Ecology

Schwarzenbach RP, Escher BI, Fenner K, Hofstetter TB, Johnson CA, Von Gunten U, et al. The challenge of micropollutants in aquatic systems. Science (80-). 2006;313:1072–7.Article 

Google Scholar 
Deblonde T, Cossu-Leguille C, Hartemann P. Emerging pollutants in wastewater: a review of the literature. Int J Hyg Environ Health. 2011;214:442–8.Article 

Google Scholar 
Wang M, Cernava T. Overhauling the assessment of agrochemical-driven interferences with microbial communities for improved global ecosystem integrity. Environ Sci Ecotechnol. 2020;4:100061.Article 

Google Scholar 
Luo Y, Guo W, Ngo HH, Nghiem LD, Hai FI, Zhang J, et al. A review on the occurrence of micropollutants in the aquatic environment and their fate and removal during wastewater treatment. Sci Total Environ. 2014;473–474:619–41.Article 

Google Scholar 
Wang Z, Zhang XH, Huang Y, Wang H. Comprehensive evaluation of pharmaceuticals and personal care products (PPCPs) in typical highly urbanized regions across China. Environ Pollut. 2015;204:223–32.Article 

Google Scholar 
Eggen RIL, Hollender J, Joss A, Schärer M, Stamm C. Reducing the discharge of micropollutants in the aquatic environment: the benefits of upgrading wastewater treatment plants. Environ Sci Technol. 2014;48:7683–9.Article 

Google Scholar 
Vila-Costa M, Cerro-Gálvez E, Martínez-Varela A, Casas G, Dachs J. Anthropogenic dissolved organic carbon and marine microbiomes. ISME J. 2020;14:2646–8.Article 

Google Scholar 
da Silva GCX, Medeiros de Abreu CH, Ward ND, Belúcio LP, Brito DC, Cunha HFA, et al. Environmental impacts of dam reservoir filling in the East Amazon. Front Water. 2020;2:11.Article 

Google Scholar 
Kuroda K, Murakami M, Oguma K, Muramatsu Y, Takada H, Takizawa S. Assessment of groundwater pollution in Tokyo using PPCPs as sewage markers. Environ Sci Technol. 2012;46:1455–64.Article 

Google Scholar 
Liu WR, Zhao JL, Liu YS, Chen ZF, Yang YY, Zhang QQ, et al. Biocides in the Yangtze River of China: spatiotemporal distribution, mass load and risk assessment. Environ Pollut. 2015;200:53–63.Article 

Google Scholar 
Roberts J, Kumar A, Du J, Hepplewhite C, Ellis DJ, Christy AG, et al. Pharmaceuticals and personal care products (PPCPs) in Australia’s largest inland sewage treatment plant, and its contribution to a major Australian river during high and low flow. Sci Total Environ. 2016;541:1625–37.Article 

Google Scholar 
Rodea-Palomares I, Gonzalez-Pleiter M, Gonzalo S, Rosal R, Leganes F, Sabater S, et al. Hidden drivers of low-dose pharmaceutical pollutant mixtures revealed by the novel GSA-QHTS screening method. Sci Adv. 2016;2:1–12.Article 

Google Scholar 
Yang X, Chen F, Meng F, Xie Y, Chen H, Young K, et al. Occurrence and fate of PPCPs and correlations with water quality parameters in urban riverine waters of the Pearl River Delta, South China. Environ Sci Pollut Res. 2013;20:5864–75.Article 

Google Scholar 
Cerro-Gálvez E, Dachs J, Lundin D, Fernández-Pinos MC, Sebastián M, Vila-Costa M. Responses of coastal marine microbiomes exposed to anthropogenic dissolved organic carbon. Environ Sci Technol. 2021;55:9609–21.Article 

Google Scholar 
Martinez-Varela A, Cerro-Gálvez E, Auladell A, Sharma S, Moran MA, Kiene RP, et al. Bacterial responses to background organic pollutants in the northeast subarctic Pacific Ocean. Environ Microbiol. 2021;23:4532–46.Article 

Google Scholar 
Bob A, Shen D, Li S, Zhang L, Rashid A, Sun Q, et al. Strong impact of micropollutants on prokaryotic communities at the horizontal but not vertical scales in a subtropical reservoir, China. Sci Total Environ. 2020;721:137767.Article 

Google Scholar 
Tlili A, Corcoll N, Arrhenius Å, Backhaus T, Hollender J, Creusot N, et al. Tolerance patterns in stream biofilms link complex chemical pollution to ecological impacts. Environ Sci Technol. 2020;54:10745–53.Article 

Google Scholar 
Chalew TEA, Halden RU. Environmental exposure of aquatic and terrestrial biota to triclosan and triclocarban. J Am Water Resour Assoc. 2009;45:4–13.Article 

Google Scholar 
Zhang W, Yin K, Chen L. Bacteria-mediated bisphenol A degradation. Appl Microbiol Biotechnol. 2013;97:5681–9.Article 

Google Scholar 
Staples CA, Dorn PB, Klecka GM, O’Block ST, Harris LR. A review of the environmental fate, effects, and exposures of bisphenol A. Chemosphere. 1998;36:2149–73.Article 

Google Scholar 
Choi YJ, Lee LS. Aerobic soil biodegradation of bisphenol (BPA) alternatives bisphenol S and bisphenol AF compared to BPA. Environ Sci Technol. 2017;51:13698–704.Article 

Google Scholar 
McMurry LM, Oethinger M, Levy SB. Triclosan targets lipid synthesis [4]. Nature. 1998;394:531–2.Article 

Google Scholar 
Cabana H, Jiwan JLH, Rozenberg R, Elisashvili V, Penninckx M, Agathos SN, et al. Elimination of endocrine disrupting chemicals nonylphenol and bisphenol A and personal care product ingredient triclosan using enzyme preparation from the white rot fungus Coriolopsis polyzona. Chemosphere. 2007;67:770–8.Article 

Google Scholar 
Hu A, Ju F, Hou L, Li J, Yang X, Wang H, et al. Strong impact of anthropogenic contamination on the co-occurrence patterns of a riverine microbial community. Environ Microbiol. 2017;19:4993–5009.Article 

Google Scholar 
Boyd TJ, Smith DC, Apple JK, Hamdan LJ, Osburn CL, Montgomery MT. Evaluating PAH biodegradation relative to total bacterial carbon demand in coastal ecosystems: Are PAHs truly recalcitrant? In: Van Dijk T. (ed). Microbial Ecology Research Trends. Nova Science Publishers, 2008. pp 1–38.Okere UV, Cabrerizo A, Dachs J, Ogbonnaya UO, Jones KC, Semple KT. Effects of pre-exposure on the indigenous biodegradation of 14C-phenanthrene in Antarctic soils. Int Biodeterior Biodegrad. 2017;125:189–99.Article 

Google Scholar 
Coll C, Bier R, Li Z, Langenheder S, Gorokhova E, Sobek A. Association between aquatic micropollutant dissipation and river sediment bacterial communities. Environ Sci Technol. 2020;54:14380–92.Article 

Google Scholar 
Bender EA, Case TJ, Gilpin ME. Perturbation experiments in community ecology: Theory and practice. Ecology. 1984;65:1–13.Shade A, Peter H, Allison SD, Baho DL, Berga M, Bürgmann H, et al. Fundamentals of microbial community resistance and resilience. Front Microbiol. 2012;3:1–19.Article 

Google Scholar 
Buerger S, Spoering A, Gavrish E, Leslin C, Ling L, Epstein SS. Microbial scout hypothesis, stochastic exit from dormancy, and the nature of slow growers. Appl Environ Microbiol. 2012;78:3221–8.Article 

Google Scholar 
Lee SH, Sorensen JW, Grady KL, Tobin TC, Shade A. Divergent extremes but convergent recovery of bacterial and archaeal soil communities to an ongoing subterranean coal mine fire. ISME J. 2017;11:1447–59.Article 

Google Scholar 
Lennon JT, den Hollander F, Wilke-Berenguer M, Blath J. Principles of seed banks and the emergence of complexity from dormancy. Nat Commun. 2021;12:1–16.Article 

Google Scholar 
Philippot L, Griffiths BS, Langenheder S. Microbial community resilience across ecosystems and multiple disturbances. Microbiol Mol Biol Rev. 2021;85:e00026–20.Article 

Google Scholar 
Hu A, Li S, Zhang L, Wang H, Yang J, Luo Z, et al. Prokaryotic footprints in urban water ecosystems: a case study of urban landscape ponds in a coastal city, China. Environ Pollut. 2018;242:1729–39.Article 

