices in Zambia. Agricultural Ecosyst. Environ. 254, 124–134. https://doi.org/10.1016/j.agee.2017.11.025 (2020).
Google Scholar 
Slavchevska, V., de la Campos, O., Brunelli, A. P., Doss, C. & C., & Beyond ownership: women’s and men’s land rights in sub-Saharan Africa. Food Policy. 102, 102065. https://doi.org/10.1016/j.foodpol.2021.102065 (2021).
Google Scholar 
Marty, E., Bullock, R., Cashmore, M., Crane, T. & Eriksen, S. Adapting to climate change among transitioning Maasai pastoralists in Southern kenya: an intersectional analysis of differentiated abilities to benefit from diversification processes. J. Peasant Stud. 50 (1), 136–161. https://doi.org/10.1080/03066150.2022.2121918 (2022).
Google Scholar 
Oyetunde-Usman, Z., Olagunju, K. O. & Ogunpaimo, O. R. Determinants of adoption of multiple sustainable agricultural practices among smallholder farmers in Nigeria. Int. Soil. Water Conserv. Res. 9 (2), 241–248. https://doi.org/10.1016/j.iswcr.2020.10.007 (2021).
Google Scholar 
Mgomezulu, W. R., Edriss, A. K., Machira, K. & Pangapanga-Phiri, I. Towards sustainability in the adoption of sustainable agricultural practices: implications on household poverty, food and nutrition security. Innov. Green. Dev. 2 (3), 100054. https://doi.org/10.1016/j.igd.2023.100054 (2023).
Google Scholar 
Adigun, G. T. Determinants of credit access among smallholder women farmers in Kwara State, Nigeria. Nigeria Agricultural J. 53 (2), 121–128 (2022).
Google Scholar 
Fares, M. H., Raza, S. & Ahmad, T. I. Complementarity in mixed farming systems enhances the smallholders income: evidence from Punjab, Pakistan. PloS One. 20 (4), e0319995. https://doi.org/10.1371/journal.pone.0319995 (2025).
Google Scholar 
Achmad, B., Sanudin, Siarudin, M., Widiyanto, A., Diniyati, D., Sudomo, A., Hani,A., Fauziyah, E., Suhaendah, E., Widyaningsih, T. S., Handayani, W., Maharani, D.,Suhartono, Palmolina, M., Swestiani, D., Budi Santoso Sulistiadi, H., Winara, A.,Nur, Y. H., Diana, M., … Ruswandi, A. (2022). Traditional Subsistence Farming of Smallholder Agroforestry Systems in Indonesia: A Review. Sustainability, 14(14), 8631. https://doi.org/10.3390/su14148631.Roy, D. et al. Impact of long term conservation agriculture on soil quality under cereal based systems of North West India. Geoderma 405, 115391. https://doi.org/10.1016/j.geoderma.2021.115391 (2022).
Google Scholar 
Chaudhuri, S. et al. Reflections on farmers’ social networks: a means for sustainable agricultural development? Environ. Dev. Sustain. 23, 2973–3008. https://doi.org/10.1007/s10668-020-00762-6 (2021).
Google Scholar 
Kiros, S. & Meshesha, G. B. Factors affecting farmers’ access to formal financial credit in Basona Worana District, North Showa Zone, Amhara regional State, Ethiopia. Cogent Econ. Finance. 10 (1). https://doi.org/10.1080/23322039.2022.2035043 (2022).Habib, N., Alauddin, M., Cramb, R. & Rankin, P. A differential analysis for men and women’s determinants of livelihood diversification in rural rain-fed region of pakistan: an ordered logit model (OLOGIT) approach. Social Sci. Humanit. Open. 5 (1), 100257. https://doi.org/10.1016/j.ssaho.2022.100257 (2022).
Google Scholar 
Download referencesFundingWe would like to express our gratitude to the Faculty of Tropical AgriSciences of the Czech University of Life Sciences in Prague for financially supporting this research (IGA 20223113). This project has also received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101084201 (ECO-READY). Moreover, we acknowledge funding from the European Research Council for the BeyondSDG project, the European Union (101077492). The sponsors had no role in the study design, data collection and analysis, decision to publish, or preparation of this manuscript.Author informationAuthors and AffiliationsFaculty of Agrobiology, Food, and Natural Resources, Czech University of Life Sciences Prague, Kamycka 129, Prague, Suchdol, 16500, Czech RepublicGiri Prasad Kandel & Ioannis ManikasFaculty of Tropical AgriSciences, Czech University of Life Sciences Prague, Kamycka 129, Prague, Suchdol, 16500, Czech RepublicMustapha Yakubu Madaki, Tereza Pilarova, Ayat Ullah & Miroslava BavorovaEnergy and Sustainability Research Institute Groningen (ESRIG), University of Groningen, Groningen, 9747 AG, NetherlandsPrajal PradhanPotsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, Potsdam, D-14412, GermanyPrajal PradhanAuthorsGiri Prasad KandelView author publicationsSearch author on:PubMed Google ScholarMustapha Yakubu MadakiView author publicationsSearch author on:PubMed Google ScholarTereza PilarovaView author publicationsSearch author on:PubMed Google ScholarAyat UllahView author publicationsSearch author on:PubMed Google ScholarPrajal PradhanView author publicationsSearch author on:PubMed Google ScholarIoannis ManikasView author publicationsSearch author on:PubMed Google ScholarMiroslava BavorovaView author publicationsSearch author on:PubMed Google ScholarContributionsGiri Prasad Kandel: Conceptualization; Methodology; Data curation; Formal analysis; Writing – original draft; Project administration.Mustapha Yakubu Madaki: Methodology; Validation; Writing – review & editing.Tereza Pilarova: Methodology, Validation, Writing – review & editing.Ayat Ullah: Formal analysis; Visualization; Writing – review & editing.Prajal Pradhan: Supervision; Writing – review & editing.Ioannis Manikas: Supervision; Funding acquisition, Writing – review & editing.Miroslava Bavorova: Conceptualization; Supervision; Funding acquisition; Writing – review & editing.Corresponding authorCorrespondence to
Giri Prasad Kandel.Ethics declarations

