Philippa Kaur

Abicair K, Manning AD, Ford F, Newport J, Banks SC (2020) Habitat selection and genetic diversity of a reintroduced ‘refugee species’. Anim Conserv 23:330–341Article 

Google Scholar 
Adamack AT, Gruber B (2014) PopGenReport: Simplifying basic population genetic analyses in R. Methods Ecol Evol 5:384–387Article 

Google Scholar 
Amori G, Gippoliti S (2003) A higher-taxon approach to rodent conservation priorities for the 21st century. Anim Biodivers Conserv 26.2:1–18
Google Scholar 
Andersen LW, Fog K, Damgaard C (2004) Habitat fragmentation causes bottlenecks and inbreeding in the European tree frog (Hyla arborea). Proc Biol Sci 271:1293–1302Article 
PubMed 
PubMed Central 

Google Scholar 
Ball IR, Possingham H, Watts M (2009) Marxan and relatives: Software for spatial conservation prioritization. In: Moilanen A, Wilson KA, Possingham H (eds) Spatial Conservation Prioritisation: Quantitative Methods and Computational Tools. Oxford University Press, Oxford, U.K., p 320
Google Scholar 
Barbato M, Orozco-terWengel P, Tapio M, Bruford MW (2015). SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data. Front Genet 6:109Barbosa S, Mestre F, White TA, Paupério J, Alves PC, Searle JB (2018) Integrative approaches to guide conservation decisions: Using genomics to define conservation units and functional corridors. Mol Ecol 27:3452–3465Article 
PubMed 

Google Scholar 
Bowman DMJS, Brown GK, Braby MF, Brown JR, Cook LG, Crisp MD et al. (2010) Biogeography of the Australian monsoon tropics. J Biogeogr 37:201–216Article 

Google Scholar 
Caballero A, García-Dorado A (2013) Allelic diversity and its implications for the rate of adaptation. Genetics 195:1373–1384Article 
PubMed 
PubMed Central 

Google Scholar 
Cardillo M, Mace GM, Gittleman JL, Jones KE, Bielby J, Purvis A (2008) The predictability of extinction: biological and external correlates of decline in mammals. Proc R Soc B: Biol Sci 275:1441–1448Article 

Google Scholar 
Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA (2013) Stacks: an analysis tool set for population genomics. Mol Ecol 22:3124–3140Article 
PubMed 
PubMed Central 

Google Scholar 
Catullo RA, Lanfear R, Doughty P, Keogh JS (2014) The biogeographical boundaries of northern Australia: evidence from ecological niche models and a multi-locus phylogeny of Uperoleia toadlets (Anura: Myobatrachidae). J Biogeogr 41:659–672Article 

Google Scholar 
Caye K, Deist TM, Martins H, Michel O, François O (2016) TESS3: fast inference of spatial population structure and genome scans for selection. Mol Ecol Resour 16:540–548Article 
CAS 
PubMed 

Google Scholar 
Caye K, Jay F, Michel O, François O (2018) Fast inference of individual admixture coefficients using geographic data. Ann Appl Stat 12:586–608Article 

Google Scholar 
CBD (2021) First draft of the post-2020 global biodiversity framework. UN Convention on Biological DiversityCeballos G, Ehrlich PR, Barnosky AD, García A, Pringle RM, Palmer TM (2015) Accelerated modern human–induced species losses: Entering the sixth mass extinction. Sci Adv 1:e1400253Article 
PubMed 
PubMed Central 

Google Scholar 
Corbin LJ, Liu AYH, Bishop SC, Woolliams JA (2012) Estimation of historical effective population size using linkage disequilibria with marker data. J Anim Breed Genet 129:257–270Article 
CAS 
PubMed 

Google Scholar 
Crichton EG (1969) Reproduction in the pseudomyine rodent Mesembriomys gouldii (Gray) (Muridae). Aust J Zool 17:785–797Article 

Google Scholar 
Davies HF, McCarthy MA, Firth RSC, Woinarski JCZ, Gillespie GR, Andersen AN et al. (2017) Top-down control of species distributions: feral cats driving the regional extinction of a threatened rodent in northern Australia (N Roura-Pascual, Ed.). Divers Distrib 23:272–283Article 

Google Scholar 
Davies HF, McCarthy MA, Firth RSC, Woinarski JCZ, Gillespie GR, Andersen AN et al. (2018) Declining populations in one of the last refuges for threatened mammal species in northern Australia. Austral Ecol 43:602–612Article 

Google Scholar 
Díaz S, Settele J, Brondízio ES, Ngo HT, Agard J, Arneth A et al. (2019) Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366:6471Dyer R (2016) gstudio: tools related to the spatial analysis of genetic marker data. R package version 152Edwards RD, Crisp MD, Cook DH, Cook LG (2017) Congruent biogeographical disjunctions at a continent-wide scale: Quantifying and clarifying the role of biogeographic barriers in the Australian tropics. PLoS One 12:e0174812Article 
PubMed 
PubMed Central 

Google Scholar 
Eldridge MDB, Potter S, Cooper SJB, Eldridge MDB, Potter S, Cooper SJB (2012) Biogeographic barriers in north-western Australia: an overview and standardisation of nomenclature. Aust J Zool 59:270–272Article 

Google Scholar 
Fitak RR (2021) OptM: estimating the optimal number of migration edges on population trees using Treemix. Biol Methods Protoc 6:bpab017Article 
PubMed 
PubMed Central 

Google Scholar 
Frichot E, François O (2015) LEA: An R package for landscape and ecological association studies. Methods Ecol Evol 6:925–929Article 

Google Scholar 
Friend GR (1987) Population ecology of Mesembriomys gouldii (Rodentia, Muridae) in the wet-dry tropics of the Northern Territory. Wildl Res 14:293–303Article 

Google Scholar 
Funk WC, McKay JK, Hohenlohe PA, Allendorf FW (2012) Harnessing genomics for delineating conservation units. Trends Ecol Evol 27:489–496Article 
PubMed 
PubMed Central 

Google Scholar 
Goudet J (2005) hierfstat, a package for r to compute and test hierarchical F‐statistics. Mol Ecol Notes 5:184–186Article 

Google Scholar 
Goudet J, Kay T, Weir BS (2018) How to estimate kinship. Mol Ecol 27:4121–4135Article 
PubMed 
PubMed Central 

Google Scholar 
Greenbaum G, Templeton AR, Zarmi Y, Bar-David S (2014) Allelic richness following population founding events – a stochastic modeling framework incorporating gene flow and genetic drift. PLoS One 9:e115203Article 
PubMed 
PubMed Central 

Google Scholar 
Hanson JO, Schuster R, Morrell N, Strimas-Mackey M, Watts ME, Arcese P et al. (2020) prioritizr: systematic conservation prioritization in R. R package version 415Hoban S, Bruford M, D’Urban Jackson J, Lopes-Fernandes M, Heuertz M, Hohenlohe PA et al. (2020) Genetic diversity targets and indicators in the CBD post-2020 Global Biodiversity Framework must be improved. Biol Conserv 248:108654Article 

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

Google Scholar 
Kim SY, Lohmueller KE, Albrechtsen A, Li Y, Korneliussen T, Tian G et al. (2011) Estimation of allele frequency and association mapping using next-generation sequencing data. BMC Bioinforma 12:231Article 

Google Scholar 
Korneliussen TS, Albrechtsen A, Nielsen R (2014) Open Access ANGSD: Analysis of Next Generation Sequencing Data. BMC Bioinforma 15:1–13Article 

Google Scholar 
Lambert C, Power V, Gaikhorst G (2016) Captive breeding of the Shark Bay mouse Pseudomys fieldi to facilitate species recovery in the wild. J Zoo Aquar Res 4:164–168
Google Scholar 
Laurie CC, Nickerson DA, Anderson AD, Weir BS, Livingston RJ, Dean MD et al. (2007) Linkage disequilibrium in wild mice. PLOS Genet 3:e144Article 
PubMed 
PubMed Central 

Google Scholar 
Leakey RE, Lewin R (1995) The sixth extinction: patterns of life and the future of humankind, 1st ed. Doubleday, New York
Google Scholar 
Li H (2013). Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv:13033997Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N et al. (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25:2078–2079Article 
PubMed 
PubMed Central 

Google Scholar 
Martins H, Caye K, Luu K, Blum MGB, François O (2016) Identifying outlier loci in admixed and in continuous populations using ancestral population differentiation statistics. Mol Ecol 25:5029–5042Article 
PubMed 

Google Scholar 
Melville J, Ritchie EG, Chapple SNJ, Glor RE, Schulte JA (2011) Evolutionary origins and diversification of dragon lizards in Australia’s tropical savannas. Mol Phylogenetics Evol 58:257–270Article 
CAS 

Google Scholar 
Morton CM (1992) Diets of three species of tree-rat: Mesembriomys gouldii (Gray), M. macrurus (Peters) and Conilurus Pencillatus (Gould) from the Mitchell Plateau, Western Australia. University of Canberra, Canberra, ACT
Google Scholar 
Ottewell KM, Bickerton DC, Byrne M, Lowe AJ (2016) Bridging the gap: a genetic assessment framework for population-level threatened plant conservation prioritization and decision-making. Divers Distrib 22:174–188Article 

Google Scholar 
Ottewell K, Dunlop J, Thomas N, Morris K, Coates D, Byrne M (2014) Evaluating success of translocations in maintaining genetic diversity in a threatened mammal. Biol Conserv 171:209–219Article 

Google Scholar 
Pacifici M, Santini L, Marco MD, Baisero D, Francucci L, Marasini GG et al. (2013) Generation length for mammals. Nat Conserv 5:89–94Article 

Google Scholar 
Palsbøll PJ, Bérubé M, Allendorf FW (2007) Identification of management units using population genetic data. Trends Ecol Evol 22:11–16Article 
PubMed 

Google Scholar 
Pembleton LW, Cogan NOI, Forster JW (2013) StAMPP: an R package for calculation of genetic differentiation and structure of mixed-ploidy level populations. Mol Ecol Resour 13:946–952Article 
CAS 
PubMed 

Google Scholar 
Penton CE, Davies HF, Radford IJ, Woolley L-A, Rangers TL, Murphy BP (2021) A hollow argument: understory vegetation and disturbance determine abundance of hollow-dependent mammals in an Australian tropical savanna. Front Ecol Evol 9:778Article 

Google Scholar 
Penton CE, Radford IJ, Woolley L-A, von Takach B, Murphy BP (2021) Unexpected overlapping use of tree hollows by birds, reptiles and declining mammals in an Australian tropical savanna. Biodivers Conserv 30:2977–3001Article 

Google Scholar 
Penton CE, Woolley L-A, Radford IJ, Murphy BP (2020) Overlapping den tree selection by three declining arboreal mammal species in an Australian tropical savanna. J Mammal 101:1165–1176Article 
PubMed 
PubMed Central 

Google Scholar 
Peterson BK, Weber JN, Kay EH, Fisher HS, Hoekstra HE (2012) Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS One 7:e37135Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Petit RJ, Mousadik AE, Pons O (1998) Identifying populations for conservation on the basis of genetic markers. Conserv Biol 12:844–855Article 

Google Scholar 
Pickrell JK, Pritchard JK (2012) Inference of population splits and mixtures from genome-wide allele frequency data. PLOS Genet 8:e1002967Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Potter S, Close RL, Taggart DA, Cooper SJB, Eldridge MDB, Potter S et al. (2014) Taxonomy of rock-wallabies, Petrogale (Marsupialia: Macropodidae). IV. Multifaceted study of the brachyotis group identifies additional taxa. Aust J Zool 62:401–414Article 

Google Scholar 
Price O, Rankmore B, Milne D, Brock C, Tynan C, Kean L et al. (2005) Regional patterns of mammal abundance and their relationship to landscape variables in eucalypt woodlands near Darwin, northern Australia. Wildl Res 32:435–446Article 

Google Scholar 
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D et al. (2007) PLINK: A tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
R Core Team (2021). R: A language and environment for statistical computingRadford IJ, Corey B, Carnes K, Shedley E, McCaw L, Woolley L-A (2021). Landscape-scale effects of fire, cats, and feral livestock on threatened savanna mammals: unburnt habitat matters more than pyrodiversity. Front Ecol Evol 9: 739817Rankmore BR, Friend GR (2008). Black-footed tree-rat, Mesembriomys gouldii. In: The mammals of Australia, Reed New Holland: Sydney, Australia, pp 591–593Rick K, Ottewell K, Lohr C, Thavornkanlapachai R, Byrne M, Kennington WJ (2019) Population genomics of Bettongia lesueur: admixing increases genetic diversity with no evidence of outbreeding depression. Genes 10:851Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Robledo-Ruiz DA, Pavlova A, Clarke RH, Magrath MJL, Quin B, Harrisson KA et al. (2022) A novel framework for evaluating in situ breeding management strategies in endangered populations. Mol Ecol Resour 22:239–253Article 
PubMed 

Google Scholar 
Rowe KC, Reno ML, Richmond DM, Adkins RM, Steppan SJ (2008) Pliocene colonization and adaptive radiations in Australia and New Guinea (Sahul): Multilocus systematics of the old endemic rodents (Muroidea: Murinae). Mol Phylogenetics Evol 47:84–101Article 
CAS 

Google Scholar 
Roycroft E, MacDonald AJ, Moritz C, Moussalli A, Miguez RP, Rowe KC (2021) Museum genomics reveals the rapid decline and extinction of Australian rodents since European settlement. PNAS 118:e2021390118Roycroft EJ, Moussalli A, Rowe KC (2020) Phylogenomics uncovers confidence and conflict in the rapid radiation of Australo-Papuan rodents. Syst Biol. 69:431–444Sandoval-Castillo J, Robinson NA, Hart AM, Strain LWS, Beheregaray LB (2018) Seascape genomics reveals adaptive divergence in a connected and commercially important mollusc, the greenlip abalone (Haliotis laevigata), along a longitudinal environmental gradient. Mol Ecol 27:1603–1620Article 
PubMed 

Google Scholar 
Scheele BC, Foster CN, Banks SC, Lindenmayer DB (2017) Niche contractions in declining species: mechanisms and consequences. Trends Ecol Evol 32:346–355Article 
PubMed 

Google Scholar 
Schipper J, Chanson JS, Chiozza F, Cox NA, Hoffmann M, Katariya V et al. (2008) The status of the world’s land and marine mammals: diversity, threat, and knowledge. Science 322:225–230Article 
CAS 
PubMed 

Google Scholar 
Schmidt TL, Jasper M-E, Weeks AR, Hoffmann AA (2021) Unbiased population heterozygosity estimates from genome-wide sequence data. Methods Ecol Evol 12:1888–1898Article 

Google Scholar 
Smith AP, Quin DG (1996) Patterns and causes of extinction and decline in Australian conilurine rodents. Biol Conserv 77:243–267Article 

Google Scholar 
Stobo-Wilson AM, Stokeld D, Einoder LD, Davies HF, Fisher A, Hill BM et al. (2020) Bottom-up and top-down processes influence contemporary patterns of mammal species richness in Australia’s monsoonal tropics. Biol Conserv 247:108638Article 

Google Scholar 
Sved JA, Feldman MW (1973) Correlation and probability methods for one and two loci. Theor Popul Biol 4:129–132Article 
CAS 
PubMed 

Google Scholar 
von Takach Dukai B, Peakall R, Lindenmayer DB, Banks SC (2020a) The influence of fire and silvicultural practices on the landscape-scale genetic structure of an Australian foundation tree species. Conserv Genet 21:231–246Article 

Google Scholar 
von Takach B, Ahrens CW, Lindenmayer DB, Banks SC (2021) Scale-dependent signatures of local adaptation in a foundation tree species. Mol Ecol 30:2248–2261Article 

Google Scholar 
von Takach B, Jolly CJ, Dixon KM, Penton CE, Doherty TS, Banks SC (2022) Long-unburnt habitat is critical for the conservation of threatened vertebrates across Australia. Landsc Ecol 37:1469–1482Article 

