Philippa Kaur

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

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

Microbial community composition in cave sedimentsThe ITS1 dataset generated 5,793,980 raw sequence reads, resulting in 5,458,895 gene quality-filtered reads, ranging from 1252 up to 540,803 per sample. After singletons and rare taxa ( More

Alphan, H. Land use change and urbanisation of Adana, Turkey. Land Degrad. Dev. 14, 575–586 (2003).Article 

Google Scholar 
Catalàn, B., Sauri, D. & Serra, P. Urban sprawl in the Mediterranean? Patterns of growth and change in the Barcelona Metropolitan Region 1993–2000. Landsc. Urban Plan. 85(3–4), 174–184 (2008).
Google Scholar 
Chen, K., Long, H., Liao, L., Tu, S. & Li, T. Land use transitions and urban-rural integrated development: Theoretical framework and China’s evidence. Land Use Policy 92, 104465 (2020).Article 

Google Scholar 
Bianchini, L. et al. Forest transition and metropolitan transformations in developed countries: Interpreting apparent and latent dynamics with local regression models. Land 11(1), 12 (2021).Article 

Google Scholar 
Angel, S., Parent, J., Civco, D. L., Blei, A. & Potere, D. The dimensions of global urban expansion: Estimates and projections for all countries, 2000–2050. Prog. Plan. 75(2), 53–107 (2011).Article 

Google Scholar 
Fischer, A. P. Forest landscapes as social-ecological systems and implications for management. Landsc. Urban Plan. 177, 138–147 (2018).Article 

Google Scholar 
Darvishi, A., Yousefi, M. & Marull, J. Modelling landscape ecological assessments of land use and cover change scenarios. Application to the Bojnourd Metropolitan Area (NE Iran). Land Use Policy 99, 105098 (2020).Article 

Google Scholar 
Cheng, L. L., Tian, C. & Yin, T. T. Identifying driving factors of urban land expansion using Google earth engine and machine-learning approaches in Mentougou District, China. Sci. Rep. 12(1), 1–13 (2022).Article 
CAS 

Google Scholar 
Kasanko, M. et al. Are European Cities becoming dispersed? A comparative analysis of fifteen European urban areas. Landsc. Urban Plan. 77(1–2), 111–130 (2006).Article 

Google Scholar 
Terzi, F. & Bolen, F. Urban sprawl measurement of Istanbul. Eur. Plan. Stud. 17(10), 1559–1570 (2009).Article 

Google Scholar 
Angel, S., Parent, J. & Civco, D. L. Ten compactness properties of circles: measuring shape in geography. Can. Geogr. 54, 441–461 (2010).Article 

Google Scholar 
Salvati, L., Gemmiti, R. & Perini, L. Land degradation in Mediterranean urban areas: An unexplored link with planning?. Area 44(3), 317–325 (2012).Article 

Google Scholar 
Attorre, F., Bruno, M., Francesconi, F., Valenti, R. & Bruno, F. Landscape changes of Rome through tree-lined roads. Landsc. Urban Plan. 49, 115–128 (2000).Article 

Google Scholar 
Turok, I. & Mykhnenko, V. The trajectories of European cities, 1960–2005. Cities 24(3), 165–182 (2007).Article 

Google Scholar 
Ioannidis, C., Psaltis, C. & Potsiou, C. Towards a strategy for control of suburban informal buildings through automatic change detection. Comput. Environ. Urban Syst. 33, 64–74 (2009).Article 

Google Scholar 
Grekousis, G., Manetos, P. & Photis, Y. N. Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the athens metropolitan area. Cities 30, 193–203 (2013).Article 

Google Scholar 
Salvati, L. Towards a polycentric region? The socioeconomic trajectory of Rome, an ‘Eternally Mediterranean’ city. Tijdschr. Econ. Soc. Geogr. 105(3), 268–284 (2014).Article 

Google Scholar 
Chorianopoulos, I., Pagonis, T., Koukoulas, S. & Drymoniti, S. Planning, competitiveness and sprawl in the Mediterranean city: The case of Athens. Cities 27, 249–259 (2010).Article 

Google Scholar 
Munafò, M., Salvati, L. & Zitti, M. Estimating soil sealing rate at national level—Italy as a case study. Ecol. Ind. 26, 137–140 (2013).Article 

Google Scholar 
Morelli, V. G., Rontos, K. & Salvati, L. Between suburbanisation and re-urbanisation: Revisiting the urban life cycle in a Mediterranean compact city. Urban Res. Pract. 7(1), 74–88 (2014).Article 

Google Scholar 
Basem Ajjur, S. & Al-Ghamdi, S. G. Exploring urban growth–climate change–flood risk nexus in fast growing cities. Sci. Rep. 12, 12265 (2022).Article 
ADS 

Google Scholar 
Li, H. & Wu, J. Use and misuse of landscape indices. Landsc. Ecol. 19, 389–399 (2004).Article 

Google Scholar 
Salvati, L. Agro-forest landscape and the ‘fringe’city: A multivariate assessment of land-use changes in a sprawling region and implications for planning. Sci. Total Environ. 490, 715–723 (2014).Article 
ADS 
CAS 

Google Scholar 
Sang, X. et al. Intensity and stationarity analysis of land use change based on CART algorithm. Sci. Rep. 9(1), 1–12 (2019).Article 
ADS 
CAS 

Google Scholar 
Ettehadi Osgouei, P., Sertel, E. & Kabadayı, M. E. Integrated usage of historical geospatial data and modern satellite images reveal long-term land use/cover changes in Bursa/Turkey, 1858–2020. Sci. Rep. 12(1), 1–17 (2022).Article 

Google Scholar 
He, S., Yu, S., Li, G. & Zhang, J. Exploring the influence of urban form on land-use efficiency from a spatiotemporal heterogeneity perspective: Evidence from 336 Chinese cities. Land Use Policy 95, 104576 (2020).Article 

Google Scholar 
Bockarjova, M., Wouter Botzen, W. J., Bulkeley, H. A. & Toxopeus, H. Estimating the social value of nature-based solutions in European cities. Sci. Rep. 12, 19833 (2022).Article 
ADS 
CAS 

Google Scholar 
Liu, J. & Niyogi, D. Meta-analysis of urbanisation impact on rainfall modification. Sci. Rep. 9(1), 1–14 (2019).ADS 

Google Scholar 
Holland, J. H. Studying complex adaptive systems. J. Syst. Sci. Complex. 19(1), 1–8 (2006).Article 
MathSciNet 
MATH 

Google Scholar 
Salvati, L. & Serra, P. Estimating rapidity of change in complex urban systems: A multidimensional, local-scale approach. Geogr. Anal. 48(2), 132–156 (2016).Article 

Google Scholar 
Bura, S., Guerin-Pace, F., Mathian, H., Pumain, D. & Sanders, L. Multi-agents systems and the dynamics of a settlement system. Geogr. Anal. 28(2), 161–178 (1996).Article 

Google Scholar 
Hasse, J. E. & Lathrop, R. G. Land resource impact indicators of urban sprawl. Appl. Geogr. 23, 159–175 (2003).Article 

Google Scholar 
Grafius, D. R., Corstanje, R. & Harris, J. A. Linking ecosystem services, urban form and green space configuration using multivariate landscape metric analysis. Landsc. Ecol. 33(4), 557–573 (2018).Article 

Google Scholar 
Pumain, D. Hierarchy in Natural and Social Sciences (Kluwer-Springer, 2005).
Google Scholar 
Cabral, P., Augusto, G., Tewolde, M. & Araya, Y. Entropy in urban systems. Entropy 15(12), 5223–5236 (2013).Article 
ADS 

Google Scholar 
Salvati, L. & Carlucci, M. In-between stability and subtle changes: Urban growth, population structure, and the city life cycle in Rome. Popul. Space Place 22(3), 216–227 (2016).Article 

Google Scholar 
Batty, M. & Longley, P. Fractal Cities (Academic Press, 1994).MATH 

Google Scholar 
Berry, B. J. L. Cities as systems within systems of cities. Pap. Reg. Sci. 13, 147–163 (2005).Article 

Google Scholar 
Petrosillo, I. et al. The resilient recurrent behavior of mediterranean semi-arid complex adaptive landscapes. Land 10(3), 296 (2021).Article 

Google Scholar 
Portugali, J. Complexity, Cognition and the City, Understanding Complex Systems (Springer, 2011).Book 

Google Scholar 
Wu, J., Jenerette, G. D., Buyantuyev, A. & Redman, C. L. Quantifying spatiotemporal patterns of urbanisation: The case of the two fastest growing metropolitan regions in the United States. Ecol. Complex. 8(1), 1–8 (2011).Article 

Google Scholar 
Sun, Y., Gao, C., Li, J., Li, W. & Ma, R. Examining urban thermal environment dynamics and relations to biophysical composition and configuration and socioeconomic factors: A case study of the Shanghai metropolitan region. Sustain. Cities Soc. 40, 284–295 (2018).Article 

Google Scholar 
Phillips, M. A. & Ritala, P. A complex adaptive systems agenda for ecosystem research methodology. Technol. Forecast. Soc. Change 148, 119739 (2019).Article 

Google Scholar 
Walker, B., Holling, C. S., Carpenter, S. R. & Kinzig, A. Resilience, adaptability and transformability in social-ecological systems. Ecol. Soc. 9(2), 5 (2004).Article 

Google Scholar 
Kelly, C. et al. Community resilience and land degradation in forest and shrublandsocio-ecological systems: A case study in Gorgoglione, Basilicata regionn, Italy. Land Use Policy 46, 11–20 (2015).Article 

Google Scholar 
Preiser, R., Biggs, R., De Vos, A. & Folke, C. Social-ecological systems as complex adaptive systems. Ecol. Soc. 23(4), 46 (2018).Article 

Google Scholar 
Ferrara, A. et al. Shaping the role of ‘fast’ and ‘slow’ drivers of change in forest-shrubland socio-ecological systems. J. Environ. Manag. 169, 155–166 (2016).Article 

Google Scholar 
Lamy, T., Liss, K. N., Gonzalez, A. & Bennett, E. M. Landscape structure affects the provision of multiple ecosystem services. Environ. Res. Lett. 11(12), 124017 (2016).Article 
ADS 

Google Scholar 
Riitters, K. H., Vogt, P., Soille, P., Kozak, J. & Estreguil, C. Neutral model analysis of landscape patterns from mathematical morphology. Landsc. Ecol. 22(7), 1033–1043 (2007).Article 

Google Scholar 
Riitters, K., Vogt, P., Soille, P. & Estreguil, C. Landscape patterns from mathematical morphology on maps with contagion. Landsc. Ecol. 24(5), 699–709 (2009).Article 

Google Scholar 
Anas, A., Arnott, R. & Small, K. Urban spatial structure. J. Econ. Lit. 36(3), 1426–1464 (1998).
Google Scholar 
Arroyo-Mora, J. P., Sánchez-Azofeifa, G. A., Rivard, B., Calvo, J. C. & Janzen, D. H. Dynamics in landscape structure and composition for the Chorotega region, Costa Rica from 1960 to 2000. Agr. Ecosyst. Environ. 106(1), 27–39 (2005).Article 