Google Scholar 
Im J, Löffler FE. Fate of bisphenol A in terrestrial and aquatic environments. Environ Sci Technol. 2016;50:8403–16.Article 

Google Scholar 
Sun Q, Li M, Ma C, Chen X, Xie X, Yu CP. Seasonal and spatial variations of PPCP occurrence, removal and mass loading in three wastewater treatment plants located in different urbanization areas in Xiamen, China. Environ Pollut. 2016;208:371–81.Article 

Google Scholar 
Sun Q, Wang Y, Li Y, Ashfaq M, Dai L, Xie X, et al. Fate and mass balance of bisphenol analogues in wastewater treatment plants in Xiamen City, China. Environ Pollut. 2017;225:542–9.Article 

Google Scholar 
Sun Q, Li Y, Chou PH, Peng PY, Yu CP. Transformation of bisphenol A and alkylphenols by ammonia-oxidizing bacteria through nitration. Environ Sci Technol. 2012;46:4442–8.Article 

Google Scholar 
Zaayman M, Siggins A, Horne D, Lowe H, Horswell J. Investigation of triclosan contamination on microbial biomass and other soil health indicators. FEMS Microbiol Lett. 2017;364:1–6.Article 

Google Scholar 
Xie J, Zhao N, Zhang Y, Hu H, Zhao M, Jin H. Occurrence and partitioning of bisphenol analogues, triclocarban, and triclosan in seawater and sediment from East China Sea. Chemosphere. 2022;287:132218.Article 

Google Scholar 
Yamazaki E, Yamashita N, Taniyasu S, Lam J, Lam PKS, Moon HB, et al. Bisphenol A and other bisphenol analogues including BPS and BPF in surface water samples from Japan, China, Korea and India. Ecotoxicol Environ Saf. 2015;122:565–72.Article 

Google Scholar 
Kalyuzhny M, Shnerb NM. Dissimilarity-overlap analysis of community dynamics: opportunities and pitfalls. Methods Ecol Evol. 2017;8:1764–73.Article 

Google Scholar 
Wang J, Pan F, Soininen J, Heino J, Shen J. Nutrient enrichment modifies temperature-biodiversity relationships in large-scale field experiments. Nat Commun. 2016;7:1–9.
Google Scholar 
Hildebrand F, Tito RY, Voigt AY, Bork P, Raes J. Correction to: LotuS: an efficient and user-friendly OTU processing pipeline [Microbiome, 2, (2014), 30]. Microbiome. 2014;2:1–7.Article 

Google Scholar 
Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.Article 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41:590–6.Article 

Google Scholar 
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high- throughput community sequencing data Intensity normalization improves color calling in SOLiD sequencing. Nat Methods. 2010;7:335–6.Article 

Google Scholar 
Klappenbach JA, Saxman PR, Cole JR, Schmidt TM. Rrndb: the ribosomal RNA operon copy number database. Nucleic Acids Res. 2001;29:181–4.Article 

Google Scholar 
Wu L, Yang Y, Chen S, Zhao M, Zhu Z, Yang S, et al. Long-term successional dynamics of microbial association networks in anaerobic digestion processes. Water Res. 2016;104:1–10.Article 

Google Scholar 
Stegen JC, Lin X, Fredrickson JK, Chen X, Kennedy DW, Murray CJ, et al. Quantifying community assembly processes and identifying features that impose them. ISME J. 2013;7:2069–79.Article 

Google Scholar 
Stegen JC, Lin X, Fredrickson JK, Konopka AE. Estimating and mapping ecological processes influencing microbial community assembly. Front Microbiol. 2015;6:1–15.Article 

Google Scholar 
Webb CO, Ackerly DD, Kembel SW. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics. 2008;24:2098–2100.Article 

Google Scholar 
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21.Article 

Google Scholar 
Letunic I, Bork P. Interactive Tree of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:2–5.Article 

Google Scholar 
Anderson MJ. Permutation tests for univariate or multivariate analysis of variance and regression. Can J Fish Aquat Sci. 2001;58:626–39.Article 

Google Scholar 
Oksanen AJ, Blanchet FG, Friendly M, Kindt R, Legendre P, Mcglinn D, et al. Vegan: community ecology package. Encyclopedia of Food and Agricultural Ethics. 2019; 2395–6.Bashan A, Gibson TE, Friedman J, Carey VJ, Weiss ST, Hohmann EL, et al. Universality of human microbial dynamics. Nature. 2016;534:259–62.Article 

Google Scholar 
Vila JCC, Liu YY, Sanchez A. Dissimilarity–overlap analysis of replicate enrichment communities. ISME J. 2020;14:2505–13.Article 

Google Scholar 
Ahlmann-Eltze C, Patil I. ggsignif: significance Brackets for ‘ggplot2’. R package version 0.6.1. 2021.Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017;11:2639–43.Article 

Google Scholar 
Glassman SI, Martiny JBH. Broadscale ecological patterns are robust to use of exact. mSphere. 2018;3:e00148–18.Article 

Google Scholar 
Lindström ES, Östman Ö. The importance of dispersal for bacterial community composition and functioning. PLoS One. 2011;6:e25883.Article 

Google Scholar 
Shen D, Langenheder S, Jürgens K. Dispersal modifies the diversity and composition of active bacterial communities in response to a salinity disturbance. Front Microbiol. 2018;9:2188.Article 

Google Scholar 
Zhou NA, Lutovsky AC, Andaker GL, Gough HL, Ferguson JF. Cultivation and characterization of bacterial isolates capable of degrading pharmaceutical and personal care products for improved removal in activated sludge wastewater treatment. Biodegradation. 2013;24:813–27.Article 

Google Scholar 
Thelusmond JR, Strathmann TJ, Cupples AM. Carbamazepine, triclocarban and triclosan biodegradation and the phylotypes and functional genes associated with xenobiotic degradation in four agricultural soils. Sci Total Environ. 2019;657:1138–49.Article 

Google Scholar 
Danzl E, Sei K, Soda S, Ike M, Fujita M. Biodegradation of bisphenol A, bisphenol F and bisphenol S in seawater. Int J Environ Res Public Health. 2009;6:1472–84.Article 

Google Scholar 
Zaborowska M, Wyszkowska J, Borowik A. Soil microbiome response to contamination with Bisphenol A, Bisphenol F and Bisphenol S. Int J Mol Sci. 2020;21:3529.Article 

Google Scholar 
Freilich S, Zarecki R, Eilam O, Segal ES, Henry CS, Kupiec M, et al. Competitive and cooperative metabolic interactions in bacterial communities. Nat Commun. 2011;2:587–9.Article 

Google Scholar 
Pacheco AR, Moel M, Segrè D. Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. Nat Commun. 2019;10:103.Article 

Google Scholar 
Oh S, Choi D, Cha C-J. Ecological processes underpinning microbial community structure during exposure to subinhibitory level of triclosan. Sci Rep. 2019;9:4598.Article 

Google Scholar 
Hagberg A, Gupta S, Rzhepishevska O, Fick J, Burmølle M, Ramstedt M. Do environmental pharmaceuticals affect the composition of bacterial communities in a freshwater stream? A case study of the Knivsta river in the south of Sweden. Sci Total Environ. 2021;763:142991.Article 

Google Scholar 
Gao H, LaVergne JM, Carpenter CMG, Desai R, Zhang X, Gray K, et al. Exploring co-occurrence patterns between organic micropollutants and bacterial community structure in a mixed-use watershed. Environ Sci Process Impacts. 2019;21:867–80.Article 

Google Scholar 
Wolff D, Krah D, Dötsch A, Ghattas AK, Wick A, Ternes TA. Insights into the variability of microbial community composition and micropollutant degradation in diverse biological wastewater treatment systems. Water Res. 2018;143:313–24.Article 

Google Scholar 
Bajić D, Vila JCC, Blount ZD, Sánchez A. On the deformability of an empirical fitness landscape by microbial evolution. Proc Natl Acad Sci USA. 2018;115:11286–91.Article 

Google Scholar 
Nemergut DR, Schmidt SK, Fukami T, O’Neill SP, Bilinski TM, Stanish LF, et al. Patterns and Processes of Microbial Community Assembly. Microbiol Mol Biol Rev. 2013;77:342–56.Article 

Google Scholar 
Zhou J, Ning D. Stochastic community assembly: does it matter in microbial ecology? Microbiol Mol Biol Rev. 2017;81:1–32.Article 

Google Scholar 
Vellend M. Conceptual synthesis in community ecology. Q Rev Biol. 2010;85:183–206.Article 

Google Scholar 
Svoboda P, Lindström ES, Ahmed Osman O, Langenheder S. Dispersal timing determines the importance of priority effects in bacterial communities. ISME J. 2018;12:644–6.Article 