Google Scholar 
von Takach B, Penton CE, Murphy BP, Radford IJ, Davies HF, Hill BM et al. (2021) Population genomics and conservation management of a declining tropical rodent. Heredity 126:763–775Article 

Google Scholar 
von Takach B, Ranjard L, Burridge CP, Cameron SF, Cremona T, Eldridge MDB et al. (2022) Population genomics of a predatory mammal reveals patterns of decline and impacts of exposure to toxic toads. Mol Ecol 31:5468–5486Article 

Google Scholar 
von Takach B, Scheele BC, Moore H, Murphy BP, Banks SC (2020b) Patterns of niche contraction identify vital refuge areas for declining mammals. Divers Distrib 26:1467–1482Article 

Google Scholar 
Troughton E (1967) Furred animals of Australia, 9th edn. Angus and Robertson, Sydney, Australia
Google Scholar 
Van Dyck S, Gynther I, Baker A (Eds.) (2013) Field Companion to the Mammals of Australia. New Holland Publishers, London, Sydney
Google Scholar 
Van Dyck S, Strahan R (2008) The Mammals of Australia, 3rd edn. Reed New Holland, Chatswood, NSW, Australia
Google Scholar 
Vladislav K (2018) lpsymphony: Symphony integer linear programming solver in R. R package version 1180Watts ME, Ball IR, Stewart RS, Klein CJ, Wilson K, Steinback C et al. (2009) Marxan with Zones: Software for optimal conservation based land- and sea-use zoning. Environ Model Softw 24:1513–1521Article 

Google Scholar 
Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370CAS 
PubMed 

Google Scholar 
Weir BS, Goudet J (2017) A unified characterization of population structure and relatedness. Genetics 206:2085–2103Article 
PubMed 
PubMed Central 

Google Scholar 
Wheeler J (1982) Notes on the black-footed tree-rat in a modified environment. North Territ Nat 5:8–9
Google Scholar 
White LC, Thomson VA, West R, Ruykys L, Ottewell K, Kanowski J et al. (2020) Genetic monitoring of the greater stick-nest rat meta-population for strategic supplementation planning. Conserv Genet 21:941–956Article 

Google Scholar 
Whitlock MC, Lotterhos KE (2015) Reliable detection of loci responsible for local adaptation: inference of a null model through trimming the distribution of FST. Am Nat 186:S24–S36Article 
PubMed 

Google Scholar 
Wickham H (2009) ggplot2: elegant graphics for data analysis. Springer-Verlag, New YorkBook 

Google Scholar 
Wintle BA, Cadenhead NCR, Morgain RA, Legge SM, Bekessy SA, Cantele M et al. (2019) Spending to save: What will it cost to halt Australia’s extinction crisis? Conserv Lett 12:e12682Article 

Google Scholar 
Woinarski JCZ, Armstrong M, Brennan K, Fisher A, Griffiths AD, Hill B et al. (2010) Monitoring indicates rapid and severe decline of native small mammals in Kakadu National Park, northern Australia. Wildl Res 37:116Article 

Google Scholar 
Woinarski JCZ, Braby MF, Burbidge AA, Coates D, Garnett ST, Fensham RJ et al. (2019) Reading the black book: The number, timing, distribution and causes of listed extinctions in Australia. Biol Conserv 239:108261Article 

Google Scholar 
Woinarski JCZ, Burbidge AA (2016) Mesembriomys gouldii. The IUCN Red List of Threatened Species 2016:eT13211A22448856Woinarski JC, Burbidge AA, Harrison P (2014) The action plan for Australian mammals 2012. CSIRO Publishing, Collingwood, AustraliaBook 

Google Scholar 
Woinarski JCZ, Legge S, Fitzsimons JA, Traill BJ, Burbidge AA, Fisher A et al. (2011) The disappearing mammal fauna of northern Australia: context, cause, and response. Conserv Lett 4:192–201Article 

Google Scholar 
Woinarski JCZ, Milne DJ, Wanganeen G (2001) Changes in mammal populations in relatively intact landscapes of Kakadu National Park, Northern Territory, Australia. Austral Ecol 26:360–370Article 

Google Scholar 
Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS (2012) A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28:3326–3328Article 
CAS 
PubMed 
PubMed Central 

Google Scholar  More

Study area and camera trapping systemThe study area included the northern region of Tochigi Prefecture, Japan (Fig. 2). In Tochigi Prefecture, 54.4% of the land was covered by forest, 19.1% was covered by agricultural land in 2019 (Tochigi Prefecture 2021, https://www.pref.tochigi.lg.jp/a03/documents/keikakusho2267.pdf, accessed on Feb. 10, 2023). The northern region of Tochigi Prefecture has a relatively large area of forest. This area was the home range of the highest density of sika deer in Tochigi Prefecture in 2021. The camera trapping system consisted of 14 cameras (model no. 6210; Ltl-Acorn, Des Moines, IA, USA) that were placed in late April 2018 at 12 sites within the forest interior with two camera sets, namely ID 10–11, and ID 12–13 in neighboring areas (Fig. 2). The 12 sites spanned 84 km from west to east and 39 km from north to south (Fig. 2). The elevation of the sites ranged from 349 to 1033 m. The cameras were set horizontally at 50 cm above the ground and were operated until late November 2018. The cameras were checked every 1 or 2 months and the batteries and memory cards were replaced when necessary. Movements of the sika deer were reordered monthly from May to November. The month of April was excluded because the cameras were placed in late April. The virtual ecological model required the presence/absence of records for validation (described below), thus the number of deer captured in the photos was not considered. Finally, the visit and occupy of sika deer were recorded at 14 sites each month.Figure 2Study area, analytical units, and locations of the camera traps.Full size imageA grid size of approximately 1 km (termed “1-km mesh” hereafter) was used a as the study unit (Fig. 2). The 1-km mesh grid system is a standard Japanese unit used for several types of statistics (https://www.stat.go.jp/english/data/mesh/02.html, accessed on Feb. 10, 2023). To determine the appropriate number of 1-km mesh grids for the simulation study, a 10-km mesh grid, which is the high-order standard Japanese unit (i.e., one 10-km mesh includes 100 1-km meshes), was divided into the minimum number of areas to cover all 14 camera sites as the simulation target area to avoid arbitrary (Fig. 2). Finally, 4200 1-km mesh areas were included for the simulation (Fig. 2).Virtual ecological modelA simple cellular automaton (CA) model can predict the visit and occupy of a target species based on candidate habitats in consideration of the proximity to food resources32. The grid was set to the same size as the unit of the predicted ranges. The model yields a theoretical number of visits (described below) to each cell, which serves as an area preference of the target species. Each cell has two parameters: cell identification (ID) and movement path vector (Fig. 3a). The cell ID indicates the spatial location of the cell within the study area. The movement path involves four variables representing the four directional vectors into adjacent cells (i.e., top, left, bottom, and right) (Fig. 3b). Each variable is a probability value (i.e., 0 to 1) independent of the other three variables that indicates the probability of movement success to the adjacent cells. In this study, the probability value was based on the proximity to availability food resources.Figure 3Basic structure of the cellular automaton model. (a) Two values are associated with each cell: the cell ID “x,” a unique ID for each cell, and the movement probability “mx” indicating four directional vectors into adjacent cells. (b) Values m1, m2, m3, and m4 indicate the probability of movement along a path of the top, left, bottom, and right cells, respectively. If all movement probability values are 0, the virtual population in this cell cannot move to any other cell. If all movement probability values are 1, the virtual population in the cell can move to all adjacent cells.Full size imageA group of sika deer was used as the unit for analysis. The model simulates the capability of movement within the target area. Thus, if a virtual population visited a neighboring cell, the number of visits to the cell is increased without disappearance of the starting cell. The virtual population moves in accordance with the movement probability values.Movement probability between cellsThe term “movement probability” is defined as the probability of movement success into an adjacent cell to the top, left, bottom, or right (Fig. 3b) with four probability values:$$ {text{Movement probability x}} = {text{mx}};({text{m}}1,;{text{m}}2,;{text{m}}3,;{text{m}}4), $$
(1)
where m1, m2, m3, and m4 indicate the probability of movement success into the top, left, bottom, and right cells, respectively (Fig. 3b). Since these values are independent of one another, the maximum and minimum sums of m1, m2, m3, and m4 are theoretically 4 and 0, respectively. If all probability of movement success values are 0, the sika deer population in this cell cannot move to any other cell. Moreover, if all probability of movement success values are 1, the population in the cell can move to all adjacent cells.The amount of food resources of deer was acquired from remote sensing measurements35,36. Thus, two variables were used to represent food resource availability: the kernel normalized difference vegetation index (kNDVI)41 and landscape structure (Supplementary Fig. 1).The kNDVI uses remote sensing measurements to assess the components of green vegetation41. As compared to the ordinal NDVI, which is the most widely used index of the condition of vegetation on terrestrial surfaces, the kNDVI has greater resistance to saturation, bias, and complex phenological cycles, and exhibits enhanced robustness to noise and stability across spatial and temporal scales41. The kNDVI appropriately represents the condition of vegetation to reflect the food resource availability for sika deer. The kNDVI was analyzed from the atmospherically corrected surface reflectance observed with the Landsat 8 Operational Land Imager and Thermal Infrared Sensor instruments at approximately 16-day intervals with a spatial resolution of 30 m (data collected in 2018). The mean kNDVI was calculated monthly for each 1-km mesh within the study area. The probability values (m1, m2, m3, and m4) were defined as the proximity to available food resources in a destination cell divided by the maximum value of the target area as relative values throughout the study area. These values reflect the spatial positions of the available food resources in the study area. If the food resources are continuously available, then the sika deer population tend to visit and occupy linearly.The landscape structure is defined as a mixture of forests and grasslands because previous studies suggest that the forest edge has high availability of food resources for sika deer37,38,42,43. The dataset was generated from a current vegetation map that classified the dominant plant species provided by the Biodiversity Center of Japan (Ministry of the Environment, https://www.biodic.go.jp/index_e.html, accessed on Feb. 10, 2023). The types of vegetation of the forests and grasslands were retrieved from the literature, then the original vegetation classes were re-classified44 and overlayed on the 1-km mesh map. In this study, agricultural land types were classified as grassland. For a mesh with both forests and grasslands, the probability of movement was assigned a value of 1, while a mesh with either a forest or grassland was assigned a value of 0.5, because to treat these 2 components fairly. Every mesh of the study area included either a forest or grassland.Movement simulationFirst, simulations were conducted using two independent variables: kNDVI and landscape structure. Each simulation was initiated from one cell with the month, which is referred to as a “trial.” One step is defined as one day, thus the trial conducted in May consisted of 31 steps. A previous study reported that sika deer can travel about 50 km every 2 weeks34. Thus, one step (movement of 1 km) in one day was considered a reasonable distance. Each trial was repeated for all cells i.e., all cells was used as the starting cell of “trial”. The sum of all trials is termed a “run.” Thus, each “run” consisted of n trials, where n is the number of cells in the CA field. In this study, there were 4200 cells. At each step, each attempt to visit a neighboring cell (top, left, bottom, and right) was based on movement probabilities. For each successful movement, the presence/absence value assigned to the cell was increased from 0 to 1, i.e., change from absence to presence. The next step was then initiated from any newly visited cell and the previously visited cells. Cells with high values indicated the possibility of visitation by a virtual population from several other cells. The assigned value was used as a metric of the preference of the visited cell. In this study, 100 runs were conducted each month from May to November.Second, simulations were conducted using a combination of movement-related variables with two types of combination models: kNDVI AND landscape structure and kNDVI OR landscape structure. With both the logical AND and OR models, each step has two processes: probability approach with the kNDVI and landscape structure. With the AND model, if the virtual population passes the probability of the kNDVI to move to a neighboring cell, then the probability of movement to a neighboring cell is based on the landscape structure. In the logical AND model, we used kNDVI first because that could reflect a seasonal change in the availability of food resources. With the OR model, if the virtual population passes the probability of the kNDVI, or passes that of the landscape structure, the virtual population can move to any neighboring cell.Additionally, equivalence model simulation was conducted with all probability values (m1, m2, m3, and m4) set to 0.5.Validation of the simulation results using the camera trap dataThe results of the CA model simulation were validated by the presence/absence of the monthly records of sika deer collected with the cameras. The occurrence of a visit to a camera was determined using a generalized linear model with a binomial distribution (log link) and model selection based on Akaike’s information criterion (AIC). The explanatory variable was the theoretical number of simulated visits to a 1-km cell with a camera trap. If the AIC value of the model was  > 2 points lower than that of the null model45 (i.e., with no explanatory variable), the run was considered “correct”. The data from the kNDVI, landscape structure, AND/OR, and null/equivalence models were used. The number of “correct” runs of every 100 runs with each model was calculated. Therefore, all values could theoretically be 100.Then, the predictive ability of the model was evaluated using the results considered as “correct” with the AIC. The AIC values of all runs were compared, where one simulation set used four variables. If the four models (i.e., kNDVI, landscape, AND, and OR models) were all “correct” in one run, the AIC values were compared and the lowest AIC value of the model was recorded. Notably, differences among the AIC values were not considered because the effectiveness of the model was already evaluated in the first validation procedure. Calculations for all months were conducted. Therefore, the maximum value among the four models was 100, assuming that the run was “correct” with the lowest AIC.Finally, a map was generated of the theoretical number of visits by sika deer in each month based on the best performance among the four simulations. The map included the average number of theoretical visits over 100 runs. The results considered incorrect were not excluded because in real-world applications, simulated results are not evaluated.All statistical analyses were performed using R software (ver. 4.0.2; https://www.r-project.org/, accessed on Feb. 10, 2023). More

Roux, R., Gosselin, M., Desrosiers, G. & Nozais, C. Effects of reduced UV radiation on a microbenthic community during a microcosm experiment. Mar. Ecol. Prog. Ser. 225, 29–43. https://doi.org/10.3354/meps225029 (2002).Article 
ADS 

Google Scholar 
Häder, D. P., Helbling, E. W., Williamson, C. E. & Worrest, R. C. Effects of UV radiation on aquatic ecosystems and interactions with climate change. Photochem. Photobiol. Sci. 10, 242–260. https://doi.org/10.1039/C0PP90036B (2011).Article 
PubMed 

Google Scholar 
Schmidt, É. C. et al. Response of the agarophyte Gelidium floridanum after in vitro exposure to ultraviolet radiation B: changes in ultrastructure, pigments, and antioxidant systems. J. Appl. Phycol. 24, 1341–1352. https://doi.org/10.1007/s10811-012-9786-4 (2012).Article 
CAS 

Google Scholar 
Zhu, L. et al. Physiological responses of macroalga Gracilaria lemaneiformis (Rhodophyta) to UV-B radiation exposure. Chin. J. Oceanol. Limnol. 33, 389–399. https://doi.org/10.1007/s00343-015-4073-2 (2015).Article 
ADS 
CAS 

Google Scholar 
Liu, Y. N., Ao, M., Li, B. & Guan, Y. X. Effect of ultraviolet- B( UV-B) radiation on plant growth and development and its application value. Soils and Crops 9, 191–202. https://doi.org/10.11689/j.issn.2095-2961.2020.02.011 (2020).Article 

Google Scholar 
Chen, Y. Y., Xu, X. L., Shen, X. Y. & Zhang, Z. G. Advances of Research on Effects of Enhanced UV-B on Algae. JiangXi Science 23, 180–184. https://doi.org/10.13990/j.issn1001-3679.2005.02.025 (2005).Article 

Google Scholar 
Aguilera, J., Bischof, K., Karsten, U., Hanelt, D. & Wiencke, C. Seasonal variation in ecophysiological patterns in macroalgae from an Arctic fjord. II. Pigment accumulation and biochemical defence systems against high light stress. Mar. Biol. 140, 1087–1095. https://doi.org/10.1007/s00227-002-0792-y (2002).Article 
CAS 