Google Scholar 
Siles, G., Charland, A., Voirin, Y. & Bénié, G. B. Integration of landscape and structure indicators into a web-based geoinformation system for assessing wetlands status. Eco. Inform. 52, 166–176 (2019).Article 

Google Scholar 
Soille, P. Morphological Image Analysis: Principles and Applications (Springer, 2003).MATH 

Google Scholar 
Soille, P. & Vogt, P. Morphological segmentation of binary patterns. Pattern Recogn. Lett. 30, 456–459 (2009).Article 
ADS 

Google Scholar 
Vogt, P. et al. Mapping spatial patterns with morphological image processing. Landsc. Ecol. 22(2), 171–177 (2007).Article 

Google Scholar 
Bajocco, S., Ceccarelli, T., Smiraglia, D., Salvati, L. & Ricotta, C. Modeling the ecological niche of long-term land use changes: The role of biophysical factors. Ecol. Ind. 60, 231–236 (2016).Article 

Google Scholar 
Yin, Y., Zhou, K. & Chen, Y. Deconstructing the driving factors of land development intensity from multi-scale in differentiated functional zones. Sci. Rep. 12(1), 1–13 (2022).Article 

Google Scholar 
Duvernoy, I., Zambon, I., Sateriano, A. & Salvati, L. Pictures from the other side of the fringe: Urban growth and peri-urban agriculture in a post-industrial city (Toulouse, France). J. Rural. Stud. 57, 25–35 (2018).Article 

Google Scholar 
Smiraglia, D., Ceccarelli, T., Bajocco, S., Salvati, L. & Perini, L. Linking trajectories of land change, land degradation processes and ecosystem services. Environ. Res. 147, 590–600 (2016).Article 
CAS 

Google Scholar 
Shaker, R. R. Examining sustainable landscape function across the Republic of Moldova. Habitat Int. 72, 77–91 (2018).Article 
ADS 

Google Scholar 
Zheng, H. & Li, H. Spatial–temporal evolution characteristics of land use and habitat quality in Shandong Province, China. Sci. Rep. 12(1), 1–12 (2022).Article 

Google Scholar 
Tombolini, I., Munafò, M. & Salvati, L. Soil sealing footprint as an indicator of dispersed urban growth: A multivariate statistics approach. Urban Res. Pract. 9(1), 1–15 (2016).Article 

Google Scholar 
Salvati, L., Sateriano, A., Grigoriadis, E. & Carlucci, M. New wine in old bottles: The (changing) socioeconomic attributes of sprawl during building boom and stagnation. Ecol. Econ. 131, 361–372 (2017).Article 

Google Scholar 
Zambon, I., Benedetti, A., Ferrara, C. & Salvati, L. Soil matters? A multivariate analysis of socioeconomic constraints to urban expansion in Mediterranean Europe. Ecol. Econ. 146, 173–183 (2018).Article 

Google Scholar 
Paul, V. & Tonts, M. Containing urban sprawl: Trends in land use and spatial planning in the Metropolitan Region of Barcelona. J. Environ. Plann. Manag. 48(1), 7–35 (2005).Article 

Google Scholar 
Serra, P., Vera, A., Tulla, A. F. & Salvati, L. Beyond urban–rural dichotomy: Exploring socioeconomic and land-use processes of change in Spain (1991–2011). Appl. Geogr. 55, 71–81 (2014).Article 

Google Scholar 
Seifollahi-Aghmiuni, S., Kalantari, Z., Egidi, G., Gaburova, L. & Salvati, L. Urbanisation-driven land degradation and socioeconomic challenges in peri-urban areas: Insights from Southern Europe. Ambio 51(6), 1446–1458 (2022).Article 

Google Scholar 
Pili, S., Grigoriadis, E., Carlucci, M., Clemente, M. & Salvati, L. Towards sustainable growth? A multi-criteria assessment of (changing) urban forms. Ecol. Ind. 76, 71–80 (2017).Article 

Google Scholar 
Salvati, L., Sateriano, A. & Grigoriadis, E. Crisis and the city: Profiling urban growth under economic expansion and stagnation. Lett. Spat. Resour. Sci. 9(3), 329–342 (2016).Article 

Google Scholar 
Champion, T. & Hugo, G. New Forms of Urbanisation: Beyond the Urban-Rural Dichotomy (Ashgate, 2004).
Google Scholar 
Frondoni, R., Mollo, B. & Capotorti, G. A landscape analysis of land cover change in the municipality of Rome (Italy): Spatio-temporal characteristics and ecological implications of land cover transitions from 1954 to 2001. Landsc. Urban Plan. 100(1–2), 117–128 (2011).Article 

Google Scholar 
Perrin, C., Nougarèdes, B., Sini, L., Branduini, P. & Salvati, L. Governance changes in peri-urban farmland protection following decentralisation: A comparison between Montpellier (France) and Rome (Italy). Land Use Policy 70, 535–546 (2018).Article 

Google Scholar 
Salvati, L. Monitoring high-quality soil consumption driven by urban pressure in a growing city (Rome, Italy). Cities 31, 349–356 (2013).Article 

Google Scholar 
Salvati, L., Ciommi, M. T., Serra, P. & Chelli, F. M. Exploring the spatial structure of housing prices under economic expansion and stagnation: The role of socio-demographic factors in metropolitan Rome, Italy. Land Use Policy 81, 143–152 (2019).Article 

Google Scholar 
Ferrara, C., Salvati, L. & Tombolini, I. An integrated evaluation of soil resource depletion from diachronic settlement maps and soil cartography in peri-urban Rome, Italy. Geoderma 232, 394–405 (2014).Article 
ADS 

Google Scholar 
Egidi, G. & Salvati, L. Beyond the suburban-urban divide: Convergence in age structures in metropolitan Rome, Italy. J. Popul. Soc. Stud. 28(2), 130–142 (2020).Article 

Google Scholar 
Pili, S., Serra, P. & Salvati, L. Landscape and the city: Agro-forest systems, land fragmentation and the ecological network in Rome, Italy. Urban For. Urban Green. 41, 230–237 (2019).Article 

Google Scholar 
European Environment Agency. Urban Sprawl in Europe – The Ignored Challenge. Copenhagen: EEA Report no. 10 (2006).Park, S., Hepcan, Ç. C., Hepcan, Ş & Cook, E. A. Influence of urban form on landscape pattern and connectivity in metropolitan regions: a comparative case study of Phoenix, AZ, USA, and Izmir, Turkey. Environ. Monit. Assess. 186(10), 6301–6318 (2014).Article 

Google Scholar 
Luo, F., Liu, Y., Peng, J. & Wu, J. Assessing urban landscape ecological risk through an adaptive cycle framework. Landsc. Urban Plan. 180, 125–134 (2018).Article 

Google Scholar 
Ortega, M., Pascual, S., Elena-Rosselló, R. & Rescia, A. J. Land-use and spatial resilience changes in the Spanish olive socio-ecological landscape. Appl. Geogr. 117, 102171 (2020).Article 

Google Scholar 
Parcerisas, L. et al. Land use changes, landscape ecology and their socioeconomic driving forces in the Spanish Mediterranean coast (El Maresme County, 1850–2005). Environ. Sci. Policy 23, 120–132 (2012).Article 

Google Scholar 
Masini, E. et al. Urban growth, land-use efficiency and local socioeconomic context: A comparative analysis of 417 metropolitan regions in Europe. Environ. Manag. 63(3), 322–337 (2019).Article 
ADS 

Google Scholar 
Luck, M. & Wu, J. A gradient analysis of urban landscape pattern: a case study from the Phoenix metropolitan region, Arizona, USA. Landsc. Ecol. 17(4), 327–339 (2002).Article 

Google Scholar 
Pesaresi, M. & Bianchin, A. Recognising settlement structure using mathematical morphology and image texture. Remote Sensing Urban Anal. GISDATA 9, 46–60 (2003).
Google Scholar 
Schneider, A. & Woodcock, C. E. Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data, pattern metrics and census information. Urban Stud. 45(3), 659–692 (2008).Article 

Google Scholar 
Mubareka, S., Koomen, E., Estreguil, C. & Lavalle, C. Development of a composite index of urban compactness for land use modelling applications. Landsc. Urban Plan. 103(3–4), 303–317 (2011).Article 

Google Scholar 
Vogt, P. et al. Mapping landscape corridors. Ecol. Ind. 7(2), 481–488 (2007).Article 

Google Scholar 
Daya Sagar, B. S. & Murthy, K. S. R. Generation of a fractal landscape using nonlinear mathematical morphological transformations. Fractals 8(03), 267–272 (2000).Article 

Google Scholar 
Scott, A. J., Carter, C., Reed, M. R., Stonyer, B. & Coles, R. Disintegrated development at the rural-urban fringe: Re-connecting spatial planning theory and practice. Prog. Plan. 83, 1–52 (2013).Article 

Google Scholar 
Zhao, Q., Wen, Z., Chen, S., Ding, S. & Zhang, M. Quantifying land use/land cover and landscape pattern changes and impacts on ecosystem services. Int. J. Environ. Res. Public Health 17(1), 126 (2020).Article 

Google Scholar 
Parr, J. The regional economy, spatial structure and regional urban systems. Reg. Stud. 48(12), 1926–1938 (2014).Article 

Google Scholar 
Salvati, L., Zambon, I., Chelli, F. M. & Serra, P. Do spatial patterns of urbanisation and land consumption reflect different socioeconomic contexts in Europe?. Sci. Total Environ. 625, 722–730 (2018).Article 
ADS 
CAS 

Google Scholar 
Coppi, R. & Bolasco, S. Multiway Data Analysis (Elsevier, 1988).MATH 

Google Scholar 
Kroonenberg, P. M. Applied Multiway Data Analysis (Wiley, 2008).Book 
MATH 

Google Scholar 
Escofier, B. & Pages, J. Multiple factor analysis (AFMULT Package). Comput. Stat. Data Anal. 18, 121–140 (1994).Article 
MATH 

Google Scholar 
De Rosa, S. & Salvati, L. Beyond a ‘side street story’? Naples from spontaneous centrality to entropic polycentricism, towards a ‘crisis city’. Cities 51, 74–83 (2016).Article 

Google Scholar 
Favaro, J.-M. & Pumain, D. Gibrat revisited: An urban growth model incorporating spatial interaction and innovation cycles. Geogr. Anal. 43(3), 261–286 (2011).Article 

Google Scholar 
Walker, B. H., Carpenter, S. R., Rockstrom, J., Crepin, A.-S. & Peterson, G. D. “Drivers, “slow” variables, “fast” variables, shocks, and resilience. Ecol. Soc. 17(3), 30 (2012).Article 

Google Scholar 
Zhang, Z., Su, S., Xiao, R., Jiang, D. & Wu, J. Identifying determinants of urban growth from a multi-scale perspective: A case study of the urban agglomeration around Hangzhou Bay, China. Appl. Geogr. 45, 193–202 (2013).Article 