Google Scholar 
Bernstein HC. Reconciling ecological and engineering design principles for building microbiomes. mSystems. 2019;4:1–5.Article 

Google Scholar 
Borchert E, Hammerschmidt K, Hentschel U, Deines P. Enhancing microbial pollutant degradation by integrating eco-evolutionary principles with environmental biotechnology. Trends Microbiol. 2021;29:908–18.Article 

Google Scholar 
Rocca JD, Muscarella ME, Peralta AL, Izabel-Shen D, Simonin M. Guided by microbes: applying community coalescence principles for predictive microbiome engineering. mSystems. 2021;6:e00538–21.Article 

Google Scholar 
Nemergut DR, Knelman JE, Ferrenberg S, Bilinski T, Melbourne B, Jiang L, et al. Decreases in average bacterial community rRNA operon copy number during succession. ISME J. 2016;10:1147–56.Article 

Google Scholar 
Frost LS, Leplae R, Summers AO, Toussaint A, Edmonton A. Mobile genetic elements: the agents of open source evolution. Nat Rev Microbiol. 2005;3:722–32.Ullastres A, Merenciano M, Guio L, Gonz J. Transposable elements: a toolkit for stress and environmental adaptation in bacteria. Stress Environ Regul Gene Expr Adapt Bact. 2016;1:137–45.
Google Scholar 
Chang CY, Vila JCC, Bender M, Li R, Mankowski MC, Bassette M, et al. Engineering complex communities by directed evolution. Nat Ecol Evol. 2021;5:1011–23.Article 

Google Scholar  More

Lal Mohan, R. S., Dey, S. C., Bairagi, S. P. & Roy, S. On a survey of the Ganges River dolphin Platanista gangetica of Bramaputra River, Assam. J. Bombay Nat. Hist. Soc. 94, 483–495 (1997).
Google Scholar 
Sinha, R.K., et al. Status and distribution of the Ganges susu (Platanista gangetica) in Ganges River system of India and Nepal in Biology and conservation of freshwater cetaceans in Asia (eds. Reeves, R. R., Smith, B. D. & Kasuya, T). 42–48 (Switzerland: Occasional Paper of the IUCN Species Survival Commission, 2000)Sinha, R. K. & Kannan, K. Ganges River dolphin: an overview of biology, ecology, and conservation status in India. Ambio. 43,1029–1046 (2014).
Google Scholar 
Anderson, J. Anatomical and Zoological Researches: Comprising an Account of the Zoological Results of the Two Expeditions to Western Yunnan in 1868 and 1875; and A Monograph of the Two Cetacean Genera, Platanista and Orcella-Vol. 1 (Text). Vol. 1 (Bernard Quaritch, 1878).Herald, E. S. et al. Blind river dolphin: first side-swimming cetacean. Science 166, 1408–1410 (1969).ADS 
CAS 

Google Scholar 
Herald, E. S. Field and aquarium study of the blind River Dolphin (Platanista Gangetica) (California Academy of Sciences San Fransico Steinhart Aquarium, 1969).Pilleri, G., Zbinden, K., Gihr, M. & Kraus, C. Sonar clicks, directionality of the emission field and echolocating behaviour of the Indus dolphin (Platanista indi, Blyth, 1859). Invest. Cetacea Brain Anat. Inst. Berne Switzerl. 13–43 (1976).Jensen, F. H. et al. Clicking in shallow rivers: short-range echolocation of Irrawaddy and Ganges river dolphins in a shallow, acoustically complex habitat. PLoS ONE 8, e59284 (2013).ADS 
CAS 

Google Scholar 
Pence, E.A. Monofilament gill net acoustic study. (National Mammal Laboratory, Contract 40-ABNF-5-1988,1986)Jefferson, T. A., Würsig, B. & Fertl, D. Cetacean Detection and Responses to Fishing Gear in Marine Mammal Sensory Systems (eds. Thomas, J.A., Kastelein, R.A. & Supin, A.Y.) 663–684 (Springer, 1992)
Google Scholar 
Mansur, E. F., Smith, B. D., Mowgli, R. M. & Diyan, M. A. A. Two incidents of fishing gear entanglement of Ganges River dolphins (Platanista gangetica gangetica) in waterways of the Sundarbans mangrove forest, Bangladesh. Aquat. Mamm. 34, 362 (2008).
Google Scholar 
Sinha, R. K. An alternative to dolphin oil as a fish attractant in the Ganges River system: conservation of the Ganges River dolphin.
Biol. Conserv. 107, 253–257 https://doi.org/10.1016/S0006-3207(02)00058-7 (2002).Article 

Google Scholar 
Qureshi, Q. et al. Development of conservation action plan for river dolphin. 228 (Wildlife Institute of India, Dehradun, Uttarakhand, 2018).Kolipakam, V. et al. Evidence for the continued use of river dolphin oil for bait fishing and traditional medicine: implications for conservation. Heliyon 6, e04690 (2020).
Google Scholar 
Wakid, A. Initiative to reduce the fishing pressures in and around identified habitats of endangered Gangetic dolphin in Brahmaputra River system. (Assam, 2010).Braulik, G.T. & Smith, B.D. Platanista gangetica (amended version of 2017
assessment). The IUCN Red List of Threatened Species, e.T41758A151913336. https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T41758A151913336.en (2019).Dawson, S. M., Northridge, S., Waples, D. & Read, A. J. To ping or not to ping: the use of active acoustic devices in mitigating interactions between small cetaceans and gillnet fisheries. Endanger. Species Res. 19, 201–221 (2013)
Google Scholar 
Reeves, R. R., McClellan, K. & Werner, T. B. Marine mammal bycatch in gillnet and other entangling net fisheries, 1990 to 2011. Endanger. Species Res. 20, 71–97 (2013).
Google Scholar 
Moore, M. J. et al. Fatally entangled right whales can die extremely slowly in OCEANS 2006. 1–3 (IEEE, 2006).Meÿer, M.A. et al. Trends and interventions in large whale entanglement along the South African coast. Afr. J. Mar. Sci. 33, 429–439 (2011).
Google Scholar 
Knowlton, A. R., Hamilton, P. K., Marx, M. K., Pettis, H. M. & Kraus, S. D. Monitoring North Atlantic right whale Eubalaena glacialis entanglement rates: a 30 year retrospective. Mar. Ecol. Prog. Ser. 466, 293–302 (2012).ADS 

Google Scholar 
Knowlton, A. R. et al. Effects of fishing rope strength on the severity of large whale entanglements. Conserv. Biol. 30, 318–328 (2016).
Google Scholar 
Pace, R. M. III., Cole, T. V. & Henry, A. G. Incremental fishing gear modifications fail to significantly reduce large whale serious injury rates. Endanger. Species Res. 26, 115–126 (2014).
Google Scholar 
Salvador, G., Kenney, J. & Higgins, J. 2008 Supplement to the Large whale gear research summary. NOAA/Fisheries Northeast Regional Office, Protected Resources Division, Gloucester, MA (2008).van der Hoop, J. M. et al. Assessment of management to mitigate anthropogenic effects on large whales. Conserv. Biol. 27, 121–133 (2013).
Google Scholar 
Hamilton, S. & Baker, G. B. Technical mitigation to reduce marine mammal bycatch and entanglement in commercial fishing gear: lessons learnt and future directions. Rev. Fish Biol. Fish. 29, 223–247 (2019).
Google Scholar 
Bordino, P., Mackay, A. I., Werner, T. B., Northridge, S. & Read, A. Franciscana bycatch is not reduced by acoustically reflective or physically stiffened gillnets. Endanger. Species Res. 21, 1–12 (2013).
Google Scholar 
Dawson, S. M. Incidental catch of Hector’s dolphin in inshore gillnets. Mar. Mamm. Sci. 7, 283–295 (1991).
Google Scholar 
Mooney, T. A., Nachtigall, P. E. & Au, W. W. Target strength of a nylon monofilament and an acoustically enhanced gillnet: predictions of biosonar detection ranges. Aquat. Mamm. 30, 220–226 (2004).
Google Scholar 
Northridge, S., Sanderson, D., Mackay, A. & Hammond, P. Analysis and mitigation of cetacean bycatch in UK fisheries. Final Report
to DEFRA, Project MF0726, Sea Mammal Research Unit, School of Biology, University of St. Andrews (2003).Mangel, J. C. et al. Illuminating gillnets to save seabirds and the potential for multi-taxa bycatch mitigation. R. Soc. Open Sci. 5, 180254 (2018).ADS 

