Ecology

Hsu-Kim H, Kucharzyk KH, Zhang T, Deshusses MA. Mechanisms regulating mercury bioavailability for methylating microorganisms in the aquatic environment: A critical review. Environ Sci Technol. 2013;47:2441–56.Article 
CAS 

Google Scholar 
Podar M, Gilmour CC, Brandt CC, Soren A, Brown SD, Crable BR, et al. Global prevalence and distribution of genes and microorganisms involved in mercury methylation. Sci Adv. 2015;1:e1500675.Article 

Google Scholar 
Liu YR, Johs A, Bi L, Lu X, Hu HW, Sun D, et al. Unraveling microbial communities associated with methylmercury production in paddy soils. Environ Sci Technol. 2018;52:13110–8.Article 
CAS 

Google Scholar 
Lee C-S, Fisher NS. Bioaccumulation of methylmercury in a marine copepod. Environ Toxicol Chem. 2017;36:1287–93.Article 
CAS 

Google Scholar 
Parks JM, Johs A, Podar M, Bridou R, Hurt RAJ, Smith SD, et al. The genetic basis for bacterial mercury methylation. Science 2013;339:1332–5.Article 
CAS 

Google Scholar 
McDaniel EA, Peterson BD, Stevens SLR, Tran PQ, Anantharaman K, McMahon KD. Expanded phylogenetic diversity and metabolic flexibility of mercury-methylating microorganisms. mSystems 2020;5:e00299–20.Article 
CAS 

Google Scholar 
Cooper CJ, Zheng K, Rush KW, Johs A, Sanders BC, Pavlopoulos GA, et al. Structure determination of the HgcAB complex using metagenome sequence data: Insights into microbial mercury methylation. Commun Biol. 2020;3:320.Article 
CAS 

Google Scholar 
Kerin EJ, Gilmour CC, Roden E, Suzuki MT, Coates JD, Mason RP. Mercury methylation by dissimilatory iron-reducing bacteria. Appl Environ Microbiol. 2006;72:7919–21.Article 
CAS 

Google Scholar 
Gilmour CC, Podar M, Bullock AL, Graham AM, Brown SD, Somenahally AC, et al. Mercury methylation by novel microorganisms from new environments. Environ Sci Technol. 2013;47:11810–20.Article 
CAS 

Google Scholar 
Capo E, Bravo AG, Soerensen AL, Bertilsson S, Pinhassi J, Feng C, et al. Deltaproteobacteria and Spirochaetes-like bacteria are abundant putative mercury methylators in oxygen-deficient water and marine particles in the Baltic Sea. Front Microbiol. 2020;11:574080.Article 

Google Scholar 
Gionfriddo CM, Tate MT, Wick RR, Schultz MB, Zemla A, Thelen MP, et al. Microbial mercury methylation in Antarctic sea ice. Nat Microbiol. 2016;1:16127.Article 
CAS 

Google Scholar 
Jones DS, Walker GM, Johnson NW, Mitchell CPJ, Coleman Wasik JK, Bailey JV. Molecular evidence for novel mercury methylating microorganisms in sulfate-impacted lakes. ISME J. 2019;13:1659–75.Article 
CAS 

Google Scholar 
Christensen GA, Gionfriddo CM, King AJ, Moberly JG, Miller CL, Somenahally AC, et al. Determining the reliability of measuring mercury cycling gene abundance with correlations with mercury and methylmercury concentrations. Environ Sci Technol. 2019;53:8649–63.Article 
CAS 

Google Scholar 
Villar E, Cabrol L, Heimburger-Boavida LE. Widespread microbial mercury methylation genes in the global ocean. Environ Microbiol Rep. 2020;12:277–87.Article 
CAS 

Google Scholar 
Lin H, Ascher DB, Myung Y, Lamborg CH, Hallam SJ, Gionfriddo CM, et al. Mercury methylation by metabolically versatile and cosmopolitan marine bacteria. ISME J. 2021;15:1810–25.Article 
CAS 

Google Scholar 
King JK, Kostka JE, Frischer ME, Saunders FM, Jahnke RA. A quantitative relationship that demonstrates mercury methylation rates in marine sediments are based on the community composition and activity of sulfate-reducing bacteria. Environ Sci Technol. 2001;35:2491–6.Article 
CAS 

Google Scholar 
Regnell O, Watras CJ. Microbial mercury methylation in aquatic environments: A critical review of published field and laboratory studies. Environ Sci Technol. 2019;53:4–19.Article 
CAS 

Google Scholar 
Xie R, Wang Y, Huang D, Hou J, Li L, Hu H, et al. Expanding Asgard members in the domain of Archaea sheds new light on the origin of eukaryotes. Sci China Life Sci. 2022;65:818–29.Article 
CAS 

Google Scholar 
Seitz KW, Dombrowski N, Eme L, Spang A, Lombard J, Sieber JR, et al. Asgard archaea capable of anaerobic hydrocarbon cycling. Nat Commun. 2019;10:1822.Article 

Google Scholar 
Zaremba-Niedzwiedzka K, Caceres EF, Saw JH, Backstrom D, Juzokaite L, Vancaester E, et al. Asgard archaea illuminate the origin of eukaryotic cellular complexity. Nature 2017;541:353–8.Article 
CAS 

Google Scholar 
Liu Y, Makarova KS, Huang W-C, Wolf YI, Nikolskaya AN, Zhang X, et al. Expanded diversity of Asgard archaea and their relationships with eukaryotes. Nature 2021;593:553–7.Article 
CAS 

Google Scholar 
Zhang JW, Dong HP, Hou LJ, Liu Y, Ou YF, Zheng YL, et al. Newly discovered Asgard archaea Hermodarchaeota potentially degrade alkanes and aromatics via alkyl/benzyl-succinate synthase and benzoyl-CoA pathway. ISME J. 2021;15:1826–43.Article 
CAS 

Google Scholar 
Cai M, Liu Y, Yin X, Zhou Z, Friedrich MW, Richter-Heitmann T, et al. Diverse Asgard archaea including the novel phylum Gerdarchaeota participate in organic matter degradation. Sci China Life Sci. 2020;63:886–97.Article 
CAS 

Google Scholar 
Baker BJ, De Anda V, Seitz KW, Dombrowski N, Santoro AE, Lloyd KG. Diversity, ecology and evolution of Archaea. Nat Microbiol. 2020;5:887–900.Article 
CAS 

Google Scholar 
Farag Ibrahim F, Zhao R, Biddle Jennifer F, Atomi H. “Sifarchaeota,” a novel Asgard phylum from Costa Rican sediment capable of polysaccharide degradation and anaerobic methylotrophy. Appl Environ Micro. 2021;87:e02584–20.
Google Scholar 
Adam PS, Borrel G, Brochier-Armanet C, Gribaldo S. The growing tree of Archaea: new perspectives on their diversity, evolution and ecology. ISME J. 2017;11:2407–25.Article 

Google Scholar 
Cai M, Richter-Heitmann T, Yin X, Huang W-C, Yang Y, Zhang C, et al. Ecological features and global distribution of Asgard archaea. Sci Total Environ. 2021;758:143581.Article 
CAS 

Google Scholar 
Zhang C-J, Chen Y-L, Sun Y-H, Pan J, Cai M-W, Li M. Diversity, metabolism and cultivation of archaea in mangrove ecosystems. Mar Life Sci Tech. 2020;3:252–62.Article 

Google Scholar 
Dai SS, Yang Z, Tong Y, Chen L, Liu SY, Pan R, et al. Global distribution and environmental drivers of methylmercury production in sediments. J Hazard Mater. 2021;407:124700.Article 
CAS 

Google Scholar 
Tang WL, Liu YR, Guan WY, Zhong H, Qu XM, Zhang T. Understanding mercury methylation in the changing environment: Recent advances in assessing microbial methylators and mercury bioavailability. Sci Total Environ. 2020;714:136827.Article 
CAS 

Google Scholar 
Tsui MTK, Finlay JC, Balogh SJ, Nollet YH. In situ production of methylmercury within a stream channel in northern California. Environ Sci Technol. 2010;44:6998–7004.Article 
CAS 

Google Scholar 
Liu Y, Zhou Z, Pan J, Baker BJ, Gu JD, Li M. Comparative genomic inference suggests mixotrophic lifestyle for Thorarchaeota. ISME J. 2018;12:1021–31.Article 
CAS 

Google Scholar 
Lei P, Zhong H, Duan D, Pan K. A review on mercury biogeochemistry in mangrove sediments: Hotspots of methylmercury production? Sci Total Environ. 2019;680:140–50.Article 
CAS 

Google Scholar 
Beckers F, Rinklebe J. Cycling of mercury in the environment: Sources, fate, and human health implications: A review. Crit Rev Env Sci Tec. 2017;47:693–794.Article 
CAS 

Google Scholar 
de Oliveira DC, Correia RR, Marinho CC, Guimaraes JR. Mercury methylation in sediments of a Brazilian mangrove under different vegetation covers and salinities. Chemosphere 2015;127:214–21.Article 

Google Scholar 
Li R, Xu H, Chai M, Qiu GY. Distribution and accumulation of mercury and copper in mangrove sediments in Shenzhen, the world’s most rapid urbanized city. Environ Moni Assess. 2016;188:87.Article 

Google Scholar 
O’Connor D, Hou D, Ok YS, Mulder J, Duan L, Wu Q, et al. Mercury speciation, transformation, and transportation in soils, atmospheric flux, and implications for risk management: A critical review. Environ Int. 2019;126:747–61.Article 

Google Scholar 
Obrist D, Kirk JL, Zhang L, Sunderland EM, Jiskra M, Selin NE. A review of global environmental mercury processes in response to human and natural perturbations: Changes of emissions, climate, and land use. Ambio 2018;47:116–40.Article 

Google Scholar 
Capo E, Peterson BD, Kim M, Jones DS, Acinas SG, Amyot M, et al. A consensus protocol for the recovery of mercury methylation genes from metagenomes. Mol Ecol Resour. 2022; https://doi.org/10.1111/1755-0998.13687.Gionfriddo CM, Wymore AM, Jones DS, Wilpiszeski RL, Lynes MM, Christensen GA, et al. An improved hgcAB primer set and direct high-throughput sequencing expand Hg-methylator diversity in nature. Front Microbiol. 2020;11:541554.Article 

Google Scholar 
Yu R-Q, Barkay T. Chapter two – microbial mercury transformations: Molecules, functions and organisms. Adv Appl Microbiol. 2022;118:31–90.Article 

Google Scholar 
Chételat J, Richardson MC, MacMillan GA, Amyot M, Poulain AJ. Ratio of methylmercury to dissolved organic carbon in water explains methylmercury bioaccumulation across a latitudinal gradient from north-temperate to arctic lakes. Environ Sci Technol. 2018;52:79–88.Article 

Google Scholar 
Liu Y-R, Dong J-X, Han L-L, Zheng Y-M, He J-Z. Influence of rice straw amendment on mercury methylation and nitrification in paddy soils. Environ Pollut. 2016;209:53–9.Article 
CAS 

Google Scholar 
Moreau JW, Gionfriddo CM, Krabbenhoft DP, Ogorek JM, DeWild JF, Aiken GR, et al. The effect of natural organic matter on mercury methylation by Desulfobulbus propionicus 1pr3. Front Microbiol. 2015;6:1389.Article 

Google Scholar 
Chen C-F, Ju Y-R, Chen C-W, Dong C-D. The distribution of methylmercury in estuary and harbor sediments. Sci Total Environ. 2019;691:55–63.Article 
CAS 

Google Scholar 
Bravo AG, Bouchet S, Guédron S, Amouroux D, Dominik J, Zopfi J. High methylmercury production under ferruginous conditions in sediments impacted by sewage treatment plant discharges. Water Res. 2015;80:245–55.Article 
CAS 

Google Scholar 
Wang H, Su J, Zheng T, Yang X. Insights into the role of plant on ammonia-oxidizing bacteria and archaea in the mangrove ecosystem. J Soil Sediment. 2015;15:1212–23.Article 
CAS 

Google Scholar 
Imachi H, Nobu MK, Nakahara N, Morono Y, Ogawara M, Takaki Y, et al. Isolation of an archaeon at the prokaryote–eukaryote interface. Nature 2020;577:519–25.Article 
CAS 

Google Scholar 
Zhou J, Riccardi D, Beste A, Smith JC, Parks JM. Mercury methylation by HgcA: Theory supports carbanion transfer to Hg(II). Inorg Chem. 2014;53:772–7.Article 
CAS 

Google Scholar 
Smith Steven D, Bridou R, Johs A, Parks Jerry M, Elias Dwayne A, Hurt Richard A, et al. Site-directed mutagenesis of HgcA and HgcB reveals amino acid residues important for mercury methylation. Appl Environ Micro. 2015;81:3205–17.Article 
CAS 

Google Scholar 
Sousa FL, Neukirchen S, Allen JF, Lane N, Martin WF. Lokiarchaeon is hydrogen dependent. Nat Microbiol. 2016;1:16034.Article 
CAS 

Google Scholar 
Schaefer JK, Rocks SS, Zheng W, Liang L, Gu B, Morel FMM. Active transport, substrate specificity, and methylation of Hg(II) in anaerobic bacteria. Proc Natl Acad Sci USA 2011;108:8714.Article 
CAS 

Google Scholar 
Sakai S, Imachi H, Hanada S, Ohashi A, Harada H, Kamagata Y. Methanocella paludicola gen. nov., sp. nov., a methane-producing archaeon, the first isolate of the lineage ‘Rice Cluster I’, and proposal of the new archaeal order Methanocellales ord. nov. Int J Syst Evol Microbiol. 2008;58:929–36.Article 

Google Scholar 
Dridi B, Fardeau ML, Ollivier B, Raoult D, Drancourt M. Methanomassiliicoccus luminyensis gen. nov., sp. nov., a methanogenic archaeon isolated from human faeces. Int J Syst Evol Microbiol. 2012;62:1902–7.Article 
CAS 

Google Scholar 
Dietz R, Sonne C, Basu N, Braune B, O’Hara T, Letcher RJ, et al. What are the toxicological effects of mercury in arctic biota? Sci Total Environ. 2013;443:775–90.Article 
CAS 

Google Scholar 
Gilmour Cynthia C, Bullock Allyson L, McBurney A, Podar M, Elias Dwayne A, Lovley Derek R. Robust mercury methylation across diverse methanogenic archaea. mBio 2018;9:e02403–17.
Google Scholar 
Pan J, Chen Y, Wang Y, Zhou Z, Li M. Vertical distribution of Bathyarchaeotal communities in mangrove wetlands suggests distinct niche preference of Bathyarchaeota subgroup 6. Micro Ecol. 2019;77:417–28.Article 

Google Scholar 
Zhang C-J, Pan J, Duan C-H, Wang Y-M, Liu Y, Sun J, et al. Prokaryotic diversity in mangrove sediments across southeastern China fundamentally differs from that in other biomes. mSystems 2019;4:e00442–19.Article 
CAS 

Google Scholar 
Peng Y, Leung HC, Yiu SM, Chin FY. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 2012;28:1420–8.Article 
CAS 

Google Scholar 
Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015;31:1674–6.Article 
CAS 

Google Scholar 
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:119.Article 

Google Scholar 
Zhang C-J, Pan J, Liu Y, Duan C-H, Li M. Genomic and transcriptomic insights into methanogenesis potential of novel methanogens from mangrove sediments. Microbiome. 2020;8:94.Article 
CAS 

Google Scholar 
Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 2015;3:e1165.Article 

Google Scholar 
Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43.Article 
CAS 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.Article 
CAS 

Google Scholar 
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: A tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.Article 
CAS 

Google Scholar 
Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2019;36:1925–7.
Google Scholar 
Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016;428:726–31.Article 
CAS 

Google Scholar 
Huerta-Cepas J, Forslund K, Szklarczyk D, Jensen LJ, von Mering C, Bork P. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.Article 
CAS 

Google Scholar 
Finn RD, Clements J, Eddy SR. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 2011;39:W29–W37.Article 
CAS 

Google Scholar 
Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.Article 
CAS 

Google Scholar 
Edgar RC. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.Article 
CAS 

Google Scholar 
Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 2009;25:1972–3.Article 