Google Scholar 
Fratarcangeli, C., Fanelli, G., Franceschini, S., De Sanctis, M. & Travaglini, A. Beyond the urban-rural gradient: Self-organising map detects the nine landscape types of the city of Rome. Urban For. Urban Green. 38, 354–370 (2019).Article 

Google Scholar 
Crisci, M., Benassi, F., Rabiei-Dastjerdi, H., McArdle, G. Spatio-temporal variations and contextual factors of the supply of Airbnb in Rome. An initial investigation. Lett. Spat. Resour. Sci. 1–17 (2022).Lelo, K., Monni, S. & Tomassi, F. Socio-spatial inequalities and urban transformation. The case of Rome districts. Socio-Econ. Plann. Sci. 68, 100696 (2019).Article 

Google Scholar 
Crisci, M. The impact of the real estate crisis on a south european metropolis: From urban diffusion to Reurbanisation. Appl. Spat. Anal. Policy 15(3), 797–820 (2022).Article 

Google Scholar 
Wang, Y. & Zhang, X. A dynamic modeling approach to simulating socioeconomic effects on landscape changes. Ecol. Model. 140(1–2), 141–162 (2001).Article 

Google Scholar 
Voghera, A. The River agreement in Italy. Resilient planning for the co-evolution of communities and landscapes. Land Use Policy 91, 104377 (2020).Article 

Google Scholar 
Chen, A. & Partridge, M. D. When are cities engines of growth in China? Spread and backwash effects across the urban hierarchy. Reg. Stud. 47(8), 1313–1331 (2013).Article 

Google Scholar 
Ciommi, M., Chelli, F. M., Carlucci, M. & Salvati, L. Urban growth and demographic dynamics in southern Europe: Toward a new statistical approach to regional science. Sustainability 10(8), 2765 (2018).Article 

Google Scholar 
Jacobs-Crisioni, C., Rietveld, P. & Koomen, E. The impact of spatial aggregation on urban development analyses. Appl. Geogr. 47, 46–56 (2014).Article 

Google Scholar 
Kourtit, K., Nijkamp, P. & Reid, N. The new urban world: Challenges and policy. Appl. Geogr. 49, 1–3 (2014).Article 

Google Scholar 
Bruegmann, R. Sprawl: A Compact History (University of Chicago Press, 2005).Book 

Google Scholar 
Neuman, M. & Hull, A. The Futures of the City Region. Reg. Stud. 43(6), 777–787 (2009).Article 

Google Scholar 
Couch, C., Petschel-held, G. & Leontidou, L. Urban Sprawl In Europe: Landscapes, Land-use Change and Policy (Blackwell, 2007).Book 

Google Scholar 
Longhi, C. & Musolesi, A. European cities in the process of economic integration: towards structural convergence. Ann. Reg. Sci. 41, 333–351 (2007).Article 

Google Scholar 
Tian, G., Ouyang, Y., Quan, Q. & Wu, J. Simulating spatiotemporal dynamics of urbanisation with multi-agent systems—A case study of the Phoenix metropolitan region, USA. Ecol. Model. 222(5), 1129–1138 (2011).Article 

Google Scholar 
Tian, L., Chen, J. & Yu, S. X. Coupled dynamics of urban landscape pattern and socioeconomic drivers in Shenzhen, China. Landsc. Ecol. 29(4), 715–727 (2014).Article 

Google Scholar 
Fielding, A. J. Counterurbanization in Western Europe. Prog. Plan. 17, 1–52 (1982).Article 

Google Scholar 
Oueslati, W., Alvanides, S. & Garrod, G. Determinants of urban sprawl in European cities. Urban Stud. 52(9), 1594–1614 (2015).Article 

Google Scholar 
Tress, B., Tress, G., Décamps, H. & d’Hauteserre, A. M. Bridging human and natural sciences in landscape research. Landsc. Urban Plan. 57(3–4), 137–141 (2001).Article 

Google Scholar 
Xu, Z., Lv, Z., Li, J., Sun, H. & Sheng, Z. A Novel perspective on travel demand prediction considering natural environmental and socioeconomic factors. IEEE Intell. Transp. Syst. Mag. https://doi.org/10.1109/MITS.2022.3162901 (2022).Article 

Google Scholar 
Xu, Z., Lv, Z., Li, J. & Shi, A. A novel approach for predicting water demand with complex patterns based on ensemble learning. Water Resour. Manag. 36(11), 4293–4312 (2022).Article 

Google Scholar 
Lv, Z., Li, J., Dong, C., Li, H. & Xu, Z. Deep learning in the COVID-19 epidemic: A deep model for urban traffic revitalisation index. Data Knowl. Eng. 135, 101912 (2021).Article 

Google Scholar  More

Cavanaugh CM, McKiness ZP, Newton ILG, Stewart FJ. Marine chemosynthetic symbioses. Prokaryotes. 2006;1:475–507.Article 

Google Scholar 
Beinart RA, Luo C, Konstantinidis KT, Stewart FJ, Girguis PR. The bacterial symbionts of closely related hydrothermal vent snails with distinct geochemical habitats show broad similarity in chemoautotrophic gene content. Front Microbiol. 2019;10:1818.Article 

Google Scholar 
Robidart JC, Bench SR, Feldman RA, Novoradovsky A, Podell SB, Gaasterland T, et al. Metabolic versatility of the Riftia pachyptila endosymbiont revealed through metagenomics. Environ Microbiol. 2008;10:727–37.Article 
CAS 

Google Scholar 
Ponnudurai R, Sayavedra L, Kleiner M, Heiden SE, Thürmer A, Felbeck H, et al. Genome sequence of the sulfur-oxidizing Bathymodiolus thermophilus gill endosymbiont. Stand Genom Sci. 2017;12:50.Article 

Google Scholar 
Duperron S, Bergin C, Zielinski F, Blazejak A, Pernthaler A, McKiness ZP, et al. A dual symbiosis shared by two mussel species, Bathymodiolus azoricus and Bathymodiolus puteoserpentis (Bivalvia: Mytilidae), from hydrothermal vents along the northern Mid-Atlantic Ridge. Environ Microbiol. 2006;8:1441–7.Article 
CAS 

Google Scholar 
Dubilier N, Bergin C, Lott C. Symbiotic diversity in marine animals: the art of harnessing chemosynthesis. Nat Rev Microbiol. 2008;6:725–40.Article 
CAS 

Google Scholar 
Sogin EM, Leisch N, Dubilier N. Chemosynthetic symbioses. Curr Biol. 2020;30:R1137–R1142.Article 
CAS 

Google Scholar 
Roeselers G, Newton ILG. On the evolutionary ecology of symbioses between chemosynthetic bacteria and bivalves. Appl Microbiol Biotechnol. 2012;94:1–10.Article 
CAS 

Google Scholar 
Moran NA. Symbiosis as an adaptive process and source of phenotypic complexity. Proc Natl Acad Sci USA. 2007;104 Suppl 1:8627–33.Article 
CAS 

Google Scholar 
McMullen JG, Peterson BF, Forst S, Blair HG, Patricia Stock S. Fitness costs of symbiont switching using entomopathogenic nematodes as a model. BMC Evol Biol. 2017;17. https://doi.org/10.1186/s12862-017-0939-6.Taylor JD, Glover E. Biology, evolution and generic review of the chemosymbiotic bivalve family Lucinidae. London, UK: Ray Society; 2021.Osvatic JT, Wilkins LGE, Leibrecht L, Leray M, Zauner S, Polzin J, et al. Global biogeography of chemosynthetic symbionts reveals both localized and globally distributed symbiont groups. Proc Natl Acad Sci USA. 2021;118. https://doi.org/10.1073/pnas.2104378118.Petersen JM, Kemper A, Gruber-Vodicka H, Cardini U, van der Geest M, Kleiner M, et al. Chemosynthetic symbionts of marine invertebrate animals are capable of nitrogen fixation. Nat Microbiol. 2016;2:16195.Article 
CAS 

Google Scholar 
Lim SJ, Davis B, Gill D, Swetenburg J, Anderson LC, Engel AS, et al. Gill microbiome structure and function in the chemosymbiotic coastal lucinid Stewartia floridana. FEMS Microbiol Ecol. 2021;97. https://doi.org/10.1093/femsec/fiab042.Lim SJ, Davis BG, Gill DE, Walton J, Nachman E, Engel AS, et al. Taxonomic and functional heterogeneity of the gill microbiome in a symbiotic coastal mangrove lucinid species. ISME J. 2019;13:902–20.Article 
CAS 

Google Scholar 
Gros O, Liberge M, Felbeck H. Interspecific infection of aposymbiotic juveniles of Codakia orbicularis by various tropical lucinid gill-endosymbionts. Mar Biol. 2003;142:57–66.Article 

Google Scholar 
Gros O, Elisabeth NH, Gustave SDD, Caro A, Dubilier N. Plasticity of symbiont acquisition throughout the life cycle of the shallow-water tropical lucinid Codakia orbiculata (Mollusca: Bivalvia). Environ Microbiol. 2012;14:1584–95.Article 
CAS 

Google Scholar 
Gros O, Frenkiel L, Mouëza M. Embryonic, larval, and post-larval development in the symbiotic clam Codakia orbicularis (Bivalvia: Lucinidae). Invertebr Biol. 1997;116:86–101.Article 

Google Scholar 
König S, Gros O, Heiden SE, Hinzke T, Thürmer A, Poehlein A, et al. Nitrogen fixation in a chemoautotrophic lucinid symbiosis. Nat Microbiol. 2016;2:16193.Article 

Google Scholar 
Fiore CL, Jarett JK, Olson ND, Lesser MP. Nitrogen fixation and nitrogen transformations in marine symbioses. Trends Microbiol. 2010;18:455–63.Article 
CAS 

Google Scholar 
Cardini U, Bednarz VN, Foster RA, Wild C. Benthic N2 fixation in coral reefs and the potential effects of human-induced environmental change. Ecol Evol. 2014;4:1706–27.Article 

Google Scholar 
Glover EA, Taylor JD. Lucinidae of the Philippines: highest known diversity and ubiquity of chemosymbiotic bivalves from intertidal to bathyal depths (Mollusca: Bivalvia). mém Mus Natl Hist Nat. 2016;208:65–234.
Google Scholar 
Taylor JD, Glover EA, Williams ST. Diversification of chemosymbiotic bivalves: origins and relationships of deeper water Lucinidae. Biol J Linn Soc Lond. 2014;111:401–20.Article 

Google Scholar 
von Cosel R. Taxonomy of tropical West African bivalves. VI. Remarks on Lucinidae (Mollusca, Bivalvia), with description of six new genera and eight new species. Zoosystema. 2006;28:805.
Google Scholar 
Glover EA, Taylor JD, Rowden AA. Bathyaustriella thionipta, a new lucinid bivalve from a hydrothermal vent on the Kermadec Ridge, New Zealand and its relationship to shallow-water taxa (Bivalvia: Lucinidae). J Mollusca Stud. 2004;70:283–95.Article 