Google Scholar 
Stephenson, P. C. & Wells, S. Evaluation of the effectiveness of reducing dolphin catches with pingers and exclusion grids in the Pilbara trawl fishery. (Department of Fisheries, Western Australia, 2006).Santana-Garcon, J. et al. Risk versus reward: Interactions, depredation rates, and bycatch mitigation of dolphins in demersal fish trawls. Can. J. Fish. Aquat. Sci. 75, 2233–2240 (2018).
Google Scholar 
Carretta, J., Barlow, J. & Enriquez, L. Acoustic pingers eliminate beaked whale bycatch in a gill net fishery. Mar. Mamm. Sci. 24, 956–961 (2008).
Google Scholar 
Bordino, P. et al. Reducing incidental mortality of Franciscana dolphin Pontoporia blainvillei with acoustic warning devices attached to fishing nets. Mar. Mamm. Sci. 18, 833–842 (2002).
Google Scholar 
Khan, U. & Willems, D. Report of the Trinational workshop on the Irrawaddy Dolphin, 1st to 4th December 2020. 41 (WWF, Pakistan & Netherlands, 2021).Deori, S. et al. PINGERS: can be the eyes of blind ganges dolphins (Platanista Gangetica Gangetica, Roxburgh 1801). J. Sci. Trans. Environ. Technov. 11, 169–178 (2018).
Google Scholar 
Kraus, S. D. The once and future ping: challenges for the use of acoustic deterrents in fisheries. Mar. Technol. Soc. J. 33, 90 (1999).
Google Scholar 
Mate, B. R. & Harvey, J. T. Acoustical deterrents in marine mammal conflicts with fisheries. a workshop held February 17–18, 1986 at Newport, Oregon. NTIS, SPRINGFIELD, VA(USA) (1987).Favaro, L., Gnone, G. & Pessani, D. Postnatal development of echolocation abilities in a bottlenose dolphin (Tursiops truncatus): Temporal organization. Zoo Biol. 32, 210–215 (2013).
Google Scholar 
Dey, M., Krishnaswamy, J., Morisaka, T. & Kelkar, N. Interacting effects of vessel noise and shallow river depth elevate metabolic stress in Ganges river dolphins. Sci. Rep. 9, 15426. https://doi.org/10.1038/s41598-019-51664-1 (2019).ADS 

Google Scholar 
Kastelein, R. A. et al. Effects of acoustic alarms, designed to reduce small cetacean bycatch in gillnet fisheries, on the behaviour of North Sea fish species in a large tank. Mar. Environ. Res. 64, 160–180 (2007).CAS 

Google Scholar 
Kraus, S. et al. Acoustic alarms reduce porpoise mortality. Nature 388, 525 (1997).ADS 
CAS 

Google Scholar 
Roberts, B. L. & Read, A. J. Field assessment of C-POD performance in detecting echolocation click trains of bottlenose dolphins (Tursiops truncatus). Mar. Mamm. Sci. 31, 169–190 (2015).
Google Scholar 
Wickham, H. ggplot2: elegant graphics for data analysis. (Springer-Verlag, New York, 2009).RStudio Team. RStudio: Integrated Development for R. RStudio, PBC, Boston, MA. http://www.rstudio.com/ (2021).Crawley, M. J. Statistics: An Introduction using R (Wiley, 2005).MATH 

Google Scholar 
Perrin, W. F., Donovan, G.P. & Barlow, J. Report of the workshop on mortality of cetaceans in passive fishing nets and traps. Rep. Int. Whal. Commn. 1–71 (Cambridge: IWC, 1994).Read, A. J., Drinker, P. & Northridge, S. Bycatch of marine mammals in US and global fisheries. Conserv. Biol. 20, 163–169 (2006).
Google Scholar 
Reeves, R. & Leatherwood, S. Action plan for the conservation of cetaceans: dolphins, porpoises, and whales. IUCN/SSC Cetacean Specialist Group (IUCN Cambridge, 1998).Smith, B. D. & Braulik, G. Susu and Bhulan : Platanista gangetica gangetica and P. g. minor in Encyclopedia of Marine Mammals. 1135–1139 (Academic Press Ltd – Elsevier Science Ltd, 2009).Wakid, A. Status and distribution of the endangered Gangetic dolphin (Platanista gangetica gangetica) in the Brahmaputra River within India in 2005. Curr. Sci., 97, 1143–1151 (2009).
Google Scholar 
D’agrosa, C., Lennert-Cody, C. E. & Vidal, O. Vaquita bycatch in Mexico’s artisanal gillnet fisheries: driving a small population to extinction. Conserv. Biol. 14, 1110–1119 (2000).
Google Scholar 
Jaramillo-Legorreta, A. et al. Saving the vaquita: immediate action, not more data. Conserv. Biol., 21, 1653–1655 (2007).
Google Scholar 
Turvey, S. T. et al. First human-caused extinction of a cetacean species?. Biol. Lett. 3, 537–540 (2007).
Google Scholar 
Bashir, T., Khan, A., Gautam, P. & Behera, S. K. Abundance and prey availability assessment of Ganges River dolphin (Platanista gangetica gangetica) in a stretch of Upper Ganges River, India. Aquat. Mamm. 36, 19–26 (2010).
Google Scholar 
Braulik, G.T. & Smith, B.D. Platanista gangetica. The IUCN Red List of Threatened Species, e.T41758A50383612. https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T41758A50383612.en (2017).Hastie, G. D., Wilson, B., Wilson, L., Parsons, K. M. & Thompson, P. M. Functional mechanisms underlying cetacean distribution patterns: hotspots for bottlenose dolphins are linked to foraging. Mar. Biol. 144, 397–403 (2004).
Google Scholar 
Smith, A. M. & Smith, B. D. Review of status and threats to river cetaceans and recommendations for their conservation. Environ. Rev. 6, 189–206 (1998).
Google Scholar 
Wedekin, L., Daura-Jorge, F., Piacentini, V. & Simões-Lopes, P. Seasonal variations in spatial usage by the estuarine dolphin, Sotalia guianensis (van Bénéden, 1864)(Cetacea; Delphinidae) at its southern limit of distribution. Brazil. J. Biol. 67, 1–8 (2007).CAS 

Google Scholar 
Omeyer, L. et al. Assessing the effects of banana pingers as a bycatch mitigation device for harbour porpoises (Phocoena phocoena). Front. Mar. Sci. 285 (2020).Barlow, J. & Cameron, G. A. Field experiments show that acoustic pingers reduce marine mammal bycatch in the California drift gill net fishery. Mar. Mamm. Sci. 19, 265–283 (2003).
Google Scholar 
Amano, M., Kusumoto, M., Abe, M. & Akamatsu, T. Long-term effectiveness of pingers on a small population of finless porpoises in Japan. Endanger. Species Res. 32, 35–40 (2017).
Google Scholar 
Clay, T. A., Alfaro-Shigueto, J., Godley, B. J., Tregenza, N. & Mangel, J. C. Pingers reduce the activity of Burmeister’s porpoise around small-scale gillnet vessels. Mar. Ecol. Prog. Ser. 626, 197–208 (2019).ADS 

Google Scholar 
Kyhn, L. A. et al. Pingers cause temporary habitat displacement in the harbour porpoise Phocoena phocoena. Mar. Ecol. Prog. Ser. 526, 253–265 (2015).ADS 

Google Scholar 
Sugimatsu, H. et al. Study of acoustic characteristics of Ganges river dolphin calf using echolocation clicks recorded during long-term in-situ observation in 2012 OCEANS. 1–7 (IEEE, 2012).Ayadi, A., Ghorbel, M. & Bradai, M. N. Do pingers reduce interactions between bottlenose dolphins and trammel nets around the Kerkennah Islands (Central Mediterranean Sea)?. Cahiers Biol. Mar. 54, 375–383 (2013).
Google Scholar 
Carretta, J. V. & Barlow, J. Long-term effectiveness, failure rates, and “dinner bell” properties of acoustic pingers in a gillnet fishery. Mar. Technol. Soc. J. 45, 7–19 (2011).
Google Scholar 
Read, A. J., Waples, D. M., Urian, K. W. & Swanner, D. Fine-scale behaviour of bottlenose dolphins around gillnets. Proc. R. Soc. Lond. Ser. B Biol. Sci. 270, S90–S92 (2003).
Google Scholar 
Olesiuk, P. F., Nichol, L. M., Sowden, M. J. & Ford, J. K. Effect of the sound generated by an acoustic harassment device on the relative abundance and distribution of harbor porpoises (Phocoena phocoena) in Retreat Passage, British Columbia. Mar. Mamm. Sci. 18, 843–862 (2002).
Google Scholar 
Cox, T. M., Read, A. J., Solow, A. & Tregenza, N. Will harbour porpoises (Phocoena phocoena) habituate to pingers?. J. Cetacean Res. Manag. 3, 81–86 (2001).
Google Scholar 
Bruno, C. A. et al. Acoustic deterrent devices as mitigation tool to prevent dolphin-fishery interactions in the Aeolian Archipelago (Southern Tyrrhenian Sea, Italy). Mediterr. Mar. Sci. 22, 408–421 (2021).
Google Scholar 
Enger, P. S. Frequency discrimination in teleosts—central or peripheral in Hearing and sound communication in fishes (eds. Tavolga, W. N. et al.) 243–255 (Springer-Verlag, New York, 1981).
Google Scholar 
Halvorsen, M. B., Casper, B. M., Matthews, F., Carlson, T. J. & Popper, A. N. Effects of exposure to pile-driving sounds on the lake sturgeon, Nile tilapia and hogchoker. Proc. R. Soc. B Biol. Sci. 279, 4705–4714 (2012).
Google Scholar 
Ladich, F. Sound communication in fishes and the influence of ambient and anthropogenic noise. Bioacoustics 17, 34–38 (2008).
Google Scholar 
McCauley, R. D., Fewtrell, J. & Popper, A. N. High intensity anthropogenic sound damages fish ears. J. Acoust. Soc. Am. 113, 638–642 (2003).ADS 