Google Scholar 
Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.Article 
CAS 

Google Scholar 
Price MN, Dehal PS, Arkin AP. FastTree 2 – approximately maximum-likelihood trees for large alignments. Plos ONE. 2010;5:e9490.Article 

Google Scholar 
Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–5.Article 
CAS 

Google Scholar 
Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021;596:583–9.Article 
CAS 

Google Scholar 
Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31:455–61.CAS 

Google Scholar 
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.Article 
CAS 

Google Scholar 
Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinforma (Oxf, Engl). 2010;26:841–2.Article 
CAS 

Google Scholar 
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25:1754–60.Article 
CAS 

Google Scholar  More

Study areaWe focused on hiking activity in the four main islands of Japan (Honshu, Hokkaido, Kyushu, and Shikoku) and nearby small islands connected to the main islands by a bridge (Fig. 1a). These islands lie between latitudes 31.0° and 45.5°N, and the total area is 361,000 km2. The islands are generally mountainous and tallest mountains in central Honshu exceed 3000 m a.s.l. (Fig. 1c). In Tokyo, mean monthly temperatures range between 5.2 °C in January and 26.4 °C in August, while they range between − 18.4 °C in January and 6.2 °C in August at the summit of the highest mountain, Mt. Fuji (3776 m a.s.l., Japan Meteorological Agency). In northern Honshu and Hokkaido, snow depth can exceed 1 m even at low elevations and high mountains are covered with snow even in southern Japan.Vegetation excluding farmland and pasture covers 70.9% of the study area and the 93.9% is forest. Plantations of mostly evergreen conifers such as Japanese cedar (Cryptomeria japonica) occupy 37.6% of the vegetation area (National Surveys on the Natural Environment by the Biodiversity Center of Japan 2nd–7th; http://www.biodic.go.jp/trialSystem/top_en.html). Secondary vegetation after past human disturbances occupies 39.4% of the total vegetation and the remaining 23.0% is primary vegetation. The typical primary vegetation types are, from north to south, boreal mixed forest, deciduous broad leaved forest, and evergreen broad leaved forest.Grid squaresRecords of hiking activity were summarized for 4244 secondary grid squares based on Standard Grid Square System, which was defined by the Minister’s Order of Administrative Management Agency in 1973. In the system, the secondary grid was defined as a grid of 5′ in latitude and 7′ 30″ in longitude, which roughly corresponds to a 10 km grid in the study area. This is the standard grid system of the government and we adopted the system for convenience in future application uses and communication with practitioners. The grids, which are defined by latitude and longitude, are different in the area up to 22% between the north and south ends. Therefore, area of each grid was included in a model as an offset term.Hiking activityAccording to a government survey in 2016, (the Survey on Time Use and Leisure Activities by the Statistics Bureau of Japan, http://www.stat.go.jp/english/data/shakai/index.htm), 10.0% (about 10.7 million people) of Japan’s population age 15 or over enjoyed hiking/mountaineering in the last year. The census showed also that hiking is more popular among urban residents in the metropolitan areas. Both multi-day expedition to high mountains and day trek to low mountains in suburban areas are popular. Because of the severe winter climate, unskilled hikers use the high mountains in summer and early autumn only. During a summer vacation, whose peak time in Japan is August, many hikers enjoy multi-day trips to distant mountains. Spring and autumn are also popular seasons because of the mild weather and the scenic beauty of the fresh green or autumn colors.Data collectionIn this study, we used number of hiking records accumulated on the most popular social networking service for hikers in Japan (Yamareco; https://www.yamareco.com) as a surrogate for flow of recreation service. For all the registered destinations in the study area, the number of hiking records for each month and the latitude and longitude of the destination were collected from the service in September 2016 with the rvest28 package in R software29. This service launched in October 2005 hosts records of the hiking route, photos, participants, and impressions of a hiking trip and facilitates communication among users. Although monthly number of records for each destination is always available on the site, the exact date of each hiking record is not always public information for privacy reasons; therefore, all of the records from the almost 11 years since the start of the service were lumped together in our analysis. Hikers may record multiple places in a single trip, so the total number of records must be larger than the number of unique trips. Users of the service sometime record a place that is not a destination, e.g. start points and stations of trails, parking areas, stations of transports, and bus stops. Such records were excluded before analyses as far as it can be judged from the name of the place. As a result, the total number of hiking records was 4,708,229 records for 16,179 destinations. Finally, these records were assigned to the 4244 grids based on the latitude and longitude of each destination and then total number of records for each grid was used as a surrogate of the recreation service flow in our analysis. Not only total number but also monthly number was used in our analysis to examine seasonal changes in associations between the service and vegetation. Total record number of the grids was strongly right-skewed; no record (handled as 0 in our analysis) was found in 2036 grids while mean and maximum record number were 1109 and 350,384, respectively.Explanation variablesFifty ecological, environmental, and social/infrastructural variables (Table S1) were prepared for each grid by using ArcGIS version 10.5 (ESRI, Redlands, CA, USA). For vegetation and land-use attributes, National Surveys on the Natural Environment by the Biodiversity Center of Japan (2nd–7th; http://www.biodic.go.jp/trialSystem/top_en.html) and National Land Numerical Information (http://nlftp.mlit.go.jp/ksj-e/index.html) were used. The proportion of sea, that of total vegetation cover (excluding agricultural land and pasture) to land area, that of agricultural land (including pasture) to land area, that of natural vegetation (vegetation excluding plantations) to total vegetated area, and that of primary vegetation (vegetation with no record or evidence of a disturbance) to natural vegetation were summarized at four spatial scales: a radius of 10 km, 20 km, 50 km, and 100 km from the center of each grid. Spatial patterns of the three vegetation variables in 10 km radius were summarized in Fig. 1d–f.Maximum elevation, minimum elevation, and ruggedness (index of topographic heterogeneity30) were summarized at the four spatial scales based on a digital elevation model (10-m resolution) provided by the Geospatial Information Authority of Japan (https://fgd.gsi.go.jp/download/menu.php). For climatic variables (annual and monthly mean temperature, annual and monthly precipitation, annual and monthly hours of sunshine, and annual maximum snow depth), the National Land Numeric Information provided by the Ministry of Land, Infrastructure, Transport and Tourism of Japan (http://nlftp.mlit.go.jp/ksj-e/index.html) was referenced. Densities of population and roads at the four spatial scales were prepared from population census data from the Statistics Bureau of Japan (http://e-stat.go.jp/SG2/eStatGIS/page/download.html) and the National Land Numeric Information. For calculation of these densities, the sea surface was excluded. In addition, latitude and longitude of center of each grid were also used as explanatory variables to average effects of spatial coordinates.Statistical analysisIn this study, we employed BRT, a machine-learning method based on regression trees31 for modeling the complex relationship between a CES flow and landscape attributes12. BRT is an ensemble learning method where multiple regression trees are sequentially combined to minimize the loss function by means of gradient descent. This technique has advantage in the development of a model with a high predictive performance, in which high-dimensional interactions among explanatory variables and nonlinear responses are fully accounted for. In ecology, BRT has been frequently used for modeling of a species distribution32.Total and monthly numbers of hiking records were modeled as a function of the 50 variables described above under the assumption of a Poisson response. For temperature, precipitation, and hours of sunshine, annual and monthly average were used for the analysis of total and monthly records, respectively. In modeling by BRT, parameters for building of each learner and assembly of the learners must be carefully chosen to maximize generalization ability of a model31. In our case, candidate parameters were 2, 5, and 10 for the maximum depth of variable interactions for each learner; 2, 5, 10, and 20 for the minimum number of observations in the terminal nodes for each learner; 0.5 and 0.75 for the proportion of training data used for building each learner; and 1000, 2000, 4000, 6000, 8000 and 10,000 for the total number of learners (Table S2). In the model assembling process, the value of 0.01 was used as a shrinkage parameter. Ten-fold cross validation was used to obtain the best suites of parameters. R2 based on sum of squares:$${R}^{2}=1-frac{{sum ({y}_{i}-widehat{{y}_{i}})}^{2}}{{sum ({y}_{i}-overline{{y }_{i}})}^{2}}$$
was used for evaluation of the model’s prediction performance. The importance of explanatory variables was evaluated as an increase of mean absolute error after 100-times permutation of a variable33.Effects of each explanatory variable (a landscape attribute) on the response variable (record number) and the context dependence were visually inspected by individual conditional expectation (ICE) plot34. ICE plot visualizes the effect of a given explanatory variable for each observation by connecting outcome of a model for shifting values of the focal explanatory variable throughout the range while keeping other explanatory variables as the original value. Predictions were performed in log-scale and each line was centered to be zero at the left end of the x-axis to show relative effects of explanatory variables (c-ICE plot sensu Goltstein et al.34). Each line in ICE plot can be colored based on value of the second explanatory variable to assist assessment of the interactive effects of the two predictors. Friedman’s H statistic35 was used to detect explanatory variables whose interaction with the vegetation variables are important and therefore should be used for color-coding of an ICE plot. Friedman’s H is defined as a proportion of variance of partial dependence estimates explained by interactive effects for arbitrary suites of explanatory variables.Then, expected impacts of 0.1 decrease in the three local vegetation variables were assessed by the trained model and mapped. Although vegetation variables were sometimes more important at larger spatial scales (see “Results”), we focused on vegetation at a local (10 km radius) scale because most changes in vegetation occur at the scale in Japan (National Surveys on the Natural Environment by the Biodiversity Center of Japan, https://www.biodic.go.jp/kiso/fnd_list_h.html).All statistical analyses were performed using the R software packag29. The gbm36 package was used for BRT, the iml37 package was used for calculation of Friedman’s H statistic, and the cv.models (Oguro, https://github.com/Marchen/cv.models) packages was used for cross validation and parameter tuning. More

Ai H, Fang X, Yang B, Huang Z, Chen H, Mao L et al. (2015) Adaptation and possible ancient interspecies introgression in pigs identified by whole-genome sequencing. Nat Genet 47:217–225Article 
CAS 

Google Scholar 
Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664Article 
CAS 

Google Scholar 
Alexandri P, Megens HJ, Crooijmans RPMA, Groenen MAM, Goedbloed DJ, Herrero-Medrano JM et al. (2017) Distinguishing migration events of different timing for wild boar in the Balkans. J Biogeogr 44:259–270Article 

Google Scholar 
Alexandri P, Triantafyllidis A, Papakostas S, Chatzinikos E, Platis P, Papageorgiou N et al. (2012) The Balkans and the colonization of Europe: the post-glacial range expansion of the wild boar, Sus scrofa. J Biogeogr 39:713–723Article 

Google Scholar 
Alves PC, Pinheiro I, Godinho R, Vicente JJ, Gortázar C, Scandura M et al. (2010) Genetic diversity of wild boar populations and domestic pig breeds (Sus scrofa) in South-western Europe. Biol J Linn Soc 101:797–822Article 

Google Scholar 
Apollonio M, Andersen R, Putman R (2010) European ungulates and their management in the 21st century (M Apollonio, R Andersen, and R Putman, Eds.) Cambridge University Press: Cambridge, UKAzzaroli A, De Giuli C, Ficcarelli G, Torre D (1988) Late pliocene to early mid-pleistocene mammals in Eurasia: Faunal succession and dispersal events. Palaeogeogr Palaeoclimatol Palaeoecol 66:77–100Article 

Google Scholar 
Bérénos C, Ellis PA, Pilkington JG, Pemberton JM (2016) Genomic analysis reveals depression due to both individual and maternal inbreeding in a free‐living mammal population. Mol Ecol 25:3152–3168Article 

Google Scholar 
Braga RT, Rodrigues JFM, Diniz-Filho JAF, Rangel TF (2019) Genetic population structure and allele surfing during range expansion in dynamic habitats. An da Academia Brasileira de Ciências 91:e20180179Article 

Google Scholar 
Bragina EV, Ives AR, Pidgeon AM, Kuemmerle T, Baskin LM, Gubar YP, Piquer-Rodríguez M, Keuler NS, Petrosyan VG, Radeloff VC (2015) Rapid Declines of Large Mammal Populations after the Collapse of the Soviet Union. Cons Biol 29:844–853Article 

Google Scholar 
Brewer S, Cheddadi R, de Beaulieu JL, Reille M, Allen J, Almqvist-Jacobson H et al. (2002) The spread of deciduous Quercus throughout Europe since the last glacial period. For Ecol Manag 156:27–48Article 

Google Scholar 
Cahill S, Llimona F, Cabañeros L, Calomardo F (2012) Characteristics of wild boar (Sus scrofa) habituation to urban areas in the Collserola Natural Park (Barcelona) and comparison with other locations. Anim Biodivers Conserv 35:221–233Article 

Google Scholar 
Canu A, Costa S, Iacolina L, Piatti P, Apollonio M, Scandura M (2014) Are captive wild boar more introgressed than free-ranging wild boar? Two case studies in Italy. Eur J Wildl Res 60:459–467Article 

Google Scholar 
Canu A, Vilaça STT, Iacolina L, Apollonio M, Bertorelle G, Scandura M (2016) Lack of polymorphism at the MC1R wild-type allele and evidence of domestic allele introgression across European wild boar populations. Mamm Biol 81:477–479Article 

Google Scholar 
Carranza J, Salinas M, de Andrés D, Pérez-González J (2016) Iberian red deer: paraphyletic nature at mtDNA but nuclear markers support its genetic identity. Ecol Evol 6:905–922Article 

Google Scholar 
Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience 4:1–16Article 

Google Scholar 
Cheddadi R, Bar-Hen A (2009) Spatial gradient of temperature and potential vegetation feedback across Europe during the late Quaternary. Clim Dyn 32:371–379Article 

Google Scholar 
Clark PU, Dyke AS, Shakun JD, Carlson AE, Clark J, Wohlfarth B et al. (2009) The Last Glacial Maximum. Science 325:710–714Article 
CAS 

Google Scholar 
DeGiorgio M, Rosenberg NA (2013) Geographic sampling scheme as a determinant of the major axis of genetic variation in principal components analysis. Mol Biol Evol 30:480–488Article 
CAS 

Google Scholar 
Deinet S, Ieronymidou C, McRae L, Burfield IJ, Foppen RP, Collen B, et al. (2013) Wildlife comeback in Europe. The recovery of selected mammal and bird species. London, UKEckert CG, Samis KE, Lougheed SC (2008) Genetic variation across species’ geographical ranges: the central–marginal hypothesis and beyond. Mol Ecol 17:1170–1188Article 
CAS 

Google Scholar 
Fang M, Berg F, Ducos A, Andersson L (2006) Mitochondrial haplotypes of European wild boars with 2n = 36 are closely related to those of European domestic pigs with 2n = 38. Anim Genet 37:459–464Article 
CAS 

Google Scholar 
Ferenčaković M, Sölkner J, Curik I (2013) Estimating autozygosity from high-throughput information: Effects of SNP density and genotyping errors. Genet Sel Evol 45:42Article 

Google Scholar 
Ferreira E, Souto L, Soares AMVM, Fonseca C (2009) Genetic structure of the wild boar population in Portugal: Evidence of a recent bottleneck. Mamm Biol 74:274–285Article 

Google Scholar 
Franois O, Currat M, Ray N, Han E, Excoffier L, Novembre J (2010) Principal component analysis under population genetic models of range expansion and admixture. Mol Biol Evol 27:1257–1268Article 

Google Scholar 
Frantz AC, Bertouille S, Eloy MC, Licoppe A, Chaumont F, Flamand MC (2012) Comparative landscape genetic analyses show a Belgian motorway to be a gene flow barrier for red deer (Cervus elaphus), but not wild boars (Sus scrofa). Mol Ecol 21:3445–3457Article 
CAS 

Google Scholar 
Fulgione D, Rippa D, Buglione M, Trapanese M, Petrelli S, Maselli V (2016) Unexpected but welcome. Artificially selected traits may increase fitness in wild boar. Evol Appl 9:769–776Article 
CAS 

Google Scholar 
Goedbloed DJ, Megens HJ, van Hooft P, Herrero-Medrano JM, Lutz W, Alexandri P et al. (2013a) Genome-wide single nucleotide polymorphism analysis reveals recent genetic introgression from domestic pigs into Northwest European wild boar populations. Mol Ecol 22:856–866Article 
CAS 