Google Scholar 
Xu, F. H., Zhang, P. Y., Yu, D. S. & Li, Y. The effect of enhanced UV-B radiation to the growth of Ulva Pertusa Kjell man and Platy monas Hel gol andi ca Kylin var. Tsi ngt aoensis. J. Qingdao Univ. (E & T) 21, 49–53. https://doi.org/10.3969/j.issn.1006-9798.2006.02.010 (2006).Article 
CAS 

Google Scholar 
Guan, W. C., Chen, H., Wang, T., Chen, S. & Xu, J. Effect of the solar ultraviolet radiation on the growth and fluorescence parameters of Sargassum horner. J. Fish. China 40, 83–91. https://doi.org/10.11964/jfc.20150109683 (2016).Article 

Google Scholar 
Sun, Y. et al. Physiological responses and metabonomics analysis of male and female Sargassum thunbergii macroalgae exposed to ultraviolet-B stress. Front. Plant Sci. https://doi.org/10.3389/fpls.2022.778602 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Sun, Y. et al. The differing responses of central carbon cycle metabolism in male and female Sargassum thunbergii to ultraviolet-B radiation. Front. Plant Sci. 13, 10. https://doi.org/10.3389/fpls.2022.904943 (2022).Article 

Google Scholar 
Lu, P. et al. Gender differences response characteristics of Sargassum thunbergii in reactive oxygen species scavenging system to enhanced UV-B radiation. Period. Ocean Univ. China 52, 5259. https://doi.org/10.16441/j.cnki.hdxb.20210224 (2022).Article 
ADS 

Google Scholar 
Ji, Y., Xu, Z., Zou, D. & Gao, K. Ecophysiological responses of marine macroalgae to climate change factors. J. Appl. Phycol. 28, 2953–2967. https://doi.org/10.1007/s10811-016-0840-5 (2016).Article 
CAS 

Google Scholar 
Chen, S. W. & Wu, B. X. Algal responses to enhanced UV-B and its mechanism on molecular level. J. Jinan Univ. (Nat. Sci.) 21, 88–94. https://doi.org/10.3969/j.issn.1000-9965.2000.05.017 (2000).Article 
CAS 

Google Scholar 
Pescheck, F., Lohbeck, K. T., Roleda, M. Y. & Bilger, W. UV-B -induced DNA and photosystem II damage in two intertidal green macroalgae: distinct survival strategies in UV-screening and non-screening Chlorophyta. J. Photochem. Photobiol. B 132, 85–93. https://doi.org/10.1016/j.jphotobiol.2014.02.006 (2014).Article 
CAS 
PubMed 

Google Scholar 
Dong, K. Physiological and biochemical responses of Ulva pertusa and Sargassum thunbergii to UV-B radiation Master thesis, Ocean University of China (2008).Selvarajan, R., Sibanda, T., Venkatachalam, S., Ogola, H. & Msagati, T. A. Distribution, interaction and functional profiles of epiphytic bacterial communities from the rocky intertidal seaweeds, South Africa. Sci. Rep. 9, 19835. https://doi.org/10.1038/s41598-019-56269-2 (2019).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, Z. H., Tang, L. L. & Zhang, Y. Y. Algae-bacteria interactions and their ecological functions in the ocean. Microbiol. China 45, 2043–2053. https://doi.org/10.13344/j.microbiol.china.180178 (2018).Article 

Google Scholar 
Xuan, L. et al. Effects of UV-B radiation on quantity of epiphytic bacteria, endophytic bacteria and physiological mechanism of Erigeron breviscapus. Ecol. Environ. Sci. 18, 2211–2215. https://doi.org/10.16258/j.cnki.1674-5906.2009.06.055 (2009).Article 

Google Scholar 
Zheng, H. Effects of UV-B radiation on the endophytic bacteria in plants of Qinghai-Tibet plateau Master thesis, Lanzhou University (2009).Dobretsov, S., Véliz, K., Romero, M. S., Tala, F. & Thiel, M. Impact of UV radiation on the red seaweed Gelidium lingulatum and its associated bacteria. Eur. J. Phycol. 56, 129–141. https://doi.org/10.1080/09670262.2020.1775309 (2021).Article 
CAS 

Google Scholar 
Serebryakova, A., Aires, T., Viard, F., Serrao, E. & Engelen, A. Summer shifts of bacterial communities associated with the invasive brown seaweed Sargassum muticum are location and tissue dependent. PLoS ONE 13, e0206734. https://doi.org/10.1371/journal.pone.0206734 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Florez, J. Z., Carolina, C., Hengst, M. B. & Buschmann, A. H. A functional perspective analysis of macroalgae and epiphytic bacterial community interaction. Front. Microbiol. 8, 2561. https://doi.org/10.3389/fmicb.2017.02561 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Xu, X. et al. Different growth sensitivity to enhanced UV-B radiation between male and female Populus cathayana. Tree Physiol. 30, 1489–1498. https://doi.org/10.1093/treephys/tpq094 (2010).Article 
CAS 
PubMed 

Google Scholar 
Chen, M. et al. Various responses of antioxidant enzyme system and photosynthetic pigments in male and female mulberry (Morus alba L.) seedlings to UV-B radiation. J. China West Normal Univ. (Nat. Sci.) 35, 327–332. https://doi.org/10.16246/j.issn.1673-5072.2014.04.010 (2014).Article 

Google Scholar 
Norul, S. et al. Accumulation of phenolics and growth of dioecious Populus tremula (L.) seedlings over three growing seasons under elevated temperature and UV-B radiation. Plant Physiol. Biochem. 165, 114–122. https://doi.org/10.1016/j.plaphy.2021.05.012 (2021).Article 
ADS 
CAS 

Google Scholar 
Sun, Y. et al. Development and utilization status of Sargassum thunbergii. Fish. Sci. Technol. Inf. 45, 343–346. https://doi.org/10.16446/j.cnki.1001-1994.2018.06.011 (2018).Article 

Google Scholar 
Amaral-Zettler, L. A. et al. Comparative mitochondrial and chloroplast genomics of a genetically distinct form of Sargassum contributing to recent “Golden Tides” in the Western Atlantic. Ecol. Evol. 7, 516–525. https://doi.org/10.1002/ece3.2630 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Wu, H., Liu, H., Yang, D. & Li, M. Research present situation of Sargassum thunbergii. Terr. Nat. Resour. Study 1, 95–96. https://doi.org/10.16202/j.cnki.tnrs.2010.01.009 (2010).Article 

Google Scholar 
Njage, P. et al. Quantitative microbial risk assessment based on whole genome sequencing data: case of Listeria monocytogenes. Microorganisms 8, 1772. https://doi.org/10.3390/microorganisms8111772 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
McHugh, A. J. et al. Tracking the dairy microbiota from farm bulk tank to skimmed milk powder. mSystems 5, e00226-00220. https://doi.org/10.1128/mSystems.00226-20 (2020).Article 

Google Scholar 
Sun, Y. Polyphasic Taxonomy of Fluviibacterium aquatile SM1902T and Effect of starvation treatment on the variation of bacterial community in the open ocean surface seawater Master thesis, Shandong University, (2020).Gao, X. et al. Survival, virulent characteristics, and transcriptomic analyses of the pathogenic Vibrio anguillarum under starvation stress. Front. Cell. Infect. Microbiol. 16, 389. https://doi.org/10.3389/fcimb.2018.00389 (2018).Article 
CAS 

Google Scholar 
Gao, Y. Study on denitrification performance of marine anammox bacteria under UV and electron mediators Master thesis, Qingdao University, (2020).Fernández Zenoff, V., Siñeriz, F. & Farías, M. E. Diverse responses to UV-B radiation and repair mechanisms of bacteria isolated from high-altitude aquatic environments. Appl. Environ. Microbiol. 72, 7857–7863. https://doi.org/10.1128/aem.01333-06 (2006).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Kadivar, H. & Stapleton, A. E. Ultraviolet radiation alters maize phyllosphere bacterial diversity. Microb. Ecol. 45, 353–361. https://doi.org/10.1007/s00248-002-1065-5 (2003).Article 
CAS 
PubMed 

Google Scholar 
Lynch, M. D. J. & Neufeld, J. D. Ecology and exploration of the rare biosphere. Nat. Rev. Microbiol. 13, 217–229. https://doi.org/10.1038/nrmicro3400 (2015).Article 
CAS 
PubMed 

Google Scholar 
Reintjes, G., Arnosti, C., Fuchs, B. & Amann, R. Selfish, sharing and scavenging bacteria in the Atlantic Ocean: A biogeographical study of bacterial substrate utilisation. ISME J. 13, 1119–1132. https://doi.org/10.1038/s41396-018-0326-3 (2019).Article 
CAS 
PubMed 

Google Scholar 
Roth Rosenberg, D. et al. Prochlorococcus cells rely on microbial interactions rather than on chlorotic resting stages to survive long-term nutrient starvation. MBio 11, e01846-01820. https://doi.org/10.1128/mBio.01846-20 (2020).Article 

Google Scholar 
Berg, K. A. et al. High diversity of cultivable heterotrophic bacteria in association with cyanobacterial water blooms. ISME J. 3, 314–325. https://doi.org/10.1038/ismej.2008.110 (2009).Article 
CAS 
PubMed 

Google Scholar 
Pootakham, W. et al. High resolution profiling of coral-associated bacterial communities using full-length 16S rRNA sequence data from PacBio SMRT sequencing system. Sci. Rep. 7, 2774. https://doi.org/10.1038/s41598-017-03139-4 (2017).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Soto, C. Y. et al. IS6110 mediates increased transcription of the phoP virulence gene in a multidrug-resistant clinical isolate responsible for tuberculosis outbreaks. J. Clin. Microbiol. 42, 212–219. https://doi.org/10.1128/jcm.42.1.212-219.2004 (2004).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Di Cesare, A. et al. Diverse distribution of Toxin-Antitoxin II systems in Salmonella enterica serovars. Sci. Rep. 6, 28759. https://doi.org/10.1038/srep28759 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Das, S., Saha, S. K., De, A., Das, D. & Khuda-Bukhsh, A. R. Potential of the homeopathic remedy, Arnica Montana 30C, to reduce DNA damage in Escherichia coli exposed to ultraviolet irradiation through up-regulation of nucleotide excision repair genes. Zhong Xi Yi Jie He Xue Bao 10, 337–346. https://doi.org/10.3736/jcim20120314 (2012).Article 
PubMed 

Google Scholar 
Jallouli, W., Sellami, S., Sellami, M. & Tounsi, S. Efficacy of olive mill wastewater for protecting Bacillus thuringiensis formulation from UV radiations. Acta Trop. 140, 19–25. https://doi.org/10.1016/j.actatropica.2014.07.016 (2014).Article 
CAS 
PubMed 

Google Scholar 
Huang, L. et al. Effects of UV-B radiation on the expression of four pathogenic genes in the infection stage of Magnaporthe grisea. J. Agro-Environ. Sci. 38, 494–501. https://doi.org/10.11654/jaes.2018-0625 (2019).Article 

Google Scholar 
He, K., Marden, J. N., Quardokus, E. M. & Bauer, C. E. Phosphate flow between hybrid histidine kinases CheA3 and CheS3 controls Rhodospirillum centenum cyst formation. PLoS Genet. 9, e1004002. https://doi.org/10.1371/journal.pgen.1004002 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Deutscher, J., Francke, C. & Postma, P. W. How phosphotransferase system-related protein phosphorylation regulates carbohydrate metabolism in bacteria. Microbiol. Mol. Biol. Rev. 70, 939–1031. https://doi.org/10.1128/mmbr.00024-06 (2006).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, L., Zhao, Y., Zhou, B., Dong, K. S. & Tang, X. X. Effect of UV-B irradiation on the activity and isoforms of antioxidant enzymes in the Brown Alga Sargassum thunbergii(Mert.) O.Kuntze. Period. Ocean Univ. China 39, 1246–1250. https://doi.org/10.3969/j.issn.1672-5174.2009.06.012 (2009).Article 
CAS 

Google Scholar 
Li, L., Tang, T., Hai, M., Chen, J. & Zhou, P. Response and molecular mechanisms of plants to enhanced UV-B radiation. Chin. Agric. Sci. Bull. 31, 159–163. https://doi.org/10.11924/j.issn.1000-6850.2014-1871 (2015).Article 

Google Scholar 
Zhang, Y. et al. Dietary corn-resistant starch suppresses broiler abdominal fat deposition associated with the reduced cecal Firmicutes. Poult. Sci. 99, 5827–5837. https://doi.org/10.1016/j.psj.2020.07.042 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pilla, R. et al. Effects of metronidazole on the fecal microbiome and metabolome in healthy dogs. J. Vet. Intern. Med. 34, 1853–1866. https://doi.org/10.1111/jvim.15871 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Hong, S. Cloning and identification of a novel CDF family transporter gene cdffT from Planococcus sp. NEAU-ST10–9 Master thesis, Northeast Forestry University (2014).Egan, S., Thomas, T. & Kjelleberg, S. Unlocking the diversity and biotechnological potential of marine surface associated microbial communities. Curr. Opin. Microbiol. 11, 219–225. https://doi.org/10.1016/j.mib.2008.04.001 (2008).Article 
CAS 
PubMed 

Google Scholar 
Wang, J. et al. Sex plays a role in the construction of epiphytic bacterial communities on the algal bodies and receptacles of Sargassum thunbergii. Front. Microbiol. 13, 935222. https://doi.org/10.3389/fmicb.2022.935222 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, J. et al. Diversity of epiphytic bacterial communities on male and female Sargassum thunbergii. AMB Express 12, 97. https://doi.org/10.1186/s13568-022-01439-1 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tang, X. X. et al. Simulated intertidal UV-B radiation enhancement large-sized seaweed culture irradiation system, has salinity detector and temperature detector, culturing tank provided with fluorescent lamp tube and adjustable bracket. China patent CN208047639-U (2018).Lu, P. et al. Gender differences response characteristics of Sargassum thunbergii in reactive oxygen species scavenging system to enhanced UV-B radiation. Period. Ocean Univ. China 52, 52–59. https://doi.org/10.16441/j.cnki.hdxb.20210224 (2022).Article 
ADS 

Google Scholar 
Ren, G. et al. Response of soil, leaf endosphere and phyllosphere bacterial communities to elevated CO2 and soil temperature in a rice paddy. Plant Soil 392, 27–44. https://doi.org/10.1007/s11104-015-2503-8 (2015).Article 
CAS 

Google Scholar 
Mathai, P. et al. Spatial and temporal characterization of epiphytic microbial communities associated with Eurasian Watermilfoil: A highly invasive macrophyte in North America. FEMS Microbiol. Ecol. 94, 12–21. https://doi.org/10.1093/femsec/fiy178 (2018).Article 
CAS 

Google Scholar 
Czekalski, N., Berthold, T., Caucci, S., Egli, A. & Bürgmann, H. Increased levels of multiresistant bacteria and resistance genes after wastewater treatment and their dissemination into lake Geneva, Switzerland. Front. Microbiol. 3, 106–106. https://doi.org/10.3389/fmicb.2012.00106 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857. https://doi.org/10.1038/s41587-019-0252-6 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Janssen, S. et al. Phylogenetic placement of exact amplicon sequences improves associations with clinical information. MSystems 3, e00021-e118. https://doi.org/10.1128/mSystems.00021-18 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, S. et al. Exploring untapped potential of Streptomyces spp. in Gurbantunggut Desert by use of highly selective culture strategy. Sci. Total Environ. 790, 148235. https://doi.org/10.1016/j.scitotenv.2021.148235 (2021).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Qin, W. et al. Gut microbiota plasticity influences the adaptability of wild and domestic animals in co-inhabited areas. Front. Microbiol. 11, 125. https://doi.org/10.3389/fmicb.2020.00125 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Douglas, G. M., Beiko, R. G. & Langille, M. G. I. Predicting the functional potential of the microbiome from marker genes using PICRUSt. Methods Mol. Biol. 169–177, 2018. https://doi.org/10.1007/978-1-4939-8728-311 (1849).Article 