Google Scholar 
Paulus E Shedding light on deep-sea biodiversity—a highly vulnerable habitat in the face of anthropogenic change. Front Mar Sci. 2021;8. https://doi.org/10.3389/fmars.2021.667048.Brown A, Thatje S. Explaining bathymetric diversity patterns in marine benthic invertebrates and demersal fishes: physiological contributions to adaptation of life at depth. Biol Rev Camb Philos Soc. 2014;89:406–26.Article 

Google Scholar 
Smith CR, De Leo FC, Bernardino AF, Sweetman AK, Arbizu PM. Abyssal food limitation, ecosystem structure and climate change. Trends Ecol Evol. 2008;23:518–28.Article 

Google Scholar 
Gage JD, Tyler PA. Deep-sea biology: a natural history of organisms at the deep-sea floor. Cambridge, UK: Cambridge University Press; 1991.Iken K, Brey T, Wand U, Voigt J, Junghans P. Food web structure of the benthic community at the Porcupine Abyssal Plain (NE Atlantic): a stable isotope analysis. Prog Oceanogr. 2001;50:383–405.Article 

Google Scholar 
von Cosel R, Bouchet P. Tropical deep-water lucinids (Mollusca: Bivalvia) from the Indo-Pacific: essentially unknown, but diverse and occasionally gigantic. mém Mus Natl Hist Nat. 2008;196:115–213.
Google Scholar 
Stearns REC Scientific results of explorations by the US Fish Commission steamer Albatross. No. XVII. Descriptions of new West American land, fresh-water, and marine shells, with notes and comments. Proceedings of the United States National Museum. 1890. https://repository.si.edu/bitstream/handle/10088/13174/1/USNMP-13_813_1890.pdf.Taylor JD, Glover EA. The lucinid bivalve genus Cardiolucina (Mollusca, Bivalvia, Lucinidae): systematics, anatomy and relationships. Bull Br Mus Nat Hist Zoo. 1997;63:93–122.
Google Scholar 
Coan EV, Valentich-Scott P, Sadeghian PS. Bivalve seashells of tropical West America: marine bivalve mollusks from Baja California to Northern Peru. Santa Barbara, USA: Museum of Natural History; 2012.von Cosel R, Gofas S. Marine bivalves of tropical West Africa: from Rio de Oro to southern Angola. Marseille, France: Muséum national d’Histoire naturelle, Paris; 2019. p 1104.Atkinson L, Sink K. Field guide to the offshore marine invertebrates of South Africa. 2018. https://doi.org/10.15493/SAEON.PUB.10000001.Montagu G. Testacea Britannica, or natural history of British shells. London, UK: JS Hollis; 1803.Taylor J, Glover E. New lucinid bivalves from shallow and deeper water of the Indian and West Pacific Oceans (Mollusca, Bivalvia, Lucinidae). ZooKeys. 2013;326:69–90.Article 

Google Scholar 
Apprill A, McNally S, Parsons R, Weber L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat Micro Ecol. 2015;75:129–37.Article 

Google Scholar 
Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.Article 
CAS 

Google Scholar 
Pjevac P, Hausmann B, Schwarz J, Kohl G, Herbold CW, Loy A, et al. An economical and flexible dual barcoding, two-step PCR approach for highly multiplexed amplicon sequencing. Front Microbiol. 2021;12:669776.Article 

Google Scholar 
McLaren MR, Callahan BJ. Silva 138.1 prokaryotic SSU taxonomic training data formatted for DADA2 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4587955.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.Article 
CAS 

Google Scholar 
Andersen KS, Kirkegaard RH, Karst SM, Albertsen M. ampvis2: an R package to analyse and visualise 16S rRNA amplicon data. 2018. https://www.biorxiv.org/content/10.1101/299537v1.Bushnell B. BBMap: a fast, accurate, splice-aware aligner. Berkeley, CA, USA: Lawrence Berkeley National Lab. (LBNL); 2014.Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.Article 
CAS 

Google Scholar 
Nurk S, Bankevich A, Antipov D, Gurevich A, Korobeynikov A, Lapidus A, et al. Assembling genomes and mini-metagenomes from highly chimeric reads. In: Research in Computational Molecular Biology. Springer Berlin Heidelberg; 2013. p. 158–70.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.Article 

Google Scholar 
Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.Article 

Google Scholar 
Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.Article 
CAS 

Google Scholar 
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359.Article 

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 
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 
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019. https://doi.org/10.1093/bioinformatics/btz848.Parks DH, Chuvochina M, Chaumeil P-A, Rinke C, Mussig AJ, Hugenholtz P. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat Biotechnol. 2020;38:1079–86.Article 
CAS 

Google Scholar 
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.Article 
CAS 

Google Scholar 
Matsen FA, Kodner RB, Armbrust EV. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform. 2010;11:538.Article 

Google Scholar 
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.Article 

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 Bioinform. 2010;11:119.Article 

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

Google Scholar 
Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 2016;17:132.Article 

Google Scholar 
Trifinopoulos J, Nguyen L-T, von Haeseler A, Minh BQ. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 2016;44:W232–5.Article 
CAS 

Google Scholar 
Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–W296.Article 
CAS 

Google Scholar 
Varghese NJ, Mukherjee S, Ivanova N, Konstantinidis KT, Mavrommatis K, Kyrpides NC, et al. Microbial species delineation using whole genome sequences. Nucleic Acids Res. 2015;43:6761–71.Article 
CAS 

Google Scholar 
Qin Q-L, Xie B-B, Zhang X-Y, Chen X-L, Zhou B-C, Zhou J, et al. A proposed genus boundary for the prokaryotes based on genomic insights. J Bacteriol. 2014;196:2210–5.Article 

Google Scholar 
Huerta-Cepas J, Szklarczyk D, Heller D, Hernández-Plaza A, Forslund SK, Cook H, et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 2019;47:D309–D314.Article 
CAS 

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

Google Scholar 
Brettin T, Davis JJ, Disz T, Edwards RA, Gerdes S, Olsen GJ, et al. RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci Rep. 2015;5:8365.Article 

Google Scholar 
Mahram A, Herbordt MC. NCBI BLASTP on high-performance reconfigurable computing systems. ACM Trans Reconfigurable Technol Syst. 2015;7:1–20.Article 

Google Scholar 
Yang Z. PAML: a program package for phylogenetic analysis by maximum likelihood. Comput Appl Biosci. 1997;13:555–6.CAS 

Google Scholar 
Osvatic J, Wilkins L. Strength of selection scripts. FigShare. 2022;8. https://doi.org/10.6084/m9.figshare.20626746.v1.Amann RI, Binder BJ, Olson RJ, Chisholm SW, Devereux R, Stahl DA. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl Environ Microbiol. 1990;56:1919–25.Article 
CAS 

Google Scholar 
Lan Y, Sun J, Chen C, Sun Y, Zhou Y, Yang Y, et al. Hologenome analysis reveals dual symbiosis in the deep-sea hydrothermal vent snail Gigantopelta aegis. Nat Commun. 2021;12:1165.Article 
CAS 

Google Scholar 
Leray M, Wilkins LGE, Apprill A, Bik HM, Clever F, Connolly SR, et al. Natural experiments and long-term monitoring are critical to understand and predict marine host-microbe ecology and evolution. PLoS Biol. 2021;19:e3001322.Article 
CAS 

Google Scholar 
Petersen Jillian M, Yuen B, Alexandre G. The symbiotic ‘all-rounders’: partnerships between marine animals and chemosynthetic nitrogen-fixing bacteria. Appl Environ Microbiol 2020;87:e02129–20.Johnson KS, Childress JJ, Hessler RR, Sakamoto-Arnold CM, Beehler CL. Chemical and biological interactions in the Rose Garden hydrothermal vent field, Galapagos spreading center. Deep Sea Res A. 1988;35:1723–44.Article 

Google Scholar 
Kennicutt ME II, Brooks JM, Burke RA Jr. Hydrocarbon seepage, gas hydrates, and authigenic carbonate in the northwestern Gulf of Mexico. Offshore Technology Conference; 1989. https://doi.org/10.4043/5952-ms.Lilley MD, Butterfield DA, Olson EJ, Lupton JE, Macko SA, McDuff RE. Anomalous CH4 and NH4+ concentrations at an unsedimented mid-ocean-ridge hydrothermal system. Nature. 1993;364:45–47.Article 
CAS 

Google Scholar 
Von Damm KL, Edmond JM, Measures CI, Grant B. Chemistry of submarine hydrothermal solutions at Guaymas Basin, Gulf of California. Geochim Cosmochim Acta. 1985;49:2221–37.Article 

Google Scholar 
Lee RW, Thuesen EV, Childress JJ. Ammonium and free amino acids as nitrogen sources for the chemoautotrophic symbiosis Solemya reidi Bernard (Bivalvia: Protobranchia). J Exp Mar Bio Ecol. 1992;158:75–91.Article 
CAS 

Google Scholar 
Sanders JG, Beinart RA, Stewart FJ, Delong EF, Girguis PR. Metatranscriptomics reveal differences in in situ energy and nitrogen metabolism among hydrothermal vent snail symbionts. ISME J. 2013;7:1556–67.Article 
CAS 

Google Scholar 
Touchette BW, Burkholder JM. Review of nitrogen and phosphorus metabolism in seagrasses. J Exp Mar Bio Ecol. 2000;250:133–67.Article 
CAS 

Google Scholar 
Fourqurean JW, Zieman JC, Powell GVN. Relationships between porewater nutrients and seagrasses in a subtropical carbonate environment. Mar Biol. 1992;114:57–65.Article 
CAS 

Google Scholar 
Williams SL. Experimental studies of Caribbean seagrass bed development. Ecol Monogr. 1990;60:449–69.Article 

Google Scholar 
Herbert RA. Nitrogen cycling in coastal marine ecosystems. FEMS Microbiol Rev. 1999;23:563–90.Article 
CAS 

Google Scholar 
Risgaard-Petersen N, Dalsgaard T, Rysgaard S, Christensen PB, Borum J, McGlathery K, et al. Nitrogen balance of a temperate eelgrass Zostera marina bed. Mar Ecol Prog Ser. 1998;174:281–91.Article 
CAS 

Google Scholar 
Bristow LA, Dalsgaard T, Tiano L, Mills DB, Bertagnolli AD, Wright JJ, et al. Ammonium and nitrite oxidation at nanomolar oxygen concentrations in oxygen minimum zone waters. Proc Natl Acad Sci USA. 2016;113:10601–6.Article 
CAS 

Google Scholar 
Karthäuser C, Ahmerkamp S, Marchant HK, Bristow LA, Hauss H, Iversen MH, et al. Small sinking particles control anammox rates in the Peruvian oxygen minimum zone. Nat Commun. 2021;12:3235.Article 

Google Scholar 
Kuypers MMM, Lavik G, Woebken D, Schmid M, Fuchs BM, Amann R, et al. Massive nitrogen loss from the Benguela upwelling system through anaerobic ammonium oxidation. Proc Natl Acad Sci USA. 2005;102:6478–83.Article 
CAS 

Google Scholar 
Johnson KS, Beehler CL, Sakamoto-Arnold CM, Childress JJ. In situ measurements of chemical distributions in a deep-sea hydrothermal vent field. Science. 1986;231:1139–41.Article 
CAS 