Google Scholar 
Goedbloed DJ, van Hooft P, Megens HJ, Langenbeck K, Lutz W, Crooijmans RPMA et al. (2013b) Reintroductions and genetic introgression from domestic pigs have shaped the genetic population structure of Northwest European wild boar. BMC Genet 14:2–10Article 

Google Scholar 
Groenen MAM, Archibald AL, Uenishi H, Tuggle CK, Takeuchi Y, Rothschild MF et al. (2012) Analyses of pig genomes provide insight into porcine demography and evolution. Nature 491:393–398Article 
CAS 

Google Scholar 
Herrero-Medrano JM, Megens H-J, Groenen MAM, Ramis G, Bosse M, Pérez-Enciso M et al. (2013) Conservation genomic analysis of domestic and wild pig populations from the Iberian Peninsula. BMC Genet 14:1–13Article 

Google Scholar 
Hewitt GM (1999) Post-glacial re-colonization of European biota. Biol J Linn Soc 68:87–112Article 

Google Scholar 
Hewitt GM (2004) Genetic consequences of climatic oscillations in the Quaternary. Philos Trans R Soc Lond Ser B Biol Sci 359:183–195Article 
CAS 

Google Scholar 
Hiemstra PH, Pebesma EJ, Twenhöfel CJW, Heuvelink GBM (2009) Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network. Comput Geosci 35:1711–1721Article 
CAS 

Google Scholar 
Howrigan DP, Simonson MA, Keller MC (2011) Detecting autozygosity through runs of homozygosity: a comparison of three autozygosity detection algorithms. BMC Genomics 12:460Article 
CAS 

Google Scholar 
Huisman J, Kruuk LEB, Ellis PA, Clutton-Brock T, Pemberton JM (2016) Inbreeding depression across the lifespan in a wild mammal population. Proc Natl Acad Sci 113:3585–3590Article 
CAS 

Google Scholar 
Iacolina L, Corlatti L, Buzan E, Safner T, Šprem N (2019) Hybridisation in European ungulates: an overview of the current status, causes, and consequences. Mamm Rev 49:45–59Article 

Google Scholar 
Iacolina L, Pertoldi C, Amills M, Kusza S, Megens H-J, Bâlteanu VA et al. (2018) Hotspots of recent hybridization between pigs and wild boars in Europe. Sci Rep. 8:17372Article 
CAS 

Google Scholar 
Iacolina L, Scandura M, Goedbloed DJ, Alexandri P, Crooijmans RPMA, Larson G et al. (2016) Genomic diversity and differentiation of a managed island wild boar population. Heredity 116:60–67Article 
CAS 

Google Scholar 
Jombart T, Ahmed I (2011) adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 27:1–2Article 

Google Scholar 
de Jong JF, Hooft van P, Megens HJ, Crooijmans RPMA, Groot de GA, Pemberton JM, Huisman J et al. (2020) Fragmentation and translocation distort the genetic landscape of ungulates: red deer in the Netherlands. Front Ecol Evol 8:535715Article 

Google Scholar 
Kamvar ZN, Tabima JF, Grünwald NJ (2014) Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2:e281Article 

Google Scholar 
Kardos M, Åkesson M, Fountain T, Flagstad Ø, Liberg O, Olason P et al. (2018) Genomic consequences of intensive inbreeding in an isolated wolf population. Nat Ecol Evol 2:124–131Article 

Google Scholar 
Kaplan JO, Krumhardt KM, Zimmermann N (2009) The prehistoric and preindustrial deforestation of Europe. Quat Sci Rev 28:3016–3034. https://doi.org/10.1016/j.quascirev.2009.09.028Koemle D, Zinngrebe Y, Yu X (2018) Highway construction and wildlife populations: Evidence from Austria. Land use policy 73:447–457Article 

Google Scholar 
Krže B (1982) Divji prašič: biologija, gojitev, ekologija. Lovska zveza Slovenije, Ljubljana
Google Scholar 
Kusza S, Podgórski T, Scandura M, Borowik T, Jávor A, Sidorovich VE et al. (2014) Contemporary genetic structure, phylogeography and past demographic processes of wild boar Sus scrofa population in central and eastern Europe. PLoS One 9:e91401Article 

Google Scholar 
Lorenzini R, Lovari S, Masseti M (2002) The rediscovery of the Italian roe deer: Genetic differentiation and management implications. Ital J Zool 69(4):367–379Article 

Google Scholar 
Lorenzini R, San José C, Braza F, Aragón S (2003) Genetic differentiation and phylogeography of roe deer in Spain, as suggested by mitochondrial DNA and microsatellite analysis. Ital J Zool 70(1):89–99Article 
CAS 

Google Scholar 
Magri D (2013) Early to Middle Pleistocene dynamics of plant and mammal communities in South West Europe. Quat Int 288:63–72Article 

Google Scholar 
Manunza A, Zidi A, Yeghoyan S, Balteanu VA, Carsai TC, Scherbakov O et al. (2013) A high throughput genotyping approach reveals distinctive autosomal genetic signatures for European and Near Eastern wild boar. PLoS One 8:e55891Article 
CAS 

Google Scholar 
Maselli V, Rippa D, De Luca A, Larson G, Wilkens B, Linderholm A et al. (2016) Southern Italian wild boar population, hotspot of genetic diversity. Hystrix 27:137–144
Google Scholar 
McVean G (2009) A genealogical interpretation of principal components analysis. PLoS Genet 5:e1000686Article 

Google Scholar 
Megens H-J, Crooijmans RP, Cristobal M, Hui X, Li N, Groenen MA (2008) Biodiversity of pig breeds from China and Europe estimated from pooled DNA samples: differences in microsatellite variation between two areas of domestication. Genet Sel Evol 40:103
Google Scholar 
Melis C, Szafrańska PA, Jȩdrzejewska B, Bartoń K (2006) Biogeographical variation in the population density of wild boar (Sus scrofa) in western Eurasia. J Biogeogr 33:803–811Article 

Google Scholar 
Mihalik B, Stéger V, Frank K, Szendrei L, Kusza S (2018) Barrier effect of the M3 highway in Hungary on the genetic diversity of wild boar (Sus scrofa) population. Res J Biotechnol 13:32–38
Google Scholar 
NCBI (2018) Genome Organism Overview: Sus scrofa (pig). https://www.ncbi.nlm.nih.gov/genome?term=sus%20scrofa%20%5BOrganism%5D&cmd=DetailsSearch&report=OverviewNikolov IS, Gum B, Markov G, Kuehn R (2009) Population genetic structure of wild boar Sus scrofa in Bulgaria as revealed by microsatellite analysis. Acta Theriol (Warsz) 54:193–205Article 

Google Scholar 
Nykänen M, Rogan E, Foote AD, Kaschner K, Dabin W, Louis M et al. (2019) Postglacial colonization of northern coastal habitat by bottlenose dolphins: a marine leading-edge expansion? J Hered 110:662–674Article 

Google Scholar 
Palombo M, Romana AV-G (2003) Remarks on the biochronology of mammalian faunal complexes from the Pliocene to the Middle Pleistocene in France. Geol Rom: 145–163Paradis E, Claude J, Strimmer K (2004) APE: analysis of phylogenetics and evolution in R language. Bioinformatics 20:289–290Article 
CAS 

Google Scholar 
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D et al (2007) PLINK: A tool Set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575. www.cog-genomics.org/plink/1.9/Putman R, Apollonio M, Andersen R (2011) Ungulate management in Europe: problems and practices. Cambridge University Press, Cambridge, UKBook 

Google Scholar 
R Core Team (2018) R: A language and environment for statistical computing. Vienna, AustriaRejduch B, Sota E, Ró M, Ko M (2003) Chromosome number polymorphism in a litter of European wild boar (Sus scrofa scrofa L.). Anim Sci Pap Rep. 21:57–62
Google Scholar 
Scandura M, Iacolina L, Apollonio M (2011a) Genetic diversity in the European wild boar Sus scrofa: phylogeography, population structure and wild x domestic hybridization: Genetic variation in European wild boar. Mamm Rev 41:125–137Article 

Google Scholar 
Scandura M, Iacolina L, Cossu A, Apollonio M (2011b) Effects of human perturbation on the genetic make-up of an island population: The case of the Sardinian wild boar. Heredity 106:1012–1020Article 
CAS 

Google Scholar 
Scandura M, Iacolina L, Crestanello B, Pecchioli E, Di Benedetto MF, Russo V et al. (2008) Ancient vs. recent processes as factors shaping the genetic variation of the European wild boar: Are the effects of the last glaciation still detectable? Mol Ecol 17:1745–1762Article 
CAS 

Google Scholar 
Scandura M, Fabbri G, Caniglia R, Iacolina L, Mattucci F, Mengoni C, Pante G, Apollonio M, Mucci N (2022) Resilience to Historical Human Manipulations in the Genomic Variation of Italian Wild Boar Populations. Front Ecol Evol 10:833081Article 

Google Scholar 
Schmitt T, Varga Z (2012) Extra-Mediterranean refugia: the rule and not the exception. Front Zool 9:22Article 

Google Scholar 
Sommer RS, Fahlke JM, Schmölcke U, Benecke N, Zachos FE (2009) Quaternary history of the European roe deer Capreolus capreolus. Mamm Rev 39:1–16Article 

Google Scholar 
Sommer RS, Nadachowski A (2006) Glacial refugia of mammals in Europe: evidence from fossil records. Mamm Rev 36:251–265Article 

Google Scholar 
Sommer RS, Zachos FE (2009) Fossil evidence and phylogeography of temperate species: ‘glacial refugia’ and post-glacial recolonization. J Biogeogr 36:2013–2020Article 

Google Scholar 
Sommer RS, Zachos FE, Street M, Jöris O, Skog A, Benecke N (2008) Late Quaternary distribution dynamics and phylogeography of the red deer (Cervus elaphus) in Europe. Quat Sci Rev 27:714–733Article 

Google Scholar 
Stillfried M, Fickel J, Börner K, Wittstatt U, Heddergott M, Ortmann S et al. (2017) Do cities represent sources, sinks or isolated islands for urban wild boar population structure? J Appl Ecol 54:272–281Article 

Google Scholar 
Taberlet P, Fumagalli L, Wust-Saucy AG, Cossons JF (1998) Comparative phylogeography and post-glacial colonization routes in Europe. Mol Ecol 7:453–461.Article 
CAS 

Google Scholar 
Veličković N, Djan M, Ferreira E, Stergar M, Obreht D, Maletić V et al. (2015) From north to south and back: the role of the Balkans and other southern peninsulas in the recolonization of Europe by wild boar. J Biogeogr 42:716–728Article 

Google Scholar 
Veličković N, Ferreira E, Djan M, Ernst M, Obreht Vidaković D, Monaco A et al. (2016) Demographic history, current expansion and future management challenges of wild boar populations in the Balkans and Europe. Heredity 117:348–357Article 

Google Scholar 
Vernesi C, Crestanello B, Pecchioli E, Tartari D, Caramelli D, Hauffe H et al. (2003) The genetic impact of demographic decline and reintroduction in the wild boar (Sus scrofa): A microsatellite analysis. Mol Ecol 12:585–595Article 
CAS 

Google Scholar 
Vilaça ST, Biosa D, Zachos F, Iacolina L, Kirschning J, Alves PC et al. (2014) Mitochondrial phylogeography of the European wild boar: The effect of climate on genetic diversity and spatial lineage sorting across Europe. J Biogeogr 41:987–998Article 

Google Scholar 
Zachos FE, Frantz AC, Kuehn R, Bertouille S, Colyn M, Niedziałkowska M et al. (2016) Genetic structure and effective population sizes in European red deer (Cervus elaphus) at a continental scale: insights from microsatellite DNA. J Hered 107:318–326 More

Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).Article 
ADS 
CAS 

Google Scholar 
Sandel, B. et al. The influence of late Quaternary climate-change velocity on species endemism. Science 334(6056), 660–664 (2011).Article 
ADS 
CAS 

Google Scholar 
Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science https://doi.org/10.1126/science.aaf7671 (2016).Article 

Google Scholar 
Davis, M. B. & Shaw, R. G. Range shifts and adaptive responses to quaternary climate change. Science 292(5517), 673–679 (2001).Article 
ADS 
CAS 

Google Scholar 
Lane, J. E., Kruuk, L. E. B., Charmantier, A., Murie, J. O. & Dobson, F. S. Delayed phenology and reduced fitness associated with climate change in a wild hibernator. Nature 489, 554–557 (2012).Article 
ADS 
CAS 

Google Scholar 
Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science https://doi.org/10.1126/science.aai9214 (2017).Article 

Google Scholar 
Dawson, T. P., Jackson, S. T., House, J. I., Prentice, I. C. & Mace, G. M. Beyond predictions: Biodiversity conservation in a changing climate. Science https://doi.org/10.1126/science.1200303 (2011).Article 

Google Scholar 
Pacifici, M. et al. Species’ traits influenced their response to recent climate change. Nat. Clim. Change 7, 205–208 (2017).Article 
ADS 

Google Scholar 
Schloss, C. A., Nuñez, T. A. & Lawler, J. J. Dispersal will limit ability of mammals to track climate change in the Western Hemisphere. PNAS 109(22), 8606–8611 (2012).Article 
ADS 
CAS 

Google Scholar 
Perry, A. L., Low, P. J., Ellis, J. R. & Reynolds, J. D. Climate change and distribution shifts in marine fishes. Science 308(5730), 1912–1915 (2005).Article 
ADS 
CAS 

Google Scholar 
Bradshaw, W. E., Zani, P. A. & Holzapfel, C. M. Adaptation to temperate climates. Evolution 58(8), 1748–1762 (2004).
Google Scholar 
Thomas, C. D. et al. Ecological and evolutionary processes at expanding range margins. Nature 411(6837), 577–581 (2001).Article 
ADS 
CAS 

Google Scholar 
Urban, M. C. Accelerating extinction risk from climate change. Science 348(6234), 571–573 (2015).Article 
ADS 
CAS 

Google Scholar 
Waller, N. L., Gynther, I. C., Freeman, A. B., Lavery, T. H. & Leung, L. K. P. The bramble cay melomys Melomys rubicola (Rodentia:Muridae): A first mammalian extinction caused by human-induced climate change?. Wildl. Res. 44(1), 9–21 (2017).Article 

Google Scholar 
Murray, K. A., Rosauer, D., McCallum, H. & Skerratt, L. F. Integrating species traits with extrinsic threats: Closing the gap between predicting and preventing species declines. Proc. R. Soc. B: Biol. Sci. 278(1711), 1515–1523 (2011).Article 

Google Scholar 
Stevens, G. C. The latitudinal gradient in geographical range: How so many species coexist in the Tropics. Am. Nat. 133(2), 240–256 (1989).Article 

Google Scholar 
Hickling, R., Roy, D. B., Hill, J. K., Fox, R. & Thomas, C. D. The distributions of a wide range of taxonomic groups are expanding polewards. Glob. Change Biol. 12(3), 450–455 (2006).Article 
ADS 

Google Scholar 
Virkkala, R., Heikkinen, R. K., Leikola, N. & Luoto, M. Projected large-scale range reductions of northern-boreal land bird species due to climate change. Biol. Conserv. 141(5), 1343–1353 (2008).Article 

Google Scholar 
Sales, L. P. et al. Niche conservatism and the invasive potential of the wild boar. J. Anim. Ecol. 86(5), 1214–1223 (2017).Article 

Google Scholar 
Gouveia, S. F. et al. Climate and land use changes will degrade the configuration of the landscape for titi monkeys in eastern Brazil. Glob. Change Biol. 22(6), 2003–2012 (2016).Article 
ADS 

Google Scholar 
Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful?. Glob. Ecol. Biogeogr. 12(5), 361–371 (2003).Article 

Google Scholar 
Engler, R. et al. Predicting future distributions of mountain plants under climate change: does dispersal capacity matter?. Ecography 32(1), 34–45 (2009).Article 

Google Scholar 
Ozinga, W. A. et al. Predictability of plant species composition from environmental conditions is constrained by dispersal limitation. Oikos 108(3), 555–561 (2005).Article 