Google Scholar 
Kanehisa, M. et al. KEGG: Ntegrating viruses and cellular organisms. Nucleic Acids Res. 49, D545–D551. https://doi.org/10.1093/nar/gkaa970 (2021).Article 
CAS 
PubMed 

Google Scholar 
Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 28, 1947–1951. https://doi.org/10.1002/pro.3715 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30. https://doi.org/10.1093/nar/28.1.27 (2000).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Douglas, G. M. et al. PICRUSt2: An improved and extensible approach for metagenome inference. BioRxiv 7, 672295. https://doi.org/10.1101/672295 (2019).Article 

Google Scholar  More

Gillet, N. et al. Canada’s Changing Climate Report (Government of Canada, 2019).Bintanja, R. The impact of Arctic warming on increased rainfall. Sci. Rep. 8, 6–11 (2018).Article 

Google Scholar 
Camill, P. Permafrost thaw accelerates in boreal peatlands during late-20th century climate warming. Clim. Change 68, 135–152 (2005).Article 
CAS 

Google Scholar 
Hollesen, J., Matthiesen, H., Møller, A. B. & Elberling, B. Permafrost thawing in organic Arctic soils accelerated by ground heat production. Nat. Clim. Change 5, 574–578 (2015).Article 

Google Scholar 
Walvoord, M. A. & Striegl, R. G. Increased groundwater to stream discharge from permafrost thawing in the Yukon River basin: potential impacts on lateral export of carbon and nitrogen. Geophys. Res. Lett. 34, L12402 (2007).Pearson, R. G. et al. Shifts in Arctic vegetation and associated feedbacks under climate change. Nat. Clim. Change 3, 673–677 (2013).Article 

Google Scholar 
Heijmans, M. M. P. D. et al. Tundra vegetation change and impacts on permafrost. Nat. Rev. Earth Environ. 3, 68–84 (2022).Article 

Google Scholar 
Tape, K., Sturm, M. & Racine, C. The evidence for shrub expansion in Northern Alaska and the Pan-Arctic. Glob. Change Biol. 12, 686–702 (2006).Article 

Google Scholar 
Mekonnen, Z. A. et al. Arctic tundra shrubification: a review of mechanisms and impacts on ecosystem carbon balance. Environ. Res. Lett. 16, 053001 (2021).Article 
CAS 

Google Scholar 
Shevtsova, I. et al. Strong shrub expansion in tundra-taiga, tree infilling in taiga and stable tundra in central Chukotka (north-eastern Siberia) between 2000 and 2017. Environ. Res. Lett. 15, 085006 (2020).Article 

Google Scholar 
Wild, B. et al. Rivers across the Siberian Arctic unearth the patterns of carbon release from thawing permafrost. Proc. Natl Acad. Sci. USA 116, 10280–10285 (2019).Article 
CAS 

Google Scholar 
Rowland, J. C. et al. Arctic landscapes in transition: responses to thawing permafrost. Eos 91, 229–230 (2010).Article 

Google Scholar 
Walcker, R., Corenblit, D., Julien, F., Martinez, J. M. & Steiger, J. Contribution of meandering rivers to natural carbon fluxes: evidence from the Ucayali River, Peruvian Amazonia. Sci. Total Environ. 776, 146056 (2021).Article 
CAS 

Google Scholar 
Torres, M. A. et al. Model predictions of long-lived storage of organic carbon in river deposits. Earth Surf. Dyn. 5, 711–730 (2017).Article 

Google Scholar 
Allen, J. R. Sedimentary structures: their character and physical basis. Dev. Sedimentol. 30B, 1–593 (1982).
Google Scholar 
Howard, A. D. & Knutson, T. R. Sufficient conditions for river meandering: a simulation approach. Water Resour. Res. 20, 1659–1667 (1984).Article 

Google Scholar 
Chassiot, L., Lajeunesse, P. & Bernier, J. F. Riverbank erosion in cold environments: review and outlook. Earth-Sci. Rev. 207, 103231 (2020).Article 

Google Scholar 
Constantine, J. A., Dunne, T., Ahmed, J., Legleiter, C. & Lazarus, E. D. Sediment supply as a driver of river meandering and floodplain evolution in the Amazon Basin. Nat. Geosci. 7, 899–903 (2014).Article 
CAS 

Google Scholar 
Horton, A. J. et al. Modification of river meandering by tropical deforestation. Geology 45, 511–514 (2017).Article 

Google Scholar 
Ielpi, A. & Lapôtre, M. G. A. A tenfold slowdown in river meander migration driven by plant life. Nat. Geosci. 13, 82–86 (2020).Article 
CAS 

Google Scholar 
Kokelj, S. V., Lantz, T. C., Tunnicliffe, J., Segal, R. & Lacelle, D. Climate-driven thaw of permafrost preserved glacial landscapes, northwestern Canada. Geology 45, 371–374 (2017).Article 

Google Scholar 
Zhang, T. et al. Warming-driven erosion and sediment transport in cold regions. Nat. Rev. Earth Environ. 3, 832–851(2022).Brown, D. R. N. et al. Implications of climate variability and changing seasonal hydrology for subarctic riverbank erosion. Clim. Change 162, 385–404 (2020).Article 

Google Scholar 
Gautier, E. et al. Fifty-year dynamics of the Lena River islands (Russia): spatio-temporal pattern of large periglacial anabranching river and influence of climate change. Sci. Total Environ. 783, 147020 (2021).Article 
CAS 

Google Scholar 
Piliouras, A., Lauzon, R. & Rowland, J. C. Unraveling the combined effects of ice and permafrost on Arctic delta morphodynamics. J. Geophys. Res. Earth Surf. 126, e2020JF005706 (2021).Matsubara, Y. et al. Geomorphology river meandering on Earth and Mars: a comparative study of Aeolis Dorsa meanders, Mars and possible terrestrial analogs of the Usuktuk River, AK, and the Quinn River, NV. Geomorphology 240, 102–120 (2015).Article 

Google Scholar 
Lininger, K. B. & Wohl, E. Floodplain dynamics in North American permafrost regions under a warming climate and implications for organic carbon stocks: a review and synthesis. Earth-Sci. Rev. 193, 24–44 (2019).Article 
CAS 

Google Scholar 
Treat, C. C. & Jones, M. C. Near-surface permafrost aggradation in Northern Hemisphere peatlands shows regional and global trends during the past 6000 years. Holocene 28, 998–1010 (2018).Article 

Google Scholar 
Lapôtre, M. G. A., Ielpi, A., Lamb, M. P., Williams, R. M. E. & Knoll, A. H. Model for the formation of single-thread rivers in barren landscapes and implications for pre-Silurian and martian fluvial deposits. J. Geophys. Res. Earth Surf. 124, 2757–2777 (2019).Article 

Google Scholar 
Wang, G., Hu, H. & Li, T. The influence of freeze-thaw cycles of active soil layer on surface runoff in a permafrost watershed. J. Hydrol. 375, 438–449 (2009).Article 

Google Scholar 
Tananaev, N. & Lotsari, E. Defrosting northern catchments: fluvial effects of permafrost degradation. Earth-Sci. Rev. 228, 103996 (2022).Article 

Google Scholar 
Tarnocai, C., Nixon, M. F. & Kutny, L. Circumpolar-active-layer-monitoring (CALM) sites in the Mackenzie Valley, northwestern Canada. Permafr. Periglac. Process. 15, 141–153 (2004).Article 

Google Scholar 
Nguyen, T.-N., Burn, C. R., King, D. J. & Smith, S. L. Estimating the extent of near-surface permafrost using remote sensing, Mackenzie Delta, Northwest Territories. Permafr. Periglac. Process. 20, 141–153 (2009).Article 

Google Scholar 
Stephani, E., Drage, J., Miller, D., Jones, B. M. & Kanevskiy, M. Taliks, cryopegs, and permafrost dynamics related to channel migration, Colville River Delta, Alaska. Permafr. Periglac. Process. 31, 239–254 (2020).Article 

Google Scholar 
Walvoord, M. A. & Kurylyk, B. L. Hydrologic impacts of thawing permafrost—a review. Vadose Zo. J. 15, vzj2016.01.0010 (2016).Article 

Google Scholar 
Leopold, L. B., Wolman, M. G. & Miller, J. P. Fluvial Processes in Geomorphology (Dover, 1964).Sylvester, Z., Durkin, P. & Covault, J. A. High curvatures drive river meandering. Geology 47, 263–266 (2019).Article 

Google Scholar 
Lageweg, W. I. van de et al. Bank pull or bar push: what drives scroll-bar formation in meandering rivers? Geology 42, 319–322 (2014).Liljedahl, A. K., Timling, I., Frost, G. V. & Daanen, R. P. Arctic riparian shrub expansion indicates a shift from streams gaining water to those that lose flow. Commun. Earth Environ. 1, 50 (2020).Article 

Google Scholar 
Parker, G. et al. A new framework for modeling the migration of meandering rivers. Earth Surf. Process. Landf. 36, 70–86 (2011).Article 

Google Scholar 
Blanckaert, K. Topographic steering, flow recirculation, velocity redistribution, and bed topography in sharp meander bends. Water Resour. Res. 46, W09506 (2010).
Google Scholar 
Ielpi, A. & Lapôtre, M. G. A. Biotic forcing militates against river meandering in the modern Bonneville Basin of Utah. Sedimentology 66, 1896–1929 (2019).Article 

Google Scholar 
Fox, G. A. et al. Measuring streambank erosion due to ground water seepage: correlation to bank pore water pressure, precipitation and stream stage. Earth Surf. Process. Landf. 1573, 1558–1573 (2007).Article 

Google Scholar 
O’Neill, H. B., Smith, S. L. & Duchesne, C. Long-term permafrost degradation and thermokarst subsidence in the Mackenzie Delta Area indicated by thaw tube measurements. In 18th International Conference on Cold Regions Engineering and 8th Canadian Permafrost Conference (eds Bilodeau, J.-P. et al.) 643–651 (ASCE, 2019).Qiu, J. Thawing permafrost reduces river runoff. Nature https://doi.org/10.1038/nature.2012.9749 (2012).Zheng, L., Overeem, I., Wang, K. & Clow, G. D. Changing Arctic river dynamics cause localized permafrost thaw. J. Geophys. Res. Earth Surf. 124, 2324–2344 (2019).Article 

Google Scholar 
Jorgenson, M. T. et al. An Ecological Land Survey for the Colville River Delta, Alaska, 1996 (ABR, Inc., 1997).Park, H., Yoshikawa, Y., Yang, D. & Oshima, K. Warming water in arctic terrestrial rivers under climate change. J. Hydrometeorol. 18, 1983–1995 (2017).Article 

Google Scholar 
Roy-Leveillee, P. & Burn, C. R. Near-shore talik development beneath shallow water in expanding thermokarst lakes, Old Crow Flats, Yukon. J. Geophys. Res. Earth Surf. 122, 1070–1089 (2017).Article 

Google Scholar 
Langer, M. et al. Rapid degradation of permafrost underneath waterbodies in tundra landscapes—toward a representation of thermokarst in land surface models. J. Geophys. Res. Earth Surf. 121, 2446–2470 (2016).Article 

Google Scholar 
O’Neill, H. B., Roy-Leveillee, P., Lebedeva, L. & Ling, F. Recent advances (2010–2019) in the study of taliks. Permafr. Periglac. Process. 31, 346–357 (2020).Article 

Google Scholar 
French, H. The Periglacial Environment (Wiley, 2017).Prowse, T. D. River-ice ecology. I: Hydrologic, geomorphic, and water-quality aspects. J. Cold Reg. Eng. 15, 1–16 (2001).Article 
CAS 

Google Scholar 
Yang, X., Pavelsky, T. M. & Allen, G. H. The past and future of global river ice. Nature 577, 69–73 (2020).Article 
CAS 

Google Scholar 
Brown, J., Ferrians, O. J. Jr, Heginbottom, J. A. & Melkinov, E. S. Circum-Arctic Map of Permafrost and Ground-Ice Conditions (USGS, 1997); https://pubs.usgs.gov/cp/45/report.pdfIelpi, A., Lapotre, M. G. A., Finotello, A. & Roy-Léveillée, P. Large sinuous rivers are slowing down in a warming Arctic. Zenodo https://doi.org/10.5281/zenodo.7556050 (2023).Leopold, L. B. & Maddock, T. J. The Hydraulic Geometry of Stream Channels and Some Physiographic Implications (USGS, 1953).Giorgino, T. Computing and visualizing dynamic time warping alignments in R: the dtw package. J. Stat. Softw. 31, 1–24 (2009).Article 

Google Scholar 
Donovan, M., Belmont, P. & Sylvester, Z. Evaluating the relationship between meander-bend curvature, sediment supply, and migration rates. J. Geophys. Res. Earth Surf. 126, e2020JF006058 (2021).Article 

Google Scholar 
Sylvester, Z., Durkin, P. R., Hubbard, S. M. & Mohrig, D. Autogenic translation and counter point bar deposition in meandering rivers. GSA Bull. 133, 2439–2456 (2021).Titov, M. Code for dynamic time warping analysis. GitHub http://mlt.github.io/QGIS-Processing-tools/tags/dtw.html (2015).Finotello, A., D’Alpaos, A., Lazarus, E. D. & Lanzoni, S. High curvatures drive river meandering: COMMENT. Geology 47, e485 (2019).Finotello, A. et al. American Geophysical Union, Fall Meeting Abstracts (AGU, 2020).Congedo, L. Semi-automatic classification plugin: a Python tool for the download and processing of remote sensing images in QGIS. J. Open Source Softw. 6, 3172 (2021).Article 

Google Scholar  More

Inagaki, F., Takai, K., Kobayashi, H., Nealson, K. H. & Horikoshi, K. Sulfurimonas autotrophica gen. nov., sp. nov., a novel sulfur-oxidizing e-proteobacterium isolated from hydrothermal sediments in the Mid-Okinawa Trough. Int. J. Syst. Evol. Microbiol. 53, 1801–1805 (2003).Article 
CAS 
PubMed 

Google Scholar 
Timmer-Ten Hoor, A. A new type of thiosulphate oxidizing, nitrate reducing microorganism: Thiomicrospira denitrificans sp. nov. Neth. J. Sea Res. 9, 344–350 (1975).Article 
CAS 

Google Scholar 
Cai, L., Shao, M. & Zhang, T. Non-contiguous finished genome sequence and description of Sulfurimonas hongkongensis sp. nov., a strictly anaerobic denitrifying, hydrogen- and sulfur-oxidizing chemolithoautotroph isolated from marine sediment. Stand. Genom. Sci. 9, 1302–1310 (2014).Article 

Google Scholar 
Wang, S., Jiang, L., Liu, X., Yang, S. & Shao, Z. Sulfurimonas xiamenensis sp. nov. and Sulfurimonas lithotrophica sp. nov., hydrogen- and sulfur-oxidizing chemolithoautotrophs within the Epsilonproteobacteria isolated from coastal sediments, and an emended description of the genus Sulfurimonas. Int. J. Syst. Evol. Microbiol. 70, 2657–2663 (2020).Article 
CAS 
PubMed 

Google Scholar 
Takai, K. et al. Sulfurimonas paralvinellae sp. nov., a novel mesophilic, hydrogen- and sulfur-oxidizing chemolithoautotroph within the Epsilonproteobacteria isolated from a deep-sea hydrothermal vent polychaete nest, reclassification of Thiomicrospira denitrificans as Sulfurimonas denitrificans comb. nov. and emended description of the genus Sulfurimonas. Int. J. Syst. Evol. Microbiol. 56, 1725–1733 (2006).Article 
CAS 
PubMed 