Google Scholar 
Childress JJ, Girguis PR. The metabolic demands of endosymbiotic chemoautotrophic metabolism on host physiological capacities. J Exp Biol. 2011;214:312–25.Article 
CAS 

Google Scholar 
Hentschel U, Hand S, Felbeck H. The contribution of nitrate respiration to the energy budget of the symbiont-containing clam Lucinoma aequizonata: a calorimetric study. J Exp Biol. 1996;199:427–33.Article 
CAS 

Google Scholar 
Breusing C, Mitchell J, Delaney J, Sylva SP, Seewald JS, Girguis PR, et al. Physiological dynamics of chemosynthetic symbionts in hydrothermal vent snails. ISME J. 2020;14:2568–79.Article 
CAS 

Google Scholar 
Amorim K, Loick-Wilde N, Yuen B, Osvatic JT, Wäge-Recchioni J, Hausmann B, et al. Chemoautotrophy, symbiosis and sedimented diatoms support high biomass of benthic molluscs in the Namibian shelf. Sci Rep. 2022;12:9731.Article 
CAS 

Google Scholar 
Breusing C, Johnson SB, Tunnicliffe V, Clague DA, Vrijenhoek RC, Beinart RA. Allopatric and sympatric drivers of speciation in Alviniconcha hydrothermal vent snails. Mol Biol Evol. 2020;37:3469–84.Article 
CAS 

Google Scholar 
Giovannoni SJ, Cameron Thrash J, Temperton B. Implications of streamlining theory for microbial ecology. ISME J. 2014;8:1553–65.Article 

Google Scholar 
Brissac T, Gros O, Merçot H. Lack of endosymbiont release by two Lucinidae (Bivalvia) of the genus Codakia: consequences for symbiotic relationships. FEMS Microbiol Ecol. 2009;67:261–7.Article 
CAS 

Google Scholar 
Werner GDA, Cornelissen JHC, Cornwell WK, Soudzilovskaia NA, Kattge J, West SA, et al. Symbiont switching and alternative resource acquisition strategies drive mutualism breakdown. Proc Natl Acad Sci USA. 2018;115:5229–34.Article 
CAS 

Google Scholar 
Sudakaran S, Kost C, Kaltenpoth M. Symbiont acquisition and replacement as a source of ecological innovation. Trends Microbiol. 2017;25:375–90.Article 
CAS 

Google Scholar 
Li Y, Liles MR, Halanych KM. Endosymbiont genomes yield clues of tubeworm success. ISME J. 2018;12:2785–95.Article 
CAS 

Google Scholar 
Moran NA, Yun Y. Experimental replacement of an obligate insect symbiont. Proc Natl Acad Sci USA. 2015;112:2093–6.Article 
CAS 

Google Scholar 
Sørensen MES, Wood AJ, Cameron DD, Brockhurst MA. Rapid compensatory evolution can rescue low fitness symbioses following partner switching. Curr Biol. 2021;31:3721–3728.e4.Article 

Google Scholar 
Taylor JD, Glover EA, Smith L, Ikebe C, Williams ST. New molecular phylogeny of Lucinidae: increased taxon base with focus on tropical Western Atlantic species (Mollusca: Bivalvia). Zootaxa. 2016;4196:zootaxa.4196.3.2.Article 

Google Scholar 
Osvatic J. Fig1 gtdb tree and alignment. figshare. 2021. https://doi.org/10.6084/m9.figshare.16837216.v1.Osvatic J. Figure 2: GTDB alignment and phylogeny. 2021. https://doi.org/10.6084/m9.figshare.16837237. More

Auffret, M. D. et al. The role of microbial community composition in controlling soil respiration responses to temperature. PLoS ONE 11, e0165448 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Yao, Y. et al. A data-driven global soil heterotrophic respiration dataset and the drivers of its inter‐annual variability. Glob. Biogeochem. Cycle 35, e2020GB006918 (2021).Article 
CAS 

Google Scholar 
Davidson, E. A., Janssens, I. A. & Luo, Y. On the variability of respiration in terrestrial ecosystems: moving beyond Q10. Glob. Change Biol. 12, 154–164 (2006).Article 

Google Scholar 
Wang, Q. et al. Soil microbial respiration rate and temperature sensitivity along a north–south forest transect in eastern China: patterns and influencing factors. J. Geophys. Res. Biogeosci. 121, 399–410 (2016).Article 

Google Scholar 
Sihi, D. et al. Merging a mechanistic enzymatic model of soil heterotrophic respiration into an ecosystem model in two AmeriFlux sites of northeastern USA. Agric. Meteorol. 252, 155–166 (2018).Article 

Google Scholar 
Shao, P., Zeng, X., Moore, D. J. P. & Zeng, X. Soil microbial respiration from observations and Earth system models. Environ. Res. Lett. 8, 034034 (2013).Article 
CAS 

Google Scholar 
Davidson, E. A., Samanta, S., Caramori, S. S. & Savage, K. The dual Arrhenius and Michaelis–Menten kinetics model for decomposition of soil organic matter at hourly to seasonal time scales. Glob. Change Biol. 18, 371–384 (2012).Article 

Google Scholar 
Oechel, W. C. et al. Acclimation of ecosystem CO2 exchange in the Alaskan Arctic in response to decadal climate warming. Nature 406, 978–981 (2000).Article 
CAS 
PubMed 

Google Scholar 
Alster, C. J., von Fischer, J. C., Allison, S. D. & Treseder, K. K. Embracing a new paradigm for temperature sensitivity of soil microbes. Glob. Change Biol. 26, 3221–3229 (2020).Article 

Google Scholar 
Nie, M. et al. Positive climate feedbacks of soil microbial communities in a semi-arid grassland. Ecol. Lett. 16, 234–241 (2013).Article 
PubMed 

Google Scholar 
Ji, F., Wu, Z., Huang, J. & Chassignet, E. P. Evolution of land surface air temperature trend. Nat. Clim. Change 4, 462–466 (2014).Article 

Google Scholar 
Huntingford, C., Jones, P. D., Livina, V. N., Lenton, T. M. & Cox, P. M. No increase in global temperature variability despite changing regional patterns. Nature 500, 327–330 (2013).Article 
CAS 
PubMed 

Google Scholar 
Hansen, J., Sato, M. & Ruedy, R. Perception of climate change. Proc. Natl Acad. Sci. USA 109, E2415–E2423 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Byrne, M. P. Amplified warming of extreme temperatures over tropical land. Nat. Geosci. 14, 837–841 (2021).Article 
CAS 

Google Scholar 
IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Chan, W. P. et al. Seasonal and daily climate variation have opposite effects on species elevational range size. Science 351, 1437–1439 (2016).Article 
CAS 
PubMed 

Google Scholar 
Biederbeck, V. O. & Campbell, C. A. Soil microbial activity as influenced by temperature trends and fluctuations. Can. J. Soil Sci. 53, 363–375 (1973).Article 

Google Scholar 
Karhu, K. et al. Temperature sensitivity of soil respiration rates enhanced by microbial community response. Nature 513, 81–84 (2014).Article 
CAS 
PubMed 

Google Scholar 
Chen, H., Zhu, T., Li, B., Fang, C. & Nie, M. The thermal response of soil microbial methanogenesis decreases in magnitude with changing temperature. Nat. Commun. 11, 5733 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).Article 
CAS 

Google Scholar 
Nottingham, A. T. et al. Microbial responses to warming enhance soil carbon loss following translocation across a tropical forest elevation gradient. Ecol. Lett. 22, 1889–1899 (2019).Article 
PubMed 

Google Scholar 
Alster, C. J., Robinson, J. M., Arcus, V. L. & Schipper, L. A. Assessing thermal acclimation of soil microbial respiration using macromolecular rate theory. Biogeochemistry 158, 131–141 (2022).Article 
CAS 

Google Scholar 
Moinet, G. Y. K. et al. Soil microbial sensitivity to temperature remains unchanged despite community compositional shifts along geothermal gradients. Glob. Change Biol. 27, 6217–6231 (2021).Article 

Google Scholar 
Feng, J. et al. Soil microbial trait-based strategies drive metabolic efficiency along an altitude gradient. ISME Commun. 1, 71 (2021).Article 

Google Scholar 
Li, J. et al. Key microorganisms mediate soil carbon-climate feedbacks in forest ecosystems. Sci. Bull. 66, 2036–2044 (2021).Article 
CAS 

Google Scholar 
Trivedi, P. et al. Microbial regulation of the soil carbon cycle: evidence from gene–enzyme relationships. ISME J. 10, 2593–2604 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhu, B. & Cheng, W. Constant and diurnally-varying temperature regimes lead to different temperature sensitivities of soil organic carbon decomposition. Soil Biol. Biochem. 43, 866–869 (2011).Article 
CAS 

Google Scholar 
Bradford, M. A. et al. Thermal adaptation of soil microbial respiration to elevated temperature. Ecol. Lett. 11, 1316–1327 (2008).Article 
PubMed 

Google Scholar 
Hartley, I. P., Hopkins, D. W., Garnett, M. H., Sommerkorn, M. & Wookey, P. A. Soil microbial respiration in Arctic soil does not acclimate to temperature. Ecol. Lett. 11, 1092–1100 (2008).Article 
PubMed 

Google Scholar 
Bradford, M. A. et al. Cross-biome patterns in soil microbial respiration predictable from evolutionary theory on thermal adaptation. Nat. Ecol. Evol. 3, 223–231 (2019).Article 
PubMed 

Google Scholar 
Tian, W. et al. Thermal adaptation occurs in the respiration and growth of widely distributed bacteria. Glob. Change Biol. 28, 2820–2829 (2022).Article 
CAS 

Google Scholar 
Bradford, M. A., Watts, B. W. & Davies, C. A. Thermal adaptation of heterotrophic soil respiration in laboratory microcosms. Glob. Change Biol. 16, 1576–1588 (2010).Article 

Google Scholar 
Walker, T. W. N. et al. Microbial temperature sensitivity and biomass change explain soil carbon loss with warming. Nat. Clim. Change 8, 885–889 (2018).Article 
CAS 

Google Scholar 
Chen, H. et al. Microbial respiratory thermal adaptation is regulated by r-/K-strategy dominance. Ecol. Lett. 25, 2489–2499 (2022).Article 
PubMed 

Google Scholar 
Wang, C. et al. The temperature sensitivity of soil: microbial biodiversity, growth, and carbon mineralization. ISME J. 15, 2738–2747 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ramadhin, C., Yi, C. & Hendrey, G. Temperature variance portends and indicates the extent of abrupt climate shifts. IOP SciNotes 2, 014002 (2021).Article 

Google Scholar 
Sun, Y. Q. & Ge, Y. Temporal changes in the function of bacterial assemblages associated with decomposing earthworms. Front. Microbiol. 12, 682224 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Shi, Z., Xu, J., Li, X., Li, R. & Li, Q. Links of extracellular enzyme activities, microbial metabolism, and community composition in the river-impacted coastal waters. J. Geophys. Res. Biogeosci. 124, 3507–3520 (2019).Article 