Google Scholar 
Takahashi, K. & Kamitani, T. Effect of dispersal capacity on forest plant migration at a landscape scale. J. Ecol. 92(5), 778–785 (2004).Article 

Google Scholar 
Koo, K. A. & Park, S. U. The effect of interplays among climate change, land-use change, and dispersal capacity on plant redistribution. Ecol. Indic. 142, 109192 (2022).Article 

Google Scholar 
Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333(6045), 1024–1026 (2011).Article 
ADS 
CAS 

Google Scholar 
Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3(10), 919–925 (2013).Article 
ADS 

Google Scholar 
Vanderwal, J. et al. Focus on poleward shifts in species’ distribution underestimates the fingerprint of climate change. Nat. Clim. Change 3, 239–243 (2013).Article 
ADS 

Google Scholar 
Lira, A. F. de A., Badillo-Montaño, R., Lira-Noriega, A. & de Albuquerque, C. M. R. Potential distribution patterns of scorpions in north-eastern Brazil under scenarios of future climate change. Austral Ecol. 45(2), 215–228 (2020).Castro, M. B. et al. Will the emblematic southern conifer Araucaria angustifolia survive to climate change in Brazil?. Biodivers. Conserv. 29(2), 591–607 (2020).Article 

Google Scholar 
Wilson, O. J., Walters, R. J., Mayle, F. E., Lingner, D. V. & Vibrans, A. C. Cold spot microrefugia hold the key to survival for Brazil’s Critically Endangered Araucaria tree. Glob. Change Biol. 25(12), 4339–4351 (2019).Article 
ADS 

Google Scholar 
Esser, L. F. et al. Future uncertainties for the distribution and conservation of Paubrasilia echinata under climate change. Acta Bot. Bras. 33(4), 770–776 (2019).Article 

Google Scholar 
Cabanne, G. S. et al. Effects of Pleistocene climate changes on species ranges and evolutionary processes in the Neotropical Atlantic Forest. Biol. J. Linn. Soc. 119(4), 856–872 (2016).Article 

Google Scholar 
Iturralde-Pólit, P., Dangles, O., Burneo, S. F. & Meynard, C. N. The effects of climate change on a mega-diverse country: predicted shifts in mammalian species richness and turnover in continental Ecuador. Biotropica 49(6), 821–831 (2017).Article 

Google Scholar 
Vu, T. T. et al. An assessment of the impact of climate change on the distribution of the grey-shanked douc Pygathrix cinerea using an ecological niche model. Primates 61(2), 267–275 (2020).Article 

Google Scholar 
Sales, L. P., Ribeiro, B. R., Pires, M. M., Chapman, C. A. & Loyola, R. Recalculating route: dispersal constraints will drive the redistribution of Amazon primates in the Anthropocene. Ecography 42(10), 1789–1801 (2019).Article 

Google Scholar 
Hill, S. E. & Winder, I. C. Predicting the impacts of climate change on Papio baboon biogeography: Are widespread, generalist primates ‘safe’?. J. Biogeogr. 46(7), 1380–1405 (2019).
Google Scholar 
Gillings, S., Balmer, D. E. & Fuller, R. J. Directionality of recent bird distribution shifts and climate change in Great Britain. Glob. Change Biol. 21(6), 2155–2168 (2015).Article 
ADS 

Google Scholar 
Fernández, D. et al. The current status of the world’s primates: Mapping threats to understand priorities for primate conservation. Int. J. Primatol. 43, 15–39 (2022).Article 

Google Scholar 
Stewart, B. M., Turner, S. E. & Matthews, H. D. Climate change impacts on potential future ranges of non-human primate species. Clim. Change 162, 2301–2318 (2020).Article 
ADS 
CAS 

Google Scholar 
Estrada, A. et al. Primates in peril: The significance of Brazil, Madagascar, Indonesia and the Democratic Republic of the Congo for global primate conservation. PeerJ 6, e4869; https://doi.org/10.7717/peerj.4869 (2018).Estrada, A. et al. Impending extinction crisis of the world’s primates: Why primates matter. Sci. Adv. https://doi.org/10.1126/sciadv.1600946 (2017).Article 

Google Scholar 
Graham, T. L., Matthews, H. D. & Turner, S. E. A global-scale evaluation of primate exposure and vulnerability to climate change. Int. J. Primatol. 37(2), 158–174 (2016).Article 

Google Scholar 
Meyer, A. L. S., Pie, M. R. & Passos, F. C. Assessing the exposure of lion tamarins (Leontopithecus spp.) to future climate change. Am. J. Primatol. 76(6), 551–562 (2014).Article 

Google Scholar 
Braz, A. G., Lorini, M. L. & Vale, M. M. Climate change is likely to affect the distribution but not parapatry of the Brazilian marmoset monkeys (Callithrix spp.). Divers. Distrib. 25(4), 536–550 (2019).Article 

Google Scholar 
Lima, A. A. de, Ribeiro, M. C., Grelle, C. E. de V. & Pinto, M. P. Impacts of climate changes on spatio-temporal diversity patterns of Atlantic Forest primates. Perspect. Ecol. Conserv. 17(2), 50–56 (2019).Colombo, A. F. & Joly, C. A. Brazilian Atlantic Forest lato sensu: the most ancient Brazilian forest, and a biodiversity hotspot, is highly threatened by climate change. Braz. J. Biol. 70(3), 697–708 (2010).Article 
CAS 

Google Scholar 
Zwiener, V. P., Lira-Noriega, A., Grady, C. J., Padial, A. A. & Vitule, J. R. Climate change as a driver of biotic homogenization of woody plants in the Atlantic Forest. Glob. Ecol. Biogeogr. 27(3), 298–309 (2018).Article 

Google Scholar 
Lemes, P., Melo, A. S. & Loyola, R. D. Climate change threatens protected areas of the Atlantic Forest. Biodivers. Conserv. 23(2), 357–368 (2014).Article 

Google Scholar 
Rezende, G. C., Sobral-Souza, T. & Culot, L. Integrating climate and landscape models to prioritize areas and conservation strategies for an endangered arboreal primate. Am. J. Primatol. 82(12), e23202. https://doi.org/10.1002/ajp.23202 (2020).Article 

Google Scholar 
Silva, L. B. et al. How future climate change and deforestation can drastically affect the species of monkeys endemic to the eastern Amazon, and priorities for conservation. Biodivers. Conserv. 31, 971–988 (2022).Article 

Google Scholar 
Sales, L., Ribeiro, B. R., Chapman, C. A. & Loyola, R. Multiple dimensions of climate change on the distribution of Amazon primates. Perspect. Ecol. Conserv. 18(2), 83–90 (2020).
Google Scholar 
Moraes, B., Razgour, O., Souza-Alves, J., Boubli, J. & Bezerra, B. Habitat suitability for primate conservation in north-east Brazil. Oryx 54(6), 803–813 (2020).Article 

Google Scholar 
Hanson, J. O. et al. Global conservation of species’ niches. Nature 580, 232–234 (2020).Article 
ADS 
CAS 

Google Scholar 
Hanson, J. O., Rhodes, J. R., Riginos, C. & Fuller, R. A. Environmental and geographic variables are effective surrogates for genetic variation in conservation planning. PNAS 114(48), 12755–12760 (2017).Article 
ADS 
CAS 

Google Scholar 
Scheele, B. C., Foster, C. N., Banks, S. C. & Lindenmayer, D. B. Niche contractions in declining species: Mechanisms and consequences. Trends Ecol. Evol. 32(5), 346–355 (2017).Article 

Google Scholar 
Travis, J. M. J. et al. Dispersal and species’ responses to climate change. Oikos 122, 1532–1540 (2013).Article 

Google Scholar 
Lenoir, J. & Svenning, J.-C. Climate-related range shifts – a global multidimensional synthesis and new research directions. Ecography 38, 15–28 (2015).Article 

Google Scholar 
Raghunathan, N., François, L., Huynen, M. C., Oliveira, L. C. & Hambuckers, A. Modelling the distribution of key tree species used by lion tamarins in the Brazilian Atlantic forest under a scenario of future climate change. Reg. Environ. Change 15, 683–693 (2015).Article 

Google Scholar 
Lawler, J. J., Ruesch, A. S., Olden, J. D. & McRae, B. H. Projected climate-driven faunal movement routes. Ecol. Lett. 16(8), 1014–1022 (2013).Article 
CAS 

Google Scholar 
Årevall, J., Early, R., Estrada, A., Wennergren, U. & Eklöf, A. C. Conditions for successful range shifts under climate change: The role of species dispersal and landscape configuration. Divers. Distrib. 24, 1598–1611 (2018).Article 

Google Scholar 
Carroll, C., Lawler, J. J., Roberts, D. R. & Hamann, A. Biotic and climatic velocity identify contrasting areas of vulnerability to climate change. PLoS ONE 10(10), e0142024. https://doi.org/10.1371/journal.pone.0140486 (2015).Article 
CAS 

Google Scholar 
Davies, T. J., Purvis, A. & Gittleman, J. L. Quaternary climate change and the geographic ranges of mammals. Am. Nat. 174(3), 297–307 (2009).Article 

Google Scholar 
Gaston, K.J. The structure and dynamics of geographic ranges (Oxford University Press, 2003).Meyer, A. L. S. & Pie, M. R. Climate change estimates surpass rates of climatic niche evolution in primates. Int. J. Primatol. 43, 40–56 (2021).Article 

Google Scholar 
Zeigler, S. L., Fagan, W. F., DeFries, R. & Raboy, B. E. Identifying important forest patches for the long-term persistence of the endangered golden-headed lion tamarin (Leontopithecus chrysomelas). Trop. Conserv. Sci. 3(1), 63–77 (2010).Article 

Google Scholar 
Dosen, J., Fortin, M. J. & Raboy, B. E. Restoration strategies to improve connectivity for golden-headed lion tamarins (Leontopithecus chrysomelas) in the Bahian Atlantic Forest. Brazil. Int. J. Primatol. 38(5), 962–983 (2017).Article 

Google Scholar 
Piffer, P. R., Rosa, M. R., Tambosi, L. R., Metzger, J. P. & Uriarte, M. Turnover rates of regenerated forests challenge restoration efforts in the Brazilian Atlantic Forest. Environ. Res. Lett. 17(4), 045009. https://doi.org/10.1088/1748-9326/ac5ae1 (2022).Article 
ADS 

Google Scholar 
Estrada, A., Raboy, B. E. & Oliveira, L. C. Agroecosystems and primate conservation in the tropics: A review. Am. J. Primatol. 74, 696–711 (2012).Article 

Google Scholar 
Galea, B., Humle, T. Identifying and mitigating the impacts on primates of transportation and service corridors. Conserv. Biol. 36, e13836; https://doi.org/10.1111/cobi.13836 (2022).Gouveia, S. F. et al. Functional planning units for the management of an endangered Brazilian titi monkey. Am. J. Primatol. 79(5), e22637; https://doi.org/10.1002/ajp.22637 (2017).Rezende, G. et al. Leontopithecus chrysopygus. The IUCN Red List of Threatened Species, e.T11505A17935400; https://doi.org/10.2305/IUCN.UK.2020-2.RLTS.T11505A17935400.en (2020).Culot, L. et al. ATLANTIC-PRIMATES: A dataset of communities and occurrences of primates in the Atlantic Forests of South America. Ecology 100(1), e02525; https://doi.org/10.1002/ecy.2525 (2018).Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12), 4302–4315 (2017).Article 

Google Scholar 
Quinn, G. P. & Keough, M. J. Experimental design and data analysis for biologists (Cambridge University Press, 2002).Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1(1), 3–14 (2010).Article 

Google Scholar 
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17(1), 43–57 (2011).Article 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190(3–4), 231–259 (2006).Article 

Google Scholar 
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29(2), 129–151 (2006).Article 

Google Scholar 
Muscarella, R. et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for ecological niche models. Methods Ecol. Evol. 5(11), 1198–1205 (2014).Article 

Google Scholar 
Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J. Biogeogr. 34(1), 102–117 (2007).Article 

Google Scholar 
Shcheglovitova, M. & Anderson, R. P. Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes. Ecol. Modell. 269, 9–17 (2013).Article 

Google Scholar 
Wenger, S. J. & Olden, J. D. Assessing transferability of ecological models: An underappreciated aspect of statistical validation. Methods Ecol. Evol. 3(2), 260–267 (2012).Article 

Google Scholar 
Hidasi-Neto, J. et al. Climate change will drive mammal species loss and biotic homogenization in the Cerrado Biodiversity Hotspot. Perspect. Ecol. Conserv. 17(2), 57–63 (2019).
Google Scholar 
Bowman, J., Jaeger, J. A. G. & Fahrig, L. Dispersal distance of mammals is proportional to home range size. Ecology 83(7), 2049–2055 (2002).Article 

Google Scholar 
Galán-Acedo, C., Arroyo-Rodríguez, V., Andresen, E. & Arasa-Gisbert, R. Ecological traits of the world’s primates. Sci. Data 6, 55. https://doi.org/10.1038/s41597-019-0059-9 (2019).Article 

Google Scholar 
Pacifici, M. et al. Generation length for mammals. Nat. Conserv. 5, 89–94 (2013).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing Vienna Austria (2017).QGIS Development Team. QGIS Geographic Information System (2016). More

Cairney, J. W. G. & Bastias, B. A. Influences of fire on forest soil fungal communities. Can. J. For. Res. 37, 207–215 (2007).Article 

Google Scholar 
Fernández, C., Vega, J. A. & Fonturbel, T. The effects of fuel reduction treatments on runoff, infiltration and erosion in two shrubland areas in the north of Spain. J. Environ. Manage. 105, 96–102 (2012).Article 

Google Scholar 
Reazin, C., Morris, S., Smith, J. E., Cowan, A. D. & Jumpponen, A. Fires of differing intensities rapidly select distinct soil fungal communities in a Northwest US ponderosa pine forest ecosystem. For. Ecol. Manage. 377, 118–127 (2016).Article 

Google Scholar 
Durán-Manual, F. et al. Prescribed burning in Pinus cubensis-dominated tropical natural forests: A myco-friendly fire-prevention tool. For. Syst. 31, e012 (2022).
Google Scholar 
Busse, M. D., Hubbert, K. R., Fiddler, G. O., Shestak, C. J. & Powers, R. F. Lethal soil temperatures during burning of masticated forest residues. Int. J. Wildl. Fire 14, 267–276 (2005).Article 

Google Scholar 
Frazão, D. F. et al. Cistus ladanifer (Cistaceae): A natural resource in Mediterranean-type ecosystems. Planta 247, 289–300 (2018).Article 

Google Scholar 
Keeley, J. E., Bond, W. J., Bradstock, R. A., Pausas, J. G. & Rundel, P. W. Fire in mediterranean ecosystems. Fire Medit. Ecosyst. https://doi.org/10.1017/cbo9781139033091 (2011).Article 

Google Scholar 
Louro, R., Peixe, A. & Santos-silva, C. New insights on Cistus salviifolius L. micropropagation. J. Bot. Sci. 6, 10–14 (2017).CAS 

Google Scholar 
Valbuena, L., Tarrega, R. & Luis, E. Influence of heat on seed germination of Cistus laurifolius and Cistus ladanifer. J. Wildl. Fire 2, 15–20 (1992).Article 

Google Scholar 
Martín-Pinto, P., Vaquerizo, H., Peñalver, F., Olaizola, J. & Oria-De-Rueda, J. A. Early effects of a wildfire on the diversity and production of fungal communities in Mediterranean vegetation types dominated by Cistus ladanifer and Pinus pinaster in Spain. For. Ecol. Manage. 225, 296–305 (2006).Article 

Google Scholar 
Comandini, O., Contu, M. & Rinaldi, A. C. An overview of Cistus ectomycorrhizal fungi. Mycorrhiza 16, 381–395 (2006).Article 
CAS 

Google Scholar 
Zuzunegui, M. et al. Growth response of Halimium halimifolium at four sites with different soil water availability regimes in two contrasted hydrological cycles. Plant Soil 247, 271–281 (2002).Article 

Google Scholar 
Civeyrel, L. et al. Molecular systematics, character evolution, and pollen morphology of Cistus and Halimium (Cistaceae). Plant Syst. Evol. 295, 23–54 (2011).Article 