Google Scholar 
Slabbekoorn, H. et al. A noisy spring: the impact of globally rising underwater sound levels on fish. Trends Ecol. Evol. 25, 419–427 (2010).
Google Scholar 
Gazo, M., Gonzalvo, J. & Aguilar, A. Pingers as deterrents of bottlenose dolphins interacting with trammel nets. Fish. Res. 92, 70–75 (2008).
Google Scholar 
Waples, D. M. et al. A field test of acoustic deterrent devices used to reduce interactions between bottlenose dolphins and a coastal gillnet fishery. Biol. Conserv. 157, 163–171 (2013).
Google Scholar 
Leaper, R. & Calderan, S. Review of methods used to reduce risks of cetacean bycatch and entanglements. CMS Tech. Ser. 38 (UNEP/CMS Secretariat, Bonn, Germany, 2018). More

Ritchie, H. & Roser, M. Urbanization. Our World in Data (2018). https://ourworldindata.org/urbanization.Chen, H., Weersink, A., Beaulieu, M., Lee, Y. N. & Nagelschmitz, K. A historical review of changes in farm size in canada. Tech. Rep., University of Guelph, Institute for the Advanced Study of Food and and Agricultural Policy (2019).Gudelj, I. & White, K. Spatial heterogeneity, social structure and disease dynamics of animal populations. Theor. Popul. Biol. 66, 139–149 (2004).CAS 
MATH 
Article 

Google Scholar 
Augustin, N., Mugglestone, M. A. & Buckland, S. T. An autologistic model for the spatial distribution of wildlife. J. Appl. Ecol. 339–347 (1996).Karlsson, E. K., Kwiatkowski, D. P. & Sabeti, P. C. Natural selection and infectious disease in human populations. Nat. Rev. Genetics 15, 379–393 (2014).CAS 
Article 

Google Scholar 
Fornaciari, A. Environmental microbial forensics and archaeology of past pandemics. Microbiol. Spect. 5, 5–1 (2017).Article 

Google Scholar 
Thèves, C., Crubézy, E. & Biagini, P. History of smallpox and its spread in human populations. Microbiol. Spect. 4, 4–4 (2016).Article 

Google Scholar 
Coltart, C. E., Lindsey, B., Ghinai, I., Johnson, A. M. & Heymann, D. L. The ebola outbreak, 2013–2016: old lessons for new epidemics. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160297 (2017).Article 

Google Scholar 
Colizza, V., Barrat, A., Barthelemy, M., Valleron, A.-J. & Vespignani, A. Modeling the worldwide spread of pandemic influenza: Baseline case and containment interventions. PLOS Med. 4, e13 (2007).Article 

Google Scholar 
Lüthy, I. A., Ritacco, V. & Kantor, I. N. One hundred years after the “Spanish” flu. Medicina 78, 113–118 (2018).
Google Scholar 
Zhang, Y., Zhang, A. & Wang, J. Exploring the roles of high-speed train, air and coach services in the spread of COVID-19 in China. Transport Policy 94, 34–42 (2020).Article 

Google Scholar 
Coelho, M. T. P. et al. Global expansion of COVID-19 pandemic is driven by population size and airport connections. PeerJ 8, e9708 (2020).Article 

Google Scholar 
Tompkins, D. M., Carver, S., Jones, M. E., Krkošek, M. & Skerratt, L. F. Emerging infectious diseases of wildlife: A critical perspective. Trends Parasitol. 31, 149–159 (2015).Article 

Google Scholar 
Soulsbury, C. D. & White, P. C. Human-wildlife interactions in urban areas: A review of conflicts, benefits and opportunities. Wildl. Res. 42, 541–553 (2015).Article 

Google Scholar 
VanderWaal, K. L. et al. Network analysis of cattle movements in Uruguay: Quantifying heterogeneity for risk-based disease surveillance and control. Prevent. Vet. Med. 123, 12–22 (2016).Article 

Google Scholar 
Rossi, G. et al. The potential role of direct and indirect contacts on infection spread in dairy farm networks. PLOS Comput. Biol. 13, e1005301 (2017).Article 

Google Scholar 
Stoddard, S. T. et al. The role of human movement in the transmission of vector-borne pathogens. PLOS Neg. Trop. Dis. 3, e481 (2009).Article 

Google Scholar 
Cosner, C. Models for the effects of host movement in vector-borne disease systems. Math. Biosci. 270, 192–197 (2015).MathSciNet 
MATH 
Article 

Google Scholar 
Scherer, P.C. Infection on the move: individual host movement drives disease persistence in spatially structured landscapes. Dr. rer. nat. thesis, Universität Potsdam (2019).Riley, S. Large-scale spatial-transmission models of infectious disease. Science 316, 1298–1301 (2007).ADS 
CAS 
Article 

Google Scholar 
Dougherty, E. R., Seidel, D. P., Carlson, C. J., Spiegel, O. & Getz, W. M. Going through the motions: Incorporating movement analyses into disease research. Ecol. Lett. 21, 588–604 (2018).Article 

Google Scholar 
Daversa, D., Fenton, A., Dell, A., Garner, T. & Manica, A. Infections on the move: How transient phases of host movement influence disease spread. Proc. R. Soc. B Biol. Sci. 284, 20171807 (2017).Article 

Google Scholar 
MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton University Press, 2001).Book 

Google Scholar 
Kobayashi, K. & Okumura, M. The growth of city systems with high-speed railway systems. Ann. Region. Sci. 31, 39–56 (1997).Article 

Google Scholar 
VanderWaal, K., Perez, A., Torremorrell, M., Morrison, R. M. & Craft, M. Role of animal movement and indirect contact among farms in transmission of porcine epidemic diarrhea virus. Epidemics 24, 67–75 (2018).Article 

Google Scholar 
Hanski, I. Metapopulation dynamics. Nature 396, 41–49 (1998).ADS 
CAS 
Article 

Google Scholar 
Colizza, V. & Vespignani, A. Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: Theory and simulations. J. Theor. Biol. 251, 450–467 (2008).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
Wang, L. & Li, X. Spatial epidemiology of networked metapopulation: An overview. Chin. Sci. Bull. 59, 3511–3522 (2014).Article 

Google Scholar 
Ruxton, G. D. Low levels of immigration between chaotic populations can reduce system extinctions by inducing asynchronous regular cycles. Proc. R. Soc. Lond. Seri. B Biol. Sci. 256, 189–193 (1994).ADS 
Article 

Google Scholar 
Earn, D. J. D., Rohani, P. & Grenfell, B. T. Persistence chaos and synchrony in ecology and epidemiology. Proc. R. Soc. Lond. Seri. B Biol. Sci. 265, 7–10 (1998).CAS 
Article 

Google Scholar 
Rosenzweig, M. L. Paradox of enrichment: Destabilization of exploitation ecosystems in ecological time. Science 171, 385–387 (1971).ADS 
CAS 
Article 

Google Scholar 
Hilker, F. M. & Schmitz, K. Disease-induced stabilization of predator-prey oscillations. J. Theor. Biol. 255, 299–306 (2008).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
Brown, J. H. & Kodric-Brown, A. Turnover rates in insular biogeography: Effect of immigration on extinction. Ecology 58, 445–449 (1977).Article 

Google Scholar 
Philipson, T. Economic epidemiology and infectious diseases. Handb. Health Econ. 1, 1761–1799 (2000).Article 