Google Scholar 
Razanamalala, K. et al. Soil microbial diversity drives the priming effect along climate gradients: a case study in Madagascar. ISME J. 12, 451–462 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Xu, M. et al. High microbial diversity stabilizes the responses of soil organic carbon decomposition to warming in the subsoil on the Tibetan Plateau. Glob. Change Biol. 27, 2061–2075 (2021).Article 
CAS 

Google Scholar 
Clemmensen, K. E. et al. Roots and associated fungi drive long-term carbon sequestration in boreal forest. Science 339, 1615–1618 (2013).Article 
CAS 
PubMed 

Google Scholar 
Qiao, N. et al. Labile carbon retention compensates for CO2 released by priming in forest soils. Glob. Change Biol. 20, 1943–1954 (2014).Article 

Google Scholar 
Ning, Q. et al. Carbon limitation overrides acidification in mediating soil microbial activity to nitrogen enrichment in a temperate grassland. Glob. Change Biol. 27, 5976–5988 (2021).Article 
CAS 

Google Scholar 
Wan, S. & Luo, Y. Substrate regulation of soil respiration in a tallgrass prairie: results of a clipping and shading experiment. Glob. Biogeochem. Cycle 17, 1054 (2003).Article 

Google Scholar 
Gillabel, J., Cebrian-Lopez, B., Six, J. & Merckx, R. Experimental evidence for the attenuating effect of SOM protection on temperature sensitivity of SOM decomposition. Glob. Change Biol. 16, 2789–2798 (2010).Article 

Google Scholar 
Xia, J. et al. Terrestrial carbon cycle affected by non-uniform climate warming. Nat. Geosci. 7, 173–180 (2014).Article 
CAS 

Google Scholar 
Balesdent, J. et al. Atmosphere–soil carbon transfer as a function of soil depth. Nature 559, 599–602 (2018).Article 
CAS 
PubMed 

Google Scholar 
Howard, D. M. & Howard, P. J. A. Relationships between CO2 evolution, moisture-content and temperature for a range of soil types. Soil Biol. Biochem. 25, 1537–1546 (1993).Article 

Google Scholar 
Hoyle, F. C., Murphy, D. V. & Brookes, P. C. Microbial response to the addition of glucose in low-fertility soils. Biol. Fertil. Soils 44, 571–579 (2008).Article 
CAS 

Google Scholar 
Mau, R. L. et al. Linking soil bacterial biodiversity and soil carbon stability. ISME J. 9, 1477–1480 (2015).Article 
CAS 
PubMed 

Google Scholar 
Tucker, C. L., Bell, J., Pendall, E. & Ogle, K. Does declining carbon-use efficiency explain thermal acclimation of soil respiration with warming? Glob. Change Biol. 19, 252–263 (2013).Article 

Google Scholar 
Billings, S. A. & Ballantyne, F. T. How interactions between microbial resource demands, soil organic matter stoichiometry, and substrate reactivity determine the direction and magnitude of soil respiratory responses to warming. Glob. Change Biol. 19, 90–102 (2013).Article 

Google Scholar 
Li, J. et al. Biogeographic variation in temperature sensitivity of decomposition in forest soils. Glob. Change Biol. 26, 1873–1885 (2020).Article 

Google Scholar 
Min, K. et al. Temperature sensitivity of biomass-specific microbial exo-enzyme activities and CO2 efflux is resistant to change across short- and long-term timescales. Glob. Change Biol. 5, 1793–1807 (2019).Article 

Google Scholar 
Dacal, M., Bradford, M. A., Plaza, C., Maestre, F. T. & Garcia-Palacios, P. Soil microbial respiration adapts to ambient temperature in global drylands. Nat. Ecol. Evol. 3, 232–238 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Field-Fote, E. E. Mediators and moderators, confounders and covariates: exploring the variables that illuminate or obscure the “active ingredients” in neurorehabilitation. J. Neurol. Phys. Ther. 43, 83–84 (2019).Article 
PubMed 

Google Scholar 
Anderson, T. H. & Domsch, K. H. Soil microbial biomass: the eco-physiological approach. Soil Biol. Biochem. 12, 2039–2043 (2010).Article 

Google Scholar 
Vance, E. D., Brookes, P. C. & Jenkinson, D. S. Microbial biomass measurements in forest soils—the use of the chloroform fumigation incubation method in strongly acid soils. Soil Biol. Biochem. 19, 697–702 (1987).Article 
CAS 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).Article 

Google Scholar 
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).Article 
CAS 
PubMed 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).Article 
CAS 
PubMed 

Google Scholar 
Koljalg, U. et al. UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. N. Phytol. 166, 1063–1068 (2005).Article 
CAS 

Google Scholar 
German, D. P. et al. Optimization of hydrolytic and oxidative enzyme methods for ecosystem studies. Soil Biol. Biochem. 43, 1387–1397 (2011).Article 
CAS 

Google Scholar 
Mazerolle, M. Improving data analysis in herpetology: using Akaike’s information criterion (AIC) to assess the strength of biological hypotheses. Amphib. Reptil. 2, 169–180 (2006).Article 

Google Scholar 
Moinet, G. Y. K. et al. Temperature sensitivity of decomposition decreases with increasing soil organic matter stability. Sci. Total Environ. 704, 135460 (2020).Article 
CAS 
PubMed 

Google Scholar 
Moinet, G. Y. K. et al. The temperature sensitivity of soil organic matter decomposition is constrained by microbial access to substrates. Soil Biol. Biochem. 116, 333–339 (2018).Article 
CAS 

Google Scholar 
Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).Article 

Google Scholar  More

Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73. https://doi.org/10.1038/nature22900 (2017).Article 
ADS 
CAS 

Google Scholar 
Laurance, W. F., Sayer, J. & Cassman, K. G. Agricultural expansion and its impacts on tropical nature. Trends Ecol. Evol. 29, 107–116. https://doi.org/10.1016/j.tree.2013.12.001 (2014).Article 

Google Scholar 
Ceballos, G. et al. Accelerated modern human–induced species losses: Entering the sixth mass extinction. J. Sci. Adv. 1, e1400253. https://doi.org/10.1126/sciadv.1400253 (2015).Article 
ADS 

Google Scholar 
Cardillo, M. et al. Human population density and extinction risk in the world’s carnivores. PLoS Biol. 2, e197. https://doi.org/10.1371/journal.pbio.0020197 (2004).Article 

Google Scholar 
Estes, J. A. et al. Trophic downgrading of planet earth. Science 333, 301–306 (2011).Article 
ADS 
CAS 

Google Scholar 
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 124–148 (2014).Article 

Google Scholar 
Bauer, H. et al. Lion (Panthera leo) populations are declining rapidly across Africa, except in intensively managed areas. Proc. Natl. Acad. Sci. 112, 14895–14899 (2015).Article 
ADS 

Google Scholar 
Bauer, H., Page-Nicholson, S., Hinks, A. & Dickman, A. Guidelines for the Conservation of lion in Africa 17–24 (IUCN SSC Cat Specialist Group, 2018).
Google Scholar 
Lindsey, P. A., Roulet, P. A. & Romanach, S. S. Economic and conservation significance of the trophy hunting industry in sub-Saharan Africa. Biol. Conserv. 134, 455–469. https://doi.org/10.1016/j.biocon.2006.09.005 (2007).Article 

Google Scholar 
Vucetich, J. A. et al. The value of argument analysis for understanding ethical considerations pertaining to trophy hunting and lion conservation. Biol. Conserv. 235, 260–272. https://doi.org/10.1016/j.biocon.2019.04.012 (2019).Article 

Google Scholar 
Dube, N. Voices from the village on trophy hunting in Hwange district, Zimbabwe. Ecol. Econ. 159, 335–343. https://doi.org/10.1016/j.ecolecon.2019.02.006 (2019).Article 

Google Scholar 
Murombedzi, J. African wildlife and livelihoods. In The Promise and Performance of Community Conservation (eds Hulme, D. & Murphree, M.) 244–255 (James Currey, 2001).
Google Scholar 
Leader-Williams, N., Baldus, R. D. & Smith, R. J. Recreational hunting. In Conservation and Rural Livelihoods (eds Dickson, B. et al.) 296–316 (Blackwell Publishing Ltd., 2009).Chapter 

Google Scholar 
DiMinin, E., Leader-Williams, N. & Bradshaw, C. J. A. Banning trophy hunting will exacerbate biodiversity loss. Trends Ecol. Evol. 31, 99–102 (2016).Article 

Google Scholar 
Whitman, K., Starfield, A. M., Quadling, H. S. & Packer, C. Sustainable trophy hunting of African lions. Nature 428, 175–178 (2004).Article 
ADS 
CAS 

Google Scholar 
Packer, C. et al. Sport hunting, predator control and conservation of large carnivores. PLoS ONE 4, e5941. https://doi.org/10.1371/journal.pone.0005941 (2009).Article 
ADS 
CAS 

Google Scholar 
Mweetwa, T. et al. Quantifying lion (Panthera leo) demographic response following a three-year moratorium on trophy hunting. PLoS ONE 13, e0197030. https://doi.org/10.1371/journal.pone.0197030 (2018).Article 
CAS 

Google Scholar 
Loveridge, A. J. et al. Conservation of large predator populations: Demographic and spatial responses of African lions to the intensity of trophy hunting. Biol. Conserv. 204, 247–254. https://doi.org/10.1016/j.biocon.2016.10.024 (2016).Article 

Google Scholar 
Starfield, A. M., Shiell, J. D. & Smuts, G. L. Simulation of lion control strategies in a large game reserve. Ecol. Model. 13, 17–28 (1981).Article 

Google Scholar 
Venter, J. & Hopkins, M. E. Use of a simulation model in the management of a lion population. S. Afr. J. Wildl. Res. 18, 126–130 (1988).
Google Scholar 
Starfield, A. M. & Bleloch, A. L. Modelling the effect of contraception on part of the lion population in Etosha National Park. Applied Mathematic Dept. Report R3/82, Witwaterstrand University, South Africa. 7 (1982).Dickman, A., Becker, M., Begg, C., Loveridge, A. J. & Macdonald, D. W. Guidelines for the Conservation of Lions in Africa, Ch. 6 69–75 (IUCN SSC Cat Specialist Group, 2018).
Google Scholar 
Creel, S. et al. Assessing the sustainability of lion trophy hunting with recomendations for policy. Ecol. Appl. 26, 2347–2357. https://doi.org/10.1002/eap.1377 (2016).Article 

Google Scholar 
Barthold, J., Loveridge, A. J., Macdonald, D. W., Packer, C. & Colchero, F. Bayesian estimates of male and female African lion mortality for future use in population management. J. Appl. Ecol. 53, 295–304 (2016).Article 

Google Scholar 
Loveridge, A. J., Valeix, M., Elliot, N. B. & Macdonald, D. W. The landscape of anthropogenic mortality: How African lions respond to spatial variation in risk. J. Appl. Ecol. 54, 815–825. https://doi.org/10.1111/1365-2664.12794 (2017).Article 

Google Scholar 
Loveridge, A. J. et al. Evaluating the spatial intensity and demographic impacts of wire-snare bush-meat poaching on large carnivores. Biol. Conserv. 244, 108504 (2020).Article 