Google Scholar 
Leonardi, M., Furtado, A. N. M., Comandini, O., Geml, J. & Rinaldi, A. C. Halimium as an ectomycorrhizal symbiont: New records and an appreciation of known fungal diversity. Mycol. Prog. 19, 1495–1509 (2020).Article 

Google Scholar 
Oria-De-Rueda, J. A., Martín-Pinto, P. & Olaizola, J. Bolete productivity of cistaceous scrublands in northwestern Spain. Econ. Bot. 62, 323–330 (2008).Article 

Google Scholar 
Fernández, C., Vega, J. A. & Fonturbel, T. Does shrub recovery differ after prescribed burning, clearing and mastication in a Spanish heathland?. Plant Ecol. 216, 429–437 (2015).Article 

Google Scholar 
Ponte, E. D., Costafreda-Aumedes, S. & Vega-Garcia, C. Lessons learned from arson wildfire incidence in reforestations and natural stands in Spain. Forests 10, 1–18 (2019).Article 

Google Scholar 
Franco-Manchón, I., Salo, K., Oria-de-Rueda, J. A., Bonet, J. A. & Martín-Pinto, P. Are wildfires a threat to fungi in European Pinus forests? A case study of boreal and Mediterranean forests. Forests 10, 309 (2019).Article 

Google Scholar 
Mediavilla, O., Oria-de-Rueda, J. A. & Martin-Pinto, P. Changes in sporocarp production and vegetation following wildfire in a Mediterranean Forest Ecosystem dominated by Pinus nigra in Northern Spain. For. Ecol. Manage. 331, 85–92 (2014).Article 

Google Scholar 
Tomao, A., Antonio Bonet, J., Castaño, C. & De-Miguel, S. How does forest management affect fungal diversity and community composition? Current knowledge and future perspectives for the conservation of forest fungi. For. Ecol. Manage. 457, 117678 (2020).
Article 

Google Scholar 
Espinosa, J., Rodríguez de Rivera, O., Madrigal, J., Guijarro, M. & Hernando, C. Predicting potential cambium damage and fire resistance in Pinus nigra Arn. ssp. salzmannii. For. Ecol. Manage. 474, 118372 (2020).Article 

Google Scholar 
Potts, J. B. & Stephens, S. L. Invasive and native plant responses to shrubland fuel reduction: Comparing prescribed fire, mastication and treatment season. Biol. Conserv. 142, 1657–1664 (2009).Article 

Google Scholar 
Agee, J. K. & Skinner, C. N. Basic principles of forest fuel reduction treatments. For. Ecol. Manage. 211, 83–96 (2005).Article 

Google Scholar 
Fernández, C., Vega, J. A. & Fonturbel, T. Fuel reduction at a Spanish heathland by prescribed fire and mechanical shredding: Effects on seedling emergence. J. Environ. Manage. 129, 621–627 (2013).Article 

Google Scholar 
Huggett, R. J., Abt, K. L. & Shepperd, W. Efficacy of mechanical fuel treatments for reducing wildfire hazard. For. Policy Econ. 10, 408–414 (2008).Article 

Google Scholar 
Fernández, C. & Vega, J. A. Shrub recovery after fuel reduction treatments and a subsequent fire in a Spanish heathland. Plant Ecol. 215, 1233–1243 (2014).Article 

Google Scholar 
Fernández, C., Vega, J. A. & Fonturbel, T. Does fire severity influence shrub resprouting after spring prescribed burning?. Acta Oecologica 48, 30–36 (2013).Article 
ADS 

Google Scholar 
Ellsworth, J. W., Harrington, R. A. & Fownes, J. H. Seedling emergence, growth, and allocation of Oriental bittersweet: Effects of seed input, seed bank, and forest floor litter. For. Ecol. Manage. 190, 255–264 (2004).Article 

Google Scholar 
Castaño, C. et al. Resistance of the soil fungal communities to medium-intensity fire prevention treatments in a Mediterranean scrubland. For. Ecol. Manage. 472, 118217 (2020).Article 

Google Scholar 
Anderson, I. C., Bastias, B. A., Genney, D. R., Parkin, P. I. & Cairney, J. W. G. Basidiomycete fungal communities in Australian sclerophyll forest soil are altered by repeated prescribed burning. Mycol. Res. 111, 482–486 (2007).Article 
CAS 

Google Scholar 
Hernández-Rodríguez, M. et al. Soil fungal community composition in a Mediterranean shrubland is primarily shaped by history of major disturbance, less so by current fire fuel reduction treatments. Unpublished (2015).Oria de Rueda, J. A., Martín-Pinto, P. & Olaizola, J. Boletus edulis PRODUCTION IN XEROPHILIC AND PIROPHITIC SCHRUBS OF Cistus ladanifer AND Halimium lasianthum IN WESTERN SPAIN. in IV International Workshop on Edible Mycorrhizal Mushrooms (2005).Hart, B. T. N., Smith, J. E., Luoma, D. L. & Hatten, J. A. Recovery of ectomycorrhizal fungus communities fifteen years after fuels reduction treatments in ponderosa pine forests of the Blue Mountains. Oregon. For. Ecol. Manage. 422, 11–22 (2018).Article 

Google Scholar 
Hernández-Rodríguez, M., Oria-de-Rueda, J. A., Pando, V. & Martín-Pinto, P. Impact of fuel reduction treatments on fungal sporocarp production and diversity associated with Cistus ladanifer L. ecosystems. For. Ecol. Manage. 353, 10–20 (2015).Article 

Google Scholar 
Fernandes, P. M. Scientific support to prescribed underburning in southern Europe: What do we know?. Sci. Total Environ. 630, 340–348 (2018).Article 
ADS 
CAS 

Google Scholar 
Day, N. J. et al. Wildfire severity reduces richness and alters composition of soil fungal communities in boreal forests of western Canada. Glob. Chang. Biol. 25, 2310–2324 (2019).Article 
ADS 

Google Scholar 
Salo, K., Domisch, T. & Kouki, J. Forest wildfire and 12 years of post-disturbance succession of saprotrophic macrofungi (Basidiomycota, Ascomycota). For. Ecol. Manage. 451, 117454 (2019).Article 

Google Scholar 
Zakaria, A. J. & Boddy, L. Mycelial foraging by Resinicium bicolor: Interactive effects of resource quantity, quality and soil composition. FEMS Microbiol. Ecol. 40, 135–142 (2002).Article 
CAS 

Google Scholar 
Hul, S. et al. Fungal community shifts in structure and function across a boreal forest fire chronosequence. Appl. Environ. Microbiol. 81, 7869–7880 (2015).Article 
ADS 

Google Scholar 
Vázquez-Veloso, A. et al. Prescribed burning in spring or autumn did not affect the soil fungal community in Mediterranean Pinus nigra natural forests. For. Ecol. Manage. 512, 120161 (2022).Article 

Google Scholar 
Lindahl, B. D. et al. Spatial separation of litter decomposition and mycorrhizal nitrogen uptake in a boreal forest. New Phytol. 173, 611–620 (2007).Article 
CAS 

Google Scholar 
Salomón, R., Rodríguez-Calcerrada, J., González-Doncel, I., Gil, L. & Valbuena-Carabaña, M. On the general failure of coppice conversion into high forest in Quercus pyrenaica stands: A genetic and physiological approach. Folia Geobot. 52, 101–112 (2017).Article 

Google Scholar 
Williams, R. J., Hallgren, S. W. & Wilson, G. W. T. Frequency of prescribed burning in an upland oak forest determines soil and litter properties and alters the soil microbial community. For. Ecol. Manage. 265, 241–247 (2012).Article 

Google Scholar 
Semenova-Nelsen, T. A., Platt, W. J., Patterson, T. R., Huffman, J. & Sikes, B. A. Frequent fire reorganizes fungal communities and slows decomposition across a heterogeneous pine savanna landscape. New Phytol. 224, 916–927 (2019).Article 

Google Scholar 
Oliver, A. K., Callaham, M. A. & Jumpponen, A. Soil fungal communities respond compositionally to recurring frequent prescribed burning in a managed southeastern US forest ecosystem. For. Ecol. Manage. 345, 1–9 (2015).Article 

Google Scholar 
Sanz-Benito, I., Mediavilla, O., Casas, A., Oria-de-Rueda, J. A. & Martín-Pinto, P. Effects of fuel reduction treatments on the sporocarp production and richness of a Quercus/Cistus mixed system. For. Ecol. Manage. 503, 119798 (2022).Article 

Google Scholar 
Santos-Silva, C., Gonçalves, A. & Louro, R. Canopy cover influence on macrofungal richness and sporocarp production in montado ecosystems. Agrofor. Syst. 82, 149–159 (2011).Article 

Google Scholar 
Lin, W. R. et al. The impacts of thinning on the fruiting of saprophytic fungi in Cryptomeria japonica plantations in central Taiwan. For. Ecol. Manage. 336, 183–193 (2015).Article 

Google Scholar 
Aragón, G., López, R. & Martínez, I. Effects of Mediterranean dehesa management on epiphytic lichens. Sci. Total Environ. 409, 116–122 (2010).Article 
ADS 

Google Scholar 
Hämäläinen, A., Kouki, J. & Lohmus, P. The value of retained Scots pines and their dead wood legacies for lichen diversity in clear-cut forests: The effects of retention level and prescribed burning. For. Ecol. Manage. 324, 89–100 (2014).Article 

Google Scholar 
Schimmel, J. & Granstrom, A. Fire severity and vegetation response in the boreal Swedish. Ecol. Soc. Am. 77, 1436–1450 (1996).
Google Scholar 
Hinojosa, M. B., Albert-Belda, E., Gómez-Muñoz, B. & Moreno, J. M. High fire frequency reduces soil fertility underneath woody plant canopies of Mediterranean ecosystems. Sci. Total Environ. 752, 141877 (2021).Article 
ADS 
CAS 

Google Scholar 
Clemmensen, K. E. et al. Carbon sequestration is related to mycorrhizal fungal community shifts during long-term succession in boreal forests. New Phytol. 205, 1525–1536 (2015).Article 
CAS 

Google Scholar 
Tedersoo, L. et al. Disentangling global soil fungal diversity. Science 346, 1052–1053 (2014).Article 

Google Scholar 
Adamo, I. et al. Sampling forest soils to describe fungal diversity and composition. Which is the optimal sampling size in Mediterranean pure and mixed pine oak forests?. Fungal Biol. https://doi.org/10.1016/j.funbio.2021.01.005 (2021).Article 

Google Scholar 
Tedersoo, L. et al. Regional-scale in-depth analysis of soil fungal diversity reveals strong pH and plant species effects in northern Europe. Front. Microbiol. 11, 1953 (2020).Article 

Google Scholar 
Peay, K., Garbelotto, M. & Bruns, T. Evidence of dispersal limitation in soil microorganisms: Isolation reduces species richness on mycorrhizal tree islands. Ecology 91, 3631–3640 (2010).Article 

Google Scholar 
Koivula, M. & Vanha-Majamaa, I. Experimental evidence on biodiversity impacts of variable retention forestry, prescribed burning, and deadwood manipulation in Fennoscandia. Ecol. Process. 9, 1–22 (2020).Article 

Google Scholar 
Fox, S. et al. Fire as a driver of fungal diversity—A synthesis of current knowledge. Mycologia 00, 1–27 (2022).
Google Scholar 
Raudabaugh, D. B. et al. Where are they hiding? Testing the body snatchers hypothesis in pyrophilous fungi. Fungal Ecol. 43, 100870 (2020).Article 

Google Scholar 
Izzo, A., Canright, M. & Bruns, T. D. The effects of heat treatments on ectomycorrhizal resistant propagules and their ability to colonize bioassay seedlings. Mycol. Res. 110, 196–202 (2006).Article 

Google Scholar 
Kipfer, T., Moser, B., Egli, S., Wohlgemuth, T. & Ghazoul, J. Ectomycorrhiza succession patterns in Pinus sylvestris forests after stand-replacing fire in the Central Alps. Oecologia 167, 219–228 (2011).Article 
ADS 

Google Scholar 
Glassman, S. I., Levine, C. R., Dirocco, A. M., Battles, J. J. & Bruns, T. D. Ectomycorrhizal fungal spore bank recovery after a severe forest fire: Some like it hot. ISME J. 10, 1228–1239 (2016).Article 

Google Scholar 
Buscardo, E. et al. Impact of wildfire return interval on the ectomycorrhizal resistant propagules communities of a Mediterranean open forest. Fungal Biol. 114, 628–636 (2010).Article 

Google Scholar 
Pringle, A., Vellinga, E. & Peay, K. The shape of fungal ecology: Does spore morphology give clues to a species’ niche?. Fungal Ecol. 17, 213–216 (2015).Article 

Google Scholar 
Zhang, K., Cheng, X., Shu, X., Liu, Y. & Zhang, Q. Linking soil bacterial and fungal communities to vegetation succession following agricultural abandonment. Plant Soil 431, 19–36 (2018).Article 
ADS 
CAS 

Google Scholar 
Xiang, X. et al. Arbuscular mycorrhizal fungal communities show low resistance and high resilience to wildfire disturbance. Plant Soil 397, 347–356 (2015).Article 
CAS 

Google Scholar 
Dove, N. C., Klingeman, D. M., Carrell, A. A., Cregger, M. A. & Schadt, C. W. Fire alters plant microbiome assembly patterns: Integrating the plant and soil microbial response to disturbance. New Phytol. 230, 2433–2446 (2021).Article 
CAS 

Google Scholar 
Fernandes, P. M. Fire-smart management of forest landscapes in the Mediterranean basin under global change. Landsc. Urban Plan. 110, 175–182 (2013).Article 

Google Scholar 
Fontúrbel, M. T., Fernández, C. & Vega, J. A. Prescribed burning versus mechanical treatments as shrubland management options in NW Spain: Mid-term soil microbial response. Appl. Soil Ecol. 107, 334–346 (2016).Article 

Google Scholar 
Geml, J. et al. Large-scale fungal diversity assessment in the Andean Yungas forests reveals strong community turnover among forest types along an altitudinal gradient. Mol. Ecol. 23, 2452–2472 (2014).Article 
CAS 

Google Scholar 
Chu, H. et al. Effects of slope aspects on soil bacterial and arbuscular fungal communities in a boreal forest in China. Pedosphere 26, 226–234 (2016).Article 

Google Scholar 
Geml, J. Soil fungal communities reflect aspect-driven environmental structuring and vegetation types in a Pannonian forest landscape. Fungal Ecol. 39, 63–79 (2019).Article 

Google Scholar 
Castaño, C. et al. Soil microclimate changes affect soil fungal communities in a Mediterranean pine forest. New Phytol. 220, 1211–1221 (2018).Article 

Google Scholar 
Collado, E. et al. Mushroom productivity trends in relation to tree growth and climate across different European forest biomes. Sci. Total Environ. 689, 602–615 (2019).Article 
ADS 
CAS 

Google Scholar 
Ihrmark, K., Bödeker, I. & Cruz-Martinez, K. New primers to amplify the fungal ITS2 region—Evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677 (2012).Article 
CAS 

Google Scholar 
White, T., Bruns, S., Lee, S. & Taylor, J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In PCR Protocols: A Guide to Methods and Applications (eds Innis, M. A. et al.) 315–322 (Academic Press, 1990).
Google Scholar 
Kent, M. Vegetation Description and Data Analysis: A Practical Approach (Wiley, 2011).