Google Scholar 
Murdoch, W. W., Briggs, C. J. & Nisbet, R. M. Consumer-Resource Dynamics, Monographs in Population Biology Vol. 36 (Princeton University Press, 2003).
Google Scholar 
Murdoch, W. W. & Oaten, A. Predation and population stability. In Advances in Ecological Research, vol. 9, 1–131 (Elsevier, 1975).Bolker, B. & Grenfell, B. T. Space, persistence and dynamics of measles epidemics. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 348, 309–320 (1995).ADS 
CAS 
Article 

Google Scholar 
Keeling, M. J. & Rohani, P. Estimating spatial coupling in epidemiological systems: a mechanistic approach. Ecol. Lett. 5, 20–29 (2002).Article 

Google Scholar 
Arino, J. Spatio-temporal spread of infectious pathogens of humans. Infect. Dis. Model. 2, 218–228 (2017).
Google Scholar 
Keeling, M. J. & Rohani, P. Modeling Infectious Diseases in Humans and Animals (Princeton University Press, 2011).MATH 
Book 

Google Scholar 
Wilson, E. B. & Worcester, J. The spread of an epidemic. Proc. Nat. Acad. Sci. 31, 327 (1945).ADS 
CAS 
Article 

Google Scholar 
Rushton, S. & Mautner, A. The deterministic model of a simple epidemic for more than one community. Biometrika 42, 126–132 (1955).MathSciNet 
MATH 
Article 

Google Scholar 
Lourenço, J. & Recker, M. Natural, persistent oscillations in a spatial multi-strain disease system with application to dengue. PLOS Comput. Biol. 9, e1003308 (2013).ADS 
Article 

Google Scholar 
Wikramaratna, P. S., Pybus, O. G. & Gupta, S. Contact between bird species of different lifespans can promote the emergence of highly pathogenic avian influenza strains. Proc. Natl. Acad. Sci. 111, 10767–10772 (2014).ADS 
CAS 
Article 

Google Scholar 
Xiao, Y., Zhou, Y. & Tang, S. Modelling disease spread in dispersal networks at two levels. Math. Med. Biol. J. IMA 28, 227–244 (2011).MathSciNet 
MATH 
Article 

Google Scholar 
Arino, J., Ducrot, A. & Zongo, P. A metapopulation model for malaria with transmission-blocking partial immunity in hosts. J. Math. Biol. 64, 423–448 (2012).MathSciNet 
MATH 
Article 

Google Scholar 
De Roos, A. M., Mccauley, E. & Wilson, W. G. Mobility versus density-limited predator-prey dynamics on different spatial scales. Proc. R. Soc. Lond. Ser. B Biol. Sci. 246, 117–122 (1991).ADS 
Article 

Google Scholar 
Dey, S., Goswami, B. & Joshi, A. Effects of symmetric and asymmetric dispersal on the dynamics of heterogeneous metapopulations: Two-patch systems revisited. J. Theor. Biol. 345, 52–60 (2014).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
Anderson, R. M., Jackson, H. C., May, R. M. & Smith, A. M. Population dynamics of fox rabies in Europe. Nature 289, 765–771 (1981).ADS 
CAS 
Article 

Google Scholar 
Gupta, S., Ferguson, N. & Anderson, R. Chaos persistence, and evolution of strain structure in antigenically diverse infectious agents. Science 280, 912–915 (1998).ADS 
CAS 
Article 

Google Scholar 
Holland, M. D. & Hastings, A. Strong effect of dispersal network structure on ecological dynamics. Nature 456, 792–794 (2008).ADS 
CAS 
Article 

Google Scholar 
McCann, K., Hastings, A. & Huxel, G. R. Weak trophic interactions and the balance of nature. Nature 395, 794–798 (1998).ADS 
CAS 
Article 

Google Scholar 
Singh, A. & Gakkhar, S. Synchronization of chaos in a food web in ecological systems. World Acad. Sci. Eng. Technol. 70, 94–98 (2010).
Google Scholar 
Gotelli, N. J. Metapopulation models: The rescue effect, the propagule rain, and the core-satellite hypothesis. American Naturalist 138, 768–776 (1991).Article 

Google Scholar 
Heino, M., Kaitala, V., Ranta, E. & Lindström, J. Synchronous dynamics and rates of extinction in spatially structured populations. Proc. R. Soc. Lond. Ser. B Biol. Sci. 264, 481–486 (1997).ADS 
Article 

Google Scholar 
Molofsky, J. & Ferdy, J.-B. Extinction dynamics in experimental metapopulations. Proc. Natl. Acad. Sci. 102, 3726–3731 (2005).ADS 
CAS 
Article 

Google Scholar 
Saxena, G., Prasad, A. & Ramaswamy, R. Amplitude death: The emergence of stationarity in coupled nonlinear systems. Phys. Rep. 521, 205–228 (2012).ADS 
Article 

Google Scholar 
Majhi, S. & Ghosh, D. Amplitude death and resurgence of oscillation in networks of mobile oscillators. Europhys. Lett. 118, 40002 (2017).ADS 
Article 

Google Scholar 
Shen, C., Chen, H. & Hou, Z. Mobility and density induced amplitude death in metapopulation networks of coupled oscillators. Chaos 24, 043125 (2014).MATH 
Article 

Google Scholar 
Karnatak, R., Ramaswamy, R. & Feudel, U. Conjugate coupling in ecosystems: Cross-predation stabilizes food webs. Chaos Solitons Fractals 68, 48–57 (2014).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
Bolker, B. M. & Grenfell, B. T. Chaos and biological complexity in measles dynamics. Proc. R. Soc. Lond. Ser. B Biol. Sci. 251, 75–81 (1993).ADS 
CAS 
Article 

Google Scholar 
Olsen, L. F., Truty, G. L. & Schaffer, W. M. Oscillations and chaos in epidemics: A nonlinear dynamic study of six childhood diseases in Copenhagen, Denmark. Theor. Popul. Biol. 33, 344–370 (1988).MathSciNet 
CAS 
MATH 
Article 

Google Scholar 
Lundberg, P., Ranta, E., Ripa, J. & Kaitala, V. Population variability in space and time. Trends Ecol. Evolut. 15, 460–464 (2000).CAS 
Article 

Google Scholar 
Dekker, A. Realistic social networks for simulation using network rewiring. In International Congress on Modelling and Simulation, 677–683 (2007).Milgram, S. The small world problem. Psychol. Today 2, 60–67 (1967).
Google Scholar 
Sallaberry, A., Zaidi, F. & Melançon, G. Model for generating artificial social networks having community structures with small-world and scale-free properties. Soc. Netw. Anal. Min. 3, 597–609 (2013).Article 

Google Scholar 
Olesen, J. M., Bascompte, J., Dupont, Y. L. & Jordano, P. The modularity of pollination networks. Proc. Natl. Acad. Sci. 104, 19891–19896 (2007).ADS 
CAS 
MATH 
Article 

Google Scholar 
Stouffer, D. B. & Bascompte, J. Compartmentalization increases food-web persistence. Proc. Natl. Acad. Sci. 108, 3648–3652 (2011).ADS 
CAS 
Article 

Google Scholar 
Girvan, M. & Newman, M. E. Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99, 7821–7826 (2002).ADS 
MathSciNet 
CAS 
MATH 
Article 

Google Scholar 
Krause, A. E., Frank, K. A., Mason, D. M., Ulanowicz, R. E. & Taylor, W. W. Compartments revealed in food-web structure. Nature 426, 282–285 (2003).ADS 
CAS 
Article 

Google Scholar 
Rezende, E. L., Albert, E. M., Fortuna, M. A. & Bascompte, J. Compartments in a marine food web associated with phylogeny, body mass, and habitat structure. Ecol. Lett. 12, 779–788 (2009).Article 

Google Scholar 
Pastor-Satorras, R. & Vespignani, A. Epidemics and immunization in scale-free networks. In Handbook of Graphs and Networks, 111–130 (Wiley Online Library, 2002).Lloyd-Smith, J. O., Schreiber, S. J., Kopp, P. E. & Getz, W. M. Superspreading and the effect of individual variation on disease emergence. Nature 438, 355–359 (2005).ADS 
CAS 
Article 

Google Scholar 
Shirley, M. D. & Rushton, S. P. The impacts of network topology on disease spread. Ecol. Complex. 2, 287–299 (2005).Article 

Google Scholar 
Keeling, M. J. & Eames, K. T. Networks and epidemic models. J. R. Soc. Interface 2, 295–307 (2005).Article 