Google Scholar 
Becker, M. S. et al. Estimating past and future male loss in three Zambian lion populations. J. Wildl. Manag. 77, 128–142 (2013).Article 

Google Scholar 
Kiffner, C., Meyer, B., Muhlenberg, M. & Waltert, M. Plenty of prey, few predators: What limits lions Panthera leo in Katavi National park, western Tanzania?. Oryx 43, 52–59 (2009).Article 

Google Scholar 
Loveridge, A. J., Searle, A. W., Murindagomo, F. & Macdonald, D. W. The impact of sport hunting on the population dynamics of an African lion population in a protected area. Biol. Conserv. 134, 548–558 (2007).Article 

Google Scholar 
Miller, J. R. B. et al. Aging traits and sustainable trophy hunting of African lions. Biol. Conserv. 201, 160–168 (2016).Article 

Google Scholar 
Woodroffe, R. & Ginsberg, J. R. Edge effects and the extinction of populations inside protected areas. Science 280, 2126–2128 (1998).Article 
ADS 
CAS 

Google Scholar 
Gervasi, V., Linnell, J. D. C., Brøseth, H. & Gimenez, O. Failure to coordinate management in transboundary populations hinders the achievement of national management goals: The case of wolverines in Scandinavia. J. Appl. Ecol. 56, 1905–1915. https://doi.org/10.1111/1365-2664.13379 (2019).Article 

Google Scholar 
Breitenmoser, U. & Nobbe, C. Guidelines for the Conservation of Lions in Africa (ed IUCN CSG/SSC) 29–30 (IUCN, 2018).du Preez, B. & Lopez-Bao, J. V. Guidelines for the Conservation of the Lion in Africa (ed IUCN CSG/SSC) 76–78 (IUCN, 2018).Loveridge, A. J., Hemson, G., Davidson, Z. & Macdonald, D. W. African lions on the edge: reserve boundaries as ‘attractive sinks’ In Biology and Conservation of Wild Felids, Ch. 11 (eds Macdonald, D. W. & Loveridge, A. J.) 283–304 (Oxford University Press, London, 2010).

Google Scholar 
Borrego, N., Ozgul, A., Slotow, R. & Packer, C. Lion population dynamics: Do nomadic males matter?. Behav. Ecol. 29, 660–666. https://doi.org/10.1093/beheco/ary018%JBehavioralEcology (2018).Article 

Google Scholar 
Packer, C. et al. The case for fencing remains intact. Ecol. Lett. https://doi.org/10.1111/ele.12171 (2013).Balme, G. et al. Big cats at large: Density, structure, and spatio-temporal patterns of a leopard population free of anthropogenic mortality. Popul. Ecol. 61, 256–267. https://doi.org/10.1002/1438-390x.1023 (2019).Article 

Google Scholar 
Grünewald, C., Schleuning, M. & Böhning-Gaese, K. Biodiversity, scenery and infrastructure: Factors driving wildlife tourism in an African savannah national park. Biol. Conserv. 201, 60–68. https://doi.org/10.1016/j.biocon.2016.05.036 (2016).Article 

Google Scholar 
Pulliam, H. R. Sources, sinks, and population. Regulation 132, 652–661. https://doi.org/10.1086/284880 (1988).Article 

Google Scholar 
Lamb, C. T. et al. The ecology of human–carnivore coexistence. Proc. Natl. Acad. Sci. 117, 17876–17883. https://doi.org/10.1073/pnas.1922097117 (2020).Article 
ADS 
CAS 

Google Scholar 
Robinson, H. S., Weilgus, R. B., Cooley, H. & Cooley, S. Source—sink populations in carnivore management: cougar demography and immigration in a hunted population. Ecol. Appl. 18, 1028–1037 (2008).Article 

Google Scholar 
Creel, S. et al. Questionable policy for large carnivore hunting. Science 350, 1473–1475 (2015).Article 
ADS 
CAS 

Google Scholar 
Cushman, S. A. et al. Prioritizing core areas, corridors and conflict hotspots for lion conservation in southern Africa. PLoS ONE 13, e0196213. https://doi.org/10.1371/journal.pone.0196213 (2018).Article 
CAS 

Google Scholar 
Kelly, M. J. & Durant, S. M. Viability of the Serengeti cheetah population. Conserv. Biol. 14, 786–797 (2000).Article 

Google Scholar 
Skalski, J. R., Ryding, K. & Millspaug, J. J. Wildlife Demography: Analysis of Sex, Age, and Count Data (Elsevier Academic Press, 2005).
Google Scholar 
Hamlin, K. L., Pac, D. F., Sime, C. A., DeSimone, R. M. & Dusek, G. L. Evaluating the accuracy of ages obtained by two methods for montana ungulates. J. Wildl. Manag. 64, 441–449. https://doi.org/10.2307/3803242 (2000).Article 

Google Scholar 
Storm, D. J. et al. Estimating ages of white-tailed deer: Age and sex patterns of error using tooth wear-and-replacement and consistency of cementum annuli. Wildl Soc Bull 38, 849–856. https://doi.org/10.1002/wsb.457 (2014).Article 
ADS 

Google Scholar 
Balme, G. A., Hunter, L. & Braczkowski, A. R. Applicability of age-based hunting regulations for African Leopards. PLoS ONE 7, e35209. https://doi.org/10.1371/journal.pone.0035209 (2012).Article 
ADS 
CAS 

Google Scholar 
Gipson, P. S., Ballard, W. B., Nowak, R. M. & Mech, L. D. Accuracy and precision of estimating age of gray wolves by tooth wear. J. Wildl. Manag. 64, 752–758. https://doi.org/10.2307/3802745 (2000).Article 

Google Scholar 
Hiller, T. L. Comparison of two age-estimation techniques for cougars. J. Northwest. Nat. 77–82, 76 (2014).
Google Scholar 
Begg, C. M., Miller, J. R. B. & Begg, K. S. Effective implementation of age restrictions increases selectivity of sport hunting of the African lion. J. Appl. Ecol. 55, 139–146. https://doi.org/10.1111/1365-2664.12951 (2018).Article 

Google Scholar 
Mandisodza-Chikerema, R., Jooste, D. & Funston, P. J. Lion aging and adaptive quota management report: Ages of lions hunted and recommended quotas for 2019 in Zimbabwe. 12 (Unpublished report, Zimbabwe Parks and Wildlife Management and Panthera, Harare, Zimbabwe, 2019).Smuts, G. L., Anderson, J. L. & Austin, J. C. Age determination of the African lion (Panthera leo). J. Zool. Lond. 185, 115–146 (1978).Article 

Google Scholar 
Lindsey, P. A. et al. The trophy hunting of African lions: Scale, current management practices and factors undermining sustainability. PLoS ONE 8, 1–11 (2013).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2022).Packer, C. et al. Effects of trophy hunting on lion and leopard populations in Tanzania. Conserv. Biol. 25, 142–153 (2011).Article 
CAS 

Google Scholar 
Mace, G. M. & Reynolds, J. Exploitation as a conservation issue. In Conservation of Exploited Species, Ch. 1 (eds Reynolds, J. et al.) 3–15 (Cambridge University Press, Cambridge, 2001).
Google Scholar 
Struhsaker, T. T. A biologists perspective on the role of sustainable harvest in conservation. Conserv. Biol. 12, 930–932 (1998).Article 

Google Scholar  More

Humpenöder, F. et al. Projected environmental benefits of replacing beef with microbial protein. Nature 605, 90–96 (2022).Article 

Google Scholar 
Tilman, D. & Clark, M. Global diets link environmental sustainability and human health. Nature 515, 518–522 (2014).Article 
CAS 

Google Scholar 
Clark, M. A. et al. Global food system emissions could preclude achieving the 1.5° and 2°C climate change targets. Science 370, 705–708 (2020).Article 
CAS 

Google Scholar 
Eisler, M. C. et al. Agriculture: steps to sustainable livestock. Nature 507, 32–34 (2014).Article 

Google Scholar 
Kamke, J. et al. Rumen metagenome and metatranscriptome analyses of low methane yield sheep reveals a Sharpea-enriched microbiome characterised by lactic acid formation and utilisation. Microbiome 4, 56 (2016).Article 

Google Scholar 
Kruger Ben Shabat, S. et al. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME J. 10, 2958–2972 (2016).Article 

Google Scholar 
Janssen, P. H. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Anim. Feed Sci. Technol. 160, 1–22 (2010).Article 
CAS 

Google Scholar 
Wallace, R. J. et al. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci. Adv. 5, eaav8391 (2019).Article 
CAS 

Google Scholar 
Urrutia, N. L. & Harvatine, K. J. Acetate dose-dependently stimulates milk fat synthesis in lactating dairy cows. J. Nutr. 147, 763–769 (2017).Article 
CAS 

Google Scholar 
Seshadri, R. et al. Cultivation and sequencing of rumen microbiome members from the Hungate1000 Collection. Nat. Biotechnol. 36, 359–367 (2018).Article 
CAS 

Google Scholar 
Anderson, C. J., Koester, L. R. & Schmitz-Esser, S. Rumen epithelial communities share a core bacterial microbiota: a meta-analysis of 16S rRNA Gene Illumina MiSeq sequencing datasets. Front. Microbiol. 12, 625400 (2021).Wallace, R. J., Cheng, K.-J., Dinsdale, D. & Ørskov, E. R. An independent microbial flora of the epithelium and its role in the ecomicrobiology of the rumen. Nature 279, 424–426 (1979).Article 
CAS 

Google Scholar 
Mann, E., Wetzels, S. U., Wagner, M., Zebeli, Q. & Schmitz-Esser, S. Metatranscriptome sequencing reveals insights into the gene expression and functional potential of rumen wall bacteria. Front. Microbiol. 9, 43 (2018).Pacífico, C. et al. Unveiling the bovine epimural microbiota composition and putative function. Microorganisms 9, 342 (2021).Article 

Google Scholar 
VanInsberghe, D., Arevalo, P., Chien, D. & Polz, M. F. How can microbial population genomics inform community ecology?. Phil. Trans. R. Soc. B 375, 20190253 (2020).Article 

Google Scholar 
Hunt, D. E. et al. Resource partitioning and sympatric differentiation among closely related bacterioplankton. Science 320, 1081–1085 (2008).Article 
CAS 

Google Scholar 
Fraser, C., Hanage, W. P. & Spratt, B. G. Recombination and the nature of bacterial speciation. Science 315, 476–480 (2007).Article 
CAS 

Google Scholar 
Shapiro, B. J. et al. Population genomics of early events in the ecological differentiation of bacteria. Science 335, 48–51 (2012).Article 

Google Scholar 
Cadillo-Quiroz, H. et al. Patterns of gene flow define species of thermophilic Archaea. PLoS Biol. 10, e1001265 (2012).Article 
CAS 

Google Scholar 
Koeppel, A. et al. Identifying the fundamental units of bacterial diversity: a paradigm shift to incorporate ecology into bacterial systematics. Proc. Natl Acad. Sci. USA 105, 2504–2509 (2008).Article 
CAS 