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).Article 
CAS 

Google Scholar 
Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277 (2013).Article 

Google Scholar 
Abarenkov, K. et al. Plutof-a web based workbench for ecological and taxonomic research, with an online implementation for fungal its sequences. Evol. Bioinforma. 2010, 189–196 (2010).
Google Scholar 
Põlme, S. et al. FungalTraits: A user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105, 1–16 (2020).Article 

Google Scholar 
Agerer, R. Fungal relationships and structural identity of their ectomycorrhizae. Mycol. Prog. 5, 67–107 (2006).Article 

Google Scholar 
Tedersoo, L. & Smith, M. E. Lineages of ectomycorrhizal fungi revisited: Foraging strategies and novel lineages revealed by sequences from belowground. Fungal Biol. Rev. 27, 83–99 (2013).Article 

Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & Team, R. C. Nlme: Linear and Nonlinear Mixed Effects Models. R Package Version 3.1–128. http://CRAN.R-project.org/package=nlme (2016).Chao, A. & Chiu, C. Species richness: Estimation and comparison. Wiley StatsRef https://doi.org/10.1002/9781118445112.stat03432.pub2 (2016).Article 

Google Scholar 
Chiu, C. H., Wang, Y. T., Walther, B. A. & Chao, A. An improved nonparametric lower bound of species richness via a modified good-turing frequency formula. Biometrics 70, 671–682 (2014).Article 
MathSciNet 
MATH 

Google Scholar 
Oksanen, J. et al. Vegan: Community Ecology Package. R package version 2.4–2. https://CRAN.R-project.org/package=vegan. (2017).Oksanen, J., Blanchet, F., Kindt, R. & Al, E. vegan: Community Ecology Package. R package version 2.3–0. (2015). More

Research Group for Genomic Epidemiology, Technical University of Denmark, Kgs, Lyngby, DenmarkPatrick Munk, Christian Brinch, Frederik Duus Møller, Thomas N. Petersen, Rene S. Hendriksen, Anne Mette Seyfarth, Jette S. Kjeldgaard, Christina Aaby Svendsen & Frank M. AarestrupCentre for Immunity, Infection and Evolution, University of Edinburgh, Edinburgh, UKBram van Bunnik & Mark WoolhouseCentre for Antibiotic Resistance Research (CARe), University of Gothenburg, Gothenburg, SwedenFanny Berglund & D. G. 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RahubeUniversidade Federal de Minas Gerais, Belo Horizonte, BrazilCarlos Alberto Tagliati & Larissa Camila Ribeiro de SouzaOswaldo Cruz Institute, Rio de Janeiro, BrazilDalia RodriguesVale Institute of Technology, Belιm, PA, BrazilGuilherme OliveiraNational Center of Infectious and Parasitic Diseases, Sofia, BulgariaIvan IvanovUniversity of Ouagadougou, Ouagadougou, Burkina FasoBonkoungou Isidore Juste & Traoré OumarInstitut Pasteur du Cambodge, Phnom Penh, CambodiaThet Sopheak & Yith VuthyCentre Pasteur du Cameroun, Yaoundι, CameroonAntoinette Ngandjio, Ariane Nzouankeu & Ziem A. Abah Jacques OlivierUniversity of Regina, Regina, CanadaChristopher K. YostEau Terre Environnement Research Centre (INRS-ETE), Quebec City G1K 9A9, Canada and Indian Institute of Technology, Jammu, IndiaPratik KumarEau Terre Environnement Research Centre (INRS-ETE), Quebec City G1K 9A9, Canada and Lassonde School of Enginerring, York University, Toronto, CanadaSatinder Kaur BrarUniversity of N’Djamena, N’Djamena, ChadDjim-Adjim TaboEscuela de Medicina Veterinaria, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, ChileAiko D. AdellInstitute of Public Health, Santiago, ChileEsteban Paredes-Osses & Maria Cristina MartinezUniversidad Catolica del Maule, Centro de Biotecnología de los Recursos Naturales, Facultad de Ciencias Agrarias y Forestales, Talca, ChileSara Cuadros-OrellanaGuangdong Provincial Center for Disease Control and Prevention, Guangzhou, ChinaChangwen Ke, Huanying Zheng & Li BaishengThe Hong Kong Polytechnic University, Hong Kong, ChinaLok Ting Lau & Teresa ChungShantou University Medical College, Shantou, ChinaXiaoyang JiaoNanjing University of Information Science and Technology, Nanjing, ChinaYongjie YuCenter for Disease Control and Prevention of Henan province, Zhengzhou, ChinaZhao JiaYongColombian Integrated Program for Antimicrobial Resistance Surveillance – Coipars, CI Tibaitatα, Corporaciσn Colombiana de Investigaciσn Agropecuaria (AGROSAVIA), Tibaitatα – Mosquera, Cundinamarca, ColombiaJohan F. Bernal Morales, Maria Fernanda Valencia & Pilar Donado-GodoyInstitut Pasteur de Côte d’Ivoire, Abidjan, Côte d’IvoireKalpy Julien CoulibalyUniversity of Zagreb, Zagreb, CroatiaJasna HrenovicAndrija Stampar Teaching Institute of Public Health, Zagreb, CroatiaMatijana JergovićVeterinary Research Institute, Brno, Czech RepublicRenáta KarpíškováCentre de Recherche en Sciences Naturelles de Lwiro (CRSN-LWIRO), Bukavu, Democratic Republic of CongoZozo Nyarukweba DeogratiasBIOFOS A/S, Copenhagen K, DenmarkBodil ElsborgTechnical University of Denmark, Kgs., Lyngby, DenmarkLisbeth Truelstrup Hansen & Pernille Erland JensenSuez Canal University, Ismailia, EgyptMohamed AbouelnagaUniversity of Sadat City, Sadat City, EgyptMohamed Fathy SalemMinistry of Health, Environmental Microbiology, Tallinn, EstoniaMarliin KoolmeisterAddis Ababa University, Addis Ababa, EthiopiaMengistu Legesse & Tadesse EgualeUniversity of Helsinki, Helsinki, FinlandAnnamari HeikinheimoFrench Institute Search Pour L’exploitation De La Mer (Ifremer), Nantes, FranceSoizick Le Guyader & Julien SchaefferInstituto Nacional de Investigaciσn en Salud Pϊblica-INSPI (CRNRAM), Galαpagos, Quito, EcuadorJose Eduardo VillacisNational Public Health Laboratories, Ministry of Health and Social Welfare, Kotu, GambiaBakary SannehNational Center for Disease Control and Public Health, Tbilisi, GeorgiaLile MalaniaRobert Koch Institute, Berlin, GermanyAndreas Nitsche & Annika BrinkmannTechnische Universitδt Dresden, Institute of Hydrobiology, Dresden, GermanySara Schubert, Sina Hesse & Thomas U. BerendonkUniversity for Development Studies, Tamale, GhanaCourage Kosi Setsoafia SabaUniversity of Ghana, Accra, GhanaJibril MohammedKwame Nkrumah University of Science and Technology, Kumasi, PMB, GhanaPatrick Kwame FegloCouncil for Scientific and Industrial Research Water Research Institute, Accra, GhanaRegina Ama BanuVeterinary Research Institute of Thessaloniki, Hellenic Agricultural Organisation-DEMETER, Thermi, GreeceCharalampos KotzamanidisAthens Water Supply and Sewerage Company (EYDAP S.A.), Athens, GreeceEfthymios LytrasUniversidad de San Carlos de Guatemala, Guatemala City, GuatemalaSergio A. LickesSemmelweis University, Institute of Medical Microbiology, Budapest, HungaryBela KocsisUniversity of Veterinary Medicine, Budapest, HungaryNorbert SolymosiUniversity of Iceland, Reykjavνk, IcelandThorunn R. ThorsteinsdottirCochin University of Science and Technology, Cochin, IndiaAbdulla Mohamed HathaKasturba Medical College, Manipal, IndiaMamatha BallalApollo Diagnostics, Mangalore, IndiaSohan Rodney BangeraShiraz University of Medical Sciences, Shiraz, IranFereshteh FaniShahid Beheshti University of Medical Sciences, Tehran, IranMasoud AlebouyehNational University of Ireland Galway, Galway, IrelandDearbhaile Morris, Louise O’Connor & Martin CormicanBen Gurion University of the Negev and Ministry of Health, Beer-Sheva, IsraelJacob Moran-GiladIstituto Zooprofilattico Sperimentale del Lazio e della Toscana, Rome, ItalyAntonio Battisti, Elena Lavinia Diaconu & Patricia AlbaCNR – Water Research Institute, Verbania, ItalyGianluca Corno & Andrea Di CesareNational Institute of Infectious Diseases, Tokyo, JapanJunzo Hisatsune, Liansheng Yu, Makoto Kuroda, Motoyuki Sugai & Shizuo KayamaNational Center of Expertise, Taldykorgan, KazakhstanZeinegul ShakenovaMount Kenya University, Thika, KenyaCiira KiiyukiaKenya Medical Research Institute, Nairobi, KenyaEric Ng’enoUniversity of Prishtina “Hasan Prishtina” & National Institute of Public Health of Kosovo, Pristina, KosovoLul RakaKuwait Institute for Scientific Research, Kuwait City, KuwaitKazi Jamil, Saja Adel Fakhraldeen & Tareq AlaatiInstitute of Food Safety, Riga, LatviaAivars Bērziņš, Jeļena Avsejenko, Kristina Kokina, Madara Streikisa & Vadims BartkevicsAmerican University of Beirut, Beirut, LebanonGhassan M. MatarCentral Michigan University & Michigan Health Clinics, Saginaw, MI, USAZiad DaoudNational Food and Veterinary Risk Assessment Institute, Vilnius, LithuaniaAsta Pereckienė & Ceslova Butrimaite-AmbrozevicieneLuxembourg Institute of Science and Technology, Belvaux, LuxembourgChristian PennyInstitut Pasteur de Madagascar, Antananarivo, MadagascarAlexandra Bastaraud & Jean-Marc CollardUniversity of Antananarivo, Centre d’Infectiologie Charles Mιrieux, Antananarivo, MadagascarTiavina Rasolofoarison, Luc Hervé Samison & Mala Rakoto AndrianariveloUniversity of Malawi, Blantyre, MalawiDaniel Lawadi BandaMalaysian Genomics Resource Centre Berhad, Kuala Lumpur, MalaysiaArshana AminAIMST University, COMBio, Kedah, MalaysiaHeraa Rajandas & Sivachandran ParimannanWater Services Corporation, Luqa, MaltaDavid SpiteriEnvironmental Health Directorate, St. Venera, MaltaMalcolm Vella HaberUniversity of Mauritius, Reduit, MauritiusSunita J. SantchurnInstitute for Public Health Montenegro, Podgorica, MontenegroAleksandar Vujacic & Dijana DjurovicInstitut Pasteur du Maroc, Casablanca, MoroccoBrahim Bouchrif & Bouchra KarraouanCentro de Investigaηγo em Saϊde de Manhiηa (CISM), Maputo, MozambiqueDelfino Carlos VubilAgriculture and Forestry University, Kathmandu, NepalPushkar PalNational Institute for Public, Health and the Environment (RIVM), Bilthoven, The NetherlandsHeike Schmitt & Mark van PasselUniversity of Otago, Dunedin, New ZealandGert-Jan Jeunen & Neil GemmellUniversity of Otago, Christchurch, New ZealandStephen T. ChambersUniversity of Central America, Managua, NicaraguaFania Perez Mendoza & Jorge Huete-PιrezUniversidad Nacional Autσnoma de Nicaragua-Leσn, Leσn, NicaraguaSamuel VilchezUniversity of Ilorin, Ilorin, NigeriaAkeem Olayiwola Ahmed, Ibrahim Raufu Adisa & Ismail Ayoade OdetokunUniversity of Ibadan, Ibadan, NigeriaKayode FashaeNorwegian Institute of Public Health, Oslo, NorwayAnne-Marie Sørgaard & Astrid Louise WesterVEAS, Slemmestad, NorwayPia Ryrfors & Rune HolmstadUniversity of Agriculture, Faisalabad, PakistanMashkoor MohsinAga Khan University, Karachi, PakistanRumina Hasan & Sadia ShakoorLaboratorio Central de Salud Publica, Asuncion, ParaguayNatalie Weiler Gustafson & Claudia Huber SchillInstituto Nacional de Salud, Lima, PeruMaria Luz Zamudio RojasUniversidad de Piura, Piura, PeruJorge Echevarria Velasquez & Felipe Campos YauceWHO Environmental and Occupational Health, Manila, PhilippinesBonifacio B. MagtibayMaynilad Water Services, Inc., Quezon City, PhilippinesKris Catangcatang & Ruby SibuloNational Veterinary Research Institute, Pulawy, PolandDariusz WasylUniversidade Catσlica Portuguesa, CBQF – Centro de Biotecnologia e Quνmica Fina – Laboratσrio Associado, Escola Superior de Biotecnologia, Porto, PortugalCelia Manaia & Jaqueline RochaAguas do Tejo Atlantico, Lisboa, PortugalJose Martins & Pedro ÁlvaroGwangju Institute of Science and Technology, Gwangju, Republic of KoreaDoris Di Yoong Wen, Hanseob Shin & Hor-Gil HurKorea Advanced Institute of Science and Technology, Daejeon, Republic of KoreaSukhwan YoonInstitute of Public Health of the Republic of North Macedonia, Skopje, Republic of North MacedoniaGolubinka Bosevska & Mihail KochubovskiState Medical and Pharmaceutical University, Chișinău, Republic of MoldovaRadu CojocaruNational Agency for Public Health, Chișinău, Republic of MoldovaOlga BurduniucKing Abdullah University of Science and Technology, Thuwal, Saudi ArabiaPei-Ying HongUniversity of Edinburgh, Edinburgh, Scotland, UKMeghan Rose PerryInstitut Pasteur de Dakar, Dakar, SenegalAmy GassamaInstitute of Veterinary Medicine of Serbia, Belgrade, SerbiaVladimir RadosavljevicNanyang Technological University, Singapore, SingaporeMoon Y. F. Tay, Rogelio Zuniga-Montanez & Stefan WuertzPublic Health Authority of the Slovak Republic, Bratislava, SlovakiaDagmar Gavačová, Katarína Pastuchová & Peter TruskaNational Laboratory of Health, Environment and Food, Ljubljana, SloveniaMarija TrkovIndependent consultant, Johannesburg, South AfricaKaren KeddyDaspoort Waste Water Treatment Works, Pretoria, South AfricaKerneels EsterhuyseKorea Advanced Institute of Science and Technology, Daejeon, South KoreaMin Joon SongSchool of Veterinary Sciences, Lugo, SpainMarcos Quintela-BalujaLabaqua, Santiago de Compostela, SpainMariano Gomez LopezIRTA, Centre de Recerca en Sanitat Animal (CReSA, IRTA-UAB), Campus de la Universitat Autonoma de Barcelona, Bellaterra, SpainMarta Cerdà-CuéllarUniversity of Kelaniya, Ragama, Sri LankaR. R. D. P. Perera, N. K. B. K. R. G. W. Bandara & H. I. PremasiriMedical Research Institute, Colombo, Sri LankaSujatha PathirageCaribbean Public Health Agency, Catries, Saint LuciaKareem CharlemagneThe Sahlgrenska Academy at the University of Gothenburg, Gothenburg, SwedenCarolin RutgerssonSwedish University of Agricultural Sciences, Uppsala, SwedenLeif Norrgren & Stefan ÖrnFederal Food Safety and Veterinary Office (FSVO), Bern, SwitzerlandRenate BossAra Region Bern AG, Herrenschwanden, SwitzerlandTanja Van der HeijdenCenters for Disease Control, Taipei, TaiwanYu-Ping HongKilimanjaro Clinical Research Institute, Moshi, TanzaniaHappiness Houka KumburuSokoine University of Agriculture, Morogoro, TanzaniaRobinson Hammerthon MdegelaFaculty of Science and Technology, Suratthani Rajabhat University, Surat Thani, ThailandKaknokrat ChonsinFaculty of Public Health, Mahidol University, Bangkok, ThailandOrasa SuthienkulFaculty of Medicine Siriraj Hospital, Bangkok, ThailandVisanu ThamlikitkulNational Institute for Public Health and the Environment (RIVM), Bilthoven, NetherlandsAna Maria de Roda HusmanNational Institute of Hygiene, Lomι, TogoBawimodom BidjadaAgence de Mιdecine Prιventive, Dapaong, TogoBerthe-Marie Njanpop-LafourcadeDivision of Integrated Surveillance of Health Emergencies and Response, Lomι, TogoSomtinda Christelle Nikiema-PessinabaPublic Health Institution of Turkey, Ankara, TurkeyBelkis LeventHatay Mustafa Kemal University, Hatay, TurkeyCemil KurekciMakerere University, Kampala, UgandaFrancis Ejobi & John Bosco KaluleAbu Dhabi Public Health Center, Abu Dhai, United Arab EmiratesJens ThomsenDubai municipality, WWTP Al Aweer, Dubai, UAEOuidiane ObaidiRashid Hospital, Dubai, UAELaila Mohamed JassimNorthumbrian Water, Northumbria House, Abbey Road, Pity Me, Durham, UKAndrew MooreUniversity of Exeter Medical School, Cornwall, UKAnne Leonard, Lihong Zhang & William H. GazeNewcastle University, Newcastle upon Tyne, UKDavid W. Graham & Joshua T. BunceBrightwater Treatment Plant, Woodinville, WA, USABrett LeforDepartment of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USADrew Capone & Joe BrownUniversity of North Carolina, Chapel Hill, USAEmanuele Sozzi & Mark D. SobseyUniversity of Washington, Seattle, WA, USAJohn Scott Meschke, Nicola Koren Beck, Pardi Sukapanpatharam & Phuong TruongBaylor University, Waco, USAMichael DavisColumbia Boulevard WWTP, Portland, USARonald LilienthalEastern Illinois University, Charleston, USASanghoon KangThe Ohio State University, Columbus Ohio, USAThomas E. WittumLaboratorio Tecnolσgico del Uruguay, Montevideo, UruguayNatalia Rigamonti & Patricia BaklayanInstitute of Public Health in Ho Chi Minh City, Ho Chi Minh, VietnamChinh Dang Van, Doan Minh Nguyen Tran & Nguyen Do PhucUniversity of Zambia, Lusaka, ZambiaGeoffrey Kwenda More