Google Scholar 
Hu, Q., Wang, S., Lai, Q., Shao, Z. & Jiang, L. Sulfurimonas indica sp. nov., a hydrogen- and sulfur-oxidizing chemolithoautotroph isolated from a hydrothermal sulfide chimney in the Northwest Indian Ocean. Int. J. Syst. Evol. Microbiol. 71, 1466–5034 (2021).Article 

Google Scholar 
Wang, S. et al. Sulfurimonas sediminis sp. nov., a novel hydrogen- and sulfur-oxidizing chemolithoautotroph isolated from a hydrothermal vent at the Longqi system, southwestern Indian ocean. Antonie Van Leeuwenhoek 114, 813–822 (2021).Article 
CAS 
PubMed 

Google Scholar 
Wang, S. et al. Characterization of Sulfurimonas hydrogeniphila sp. nov., a novel bacterium predominant in deep-sea hydrothermal vents and comparative genomic analyses of the genus Sulfurimonas. Front. Microbiol. 12, 626705 (2021).Labrenz, M. et al. Sulfurimonas gotlandica sp. nov., a chemoautotrophic and psychrotolerant epsilonproteobacterium isolated from a pelagic redoxcline, and an emended description of the genus Sulfurimonas. Int. J. Syst. Evol. Microbiol. 63, 4141–4148 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Henkel, J. V. et al. Candidatus Sulfurimonas marisnigri sp. nov. and Candidatus Sulfurimonas baltica sp. nov., thiotrophic manganese oxide reducing chemolithoautotrophs of the class Campylobacteria isolated from the pelagic redoxclines of the Black Sea and the Baltic Sea. Syst. Appl. Microbiol. 44, 126155 (2021).Article 
CAS 
PubMed 

Google Scholar 
Ratnikova, N. M. et al. Sulfurimonas crateris sp. nov., a facultative anaerobic sulfur-oxidizing chemolithoautotrophic bacterium isolated from a terrestrial mud volcano. Int. J. Syst. Evol. Microbiol. 70, 487–492 (2020).Article 
CAS 
PubMed 

Google Scholar 
Han, Y. & Perner, M. The globally widespread genus Sulfurimonas: versatile energy metabolisms and adaptations to redox clines. Front. Microbiol. 6, 989 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
López-garcía, P. et al. Bacterial diversity in hydrothermal sediment and epsilonproteobacterial dominance in experimental microcolonizers at the Mid-Atlantic Ridge. Environ. Microbiol. 5, 961–976 (2003).Article 
PubMed 

Google Scholar 
Nakagawa, S. et al. Distribution, phylogenetic diversity and physiological characteristics of epsilon-Proteobacteria in a deep-sea hydrothermal field. Environ. Microbiol. 7, 1619–1632 (2005).Article 
CAS 
PubMed 

Google Scholar 
Huber, J. A. et al. Isolated communities of Epsilonproteobacteria in hydrothermal vent fluids of the Mariana Arc seamounts. FEMS Microbiol. Ecol. 73, 538–549 (2010).CAS 
PubMed 

Google Scholar 
Meier, D. V. et al. Niche partitioning of diverse sulfur-oxidizing bacteria at hydrothermal vents. ISME J. 11, 1545–1558 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mino, S. et al. Endemicity of the cosmopolitan mesophilic chemolithoautotroph Sulfurimonas at deep-sea hydrothermal vents. ISME J. 11, 909–919 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Akerman, N. H., Butterfield, D. A., Huber, J. A., Huber, J. A. & Paul, J. B. Phylogenetic diversity and functional gene patterns of sulfur-oxidizing subseafloor Epsilonproteobacteria in diffuse hydrothermal vent fluids. Front. Microbiol. 4, 185 (2013).Rogge, A., Vogts, A., Voss, M. & Labrenz, M. Success of chemolithoautotrophic SUP05 and Sulfurimonas GD17 cells in pelagic Baltic Sea redox zones is facilitated by their lifestyles as K- and r -strategists. Environ. Microbiol. 19, 2495–2506 (2017).Article 
CAS 
PubMed 

Google Scholar 
German, C. R. et al. Diverse styles of submarine venting on the ultraslow spreading Mid-Cayman Rise. Proc. Natl Acad. Sci. USA 107, 14020–14025 (2010).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sylvan, J. B., Pyenson, B. C., Rouxel, O., German, C. R. & Edwards, K. J. Time-series analysis of two hydrothermal plumes at 9°50’ N East Pacific Rise reveals distinct, heterogeneous bacterial populations. Geobiology 10, 178–192 (2012).Article 
CAS 
PubMed 

Google Scholar 
Perner, M. et al. In situ chemistry and microbial community compositions in five deep-sea hydrothermal fluid samples from Irina II in the Logatchev field. Environ. Microbiol. 15, 1551–1560 (2013).Article 
CAS 
PubMed 

Google Scholar 
Haalboom, S. et al. Patterns of (trace) metals and microorganisms in the Rainbow hydrothermal vent plume at the Mid-Atlantic Ridge. Biogeosciences 17, 2499–2519 (2020).Article 
CAS 

Google Scholar 
Li, J. et al. Distribution and succession of microbial communities along the dispersal pathway of hydrothermal plumes on the Southwest Indian Ridge. Front. Mar. Sci. 7, 581381 (2020).Article 

Google Scholar 
Dick, G. J. et al. The microbiology of deep-sea hydrothermal vent plumes: ecological and biogeographic linkages to seafloor and water column habitats. Front. Microbiol. 4, 124 (2013).Dick, G. J. The microbiomes of deep-sea hydrothermal vents: distributed globally, shaped locally. Nat. Rev. Microbiol. 17, 271–283 (2019).Article 
CAS 
PubMed 

Google Scholar 
German, C. R. & Seyfried, W. E. in Treatise on Geochemistry 2nd edn (eds Holland, H. D. & Turekian, K. K.), 8, 191–233 (Elsevier, 2014).Kadko, D., Baross, J. & Alt, J. The magnitude and global implications of hydrothermal flux. Geophys. Monogr. Ser. 91, 446–466 (1995).
Google Scholar 
German, C. R. et al. Volcanically hosted venting with indications of ultramafic influence at Aurora hydrothermal field on Gakkel Ridge. Nat. Commun. 13, 6517 (2022).Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Konstantinidis, K. T., Rosselló-móra, R. & Amann, R. Uncultivated microbes in need of their own taxonomy. ISME J. 11, 2399–2406 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Murray, A. E. et al. Roadmap for naming uncultivated Archaea and Bacteria. Nat. Microbiol. 5, 987–994 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Eren, A. M. et al. Minimum entropy decomposition: unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences. ISME J. 9, 968–979 (2015).Article 
CAS 
PubMed 

Google Scholar 
Dick, G. J. & Tebo, B. M. Microbial diversity and biogeochemistry of the Guaymas Basin deep-sea hydrothermal plume. Environ. Microbiol. 12, 1334–1347 (2010).Article 
CAS 
PubMed 

Google Scholar 
Lesniewski, R. A., Jain, S., Anantharaman, K., Schloss, P. D. & Dick, G. J. The metatranscriptome of a deep-sea hydrothermal plume is dominated by water column methanotrophs and lithotrophs. ISME J. 6, 2257–2268 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sheik, C. S. et al. Spatially resolved sampling reveals dynamic microbial communities in rising hydrothermal plumes across a back-arc basin. ISME J. 9, 1434–1445 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Reed, D. C. et al. Predicting the response of the deep-ocean microbiome to geochemical perturbations by hydrothermal vents. ISME J. 9, 1857–1869 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Han, Y. & Perner, M. The role of hydrogen for Sulfurimonas denitrificans’ metabolism. PLoS ONE 9, 8–14 (2014).
Google Scholar 
Ilbert, M. & Bonnefoy, V. Insight into the evolution of the iron oxidation pathways. Biochim. Biophys. Acta 1827, 161–175 (2013).Article 
CAS 
PubMed 

Google Scholar 
Yu, H. & Leadbetter, J. R. Bacterial chemolithoautotrophy via manganese oxidation. Nature 583, 453–458 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Andrews, S. C. Iron storage in bacteria. Adv. Microb. Physiol. 40, 281–351 (1998).Article 
CAS 
PubMed 

Google Scholar 
Pitcher, R. S. & Watmough, N. J. The bacterial cytochrome cbb 3 oxidases. Biochim. Biophys. Acta 1655, 388–399 (2004).Article 
CAS 
PubMed 

Google Scholar 
Sousa, F. L. et al. The superfamily of heme–copper oxygen reductases: types and evolutionary considerations. Biochim. Biophys. Acta 1817, 629–637 (2012).Article 
CAS 
PubMed 

Google Scholar 
Park, B. et al. Cultivation of autotrophic ammonia-oxidizing archaea from marine sediments in coculture with sulfur-oxidizing bacteria. Appl. Environ. Microbiol. 76, 7575–7587 (2010).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Fuchs, G. Alternative pathways of carbon dioxide fixation: insights into the early evolution of life? Annu. Rev. Microbiol. 65, 631–658 (2011).Article 
CAS 
PubMed 

Google Scholar 
Bayer, B. et al. Metabolic versatility of the nitrite-oxidizing bacterium Nitrospira marina and its proteomic response to oxygen-limited conditions. ISME J. 15, 1025–1039 (2021).Article 
CAS 
PubMed 

Google Scholar 
Yamamoto, M., Arai, H., Ishii, M. & Igarashi, Y. Role of two 2-oxoglutarate: ferredoxin oxidoreductases in Hydrogenobacter thermophilus under aerobic and anaerobic conditions. FEMS Microbiol. Lett. 263, 189–193 (2006).Article 
CAS 
PubMed 

Google Scholar 
Yamamoto, M., Ikeda, T., Arai, H., Ishii, M. & Igarashi, Y. Carboxylation reaction catalyzed by 2-oxoglutarate:ferredoxin oxidoreductases from Hydrogenobacter thermophilus. Extremophiles 14, 79–85 (2010).Article 
CAS 
PubMed 

Google Scholar 
Berg, I. A. Ecological aspects of the distribution of different autotrophic CO2 fixation pathways. Appl. Environ. Microbiol. 77, 1925–1936 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
French, C. E., Bell, J. M. L. & Ward, F. B. Diversity and distribution of hemerythrin-like proteins in prokaryotes. FEMS Microbiol. Lett. 279, 131–145 (2008).Article 
CAS 
PubMed 

Google Scholar 
Isaza, C. E., Silaghi-dumitrescu, R., Iyer, R. B., Kurtz, D. M. & Chan, M. K. Structural basis for O2 sensing by the hemerythrin-like domain of a bacterial chemotaxis protein: substrate tunnel and fluxional n terminus. Biogeochemistry 45, 9023–9031 (2006).Article 
CAS 

Google Scholar 
Kendall, J. J., Barrero-tobon, A. M., Hendrixson, D. R. & Kelly, D. J. Hemerythrins in the microaerophilic bacterium Campylobacter jejuni help protect key iron–sulphur cluster enzymes from oxidative damage. Environ. Microbiol. 16, 1105–1121 (2014).Article 
CAS 
PubMed 

Google Scholar 
Nariya, S. & Kalyuzhnaya, M. G. Hemerythrins enhance aerobic respiration in Methylomicrobium alcaliphilum 20Z R, a methane-consuming bacterium. FEMS Microbiol. Lett. 367, fnaa003 (2020).Sheng, Y. et al. Superoxide dismutases and superoxide reductases. Chem. Rev. 114, 3854–3918 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Anantharaman, K., Breier, J. A., Sheik, C. S. & Dick, G. J. Evidence for hydrogen oxidation and metabolic plasticity in widespread deep-sea sulfur-oxidizing bacteria. Proc. Natl Acad. Sci. USA 110, 330–335 (2013).Article 
CAS 
PubMed 

Google Scholar 
Dede, B. et al. Niche differentiation of sulfur-oxidizing bacteria (SUP05) in submarine hydrothermal plumes. ISME J. 16, 1479–1490 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schlindwein, V. (ed.) The Expedition of the Research Vessel ‘Polarstern’ to the Antarctic in 2013 (ANT-XXIX/8). Reports on polar and marine research, Bremerhaven, Alfred Wegener Institute for Polar and Marine Research, 672, 111 (2014); https://doi.org/10.2312/BzPM_0672_2014Boetius, A. The Expedition PS86 of the Research Vessel POLARSTERN to the Arctic Ocean in 2014. Reports on polar and marine research, Bremerhaven, Alfred Wegener Institute for Polar and Marine Research, 685, 133 (2015); https://doi.org/10.2312/BzPM_0685_2015Boetius, A. & Purser, A. The Expedition PS101 of the Research Vessel POLARSTERN to the Arctic Ocean in 2016. Reports on polar and marine research, Bremerhaven, Alfred Wegener Institute for Polar and Marine Research, 706, 230 (2017); https://doi.org/10.2312/BzPM_0706_2017Varliero, G., Bienhold, C., Schmid, F., Boetius, A. & Molari, M. Microbial diversity and connectivity in deep-sea sediments of the South Atlantic Polar Front. Front. Microbiol. 10, 665 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Pernthaler, A., Pernthaler, J. & Amann, R. Fluorescence in situ hybridization and catalyzed reporter deposition for the identification of marine bacteria. Appl. Environ. Microbiol. 68, 3094–3101 (2002).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Alm, E. W., Oerther, D. B., Larsen, N., Stahl, D. A. & Raskin, L. The oligonucleotide probe database. Appl. Environ. Microbiol. 62, 3557–3559 (1996).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Amann, R. I. et al. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl. Environ. Microbiol. 56, 1919–1925 (1990).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).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, 590–596 (2013).Article 

Google Scholar 
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).Hassenrück, C., Quast, C., Rapp, J. & Buttigieg, P. Amplicon (GitHub, accessed 15 April 2019); https://github.com/chassenr/NGS/tree/master/AMPLICONMahé, F., Rognes, T., Quince, C., de Vargas, C. & Dunthorn, M. Swarm: robust and fast clustering method for amplicon-based studies. PeerJ 2, e593 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Bushnell, B. BBMap: A Fast, Accurate, Splice-Aware Aligner. United States (2014). https://www.osti.gov/servlets/purl/1241166Bolger, 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 
Kopylova, E., Noe, L. & Touzet, H. Sequence analysis SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).Article 
CAS 
PubMed 

Google Scholar 
Gruber-vodicka, H. R., Seah, B. K. & Pruesse, E. phyloFlash: rapid small-subunit rRNA profiling and targeted assembly from metagenomes. mSystems 5, e00920 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. https://doi.org/10.14806/ej.17.1.200 (2011).Zhang, J., Kobert, K., Fluori, T. & Stamatakis, A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2014).Article 
CAS 
PubMed 

Google Scholar 
Pruesse, E., Peplies, J. & Glöckner, F. O. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28, 1823–1829 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, D., Liu, C., Luo, R., Sadakane, K. & Lam, T. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).Article 
CAS 
PubMed 

Google Scholar 
Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).Article 
CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).Article 
CAS 
PubMed 

Google Scholar 
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 11, 2864–2868 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 17, 132 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Varghese, N. J. et al. Microbial species delineation using whole genome sequences. Nucleic Acids Res. 43, 6761–6771 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Eren, A. M. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 3, e1319 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Wheeler, T. J. & Eddy, S. R. nhmmer: DNA homology search with profile HMMs. Bioinformatics 29, 2487–2489 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).Article 
CAS 
PubMed 

Google Scholar 
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).Article 
CAS 
PubMed 

Google Scholar 
Lagesen, K. et al. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 35, 3100–3108 (2007).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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 
PubMed 
PubMed Central 

Google Scholar 
Chklovski, A., Parks, D. H., Woodcroft, B. J. & Tyson, G. W. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Preprint at bioRxiv https://doi.org/10.1101/2022.07.11.499243 (2022).Manni, M., Berkeley, M. R., Seppey, M. & Zdobnov, E. M. BUSCO: assessing genomic data quality and beyond. Curr. Protoc. 1, e323 (2021).Article 
PubMed 