Google Scholar 
Arevalo, P., VanInsberghe, D., Elsherbini, J., Gore, J. & Polz, M. F. A reverse ecology approach based on a biological definition of microbial populations. Cell 178, 820–834.e14 (2019).Article 
CAS 

Google Scholar 
Wetzels, S. U. et al. Epimural bacterial community structure in the rumen of Holstein cows with different responses to a long-term subacute ruminal acidosis diet challenge. J. Dairy Sci. 100, 1829–1844 (2017).Article 
CAS 

Google Scholar 
Neubauer, V. et al. Effects of clay mineral supplementation on particle-associated and epimural microbiota, and gene expression in the rumen of cows fed high-concentrate diet. Anaerobe 59, 38–48 (2019).Article 
CAS 

Google Scholar 
Stewart, R. D. et al. Compendium of 4,941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery. Nat. Biotechnol. 37, 953–961 (2019).Article 
CAS 

Google Scholar 
Waite, D. W. et al. Comparative genomic analysis of the class Epsilonproteobacteria and proposed reclassification to Epsilonbacteraeota (phyl. nov.). Front. Microbiol. 8, 682 (2017).Article 

Google Scholar 
Rodriguez-R, L. M. & Konstantinidis, K. T. Bypassing cultivation to identify bacterial species. Microbe Mag. 9, 111–118 (2014).Article 

Google Scholar 
Bendall, M. L. et al. Genome-wide selective sweeps and gene-specific sweeps in natural bacterial populations. ISME J. 10, 1589–1601 (2016).Article 

Google Scholar 
Birky, C. W., Adams, J., Gemmel, M. & Perry, J. Using population genetic theory and DNA sequences for species detection and identification in asexual organisms. PLoS ONE 5, e10609 (2010).Article 

Google Scholar 
Li, W.-H. Unbiased estimation of the rates of synonymous and nonsynonymous substitution. J. Mol. Evol. 36, 96–99 (1993).Article 
CAS 

Google Scholar 
Novichkov, P. S., Wolf, Y. I., Dubchak, I. & Koonin, E. V. Trends in prokaryotic evolution revealed by comparison of closely related bacterial and archaeal genomes. J. Bacteriol. 191, 65–73 (2009).Article 
CAS 

Google Scholar 
Tilman, D. Resource competition between plankton algae: an experimental and theoretical approach. Ecology 58, 338–348 (1977).Article 
CAS 

Google Scholar 
Yawata, Y. et al. Competition–dispersal tradeoff ecologically differentiates recently speciated marine bacterioplankton populations. Proc. Natl Acad. Sci. USA 111, 5622–5627 (2014).Article 
CAS 

Google Scholar 
Basan, M. et al. A universal trade-off between growth and lag in fluctuating environments. Nature 584, 470–474 (2020).Article 
CAS 

Google Scholar 
Flamholz, A., Noor, E., Bar-Even, A., Liebermeister, W. & Milo, R. Glycolytic strategy as a tradeoff between energy yield and protein cost. Proc. Natl Acad. Sci. USA 110, 10039–10044 (2013).Article 
CAS 

Google Scholar 
Szymanski, C. M., Yao, R., Ewing, C. P., Trust, T. J. & Guerry, P. Evidence for a system of general protein glycosylation in Campylobacter jejuni. Mol. Microbiol. 32, 1022–1030 (1999).Article 
CAS 

Google Scholar 
Roux, D. et al. Identification of poly-N-acetylglucosamine as a major polysaccharide component of the Bacillus subtilis biofilm matrix. J. Biol. Chem. 290, 19261–19272 (2015).Article 
CAS 

Google Scholar 
Troutman, J. M. & Imperiali, B. Campylobacter jejuni PglH is a single active site processive polymerase that utilizes product inhibition to limit sequential glycosyl transfer reactions. Biochemistry 48, 2807–2816 (2009).Article 
CAS 

Google Scholar 
Hehemann, J. H. et al. Adaptive radiation by waves of gene transfer leads to fine-scale resource partitioning in marine microbes. Nat. Commun. 7, 12860 (2016).Article 
CAS 

Google Scholar 
Treangen, T. J. & Rocha, E. P. C. Horizontal transfer, not duplication, drives the expansion of protein families in prokaryotes. PLoS Genet. 7, e1001284 (2011).Article 
CAS 

Google Scholar 
Castric, P. pilO, a gene required for glycosylation of Pseudomonas aeruginosa 1244 pilin. Microbiology 141, 1247–1254 (1995).Article 
CAS 

Google Scholar 
Mourkas, E. et al. Host ecology regulates interspecies recombination in bacteria of the genus Campylobacter. eLife 11, e73552 (2022).Article 
CAS 

Google Scholar 
Sheppard, S. K. et al. Genome-wide association study identifies vitamin B 5 biosynthesis as a host specificity factor in Campylobacter. Proc. Natl Acad. Sci. USA 110, 11923–11927 (2013).Article 
CAS 

Google Scholar 
Bobay, L.-M. & Ochman, H. Biological species are universal across life’s domains. Genome Biol. Evol. https://doi.org/10.1093/gbe/evx026 (2017).Dieho, K. et al. Morphological adaptation of rumen papillae during the dry period and early lactation as affected by rate of increase of concentrate allowance. J. Dairy Sci. 99, 2339–2352 (2016).Article 
CAS 

Google Scholar 
Lawson, C. E. et al. Autotrophic and mixotrophic metabolism of an anammox bacterium revealed by in vivo 13C and 2H metabolic network mapping. ISME J. 15, 673–687 (2021).Article 
CAS 

Google Scholar 
Kwong, W. K., Zheng, H. & Moran, N. A. Convergent evolution of a modified, acetate-driven TCA cycle in bacteria. Nat. Microbiol. 2, 17067 (2017).Article 
CAS 

Google Scholar 
Kather, B., Stingl, K., van der Rest, M. E., Altendorf, K. & Molenaar, D. Another unusual type of citric acid cycle enzyme in Helicobacter pylori: the malate:quinone oxidoreductase. J. Bacteriol. 182, 3204–3209 (2000).Article 
CAS 

Google Scholar 
Mullins, E. A. & Kappock, T. J. Crystal structures of Acetobacter aceti succinyl-coenzyme A (CoA):acetate CoA-transferase reveal specificity determinants and illustrate the mechanism used by class I CoA-transferases. Biochemistry 51, 8422–8434 (2012).Article 
CAS 

Google Scholar 
Letten, A. D., Hall, A. R. & Levine, J. M. Using ecological coexistence theory to understand antibiotic resistance and microbial competition. Nat. Ecol. Evol. 5, 431–441 (2021).Article 

Google Scholar 
Park, S. Y. et al. Strain-level fitness in the gut microbiome is an emergent property of glycans and a single metabolite. Cell 185, 513–529.e21 (2022).Article 
CAS 

Google Scholar 
Kim, C. H. Control of lymphocyte functions by gut microbiota-derived short-chain fatty acids. Cell Mol. Immunol. 18, 1161–1171 (2021).Article 
CAS 

Google Scholar 
Morrison, D. J. & Preston, T. Formation of short chain fatty acids by the gut microbiota and their impact on human metabolism. Gut Microbes 7, 189–200 (2016).Article 

Google Scholar 
Frampton, J., Murphy, K. G., Frost, G. & Chambers, E. S. Short-chain fatty acids as potential regulators of skeletal muscle metabolism and function. Nat. Metab. 2, 840–848 (2020).Article 
CAS 

Google Scholar 
Good, B. H., McDonald, M. J., Barrick, J. E., Lenski, R. E. & Desai, M. M. The dynamics of molecular evolution over 60,000 generations. Nature 551, 45–50 (2017).Article 

Google Scholar 
Lang, G. I. et al. Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations. Nature 500, 571–574 (2013).Article 
CAS 

Google Scholar 
Shapiro, B. J. & Polz, M. F. Microbial speciation. Cold Spring Harb. Perspect. Biol. 7, a018143 (2015).Article 

Google Scholar 
Sheppard, S. K. et al. Evolution of an agriculture-associated disease causing Campylobacter coli clade: evidence from national surveillance data in Scotland. PLoS ONE 5, e15708 (2010).Article 
CAS 

Google Scholar 
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 

Google Scholar 
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).Article 
CAS 

Google Scholar 
Pacífico, C. et al. Bovine rumen epithelial miRNA–mRNA dynamics reveals post-transcriptional regulation of gene expression upon transition to high-grain feeding and phytogenic supplementation. Genomics 114, 110333 (2022).Article 

Google Scholar 
Rivera-Chacon, R. et al. Supplementing a phytogenic feed additive modulates the risk of subacute rumen acidosis, rumen fermentation and systemic inflammation in cattle fed acidogenic diets. Animals 12, 1201 (2022).Article 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
CAS 

Google Scholar 
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).Article 
CAS 

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).Article 
CAS 

Google Scholar 
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).Article 
CAS 

Google Scholar 
Putri, G. H., Anders, S., Pyl, P. T., Pimanda, J. E. & Zanini, F. Analysing high-throughput sequencing data in Python with HTSeq 2.0. Bioinformatics 38, 2943–2945 (2022).Article 
CAS 

Google Scholar 
O’doherty, A. et al. Development of nalidixic acid amphotericin B vancomycin (NAV) medium for the isolation of Campylobacter ureolyticus from the stools of patients presenting with acute gastroenteritis. Br. J. Biomed. Sci. 71, 6–12 (2014).Article 

Google Scholar 
Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864 (2011).Article 
CAS 

Google Scholar 
Karst, S. M., Kirkegaard, R. H. & Albertsen, M. mmgenome: a toolbox for reproducible genome extraction from metagenomes. Preprint at bioRxiv https://doi.org/10.1101/059121 (2014).Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).Article 
CAS 

Google Scholar 
Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).Article 

Google Scholar 
Jukes, T. H. & Cantor, C. R. in Mammalian Protein Metabolism (ed. Munro, H. N.) 21–132 (Elsevier, 1969).Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).Article 
CAS 

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

Google Scholar 
Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).Article 
CAS 

Google Scholar 
Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).Article 
CAS 

Google Scholar 
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).Article 
CAS 

Google Scholar 
Le, S. Q. & Gascuel, O. An improved general amino acid replacement matrix. Mol. Biol. Evol. 25, 1307–1320 (2008).Article 
CAS 

Google Scholar 
Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).Article 

Google Scholar 
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).Article 
CAS 

Google Scholar 
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).Article 

Google Scholar 
Tan, R. S. G., Zhou, M., Li, F. & Guan, L. L. Identifying active rumen epithelial associated bacteria and archaea in beef cattle divergent in feed efficiency using total RNA-seq. Curr. Res. Microbial Sci. 2, 100064 (2021).Article 
CAS 

Google Scholar 
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics https://doi.org/10.1093/bioinformatics/btz848 (2019).Brewer, M. T., Anderson, K. L., Yoon, I., Scott, M. F. & Carlson, S. A. Amelioration of salmonellosis in pre-weaned dairy calves fed Saccharomyces cerevisiae fermentation products in feed and milk replacer. Vet. Microbiol. 172, 248–255 (2014).Article 

Google Scholar  More