Pearson, P. N. & Palmer, M. R. Atmospheric carbon dioxide concentrations over the past 60 million years. Nature 406, 695–699 (2000).Article 
ADS 
CAS 

Google Scholar 
Wang, P. et al. The genome evolution and domestication of tropical fruit mango. Genome Biol. 21, 60 (2020).Article 

Google Scholar 
Yang, H. et al. Advances in the regulatory mechanisms of pollen response to heat stress in crops. Chin. Bull. Bot. 54(2), 157–167 (2019).CAS 

Google Scholar 
Liang, Q. Z. et al. Transcriptome and metabolome analyses reveal the involvement of multiple pathways in flowering intensity in mango. Front. Plant Sci. 13, 933923 (2022).Article 

Google Scholar 
Ranasinghe, C. S., Waidyarathna, K. P., Pradeep, A. P. C. & Meneripitiya, M. S. K. Approach to screen coconut varieties for high temperature tolerance by in-vitro pollen germination. COCOS. 19, 01–11 (2010).
Google Scholar 
Das, S., Krishnan, P., Nayak, M. & Ramakrishnan, B. High temperature stress effects on pollens of rice (Oryza sativa L.) genotypes. Environ. Exp. Bot. 101, 36–46 (2014).Article 

Google Scholar 
Balasubramanian, S., Sureshkumar, S., Lempe, J. & Weigel, D. Potent induction of Arabidopsis thaliana flowering by elevated growth temperature. PLoS Genet. 2(7), e106 (2006).Article 

Google Scholar 
Sakata, T., Takahashi, H., Nishiyama, I. & Higashitani, A. Effects of high temperature on the development of pollen mother cells and microspores in Barley Hordeum vulgare L.. J. Plant Res. 113(4), 395–402 (2000).Article 

Google Scholar 
Hedhly, A., Hormaza, J. I. & Herrero, M. The effect of temperature on pollen germination, pollen tube growth, and stigmatic receptivity in peach. Plant Biol. 7(5), 476–483 (2005).Article 
CAS 

Google Scholar 
Pirlak, L. The effects of temperature on pollen germination and pollen tube growth of apricot and sweet cherry. Gartenbauwissenschaft 67(2), 61–64 (2002).
Google Scholar 
Koti, S., Reddy, K. R., Reddy, V. R., Kakani, V. G. & Zhao, D. Interactive effects of carbon dioxide, temperature, and ultraviolet-B radiation on soybean (Glycine max L.) flower and pollen morphology, pollen production, germination, and tube lengths. J. Exp. Bot. 56(412), 725–736 (2004).Article 

Google Scholar 
Pham, V. T., Herrero, M. & Hormaza, J. I. Effect of temperature on pollen germination and pollen tube growth in longan (Dimocarpus longan Lour.). Sci. Hort. 197, 470–475 (2015).Article 

Google Scholar 
Meehl, T. G. A. & Tebaldi, C. More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305, 994–997 (2004).Article 
ADS 
CAS 

Google Scholar 
Reddy, K. R., Hodges, H. F. & Reddy, V. R. Temperature effects on cotton fruit retention. Agron. J. 84, 26–30 (1992).Article 

Google Scholar 
Reddy, K. R., Reddy, V. R. & Hodges, H. F. Effects of temperature on early season cotton growth and development. Agron. J. 84, 229–237 (1992).Article 

Google Scholar 
Stainforth, D. et al. Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433, 403–406 (2005).Article 
ADS 
CAS 

Google Scholar 
Liu, Z., Yuan, Y., Liu, S., Yu, X. & Rao, L. Screening for high temperature tolerant cotton cultivars by testing in vitro pollen germination, pollen tube growth and boll retention. J. Integr. Plant Biol. 48, 706–714 (2006).Article 

Google Scholar 
Kakani, V. G., Prasad, P. V. V., Craufurd, P. Q. & Wheeler, T. R. Response of in vitro pollen germination and pollen tube growth of groundnut (Arachis hypogaea L.) genotypes to temperature. Plant Cell Environ. 25, 1651–1661 (2002).Article 

Google Scholar 
Kakani, V. G. et al. Differences in in vitro pollen germination and pollen tube growth of cotton cultivars in response to high temperature. Ann. Bot. 96(1), 59–67 (2005).Article 
CAS 

Google Scholar 
Hebbar, K. B. et al. Differences in in vitro pollen germination and pollen tube growth of coconut (Cocos nucifera L.) genotypes in response to high temperature stress. Environ. Ex. Bot. 153, 35–44 (2018).Article 

Google Scholar 
Aloni, B., Peet, M., Pharr, M. & Karmi, L. The effect of high temperaturare and high atmospheric CO2 on carbohydrate changes in bell pepper (Capsicum annuum) pollen in relation to its germination. Physiol. Plant 112, 505–512 (2001).Article 
CAS 

Google Scholar 
Dai, Q., Shaobing, P., Chavez, A. Q. & Vergara, B. S. Intraspecific responses of 188 rice cultivars to enhanced UVB radiation. Environ. Exp. Bot. 34(4), 433–442 (1994).Article 

Google Scholar 
Hepler, P. K., Vidali, L. & Cheung, A. Y. Polarized cell growth in higher plants. Annu. Rev. Cell Dev. Biol. 17(1), 159–187 (2001).Article 
CAS 

Google Scholar 
Prado, A. M., Porterfield, D. M. & Feijo, J. A. Nitric oxide is involved in growth regulation and re-orientation of pollen tubes. Development 131(11), 2707–2714 (2004).Article 
CAS 

Google Scholar 
Potocky, M., Jones, M. A., Bezvoda, R., Smirnoff, N. & Zarsky, V. Reactive oxygen species produced by NADPH oxidase are involved in pollen tube growth. New Phytol. 174(4), 742–751 (2007).Article 
CAS 

Google Scholar 
Lassig, R., Gutermuth, T., Bey, T. D., Konrad, K. R. & Romeis, T. Pollen tube NAD (P)H oxidases act as a speed control to dampen growth rate oscillations during polarized cell growth. Plant J. 78(1), 94–106 (2014).Article 
CAS 

Google Scholar 
McInnis, S. M., Desikan, R., Hancock, J. T. & Hiscock, S. J. Production of reactive oxygen species and reactive nitrogen species by angiosperm stigmas and pollen: potential signalling crosstalk?. New Phytol. 172(2), 221–228 (2006).Article 
CAS 

Google Scholar 
Duan, Q. et al. Reactive oxygen species mediate pollen tube rupture to release sperm for fertilization in Arabidopsis. Nat. Commun. 5, 3129 (2014).Article 
ADS 

Google Scholar 
You, J. & Chan, Z. ROS regulation during abiotic stress responses in crop plants. Front Plant Sci. 6, 1092 (2015).Article 

Google Scholar 
Apel, K. & Hirt, H. Reactive oxygen species: Metabolism, oxidative stress, and signal transduction. Annu. Rev. Plant Biol. 55, 373–399 (2004).Article 
CAS 

Google Scholar 
Pandhair, V. & Sekhon, B. S. Reactive oxygen species and antioxidants in plants: An overview. J. Plant Biochem. Biot. 15(2), 71–78 (2006).Article 
CAS 

Google Scholar 
Sharma, P., Jha, A. B., Dubey, R. S. & Pessarakli, M. Reactive oxygen species, oxidative damage, and antioxidative defense mechanism in plants under stressful conditions. J. Bot. https://doi.org/10.1155/2012/217037 (2012).Article 

Google Scholar 
Luo, C. et al. Construction of a high-density genetic map based on large-scale marker development in mango using specific-locus amplified fragment sequencing (SLAF-seq). Front. Plant Sci. 7, 1310 (2016).Article 

Google Scholar 
IPCC. IPCC Fourth Assessment Report. http://www.ipcc.ch/. Accessed 15 Jan 2010 (2007)Reddy, K. R. & Kakani, V. G. Screening Capsicum species of different origins for high temperature tolerance by in vitro pollen germination and pollen tube length. Sci. Hort. 112, 130–135 (2007).Article 

Google Scholar 
Armendariz, B. H. C., Oropeza, C., Chan, J. L., Maust, B., Aguilar, C. C. C., & Saenz, L. Pollen Fertility and Female Flower Anatomy of Micropropagated Coconut Palms. 373–378 (Revista Fitotecnia Mexicana, Sociedad Mexicana de Fitogenetica, A C. Mexico, 2006)Binelli, G., Manincor, E. V. & Ottaviano, E. Temperature effects on pollen germination and pollen tube growth in maize. Genetica Agraria 39, 269–281 (1985).
Google Scholar 
Matlob, A. N. & Kelly, W. C. Effect of high temperature on pollen tube growth of snake melon and cucumber. J. Am. Soc. Hortic. Sci. 98, 296–300 (1973).Article 

Google Scholar 
Zhou, Q. F. An Empirical Study on the Evolution of Mango Production in China. 1–53 (Hainan University, 2017)He, L. et al. Grafting trial on mango varieties in hot-dry region Jinsha River. Subtropic. Agric. Res. 6(3), 21–24 (2010) (in Chinese with English abstract).
Google Scholar 
Gong, D. Y., Liu, Q. G., Zhang, Y. & Zhang, X. B. Studies on adaptability and application of mango varieties in south subtropical regions of Guizhou. Acta Agricult. Jiangxi 24(7), 28–31 (2012) (in Chinese).CAS 

Google Scholar 
Liu, Z. T. Performance and cultivation techniques of coconut mango in Panxi hot area. Trop. Agricult. Guangxi 3(110), 11–12 (2007).
Google Scholar 
Gajanayake, B., Trader, B. W., Reddy, K. R. & Harkess, R. L. Screening ornamental pepper cultivars for temperature tolerance using pollen and physiological parameters. Hortic. Sci. 46, 878–884 (2011).
Google Scholar 
Salem, M. A., Kakani, V. G., Koti, S. & Reddy, K. R. Pollen-based screening of soybean genotypes for high temperatures. Crop Sci. 47, 219–231 (2007).Article 

Google Scholar 
Young, L. W., Wilen, R. W. & Bonham-Smith, P. C. High temperature stress of Brassica napus during flowering reduces micro- and megagametophyte fertility, induces fruit abortion, and disrupts seed production. J. Exp. Bot. 55, 485–495 (2004).Article 
CAS 

Google Scholar 
Kafizadeh, N., Carapetian, J. & Kalantari, K. M. Effects of heat stress on pollen viability and pollen tube growth in pepper. Res. J. Biol. Sci. 3, 1159–1162 (2008).
Google Scholar 
Pressman, E., Peet, M. M. & Pharr, D. M. The effect of heat stress on tomato pollen characteristics is associated with changes in carbohydrate concentration in the developing anthers. Ann. Bot. 90, 613–636 (2002).Article 

Google Scholar 
Sukhvibul, N. et al. Effect of temperature on pollen germination and pollen tube growth of four cultivars of mango (Mangifera indica L.). J. Hortic. Sci. Biotechnol. 75(2), 214–222 (2000).Article 

Google Scholar 
Koubouris, G. C., Metzidakis, I. T. & Vasilakakis, M. D. Impact of temperature on olive (Olea europaea L.) pollen performance in relation to relative humidity and genotype. Environ. Exp. Bot. 67(1), 209–214 (2009).Article 

Google Scholar 
Huang, J. H. et al. Effects of low temperatures on sexual reproduction of ‘Tainong 1’ mango (Mangifera indica). Sci. Horticult. 126(2), 109–119 (2010) (in Chinese with English abstract).Article 

Google Scholar 
Çetinbaş-Gença, A., Cai, G., Vardara, F. & Ünal, M. Differential effects of low and high temperature stress on pollen germination and tube length of hazelnut (Corylus avellana L.) genotypes. Sci. Horticult. 255, 61–69 (2019).Article 

Google Scholar 
Sorkheh, K. et al. Interactive effects of temperature and genotype on almond (Prunus dulcis L.) pollen germination and tube length. Sci. Hortic. 227, 162–168 (2018).Article 

Google Scholar 
Wang, L. et al. Analysis of common errors of custom enzyme activity units and suggestions for standardized use. Chin. J. Sci. Technol. 24(5), 1009–1011 (2013).
Google Scholar 
Wang, W. et al. Combined cytological and transcriptomic analysis reveals a nitric oxide signaling pathway involved in cold-inhibited Camellia sinensis pollen tube growth. Front. Plant Sci. 7, 456 (2016).
Google Scholar 
He, J. M., Bai, X. L., Wang, R. B., Cao, B. & She, X. P. The involvement of nitric oxide in ultraviolet-B-inhibited pollen germination and tube growth of Paulownia tomentosa in vitro. Physiol. Plant 131(2), 273–282 (2007).CAS 

Google Scholar 
Gao, Y. et al. Mitochondrial dysfunction mediated by cytoplasmic acidification results in pollen tube growth cessation in Pyrus pyrifolia. Physiol. Plant 153(4), 603–615 (2015).Article 
CAS 

Google Scholar 
Hall, A. E. Breading for heat tolerance. Plant Breed. Rev. (SAS Institute) 10, 129–168 (1999) (SAS/STAT user’s guide, version 9.2. SAS Institute, 1992).Mearns, L. O., Easterling, W., Hays, C. & Marx, D. Comparison of agricultural impacts of climate change calculated from high and low resolution climate change scenarios. Part I. The uncertainty due to spatial scale. Clim. Change. 51, 131–172 (2001).Article 

Google Scholar 
SAS Institute SAS/STAT User’s Guide, Version 9.1.3. (SAS Institute Inc., 2004).Li, H. S., Sun, Q., Zhao, S. J. & Zhang, W. H. Experiment Principle and Technology of Plant Physiology and Biochemistry (Higher Education Press, 2000).
Google Scholar 
Cai, Q. S. Plant Physiology Experiment. Vol. 4(1). 182–186 (China Agricultural University Press, 2013) (in Chinese).Jia, M. X. et al. ROS-induced oxidative stress is closely related to pollen deterioration following cryopreservation. In Vitro Cell Dev. Biol. Plant 53(4), 433–439 (2017).Article 
CAS 