Google Scholar 
Laslett, D. & Canback, B. ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res. 32, 11–16 (2004).Article 
CAS 
PubMed 
PubMed Central 

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

Google Scholar 
El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res. 47, D427–D432 (2019).Article 
CAS 
PubMed 

Google Scholar 
Haft, D. H. et al. TIGRFAMs: a protein family resource for the functional identification of proteins. Nucleic Acids Res. 29, 41–43 (2001).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 248, 726–731 (2015).
Google Scholar 
Tatusov, R. L., Galperin, M. Y., Natale, D. A. & Koonin, E. V. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 28, 33–36 (2000).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kristensen, D. M. et al. A low-polynomial algorithm for assembling clusters of orthologous groups from intergenomic symmetric best matches. Bioinformatics 26, 1481–1487 (2010).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. L. Predicting transmembrane protein topology with a hidden markov model: application to complete genomes. J. Mol. Biol. 305, 567–580 (2001).Article 
CAS 
PubMed 

Google Scholar 
Søndergaard, D., Pedersen, C. N. S. & Greening, C. HydDB: a web tool for hydrogenase classification and analysis. Sci. Rep. 6, 34212 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Garber, A. I. et al. FeGenie: a comprehensive tool for the identification of iron genes and iron gene neighborhoods in genome and metagenome assemblies. Front. Microbiol. 11, 37 (2020).Passardi, F. et al. PeroxiBase: the peroxidase database. Phytochemistry 68, 1605–1611 (2007).Article 
CAS 
PubMed 

Google Scholar 
Lucchetti-miganeh, C., Goudenège, D., Thybert, D., Salbert, G. & Barloy-hubler, F. SORGOdb: superoxide reductase gene ontology curated database. BMC Microbiol. 11, 105 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 4–8 (2016).
Google Scholar 
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).Article 
CAS 
PubMed 

Google Scholar 
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).Article 
CAS 
PubMed 

Google Scholar 
Mistry, J. et al. Pfam: the protein families database in 2021. Nucleic Acids Res. 49, D412–D419 (2021).Article 
CAS 
PubMed 

Google Scholar 
Tu, Q., Lin, L., Cheng, L., Deng, Y. & He, Z. NCycDB: a curated integrative database for fast and accurate metagenomic profiling of nitrogen cycling genes. Bioinformatics 35, 1040–1048 (2019).Article 
CAS 
PubMed 

Google Scholar 
Li, H. et al. A cross-species alignment tool (CAT). BMC Bioinformatics 8, 349 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Vasimuddin, M., Misra, S., Li, H. & Aluru, S. Efficient architecture-aware acceleration of BWA-MEM for multicore systems. In 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Rio de Janeiro, Brazil, 314–324, doi: 10.1109/IPDPS.2019.00041 (2019); https://ieeexplore.ieee.org/document/8820962Putri, 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 
PubMed 
PubMed Central 

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

Google Scholar 
Criscuolo, A. & Gribaldo, S. BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments. BMC Evol. Biol. 10, 210 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Jalili, V. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update. Nucleic Acids Res. 48, 395–402 (2020).Article 

Google Scholar 
Trifinopoulos, J., Nguyen, L.-T., von Haeseler, A. & Minh, B. Q. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 44, W232–W235 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kalyaanamoorthy, S. et al. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Berger, S. A., Krompass, D. & Stamatakis, A. Performance, accuracy, and web server for evolutionary placement of short sequence reads under maximum likelihood. Syst. Biol. 60, 291–302 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2, W256–W259 (2019).Article 

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

Google Scholar 
van Dongen, S. & Abreu-goodger, C. in Bacterial Molecular Networks: Methods and Protocols, Methods in Molecular Biology (eds van Helden, J. et al.) 281–295 (Springer, 2012).Altschup, S. F., Gish, W., Pennsylvania, T. & Park, U. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).Article 

Google Scholar 
Nguyen, L., Schmidt, H. A., Haeseler, A., Von & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Chernomor, O., von Haeseler, A. & Minh, B. Q. Terrace aware data structure for phylogenomic inference from supermatrices. Syst. Biol. 65, 997–1008 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Delmont, T. O. & Eren, A. M. Linking pangenomes and metagenomes: the Prochlorococcus metapangenome. PeerJ 6, e4320 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Jensen, L. J. et al. eggNOG: automated construction and annotation of orthologous groups of genes. Nucleic Acids Res. 36, 250–254 (2008).Article 

Google Scholar 
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–360 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.6-4. https://CRAN.R-project.org/package=vegan (2022).Robinson, M. D., Mccarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).Article 
CAS 
PubMed 

Google Scholar 
Reiner-Benaim, A. FDR control by the BH procedure for two-sided correlated tests with implications to gene expression data analysis. Biom. J. 49, 107–126 (2007).Article 
PubMed 

Google Scholar 
Villanueva, R. A. M. & Chen, Z. J. ggplot2: elegant graphics for data analysis (2nd ed.). Meas. Interdiscip. Res. Perspect. 17, 160–167 (2019).Diepenbroek, M. et al. Towards an integrated biodiversity and ecological research data management and archiving platform: the German Federation for the Curation of Biological Data (GFBio). In Informatik 2014 – Big Data Komplexität meistern Proc. 232 (eds Plödereder, E. et al.) 1711–1725 (Gesellschaft für Informatik, 2014).Schmidt, K., Koschinsky, A., Garbe-Schönberg, D., de Carvalho, L. M. & Seifert, R. Geochemistry of hydrothermal fluids from the ultramafic-hosted Logatchev hydrothermal field, 15°N on the Mid-Atlantic Ridge: temporal and spatial investigation. Chem. Geol. 242, 1–21 (2007).Article 
CAS 

Google Scholar 
Perner, M. et al. The influence of ultramafic rocks on microbial communities at the Logatchev hydrothermal field, located 15 degrees N on the Mid-Atlantic Ridge. FEMS Microbiol. Ecol. 61, 97–109 (2007).Article 
CAS 
PubMed 

Google Scholar 
Douville, E. et al. The rainbow vent fluids (36°14’N, MAR): the influence of ultramafic rocks and phase separation on trace metal content in Mid-Atlantic Ridge hydrothermal fluids. Chem. Geol. 184, 37–48 (2002).Article 
CAS 

Google Scholar 
Ji, F. et al. Geochemistry of hydrothermal vent fluids and its implications for subsurface processes at the active Longqi hydrothermal field, Southwest Indian Ridge. Deep Sea Res. I 122, 41–47 (2017).Article 
CAS 

Google Scholar  More

To supplement the limited publicly available information on rabies risk, the US Centers for Disease Control and Prevention (CDC) performs an annual country-by-country qualitative assessment of rabies risks and protective factors. The results of this assessment are released annually in an open-access database of core metrics consisting of the presence of lyssaviruses (specifically canine or wildlife rabies virus variants, or other bat lyssaviruses), access to rabies immunoglobulins and vaccines, rabies surveillance capacity and canine rabies control capacity18. The analysis presented here builds upon the current CDC evaluation and specifically examines publicly available data to better inform the parameter of rabies surveillance capacity. This study found publicly available data regarding rabies animal testing by species, described testing practices in relation to the country’s human and dog populations, as well as by their stage of DMRVV control (defined by WHO), and used this data to calculate a surveillance testing threshold for DMRVV endemic countries.Data sources were categorized into four tiers, with the order reflecting the preference for selecting the most appropriate data for the purposes of this analysis. Tier 1 data sources were considered to be the preferential data source and included any official government data submitted to a Regional or International data repository. Official data repositories included the WHO GHO, Pan-American Health Organization Regional Information System for Epidemiologic Surveillance of Rabies (PAHO SIRVERA), and the European Rabies Bulletin. Tier 1 data sources also included official country reports found through literature search, so long as they were publicly available. Tier 2 data sources consisted of published reports in peer-reviewed literature or on a ministry of health or agriculture site that includes data from the entire country, as well as unofficial data repositories (e.g., Global Alliance on Rabies Control (GARC) Rabies Epidemiologic Bulletin). Tier 3 data consisted of one-time cross-sectional studies or studies describing sub-national testing activities and which could not be reliably extrapolated to an entire country. Tier 4 data sources include any resource not captured in the previous criteria that were obtained during literature searches. The primary data search was conducted in September 2021, with an update in September 2022. Only Tier 1 and Tier 2 data sources were included in the evaluation of animal testing rates. If multiple data sources contained conflicting testing rates, we prioritized data from surveillance repositories, then reports from ministries of health or agriculture, and, finally, peer-reviewed publications.For Tier 1 data (i.e., surveillance repository), data was included in this study if it described rabies testing conducted between the years 2010 and 2019. As political, economic, and epidemiologic factors directly influence the reliability and transparency of surveillance system data, we decided that a ten-year limit would capture any year-to-year variation in data and better characterize current passive surveillance practices. Additionally, the cutoff of 2019 was chosen so that the effects of the COVID-19 pandemic on rabies surveillance capacity would not affect this comprehensive evaluation and would account for lag time in reporting to Tier 1 data sources19,20. This study assumed data from these surveillance repositories is entered secondary to passive surveillance systems. If data was known to be from active surveillance activities, it was removed from analyses.For Tier 2 data (i.e., peer-reviewed publications), certain publications presented aggregated testing data that included years prior to the Tier 1 cutoff (i.e., 2010). To increase inclusivity of eligible data and keep the findings from this evaluation representative of current practices, eligible data must have had an end year ≥ 2012, regardless of the starting year of data (Table S1). The literature search was conducted on PubMed, Scopus, and Google for “rabies” AND “[country name]” from 2010 to December 2021. “Publicly available” was defined as any result appearing in PubMed or Scopus, or within the first three pages of a Google search. Exceptions to the first three pages were made for similar country names (e.g., Guinea, Congo). The first 10% of Spanish- and French-speaking countries were also searched for “rabia” and “raj,” respectively, to potentially capture any other sources of surveillance data. However, after no additional data was found, this was discontinued. If an article or resource quantifying animal testing capacity within these criteria was not found, the country was deemed to not have readily available data for analysis.For any countries that were part of the surveillance threshold calculation for DMRVV endemic countries, the preferred tiered data was compared to all other data sources. For one country (i.e., Brazil), there was a notable lack of dog testing data and known discrepancies in data reporting between their two reporting systems (i.e., SINAN, SIRVERA)21. In this situation, a median rate was calculated between a Tier 1 and Tier 3 data source. No other such discrepancies were noted. The type of surveillance (active or passive) was noted for each data source; we assumed passive surveillance with Tier 1 data unless compelling evidence existed to display that this was not the case. A strictly active surveillance program was excluded from all analyses. A summary of overall testing practices was performed and standardized according to the number of years each data source contained.As evaluations of rabies testing rates spanned over multiple years, population estimates were obtained to reflect the most recent year in the available data. Three separate testing rates were calculated and standardized based on the human population within the country: [1] All animal, [2] Domestic animal, and [3] Wildlife. There are different social and cultural behaviors that affect the human to dog ratio and interactions between people and animals. These differences can impact the susceptibility of dogs to rabies virus infection and the likelihood of human interactions with rabid animals. Therefore, we additionally calculated country testing rates standardized by the estimated dog population, to provide an additional indicator value of adequate surveillance capacity. Estimated dog populations were obtained from a previous study22. This resulted in up to four calculated rabies testing rates per country, depending upon available data.Equation 1: All-animal per human testing rate (AAHR)$$frac{Average,number,of,all,animals,tested/year}{{Estimated,human,population}} times 100,000$$
(1)
Equation 2: Domestic animal per human testing rate (DAHR)$$frac{Average, number, of, domestic, animals, tested/year}{{Estimated, human, population}} times 100,000$$
(2)
Equation 3: Domestic animal per dog testing rate (DADR)$$frac{Average, number, of, domestic ,animals, tested/year}{{Estimated ,dog, population}} times 100,000$$
(3)
Equation 4: Wildlife per human testing rate (WHR)$$frac{Average, number ,of, wildlife, animals, tested/year}{{Estimated ,human, population}} times 100,000$$
(4)
The WHO rabies epidemiologic Status is divided into five categories in escalating levels of dog rabies control: [1] Endemic dog-transmitted human rabies, [2] Endemic dog rabies, [3] Sporadic dog-transmitted rabies, [4] Controlled dog rabies, and [5] No dog rabies. The WHO Status was established based on existing data and expert knowledge to help better define the level of rabies control for each country23. In addition to these five WHO Statuses, countries in Status [5] were further sub-categorized into [5a] (rabies virus free), and [5b] (wildlife rabies enzootic) based on CDC’s wildlife rabies status; the CDC rabies status was also used for any country without a WHO Status (n = 11)24. Average testing rates for the aforementioned equations were calculated for each WHO Rabies Status category, treating each country as an equally weighted value in the rate calculation. Only descriptive analyses were conducted to describe surveillance and testing data, as data quality was not deemed acceptable for multi-variable statistical analysis and testing rates were heavily left-skewed. Data is presented as median and IQR as the data was noted to not reflect a parametric distribution.Ethics approvalThis activity was reviewed by CDC and was conducted consistent with applicable federal law and CDC policy. (See e.g., 45 C.F.R. part 46, 21 C.F.R. part 56; 42 U.S.C. §241(d); 5 U.S.C. §552a; 44 U.S.C. §3501 et seq.) The views and opinions of the manuscript are of the authors alone and do not represent those of CDC or any other federal agency. More

Kruuk, H. Feeding and social behaviour of the striped hyaena (Hyaena vulgaris). East Afr. Wildl. J. 14, 91–111 (1976).Article 

Google Scholar 
Wanger A.P. Behavioral ecology of the striped hyena (Hyaena hyaena) . Diss. Montana State University-Bozeman, College of Letters & Science (2006).AbiSaid, M. & Dloniak, S.M.D. Hyaena hyaena. The IUCN Red List of Threatened Species 2015: e.T10274A45195080 (2015).Abi-Said, M. R. & Abi-Said, D. M. Distribution of the striped hyena (Hyaena hyaena syriaca Matius, 1882) (Carnivora: Hyaenidae) in urban and rural areas of Lebanon. Zool. Middle East 42, 3–1 (2007).Article 

Google Scholar 
Al Younis, J. S. Hyaenas in Eastern Jordan. IUCN Hyaena Spec. Group Newsl. 6, 2–3 (1993).
Google Scholar 
Qarqaz, M. A., Abu Baker, M. A. & Amr, Z. S. Status and ecology of the Striped Hyena, Hyaena hyaena, in Jordan. Zool. Middle East 33, 87–92 (2004).Article 

Google Scholar 
Bhandari, S., Youlatos, D., Thapamagar, T. & Bhusal, D. R. Shrinking striped hyena (Hyaena hyaena Linnaeus, 1758) distribution in Nepal. Eur. J. Wildl. Res. 67, 1–4 (2021).Article 