Google Scholar 
Godfrey, S. S., Bull, C. M., James, R. & Murray, K. Network structure and parasite transmission in a group living lizard the gidgee skink, Egernia stokesii. Behav. Ecol. Sociobiol. 63, 1045–1056 (2009).Article 

Google Scholar 
VanderWaal, K. L., Atwill, E. R., Hooper, S., Buckle, K. & McCowan, B. Network structure and prevalence of Cryptosporidium in Belding’s ground squirrels. Behav. Ecol. Sociobiol. 67, 1951–1959 (2013).Article 

Google Scholar 
Proulx, S. R., Promislow, D. E. & Phillips, P. C. Network thinking in ecology and evolution. Trends Ecol. Evolut. 20, 345–353 (2005).Article 

Google Scholar 
Craft, M. E. & Caillaud, D. Network models: An underutilized tool in wildlife epidemiology?. Interdiscip. Perspect. Infect. Dis. 2011, (2011).Bajardi, P. et al. Human mobility networks, travel restrictions, and the global spread of 2009 h1n1 pandemic. PloS one 6, e16591 (2011).ADS 
CAS 
Article 

Google Scholar 
Gog, J. R. et al. Seven challenges in modeling pathogen dynamics within-host and across scales. Epidemics 10, 45–48 (2015).Article 

Google Scholar 
Cen, X., Feng, Z. & Zhao, Y. Emerging disease dynamics in a model coupling within-host and between-host systems. J. Theor. Biol. 361, 141–151 (2014).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
Meakin, S. R. & Keeling, M. J. Correlations between stochastic epidemics in two interacting populations. Epidemics 26, 58–67 (2019).Article 

Google Scholar 
Machado, G. et al. Identifying outbreaks of porcine epidemic diarrhea virus through animal movements and spatial neighborhoods. Sci. Rep. 9, 1–12 (2019).
Google Scholar 
Tonkin, J. D. et al. The role of dispersal in river network metacommunities: Patterns, processes, and pathways. Freshwater Biol. 63, 141–163 (2018).Article 

Google Scholar 
Pedersen, T. L. tidygraph: a tidy API for graph manipulation (2019). R package version 1.1.2.Rackauckas, C. & Nie, Q. Differentialequations.jl–a performant and feature-rich ecosystem for solving differential equations in Julia. J. Open Res. Softw. 5 (2017).Rackauckas, C. & Nie, Q. Confederated modular differential equation APIS for accelerated algorithm development and benchmarking. Adv. Eng. Softw. 132, 1–6 (2019).Article 

Google Scholar 
Bezanson, J., Edelman, A., Karpinski, S. & Shah, V. B. Julia: A fresh approach to numerical computing. SIAM Rev. 59, 65–98 (2017).MathSciNet 
MATH 
Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
Book 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2020). More

Zhu, X. China’s karst tiankeng and its value for science and tourism. Sci. Technol. Rev. 19, 60–63 (2001).
Google Scholar 
Zhu, X. et al. A brift study on karst tiankeng. Carsol. Sin. 22, 51–65 (2003).
Google Scholar 
Zhu, X. & Waltham, T. Tiankeng: Definition and description. Cave Karst Sci. 32, 75–79 (2005).
Google Scholar 
Zhu, X. & Chen, W. Tiankengs in the karst of China. Cave Karst Sci. 32, 55–56 (2005).
Google Scholar 
Shui, W., Chen, Y., Wang, Y., Su, Z. & Zhang, S. Origination, study progress and prospect of karst tiankeng research in China. Acta Geogr. Sin. 70, 431–446 (2015).
Google Scholar 
Alexander, K. Cave un-roofing as a large-scale geomorphic process. Carsol. Sin. 4, 1–11 (2006).
Google Scholar 
Palmer, A. & Palmer, M. Hydraulic processes in the origin of tiankengs. Speleogenesis Evol. Karst Aquifers 4, 8 (2006).
Google Scholar 
Waltham, T. Collapse processes at the tiankengs of Xingwen. Cave Karst Sci. 32, 107–110 (2005).
Google Scholar 
White, W. & White, E. Size scales for closed depression landforms: The place of tiankengs. Cave Karst Sci. 32, 111–118 (2005).
Google Scholar 
Chen, W., Zhu, X., Zhu, D. & Ma, Z. Karst geological relics and development of Xiaozhai Tiankeng and Tianjinxia Fissure Gorge, Fengjie, Chongqing. J. Mountain Sci. 22, 22–29 (2004).CAS 

Google Scholar 
Yue, Y., Wang, K., Zhang, W., Chen, H. & Wang, M. Relationships between soil and environment in Peak-Cluster Depression areas of karst region based on canonical correspondence analysis. Environ. Sci. 29, 1400–1405 (2008).
Google Scholar 
Huang, B., Cai, W., Xue, Y. & Zhu, X. Research on tourism resource characteristics of tiankeng group in Dashiwei, Guangxi. Geogr. Geo-Inf. Sci. 20, 109–112 (2004).CAS 

Google Scholar 
Zhu, X. Discovery of erosional tiankeng in Houping, Wulong and its value of science and tourism. Carsol. Sin. 2, 93–98 (2006).
Google Scholar 
Yuan, D. The development of modern karstology in China. Geol. Rev. 52, 733–736 (2006).CAS 

Google Scholar 
Gunn, J. Turloughs and tiankengs: Distinctive doline forms. Cave Karst Sci. 32, 83–84 (2005).
Google Scholar 
Klimchou, A. Cave un-roofing as a large-scale geomorphic process. Cave Karst Sci. 32, 93–98 (2005).
Google Scholar 
Zhu, X., Chen, W. & Erin, L. Wulong karst systems and as an indicator of local tectonic uplift. Carsol. Sin. 26, 119–125 (2007).
Google Scholar 
Shui, W. & Wang, X. Geological expedition and analysis on formation and evolvement of erosive Karst Tiankeng: A case study of Xingwen World Geopark. Adv. Mater. Res. 250–253, 2002–2006 (2011).Article 

Google Scholar 
Su, S., Huang, K. & Ma, B. Diversity study on pteridophyte flora in the area of Dashiwei Tiankeng group of Leye County. Hubei Agric. Sci. 51, 948–950 (2012).
Google Scholar 
Huang, K. & Su, S. Resource investigation and application research of pteridophyte flora resource in the area of Dashiwei Tiankeng Group. Anhui Agric. Sci. Bull. 21, 74–80 (2015).CAS 

Google Scholar 
Su, Y., Xue, Y., Fan, B., Mo, F. & Feng, H. Plant community structure and species diversity in Liuxing tiankeng of Guangxi. Acta Botan. Boreali-Occiden. Sin. 36, 2300–2306 (2016).
Google Scholar 
Li, W., Xiang, Y., Du, Y. & Wu, X. Underground forest communities in Zhanyi, Yunnan Province. For. Sci. Technol. 20–25 (2001).Jian, X. et al. Interspecific relationships of grassland plant community’s dominant species in moderate-degraded tiankeng of Yunnan, China. Chin. J. Appl. Ecol. 29, 1–14. https://doi.org/10.13287/j.1001 (2018).Article 

Google Scholar 
Chen, W., Zhu, D. & Zhu, X. Characteristics and evaluation of karst landscape in tiankeng-difeng scenery site, Fengjie, Chongqing. Geogr. Geo-Inf. Sci. 20, 80–83 (2004).CAS 

Google Scholar 
Wang, J. & Guo, C. Comparison between the positive and negative topographic ecosystem in karst mountainous areas and its bearing capability. Guizhou Agric. Sci. 35, 85–87 (2007).MathSciNet 

Google Scholar 
Tony, W. Tiankengs of the world, outside China. Speleogenesis Evol. Karst Aquifers 4, 1–12 (2006).
Google Scholar 
Su, Y., Tang, Q., Mo, F. & Xue, Y. Karst tiankengs as refugia for indigenous tree flora amidst a degraded landscape in southwestern China. Sci. Rep. 7, 1–10 (2017).ADS 
Article 
CAS 

Google Scholar 
Bátori, Z. et al. A comparison of the vegetation of forested and non-forested solution dolines in Hungary: A preliminary study. Biologia 69, 1339–1348 (2014).Article 
CAS 

Google Scholar 
Bátori, Z. et al. The conservation value of karst dolines for vascular plants in woodland habitats of Hungary: Refugia and climate change. Int. J. Speleol. 43, 15–26 (2014).Article 

Google Scholar 
Bátori, Z. et al. Importance of karst sinkholes in preserving relict, mountain, and wet-woodland plant species under sub-Mediterranean climate: A case study from southern Hungary. J. Cave Karst Stud. Natl. Speleol. Soc. Bull. 74, 127–134 (2012).Article 