Google Scholar  More

Strain assessmentWe did not detect any C-strain individual following analysis of 138 fully assembled mitochondrial DNA genomes (mitogenomes) from Australian samples. Our results, particularly that from Northern Territory, are not dissimilar to the finding of Piggott et al.56 who detected only two (i.e., 4.2%) C-strain mtCOI haplotype individuals from a much larger (i.e., n = 48) Northern Territory sample size. Proportions of C-strain to R-strain also varied significantly across the different SEA populations (Table S1) in contrast to the patterns observed in China, India, and African nations (e.g.,22,33,34,39,57). All Australian populations analysed for their corn or rice mitochondrial haplotypes via mitogenome assemblies of whole genome sequencing data therefore contrasted with the invasive populations from SEA where in some countries (e.g., Myanmar, Vietnam) FAW with the C-strain mtCOI haplotypes made up approximately 50% of the populations examined (see Table S1 for C- and R-strains mitogenome proportions, see also Fig. 1 ‘C-strain’ and ‘R-strain’ Maximum Likelihood cladograms).Figure 1Maximum Likelihood cladograms of unique Spodoptera frugiperda C-strain and R-strain partial mitochondrial genomes based on concatenation of the 13 PCGs (11,393 bp) using IQ-Tree with 1000 UFBoot replications. Individuals in clades I, II, III, and IV (C-strain) and in Clades I, II, V (R-strain) that are in the same colour scheme (i.e., green, orange, blue, or pinks) shared 100% nucleotide identity. Mitogenome haplotypes from native individuals for both C- and R-strains are in khaki green colour. Red and dark grey dots at branch nodes represent bootstrap values of 87–100% and 74–86%, respectively. Bootstrap values  Hetexp; see60) could likewise indicate recent mixing of distinct populations from SEA that suggest multiple introductions (e.g.,33,39 cf.46,47,61,62; i.e., due to a recent bottleneck from a recent western Africa founder event).Table 1 Population genetic differentiation via pairwise FST estimates between Spodoptera frugiperda populations from the invasive ranges of Africa (Uganda, Malawi, Benin), South Asia (India), East Asia (China (Cangyuan (CY), Xinping (XP), YuanJiang (YJ)), South Korea), Southeast Asia (Malaysia (Johor, Kedah, Penang States), Laos, Vietnam, Myanmar), and Pacific/Australia (Papua New Guinea (PNG), Australia—Kununurra (Western Australia, WA), Northern Territory (NT), Strathmore, Walkamin, Burdekin, Mackay (Queensland, Qld), Wee Waa (New South Wales, NSW).Full size tableThe observed heterozygosity excess detected in all invasive range populations could be further explained as due to population sub-structure and isolation breaking through periodic migration. Significant numbers of loci (ca. 30%) were also shown to not be in Hardy–Weinberg equilibrium (HWE) especially for the Malaysian (i.e., Kedah), but also Australian (i.e., Wee Waa, NT, Kununurra), Chinese (e.g., XP), South Korean, and Malawian populations. Taken as a whole, genetic diversity results from this study therefore suggested that the invasive Asian (i.e., SA, SEA, EA) FAW populations exhibited signatures of recent mixing of previously separated populations. Simulated patterns of moth migration of various invasive FAW populations such as between Myanmar and China (e.g.,41,42,55) and to Australia54 are incompatible with the population genomic data, which suggests these were likely discrete and non-panmictic FAW populations with the most probable explanation being due to multiple origins of founding populations.Genetic differentiation analysisEstimates of pairwise genetic differentiation (FST) between populations varied significantly (Table 1) and extended to between populations within a country (e.g., Mackay vs. rest of Australia; Kedah vs. rest of Malaysia). Of interest are the pairwise estimates between different Australian FAW populations from Kununurra (Western Australia), Northern Territory, Queensland (Strathmore, Walkamin, Burdekin, Mackay) and New South Wales (Wee Waa) that represented the most recently reported invasive populations in this study, and predominantly showed significant differentiation amongst themselves (with the exception of the two Queensland populations of Mackay and partially for Walkamin) and with other SEA/SA/EA countries. The majority of non-significant population genetic differentiation estimates were in SEA where the presence of FAW was reported earlier, i.e., since 2018 (e.g.,63,64 or as early as 200865,66; see also33), while across Asia (e.g., China) since 2016 but also potentially pre-2014 (16,67; see also33).Interestingly, significant genetic differentiation was observed between populations from Yunnan province in China and populations from Myanmar, Laos, and Vietnam. Penang and Johor (Malaysia) populations were not significantly differentiated from other SE Asian populations, nor with Ugandan and Malawian populations from east Africa. Individuals from Benin and Mackay (Queensland, Australia) showed non-significant genetic differentiation with all populations except with Kedah, and for Mackay also surprisingly with the Wee Waa population from New South Wales. The South Korean population exhibited significant genetic differentiation with SE Asian population except with Mackay, India and the Yuanjiang (YJ) population in Yunnan Province. Finally, the Kedah population, being one of the earliest collected samples from Malaysia and having been maintained as a laboratory population, showed strong differentiation with all populations (and lowest nucleotide diversity, π = 0.237; Table 2) further supporting unique, non-African, introduction events in SEA. Strong genetic differentiation suggested there was limited gene flow to breakdown sub-structure between populations, and the FST estimates from these invasive populations therefore failed to support a west-to-east spread pathway for the FAW. This observation instead suggested the widespread presence of genetically distinct FAW populations, likely due to independent introductions and therefore also highlighting likely biosecurity weaknesses especially in East Asia (e.g., China, South Korea) and SEA (e.g., Malaysia).Table 2 Population statistics for Spodoptera frugiperda populations from Southeast Asia (i.e., Malaysia (MYS; Johor, Kedah, Penang), Laos, Vietnam, Myanmar), East Asia (i.e., South Korea), and Pacific/Australia (i.e., Papua New Guinea (PNG), Australia).Full size tableThe genetic diversity of Australian populations identified surprisingly complex sub-structure patterns given the short time frame of population detections across different northern Australian regions. Significant genetic differentiation between, e.g., Kununurra (WA), Northern Territory (NT), Queensland (e.g., Strathmore, Burdekin), and Wee Waa (NSW) populations suggests these populations likely derived from separate establishment events. The WA Kununurra population was not significantly differentiated from the Johor State (Malaysia), India and the Cangyuan (CY) China populations, suggesting a potential south-eastern route from SA/SEA into north-western Australia. Contrasting this, Walkamin and Mackay populations showed non-significant genetic differentiation with the Madang (PNG) population, suggesting a potential second pathway for SEA individuals to arrive at the north-eastern region of Australia. Significant genetic differentiation between WA, NT, and Qld populations suggested that at least during the early stage of pest establishment in northern Australia, there was limited gene flow to homogenise the unique genetic background carried by these distinct individuals, some of which exhibited also distinct insecticide resistance profiles48,49.PCAWe selected specific populations to compare using Principal Component Analysis (PCA) as examples to support evidence of independent introductions, as seen from Fig. 3a between China (CY, YJ, XP) populations vs. Myanmar, in Fig. 3b (within Malaysian populations between those collected from Penang and Johor States vs. Kedah State), in Fig. 3c for between China and East Africa (e.g., Uganda, Malawi), and where Benin and India individuals that grouped with either China or east Africa; and in Fig. 3d between China, Malaysia (Kedah State), and Australia (NT, NSW)). Genetic variability between Australian populations (e.g., Strathmore (QLD) vs. NT and NSW) was also evident (Fig. 3d).Figure 3Principal component analysis (PCA) showing variability between selected FAW populations from their invasive ranges. (a) China and Myanmar; (b) Kedah and Johor/Penang populations from Malaysia, (c) China and east African (Uganda/Malawi) populations, (d) Australia (Strathmore, Qld/Northern Territory + New South Wales), China, and Malaysia (Kedah) populations, (e) Australia (Strathmore, Qld) and PNG (Madang Province) populations, (f) Lao PDR/Vietnam and South Korea populations, (g) China and SE Asian (Lao PDR/Vietnam/Myanmar/Philippines/Malaysia) and Pacific/Australia (PNG) populations, and (h) Australia, China and Malaysia (Kedah) populations. Note the overall population genomic variability between countries (e.g., a, c–g) and within countries (e.g., Malaysia (b), Australia (d)). Populations with similar genomic variability are also evident, e.g., for Strathmore (e) and South Korea (f); and for Madang (e) and Lao PDR/Vietnam (f), further supporting potential different population origins of various FAW populations across the current invasive regions. The Southeast Asian and Chinese populations are overall different (g), Australia’s FAW populations showed similarity with both Southeast Asia and China (g, h).Full size imagePCA also showed that differences existed between FAW populations from the Madang Province in PNG and with the Strathmore population from Qld (Fig. 3e). The SEA FAW populations from Lao PDR/Vietnam also exhibited diversity from the South Korean population (Fig. 3f), with the South Korean and Strathmore populations largely exhibiting similar diversity patterns, while the Madang population shared similarity with Laos and Vietnam populations. Plotting all SEA populations against China clearly showed that populations from SEA were distinct from the Chinese FAW populations (Fig. 3g), while in Australia, individuals from various populations shared similarity with both Chinese and SEA FAW. Despite the connectedness of the landscape between SEA and China, SEA largely appeared to have their own FAW populations, with FAW in SEA and in China differing in their genome compositions overall as shown via PCA.PCA further enabled visualisation of genetic diversity amongst Australia FAW populations, suggesting that arrival and establishment of FAW likely involved separate introduction events that followed closely after each other and over a short timeframe. While it had been anticipated that the southward spread of FAW from SEA would necessarily lead to Australia FAW and PNG FAW to share similar genetic backgrounds, the Madang Province FAW population appeared to be different from the Strathmore (Qld) population, with the Madang population being more similar to Lao PDR/Vietnam populations, and the Strathmore population more similar to FAW from South Korea.DivMigrate analysisDirectionality of gene flow between African, South Asia (Indian), East Asia (China) and SE Asian populations were predominantly from China to east African and SE Asian populations (e.g., Figs. 4a, b, S-1; see also Table 3), while movements of FAW in Laos and Vietnam (i.e., the Indochina region) were predominantly with other SEA countries (e.g., with Myanmar and East Africa; Figs. 4c, d, S-2; see also Table 3) but with no directional movements to the three Yunnan populations (CY, XP, YJ). Migration directionality with other SE Asian populations (e.g., Johor (JB; Fig. S-3) and Penang (PN, Fig. S-4)) showed that these two populations (but especially the Johor population) were predominantly source populations for Uganda, Malawi, Philippines, Vietnam, and PNG (Fig. S-3). Bidirectional migration between Myanmar and Laos PDR populations were also detected with the Johor population from Malaysia (Fig. S-3). When India was selected as the source population, bidirectional migration events were detected with Myanmar and with the Cangyuan (CY) populations (Fig. S-5) while unidirectional migration events from India to Uganda and Malawi and to Laos were detected, and the China Yuanjian (YJ) population showed unidirectional migration to India. Unidirectional migration events from CY and YJ populations to the PNG Madang population were detected, while bidirectional migration events between PNG and Myanmar, Laos PDR, Philippines, Vietnam, and with Uganda and Malawi were also detected (Fig. S-6). No migration events were detected between the West African Benin population and with the South Korean population.Figure 4Source populations are CY (a) and XP (b). (c, d) DivMigrate analyses with edge weight setting at 0.453 showing unidirectional (yellow arrow lines) and bidirectional (blue arrow lines) migration between countries in Africa and South Asia/East Asia/SE Asia. Migration rates between populations are as provided in Table 3. (c) Vietnam (VNM) as the source population identified an incidence of unidirectional migration from Malaysia (MYS) Johor state (JB) to Vietnam, while bidirectional migration events were detected from Vietnam to other SE Asian (e.g., Philippines (PHL), Lao PDR (Lao), Myanmar (MMR)), to Pacific/Australia (i.e., Papua New Guinea (PNG)), as well as to east Africa (Uganda (UGA), Malawi (MWI)). (d) Lao PDR (LAO) as source population identified bidirectional migration events between various SEA populations and east African populations, while unidirectional migration events were identified from India (IND) and China (CHN) Yunnan populations (CY, YJ) to Laos PDR. No migration events were evident from SE Asian populations to China.(a, b) DivMigrate analyses with edge weight setting at 0.453 showing unidirectional (yellow arrow lines) and bidirectional (blue arrow lines) gene flow between countries in Africa and South Asia/East Asia/SE Asia. Significant migration rates (at alpha = 0.5) are in red and as provided in Table 3. Incidences of unidirectional migration were predominantly detected from China (CHN) Yunnan populations (CY, XP) to SE Asian populations (e.g., Myanmar (MMR), Laos PDR (Lao), Philippines (PHL)) and to east African populations (e.g., Uganda (UGA), Malawi (MWI)) (a, b).Full size imageTable 3 DivMigrate matrix showing effective migration rates calculated using GST from source to target invasive populations.Full size tableAdmixture analysisAdmixture analyses involving all Australian, Southeast Asian and South Korean populations from this study; and native populations from the Americas and Caribbean Islands, and invasive populations from Africa (Benin, Uganda, Malawi), India, and China33, provided an overall complex picture of population structure that reflected the species’ likely introduction histories across its invasive ranges.Admixture analysis that excluded New World, African and Indian populations identified four genetic clusters (i.e., K = 4) to best describe these invasive populations from SEA, and EA (i.e., China, South Korea), and Pacific/Australia (Fig. 5a). At K = 4, Australian populations from NT and NSW, YJ population from China, South Korean, and Malaysia’s Kedah population, each showed unique admixture patterns (i.e., some individuals from NT and NSW populations lacked cluster 3; most of YJ (but also some CY and XP) individuals lacked clusters 1 and 2; South Korean (e.g., MF individuals) lacked cluster 2; Malaysia’s Kedah population lacked evidence of admixture (i.e., reflecting its laboratory culture history) and was made up predominantly by individuals that belonged to cluster 4. Populations from China also differed from most populations from SEA due to the overall absence of genetic cluster 4. Taken as a whole, establishment of the FAW populations in China, Malaysia, vs. other SE Asian populations, and between Australian populations (e.g., NT/NSW cf. WA/Qld), likely involved individuals from diverse genetic background (i.e., multiple introductions). At K = 4, the majority of Australian populations appeared to contain genetic clusters similar to China (i.e., cluster 3) and to SEA (i.e., cluster 2).Figure 5Admixture and corresponding CV plots for FAW populations from: (a) Australia, China, South Korea, Lao PDR, Myanmar, Malaysia, Philippines, PNG, and Vietnam, and (b) Benin, China, India, South Korea, Lao PDR, Myanmar, Malaysia, Philippines, PNG, Tanzania, and Vietnam. Optimal ancestral genetic clusters are K = 4 for both admixture plots. Boxed individuals have unique admixture patterns at K = 4 when compared with other populations. China FAW lacked Cluster 2 (navy blue colour; present in almost all SEA and Australian FAW), while in NSW and NT some individuals lacked cluster 3. South Korea ‘MF’ population generally lacked cluster 2, while Kedah (Malaysia) showed distinct (cluster 4) pattern for all individuals. The overall same observations are evident in the admixture plot in (b), with African FAW generally exhibiting admixture patterns similar to SEA populations than to Chinese FAW. With the exception of Kedah (Malaysia) and some Chinese FAW individuals, all FAW in the invasive range showed evidence of genomic admixture (i.e., hybrid signature). The figures were generated using the POPHELPER program  and further manipulated in Microsoft PowerPoint for Mac v16.54.Full size imageOverall admixture patterns at best K = 4 in China and SEA remained unchanged when analysed together with African and Indian individuals (Fig. 5b; excluded Australia). Benin individuals were either similar to China or to SEA, while eastern African populations (e.g., Uganda, Malawi) were similar to Southeast Asian populations from e.g., Vietnam, Laos, and is in agreement with the phylogenetic inference (Fig. 3) that identified these African individuals as having loci that were derived from Southeast Asian populations.Genome-wide SNP loci demonstrated that invasive FAW populations from SEA and Australia exhibited admixed genomic signatures similar to that observed in other invasive populations33,34. While the current invasive populations in Africa and Asia likely arrived already as hybrids as suggested by Yainna et al.68, the Malaysia Kedah State population was potentially established by offspring of a non-admixed female. Distinct admixture patterns in Malaysian FAW populations between Kedah and Johor/Penang states therefore suggested that establishment of these populations was likely as separate introduction events. As reported also in Tay et al.33, the Chinese YJ population appeared to have admixed signature that differed from XP and CY populations, and suggested that the YJ population could have a different introduction history than the XP and CY populations. Similar multiple genetic signatures based on lesser nuclear markers by Jiang et al.39 also supported likely multiple introductions of China Yunnan populations. More