Google Scholar 
Mendelssohn, H. Mass destruction of bird-life by secondary poisoning from insecticides and rodenticides. Atlantic Nat. 17, 247–248 (1962).
Google Scholar 
Mendelssohn, H. The impact of pesticides on bird life in Israel. Bull. Int. Council Bird Preserv. 11, 75–104 (1972).
Google Scholar 
Hadad, E., Kosicki, J. Z. & Yosef R. Spatial modeling of road collisions of striped hyena (Hyaena hyaena) in Israel. Ecol. Res.Ilani, G. Hyaenas in Israel. Israel Land Nat. 10–18 (1975).Tristram, H. B. The land of Israel: A journal of travels in palestine. Society for Promoting Christian Knowledge, London. 657 pp (1866).Schmitz, E. J. Wird Palästina wieder jüdisch werden?. Das Heilige Land 54, 92–95 (1910).
Google Scholar 
Schmitz, E. J. Kampf mit einem Leoparden. Das Heilige Land 56, 23–27 (1912).
Google Scholar 
Hadad, E. The persecution of the striped hyaena by humans. Teva HaDvarim 258, 68–78 (2017).
Google Scholar 
Hadad, E. 2021. Israel a heaven and a haven for striped hyaenas. Teva HaDvarim 310, 52–63 (2021).Ilani, G. Zoogeographic survey of carnivores in Israel (Golan, Judea and Samaria, Sinai). Pp. 84—94 Unpublished Internal report, Israel Nature Reserves Authority (1979).Skinner, J. D. & Ilani, G. The striped hyaena Hyaena hyaena of the Judean and Negev Deserts and a comparison with the brown hyaena H. brunnea. Israel J. Zool. 28, 229–232 (2013).
Google Scholar 
van Aarde, R. J., Skinner, J. D., Knight, M. H. & Skinner, D. C. Range use by a striped hyaena (Hyaena hyaena) in the Negev desert. J. Zool. 216, 575–577 (1988).
Google Scholar 
Dolev, A. & Pervolutzky, A. Endangered species in Israel. Red list of threatened species. Pp. 257 Hyaena hyaena (Linnaeus 1758). Nature & Parks Authority and SPNI, Keter Publishers, Jerusalem (2002).Hadad, E. Striped hyaenas in Israel. Teva HaDvarim 232, 3–14 (2015).
Google Scholar 
Albada, I. M. Primary survey of the striped hyena, Hyaena hyaena, (Linnaeus, 1758) (Carnivora:Hyaenidae) status in the West Bank Governorates, Palestine. Glob. Scholast. Res. J. Mulitidiscip. 1, 39–44 (2015).
Google Scholar 
Handal, E. N., Qumsiehm, G. H., Hammash, S. Y. & Qumsiyeh, M. B. Status and conservation of the striped hyena (Hyaena hyaena) in the occupied Palestinian Territories (West Bank). Jordan J. Nat. History 6, 11–18 (2019).
Google Scholar 
Knape, J. Decomposing trends in Swedish bird populations using generalized additive mixed models. J. Appl. Ecol. 53, 1852–1861 (2016).Article 

Google Scholar 
Wood, S. N. Generalized additive models: An introduction with R 2nd edn. (Chapman & Hall/CRC, 2017).Book 
MATH 

Google Scholar 
Mills L.S. Conservation of wildlife populations. Demography, genetics, and management. Second Edition. Wiley–Blackwell, Oxford (2013).Rieger, I. Hyaena hyaena. Mammalian Species 150, 1–5. The American Society of Mammalogists (1981).Hofer, H. & Mills, M.G.L. Worldwide distribution of Hyaenas. In: M.G.L. Mills and H. Hofer (eds), Hyaenas. Status survey and conservation action plan, pp. 39–63. IUCN/SSC Hyaena Specialist Group. IUCN, Gland, Switzerland and Cambridge, UK (1998a).Hofer, H. & Mills, M.G.L. Population size, threats and conservation status of hyaenas. In: M.G.L. Mills and H. Hofer (eds) Hyaenas. Status Survey and Conservation Action Plan, pp. 64–79. IUCN/SSC Hyaena Specialist Group. IUCN, Gland, Switzerland and Cambridge, UK (1998b).Bar-Ziv, E., Picardi, S., Kaplan, A., Avgar, T. & Berger-Tal, O. Sex differences dictate the movement patterns of Striped Hyenas, Hyaena hyaena, in a human-dominated landscape. Front. Ecol. Evol. 10, 897132 (2022).Article 

Google Scholar 
Shamon, H., Sorek, M., Dan, H. & Shapira, I. A large carnivore in a rapidly changing environment: occurrence and density of the striped hyaena hyaena hyaena in Israel. The 54th Israel Zoological Society Conference, Tel Aviv University, Ramat Aviv (2017).Shamoon, H. & Shapira, I. Limiting factors of Striped Hyaena, Hyaena hyaena, distribution and densities across climatic and geographical gradients (Mammalia: Carnivora). Zool. Middle East 65, 189–200 (2019).Article 

Google Scholar 
Yom-Tov, Y. Body sizes of carnivores commensal with humans increased over past 50 years. Funct. Ecol. 17, 323–327 (2003).Article 

Google Scholar 
Monchot, H. & Mashkour, M. Hyenas around the city (Kashan, Iran). J. Taphon. 8, 17–32 (2010).
Google Scholar 
Panda, D. et al. High striped hyena density suggests coexistence with humans in an agricultural landscape Rajasthan. PLoS ONE 17, e0266832 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mendelssohn, H. & Yom-Tov, Y. A report of birds and mammals which have increased their distribution and abundance in Israel due to human activity. Isr. J. Zool. 45, 35–47 (1998).
Google Scholar 
Meretsky, V. J. & Mannan, R. W. Supplemental feeding regimes for Egyptian vultures in the Negev Desert Israel. J. Wildl. Manag. 63, 107–115 (1999).Article 

Google Scholar 
Mallon, D. & Budd, K. (eds). Regional red list status of carnivores in the Arabian Peninsula. Cambridge, UK and Gland Switzerland: IUCN, and Sharjah, UAE: Environment and Protected Areas Authority vi+49pp (2011).Jaffa, N. A. B. K-P. S. The Arabian striped Hyena (Hyaena hyaena sultana Pocock, 1934) (Carnivora: Hyaenidae) in the Kingdom of Saudi Arabia. Gazelle Palestin. Biol. Bull. 185, 1–39 (2020).
Google Scholar 
Cogal, M., Ilemin, Y. & Sozen, M. Status and distribution of the Striped Hyaena, Hyaena hyaena, in Turkey: An updated assessment (Carnivora: Mammalia). Turk. J. Zool. 45, 131–141 (2021).Article 

Google Scholar 
Almasieh, K., Mohammadi, A. & Alvandi, R. Identifying core habitats and corridors of a near threatened carnivore, striped hyaena (Hyaena hyaena) in southwestern Iran. Sci. Rep. 12, 3425 (2022).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tourani, M., Moqanaki, E. M. & Kiabi, B. H. Vulnerability of striped hyaenas, Hyaena hyaena, in a human-dominated landscape of Central Iran. Zool. Middle East 56, 133–136 (2012).Article 

Google Scholar 
Bhandari, S. et al. Climate change threatens striped hyena (Hyaena hyaena) distribution in Nepal. Mammal Res. 67, 433–443 (2022).Article 

Google Scholar 
Tichon, J., Gilchrist, J. S., Rotem, R., Ward, P. & Spiegel, O. Social interactions in striped hyena inferred from camera trap data: Is it more social than previously thought?. Curr. Zool. 66, 345–353 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Dean, W. R. J., Seymour, C. L., Joseph, G. S. & Foord, S. H. A review of the impacts of roads on wildlife in semi-arid regions. Diversity 11, 81 (2019).Article 

Google Scholar 
Landau, Y., Abu-Rabiya, A., Avlegon, A. & Abu-Siam, S. Seasonl grazing of livestock by Beduin in KKL forests: Developments from 2009 to 2014. Forests 15, 30–39 (2015).
Google Scholar 
Jacobson, A. P. et al. Leopard (Panthera pardus) status, distribution, and the research efforts across its range. PeerJ 4, e1974 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Lazagabaster, I. A. et al. Changes in the large carnivore community structure of the Judean Desert in connection to Holocene human settlement dynamics. Sci. Rep. 11, 3548 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lazagabaster, I. A. et al. Cave paleozoology in the Judean Desert: assembling records of Holocene mammal communities. J. Quarten. Sci. 37, 651–663 (2022).Article 

Google Scholar 
Davidson, A., Malkinson, D. & Shanas, U. Wild boar foraging and risk perception—variation among urban, natural, and agricultural areas. J. Mammal. 20, 1–11 (2022).
Google Scholar 
Panda, D. et al. Competitive interactions with dominant carnivores affect carrion acquisition of striped hyena in a semi-arid landscape of Rajasthan India. Mammal Res. https://doi.org/10.1007/s13364-022-00663-1 (2022).Article 

Google Scholar 
Mandal, D., Basak, K., Mishra, R. P., Kaul, R. & Mondal, K. Staus of leopard Panthera pardus and striped hyena Hyaena hyaena and their prey in Achanakmar Tiger Reserve, Central Inida. J. Zool. Stud. 4, 34–41 (2017).
Google Scholar 
Alam, M. S., Khan, J. A. & Pathak, B. J. Striped hyena (Hyaena hyaena) status and factors affecting its distribution in the Gir National Park and Sanctuary India. Folia Zool. 64, 32–39 (2015).Article 

Google Scholar 
Singh, P., Gopalaswamy, A. M. & Karanth, K. U. Factors influencing densities of striped hyenas (Hyaena hyaena) in arid regions of India. J. Mammol. 91, 1152–1159 (2010).Article 

Google Scholar 
Handal, E. N., Amr, Z. S., Basha, W. S. & Qumsiyeh, M. B. Illegal trade in wildlife vertebrate species in the West Bank Palestine. J. Asia-Pac. Biodivers. 14, 636–639 (2021).Article 

Google Scholar 
Kumbhojkar, S., Yosef, R., Benedetti, Y. & Morelli, F. Human-leopard (Panthera pardus fusca) co-existence in Jhalana Forest Reserve India. Sustainability 11, 3912 (2019).Article 

Google Scholar 
Bhandari, S., Bhusal, D. R., Psaralexi, M. & Sgardelis, S. Habitat preference indicators for striped hyena (Hyaena hyaena) in Nepal. Glob. Ecol. Conserv. 27, e01619 (2021).Article 

Google Scholar  More

Jones, J. B., Arkhipkin, A. I., Marriott, A. L. & Pierce, G. J. Using statolith elemental signatures to confirm ontogenetic migrations of the squid Doryteuthis gahi around the Falkland Islands (Southwest Atlantic). Chemi. Geol. 481, 85–94. https://doi.org/10.1016/j.chemgeo.2018.01.034 (2018).Article 
ADS 
CAS 

Google Scholar 
Wright, P. J., Regnier, T., Gibb, F. M., Augley, J. & Devalla, S. Assessing the role of ontogenetic movement in maintaining population structure in fish using otolith microchemistry. Ecol. Evol. 8, 7907–7920. https://doi.org/10.1002/ece3.4186 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Hobbs, J. A., Lewis, L. S., Willmes, M., Denney, C. & Bush, E. Complex life histories discovered in a critically endangered fish. Sci. Rep. 9, 16772. https://doi.org/10.1038/s41598-019-52273-8 (2019).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ryan, D., Wogerbauer, C. & Roche, W. K. Otolith microchemistry to investigate nursery site fidelity and connectivity of juvenile European sea bass in Ireland. Mar. Ecol. Prog. Ser. MFCav2 https://doi.org/10.3354/meps14185 (2022).Article 

Google Scholar 
Sabetian, A. et al. Fish nearshore habitat-use patterns as ecological indicators of nursery quality. Ecol. Indic. 131, 108225. https://doi.org/10.1016/j.ecolind.2021.108225 (2021).Article 

Google Scholar 
Nelson, T. R., Hightower, C. L., Coogan, J., Walther, B. D. & Powers, S. P. Patterns and consequences of life history diversity in salinity exposure of an estuarine dependent fish. Environ. Biol. Fish. 104, 419–436. https://doi.org/10.1007/s10641-021-01080-0 (2021).Article 

Google Scholar 
Russell, A., Taylor, M. D., Barnes, T. C., Johnson, D. D. & Gillanders, B. M. Habitat transitions by a large coastal sciaenid across life history stages, resolved using otolith chemistry. Mar. Environ. Res. 176, 105614. https://doi.org/10.1016/j.marenvres.2022.105614 (2022).Article 
CAS 
PubMed 

Google Scholar 
Moore, J. W., Yeakel, J. D., Peard, D., Lough, J. & Beere, M. Life-history diversity and its importance to population stability and persistence of a migratory fish: steelhead in two large North American watersheds. J. Anim. Ecol. 83, 1035–1046. https://doi.org/10.1111/1365-2656.12212 (2014).Article 
PubMed 

Google Scholar 
Moore, B. R. & Simpfendorfer, C. A. Assessing connectivity of a tropical estuarine teleost through otolith elemental profiles. Mar. Ecol. Prog. Ser. 501, 225–238 (2014).Article 
ADS 
CAS 

Google Scholar 
Pan, X. et al. Population connectivity in a highly migratory fish, Japanese Spanish mackerel (Scomberomorus niphonius), along the Chinese coast, implications from otolith chemistry. Fish. Res. 231, 105690. https://doi.org/10.1016/j.fishres.2020.105690 (2020).Article 

Google Scholar 
Delerue-Ricard, S. et al. Extensive larval dispersal and restricted movement of juveniles on the nursery grounds of sole in the Southern North Sea. J. Sea Res. 155, 101822. https://doi.org/10.1016/j.seares.2019.101822 (2019).Article 

Google Scholar 
Hoey, J. A. et al. Using multiple natural tags provides evidence for extensive larval dispersal across space and through time in summer flounder. Mol. Ecol. 29, 1421–1435. https://doi.org/10.1111/mec.15414 (2020).Article 
CAS 
PubMed 

Google Scholar 
Campana, S. E. Chemistry and composition of fish otoliths: Pathways, mechanisms and applications. Mar. Ecol. Prog. Ser. 188, 263–297 (1999).Article 
ADS 
CAS 

Google Scholar 
Thomas, O. R., Ganio, K., Roberts, B. R. & Swearer, S. E. Trace element-protein interactions in endolymph from the inner ear of fish: Implications for environmental reconstructions using fish otolith chemistry. Metallomics 9, 239–249. https://doi.org/10.1039/c6mt00189k (2017).Article 
CAS 
PubMed 

Google Scholar 
Doubleday, Z. A., Harris, H. H., Izzo, C. & Gillanders, B. M. Strontium randomly substituting for calcium in fish otolith aragonite. Anal. Chem. 86, 865–869. https://doi.org/10.1021/ac4034278 (2014).Article 
CAS 
PubMed 

Google Scholar 
Izzo, C., Doubleday, Z. A. & Gillanders, B. M. Where do elements bind within the otoliths of fish?. Mar. Freshwater Res. 67, 1072–1076. https://doi.org/10.1071/mf15064 (2016).Article 
CAS 

Google Scholar 
Clarke, L. M., Gillanders, B., Thorrold, S. R. & Conover, D. O. Population differences in otolith chemistry have a genetic basis in Menidia menidia. Can. J. Fish. Aquat. Sci. 68, 105–114. https://doi.org/10.1139/f10-147 (2011).Article 
CAS 

Google Scholar 
Sturrock, A. M. et al. Quantifying physiological influences on otolith microchemistry. Methods in Ecology and Evolution 6(7), 806–816 (2015).Article 

Google Scholar 
Hamer, P. et al. Atypical correlation of otolith strontium : calcium and barium : calcium across a marine–freshwater life history transition of a diadromous fish. Mar. Freshwater Res. 66, 411–419. https://doi.org/10.1071/mf14001 (2015).Article 
CAS 

Google Scholar 
Izzo, C., Reis-Santos, P. & Gillanders, B. M. Otolith chemistry does not just reflect environmental conditions: A meta-analytic evaluation. Fish Fish. 19, 441–454. https://doi.org/10.1111/faf.12264 (2018).Article 

Google Scholar 
Motomura, H. Threadfins of the World (family Polynemidae). An Annotated and Illustrated Catalogue of Polynemid Species Known to Date 117 (FAO, 2004).
Google Scholar 
Huang, C. T. et al. Bioeconomic evaluation of Eleutheronema tetradactylum farming: A case study in Taiwan. Fish. Sci. 88, 437–447. https://doi.org/10.1007/s12562-022-01591-4 (2022).Article 
CAS 

Google Scholar 
Shihab, I. et al. Histological profiling of gonads depicting protandrous hermaphroditism in Eleutheronema tetradactylum. J. Fish. Biol. 90, 2402–2411. https://doi.org/10.1111/jfb.13324 (2017).Article 
CAS 
PubMed 