Competing interests
The authors declare no competing interests.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationBelow is the link to the electronic supplementary material.Supplementary Material 1Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleKandel, G.P., Madaki, M.Y., Pilarova, T. et al. Gender dynamics and remittances in the adoption of sustainable agricultural practices in Nepal.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-31848-8Download citationReceived: 29 August 2025Accepted: 05 December 2025Published: 15 December 2025DOI: https://doi.org/10.1038/s41598-025-31848-8Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative
KeywordsFeminization of agricultureGender dynamicsMale out-migrationClimate changeRemittancesSustainable agricultural practices More

“Because I am one of very few specialists in Laos, some people have started to describe me as the country’s first national herpetologist.This is accurate to my knowledge; I’m not aware of any other Laotians who were working in the field here before 2007, when I started my master’s degree. Conservation studies were instead almost always done by visiting foreign scientists. My PhD challenged that trend, however: I focused on the diversity of Tylototriton salamanders, which led to the discovery of four species.My day-to-day responsibilities at the National University of Laos in Vientiane include giving lectures, conducting research, leading studies, training future biologists and contributing to conservation initiatives. My own research focuses on amphibian and reptile diversity, with an emphasis on conserving threatened species in the country.In this photograph, I’m surveying a population of Fejervarya limnocharis frogs along a stream outside Vientiane, for a field survey. I’m recording the number of adults, tadpoles, eggs and frog calls, as well as tracking other ecological factors.

Enjoying our latest content?
Log in or create an account to continue

Access the most recent journalism from Nature’s award-winning team
Explore the latest features & opinion covering groundbreaking research

Access through your institution

or

Sign in or create an account

Continue with Google

Continue with ORCiD More