Google Scholar 
Presti, P., Johnson, G. D. & Datovo, A. Anatomy and evolution of the pectoral filaments of threadfins (Polynemidae). Sci. Rep. 10, 1–5. https://doi.org/10.1038/s41598-020-74896-y (2020).Article 
CAS 

Google Scholar 
Alshari, N. F. M. A. H. et al. Metabarcoding of fish larvae in the merbok river reveals species diversity and distribution along its mangrove environment. Zool. Stud. 60, e76. https://doi.org/10.6620/ZS.2021.60-76 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Tobin, A. J., Mapleston, A., Harry, A. V. & Espinoza, M. Big fish in shallow water; use of an intertidal surf-zone habitat by large-bodied teleosts and elasmobranchs in tropical northern Australia. Environ. Biol. Fish. 97, 821–838. https://doi.org/10.1007/s10641-013-0182-y (2013).Article 

Google Scholar 
Adkins, M. E., Simpfendorfer, C. A. & Tobin, A. J. Large tropical fishes and their use of the nearshore littoral, intertidal and subtidal habitat mosaic. Mar. Freshwater Res. 67, 1534–1545. https://doi.org/10.1071/mf14339 (2016).Article 

Google Scholar 
Xuan, Z. et al. Otolith microchemistry reveals different environmental histories for two endangered fourfinger threadfin species. Mar. Ecol. Prog. Ser. 700, 161–178 (2022).Article 
ADS 

Google Scholar 
Condini, M. V. et al. Prolonged estuarine habitat use by dusky grouper Epinephelus marginatus at subtropical latitudes revealed by otolith microchemistry. Endanger. Species Res. 29, 271–277 (2016).Article 

Google Scholar 
Teichert, N. et al. A multi-approach study to reveal eel life-history traits in an obstructed catchment before dam removal. Hydrobiologia 849, 1885–1903. https://doi.org/10.1007/s10750-022-04833-9 (2022).Article 

Google Scholar 
Wang, J., Sun, P. & Yin, F. Low mtDNA Cytb diversity and shallow population structure of Eleutheronema tetradactylum in the East China Sea and the South China Sea. Biochem. Syst. Ecol. 55, 268–274. https://doi.org/10.1016/j.bse.2014.03.026 (2014).Article 
CAS 

Google Scholar 
Du, J. et al. Connectivity of fish assemblages along the mangrove-seagrass-coral reef continuum in Wenchang, China. Acta Oceanol. Sin. 39, 43–52. https://doi.org/10.1007/s13131-019-1490-7 (2020).Article 

Google Scholar 
Pember, M. B., Newman, S.J., Hesp, S.A., Young, G.C., Skepper, C.L., Hall, N.G. & Potter, I.C. Biological parameters for managing the fisheries for Blue and King Threadfin Salmons, Estuary Rockcod, Malabar Grouper and Mangrove Jack in north-western Australia. Fisheries Research and Development Corporation. (2005).Zheng, Q., Fang, G. & Song, Y. T. Introduction to special section: Dynamics and Circulation of the Yellow, East, and South China Seas. J. Geophys. Res. https://doi.org/10.1029/2005jc003261 (2006).Article 

Google Scholar 
Zhang, P. et al. Spatiotemporal variation, speciation, and transport flux of TDP in Leizhou Peninsula coastal waters, South China Sea. Mar. Pollut. Bull. 167, 112284. https://doi.org/10.1016/j.marpolbul.2021.112284 (2021).Article 
CAS 
PubMed 

Google Scholar 
Stewart, J., Hughes, J. M., Stanley, C. & Fowler, A. M. The influence of rainfall on recruitment success and commercial catch for the large sciaenid, Argyrosomus japonicus, in eastern Australia. Mar. Environ. Res. 157, 104924. https://doi.org/10.1016/j.marenvres.2020.104924 (2020).Article 
CAS 
PubMed 

Google Scholar 
Alber, M. A conceptual model of estuarine freshwater inflow management. Estuaries 25, 1246–1261. https://doi.org/10.1007/BF02692222 (2002).Article 

Google Scholar 
Possamai, B. et al. Freshwater inflow variability affects the relative importance of allochthonous sources for estuarine fishes. Estuar. Coast. 43, 880–893. https://doi.org/10.1007/s12237-019-00693-0 (2020).Article 

Google Scholar 
Halliday, I. A., Robins, J. B., Mayer, D. G., Staunton-Smith, J. & Sellin, M. J. Effects of freshwater flow on the year-class strength of a non-diadromous estuarine finfish, king threadfin (Polydactylus macrochir), in a dry-tropical estuary. Mar. Freshwater Res. 59, 157–164. https://doi.org/10.1071/MF07077 (2008).Article 

Google Scholar 
Muñoz Sabater, J. ERA5-Land monthly averaged data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). doi:https://doi.org/10.24381/cds.68d2bb30 (2023).Xiao, J. et al. Molecular phylogenetic and morphometric analysis of population structure and demography of endangered threadfin fish Eleutheronema from Indo-Pacific waters. Sci. Rep. 12, 3455. https://doi.org/10.1038/s41598-022-07342-w (2022).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mischel, S. A., Mertz-Kraus, R., Jochum, K. P. & Scholz, D. TERMITE: An R script for fast reduction of laser ablation inductively coupled plasma mass spectrometry data and its application to trace element measurements. Rapid Commun. Mass Sp. 31, 1079–1087. https://doi.org/10.1002/rcm.7895 (2017).Article 
CAS 

Google Scholar 
Butcher, D. J. Recent advances in the determination of calcium and its use as an internal standard in environmental samples: Fundamentals and applications. Appl. Spectrosc. Rev. 55, 60–75. https://doi.org/10.1080/05704928.2019.1570520 (2020).Article 
ADS 
CAS 

Google Scholar 
Vignon, M. Extracting environmental histories from sclerochronological structures—Recursive partitioning as a mean to explore multi-elemental composition of fish otolith. Ecol. Inform. 30, 159–169. https://doi.org/10.1016/j.ecoinf.2015.10.002 (2015).Article 

Google Scholar 
Vivancos, A. et al. Hydrological connectivity drives longitudinal movement of endangered endemic Chilean darter Percilia irwini (Eigenmann, 1927). J. Fish. Biol. 98, 33–43. https://doi.org/10.1111/jfb.14554 (2021).Article 
PubMed 

Google Scholar 
R Core Team, R: A language and environment for statistical computing. (2013).Hegg, J. C. & Kennedy, B. P. Let’s do the time warp again: non-linear time series matching as a tool for sequentially structured data in ecology. Ecosphere 12, e03742. https://doi.org/10.1002/ecs2.3742 (2021).Article 

Google Scholar 
Zhong, L., Wang, M., Li, D., Tang, S. & Chen, X. Mitochondrial genome of Eleutheronema rhadinum with an additional non-coding region and novel insights into the phylogenetics. Front. Mar. Sci. 8, 746598 (2021).Article 

Google Scholar 
Leis, J. M., Piola, R. F., Hay, A. C., Wen, C. & Kan, K.-P. Ontogeny of behaviour relevant to dispersal and connectivity in the larvae of two non-reef demersal, tropical fish species. Mar. Freshwater Res. 60, 211–223 (2009).Article 

Google Scholar 
Nelson, T. R., DeVries, D. R. & Wright, R. A. Salinity and temperature effects on element incorporation of gulf killifish Fundulus grandis otoliths. Estuar. Coast. 41, 1164–1177. https://doi.org/10.1007/s12237-017-0341-z (2018).Article 
CAS 

Google Scholar 
Nelson, T. R. & Powers, S. P. Elemental concentrations of water and otoliths as salinity proxies in a Northern Gulf of Mexico estuary. Estuar. Coast. 43, 843–864. https://doi.org/10.1007/s12237-019-00686-z (2020).Article 
CAS 

Google Scholar 
Breine, J. J., Lambeens, I., Maes, Y., De Bruyn, A. & Galle, L. First record of the fourfinger threadfin, Eleutheronema tetradactylum (Shaw, 1804) in Belgium. Estuar. Coast. Shelf Sci. 187, 28–30. https://doi.org/10.1016/j.ecss.2016.12.025 (2017).Article 
ADS 

Google Scholar 
Pan, X. et al. Combining otolith elemental signatures with multivariate analytical models to verify the migratory pattern of Japanese Spanish mackerel (Scomberomorus niphonius) in the southern Yellow Sea. Acta Oceanol. Sin. 39, 54–64. https://doi.org/10.1007/s13131-020-1606-0 (2021).Article 

Google Scholar 
Miller, J. A. Effects of water temperature and barium concentration on otolith composition along a salinity gradient: Implications for migratory reconstructions. J. Exp. Mar. Biol. Ecol. 405, 42–52. https://doi.org/10.1016/j.jembe.2011.05.017 (2011).Article 
CAS 

Google Scholar 
Yokouchi, K. et al. Time lag of the response on the otolith strontium/calcium ratios of the Japanese eel, Anguilla japonica to changes in strontium/calcium ratios of ambient water. Environ. Biol. Fish. 92, 469–478. https://doi.org/10.1007/s10641-011-9864-5 (2011).Article 

Google Scholar 
Wheeler, S. G., Russell, A. D., Fehrenbacher, J. S. & Morgan, S. G. Evaluating chemical signatures in a coastal upwelling region to reconstruct water mass associations of settlement-stage rockfishes. Mar. Ecol. Prog. Ser. 550, 191–206. https://doi.org/10.3354/meps11704 (2016).Article 
ADS 
CAS 

Google Scholar 
Lin, P., Hu, J., Zheng, Q., Sun, Z. & Zhu, J. Observation of summertime upwelling off the eastern and northeastern coasts of Hainan Island, China. Ocean Dyn. 66, 387–399. https://doi.org/10.1007/s10236-016-0934-2 (2016).Article 
ADS 

Google Scholar 
Liu, W. et al. Dissolved barium as a tracer of Kuroshio incursion in the Kuroshio region east of Taiwan Island and the adjacent East China Sea. Sci. China Earth Sci. 60, 1356–1367. https://doi.org/10.1007/s11430-016-9039-7 (2017).Article 
ADS 
CAS 

Google Scholar 
Able, K. W. A re-examination of fish estuarine dependence: Evidence for connectivity between estuarine and ocean habitats. Estuar. Coast. Shelf Sci. 64, 5–17. https://doi.org/10.1016/j.ecss.2005.02.002 (2005).Article 
ADS 

Google Scholar 
Whitfield, A. K. Littoral habitats as major nursery areas for fish species in estuaries: A reinforcement of the reduced predation paradigm. Mar. Ecol. Prog. Ser. 649, 219–234. https://doi.org/10.3354/meps13459 (2020).Article 

Google Scholar 
Acha, E. M., Simionato, C. G., Carozza, C. & Mianzan, H. Climate-induced year-class fluctuations of whitemouth croaker Micropogonias furnieri (Pisces, Sciaenidae) in the Río de la Plata estuary, Argentina-Uruguay. Fish. Oceanogr. 21, 58–77. https://doi.org/10.1111/j.1365-2419.2011.00609.x (2012).Article 

Google Scholar 
Schilling, H. T. et al. Evaluating estuarine nursery use and life history patterns of Pomatomus saltatrix in eastern Australia. Mar. Ecol. Prog. Ser. 598, 187–199. https://doi.org/10.3354/meps12495 (2018).Article 
ADS 

Google Scholar 
Menezes, R. et al. Habitat use plasticity by the dog snapper (Lutjanus jocu) across the Abrolhos Bank shelf, eastern Brazil, inferred from otolith chemistry. Estuar. Coast. Shelf Sci. 263, 107637. https://doi.org/10.1016/j.ecss.2021.107637 (2021).Article 
CAS 

Google Scholar 
Santos, R. O. et al. Linking bonefish (Albula vulpes) populations to nearshore estuarine habitats using an otolith microchemistry approach. Environ. Biol. Fish. 102, 267–283. https://doi.org/10.1007/s10641-018-0839-7 (2019).Article 

Google Scholar 
Pinceel, T. et al. An empirical confirmation of diversified bet hedging as a survival strategy in unpredictably varying environments. Ecology 102, e03496. https://doi.org/10.1002/ecy.3496 (2021).Article 
PubMed 

Google Scholar 
Wang, V. H., White, J. W., Arnott, S. A. & Scharf, F. S. Population connectivity of southern flounder in the US South Atlantic revealed by otolith chemical analysis. Mar. Ecol. Prog. Ser. 596, 165–179 (2018).Article 
ADS 
CAS 

Google Scholar 
Gillson, J., Scandol, J. & Suthers, I. Estuarine gillnet fishery catch rates decline during drought in eastern Australia. Fish. Res. 99, 26–37. https://doi.org/10.1016/j.fishres.2009.04.007 (2009).Article 

Google Scholar 
Pritt, J. J., Roseman, E. F. & O’Brien, T. P. Mechanisms driving recruitment variability in fish: Comparisons between the Laurentian Great Lakes and marine systems. ICES J. Mar. Sci. 71, 2252–2267. https://doi.org/10.1093/icesjms/fsu080 (2014).Article 

Google Scholar 
Mai, A. C. G. et al. High plasticity in habitat use of Lycengraulis grossidens (Clupeiformes, Engraulididae). Estuar. Coast. Shelf Sci. 141, 17–25. https://doi.org/10.1016/j.ecss.2014.01.014 (2014).Article 
ADS 
CAS 

Google Scholar 
Horne, J. B., Momigliano, P., Welch, D. J., Newman, S. J. & Van Herwerden, L. Limited ecological population connectivity suggests low demands on self-recruitment in a tropical inshore marine fish (Eleutheronema tetradactylum: Polynemidae). Mol. Ecol. 20, 2291–2306. https://doi.org/10.1111/j.1365-294X.2011.05097.x (2011).Article 
PubMed 

Google Scholar 
Newman, S. J. et al. Stock structure of blue threadfin Eleutheronema tetradactylum across northern Australia as inferred from stable isotopes in sagittal otolith carbonate. Fish. Manag. Ecol. 18, 246–257. https://doi.org/10.1111/j.1365-2400.2010.00780.x (2011).Article 

Google Scholar 
Ballagh, A. C., Welch, D. J., Newman, S. J., Allsop, Q. & Stapley, J. M. Stock structure of the blue threadfin (Eleutheronema tetradactylum) across northern Australia derived from life-history characteristics. Fish. Res. 121–122, 63–72. https://doi.org/10.1016/j.fishres.2012.01.011 (2012).Article 

Google Scholar 
Moore, B. R. et al. Stock structure of blue threadfin Eleutheronema tetradactylum across northern Australia, as indicated by parasites. J. Fish. Biol. 78, 923–936. https://doi.org/10.1111/j.1095-8649.2011.02917.x (2011).Article 
CAS 
PubMed 

Google Scholar 
McGuigan, C. J., Schlenker, L. S., Stieglitz, J. D., Benetti, D. D. & Grosell, M. Quantifying the effects of pop-up satellite archival tags on the swimming performance and behavior of young-adult mahi-mahi (Coryphaena hippurus). Can. J. Fish. Aquat. Sci. 78, 32–39. https://doi.org/10.1139/cjfas-2020-0030 (2020).Article 

Google Scholar 
Macdonald, J. I., Drysdale, R. N., Witt, R., Cságoly, Z. & Marteinsdóttir, G. Isolating the influence of ontogeny helps predict island-wide variability in fish otolith chemistry. Rev. Fish Biol. Fisheries 30, 173–202. https://doi.org/10.1007/s11160-019-09591-x (2019).Article 

Google Scholar 
Grammer, G. L. et al. Coupling biogeochemical tracers with fish growth reveals physiological and environmental controls on otolith chemistry. Ecol. Monogr. 87, 487–507. https://doi.org/10.1002/ecm.1264 (2017).Article 

Google Scholar  More