Google Scholar 
Bátori, Z. et al. Large-and small-scale environmental factors drive distributions of cool-adapted plants in karstic microrefugia. Ann. Bot. 119, 301–309 (2017).PubMed 
Article 

Google Scholar 
Vilisics, F. et al. Small scale gradient effects on isopods (Crustacea: Oniscidea) in karstic sinkholes. Biologia 66, 499–505 (2011).Article 

Google Scholar 
Dolinar, B. & Vreš, B. Pregled flore Mišje doline in zgornjega porečja Rašice (Dolenjska, Slovenija). Hladnikia 30, 3–37 (2012).
Google Scholar 
Raschmanová, N., Miklisová, D., Ľubomír, K. & Šustr, V. Community composition and cold tolerance of soil Collembola in a collapse karst doline with strong microclimate inversion. Biologia 70, 802–811 (2016).Article 

Google Scholar 
Macarthur, R. & Wilson, E. The Theory of Island Biogeography (Princeton University Press, 1967).
Google Scholar 
Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Natl. Acad. Sci. U. S. A. 104, 5925 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhou, F. & Yu, S. Study on the plant diversity of island-like habitats in Karst Mountain Areas. Guizhou For. Sci. Technol. 40, 18–22 (2012).CAS 

Google Scholar 
Hu, F., Lou, Q. & Sun, Y. Community composition and species diversity of different island habitat on Karst Mountainous in Central Guizhou. Guizhou Sci. 29, 23–28 (2011).
Google Scholar 
Culver, D. Karst environment. Z. Geomorphol. Suppl. 60, 103–117 (2016).Article 

Google Scholar 
Daily, G. Restoring value to the world’s degraded lands. Science 269, 350–354 (1995).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Bartgis, R. The endangered sedge Scirpus ancistrochaetus and the flora of sinkhole ponds in Maryland and West Virginia. Castanea 57, 46–51 (1992).
Google Scholar 
Yu, X., Li, Y. & Ma, Z. A preliminary study on flora diversity of karst microhabitat in Shilin Park, Yunnan, China. J. Mountain Sci. 25, 438–447 (2007).
Google Scholar 
Dang, G., Feng, H., Tang, Q., Mo, F. & Xue, Y. New recorded species in Guangxi, China. J. Guangxi Normal Univ. (Nat. Sci. Edit.) 34, 147–150 (2016).
Google Scholar 
Eigenbrod, F., Gonzalez, P., Dash, J. & Steyl, I. Vulnerability of ecosystems to climate change moderated by habitat intactness. Glob. Change Biol. 21, 275–286 (2015).ADS 
Article 

Google Scholar 
Cornell, H. & Lawton, J. Species interactions, local and regional processes, and limits to the richness of ecological communities: A theoretical perspective. J. Anim. Ecol. 61, 1–12 (1992).Article 

Google Scholar 
Helmus, M., Mahler, D. & Losos, J. Island biogeography of the Anthropocene. Nature 513, 543–546 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Yuan, T., Zhang, H., Ou, Z. & Tan, Y. Effects of topography on the diversity and distribution pattern of ground plants in karst montane forests in Southwest Guangxi, China. Chin. J. Appl. Ecol. 25, 2803–2810 (2014).
Google Scholar 
Wen, L. et al. The succession characteristics and its driving mechanisms of plant community in karst region, Southwest China. Acta Ecol. Sin. 35, 5822–5833 (2015).Article 

Google Scholar 
Zhang, Z., Hu, G. & Ni, J. Erratum to: Effects of topographical and edaphic factors on the distribution of plant communities in two subtropical Karst forests, Southwestern China. J. Mt. Sci. 10, 337–338 (2013).Article 

Google Scholar 
Du, H. et al. Plant community characteristics and its coupling relationships with soil in depressions between karst hills, North Guangxi, China. Chin. J. Plant Ecol. 37, 197–208 (2013).Article 

Google Scholar 
Liu, S., Zhang, B., Yang, Q., Hu, C. & Su, C. Species composition and diversity of plant communities in Xiaoyanwan Garden of Xingwen Karst National Geopark, Sichuan Province. Subtrop. Plant Sci. 38, 37–40 (2009).
Google Scholar 
Tan, C. The preliminary discussion about Haifeng’s wetland ecosystem. For. Sci. Technol. 1–8 (2002).Ma, K. & Liu, Y. Measurement of biodiversity: The measurement of α diversity. Chin. Biodivers. 2, 231–239 (1995).
Google Scholar 
Pielou, E. The measurement of diversity in different types of biological collections. J. Theor. Biol. 13, 131–144 (1966).ADS 
Article 

Google Scholar 
Simpson, E. Measurement of diversity. Nature 163, 688 (1949).ADS 
MATH 
Article 

Google Scholar  More

Study areaThe study was conducted on two rivers in north-eastern Amazonia sensu lato, including the Guiana Shield and the Amazon River drainage (Fig. 2). The climate of the entire study area is homogeneous and the region is covered by dense, uniform lowland primary rainforest51. The altitude is in the range of 0–860 m a.s.l. The regional climate is equatorial, and the annual rainfall ranges from 3600 mm in the northeast to 2000 mm in the southwest. The Maroni River is 612 km long from its source to its estuary, and its watershed covers a surface of >68,000 km2 in Suriname and French Guiana. The Oyapock River (length, 404 km; area, 26,800 km2) is located in the state of Amapa in Brazil and in French Guiana.The foregoing river basins host nearly 400 freshwater fish species and more than 180 mammal species52,53. Most of the mammal species have a large distribution range, covering the entire study area53. The fish species have a less homogeneous distribution and a distinct upstream-downstream composition gradient54,55. Here, only large rivers were considered and most fish species were widespread over the whole study area. As habitat availability increases with river size, species richness is expected to increase upstream to dowsntream31,32. The Oyapock and Maroni river basins are among the last remaining wilderness areas on Earth17. Nevertheless, ecological disturbances are increasing there because of a growing human population and the development of small-scale gold mining activity. These disturbances have caused limited but diffuse deforestation23,56. The deforested areas currently comprise 0.67% of all Maroni and Oyapock catchments.SamplingEnvironmental DNA (eDNA) was collected from water samples at 74 locations (hereafter, sites) along the main channel and the large tributaries of the Maroni and Oyapock rivers (Fig. 2). Thirty-seven sites were sampled at each river basin. The minimum and maximum distances between adjacent sites were 1.07 and 50.20 km, respectively. The mean and median distances between adjacent sites were 10.18 and 9.14 km, respectively, and the standard deviation (SD) was 7.79 km. The sites were located from sea level to 157 m a.s.l. At all sites, the river was wider than 20 m and deeper than 1 m (Strahler orders 4–8; Supplementary Fig. 5). The physicochemical properties of the water slightly varied among sites. The temperature, pH, and conductivity were in the ranges of 28.4–33.2 °C, 6.5–7.6, and 16.9–54.6 µS/cm, respectively, at all sites except two estuarine locations where the conductivity was relatively high because of seawater incursion (Supplementary Data 2).The eDNA samples were collected during the dry seasons (October–November) of 2017 and 2018 for Maroni and Oyapock, respectively. At both rivers, the sites were sequentially sampled from downstream to upstream at a rate of 1–4 sites per day depending on the distance and travel time between sites. Following the protocol of ref. 45, we collected the eDNA by filtering two replicates of 34 L of water per site. A peristaltic pump (Vampire Sampler; Buerkle GmbH, Bad Bellingen, Germany) and single-use tubing were used to pump the water into a single-use filtration capsule (VigiDNA, pore size 0.45 μm; filtration surface 500 cm2, SPYGEN, Bourget-du-Lac, France). The tubing input was placed a few centimetres below the water surface in zones with high water flow as recommended by Cilleros et al.43. Sampling was performed in turbulent areas with rapid hydromorphologic units to ensure optimal eDNA homogeneity throughout the water column. To avoid eDNA cross-contamination among sites, the operator remained on emerging rocks downstream from the filtration area. At the end of filtration, the capsule was voided, filled with 80 mL CL1 preservation buffer (SPYGEN), and stored in the dark up to one month before the DNA extraction. No permits were required for the eDNA sampling and the access to all sites was legally permitted. The study complies with access and benefit permits ABSCH-IRCC-FR-246820-1 and ABSCH-IRCC-FR-245902-1, authorizing collection, transport and analysis of all environmental DNA samples used in this study.Laboratory procedures and bioinformatic analysesFor the DNA extraction, each filtration capsule was agitated on an S50 shaker (Ingenieurbüro CAT M. Zipperer GmbH, Ballrechten-Dottingen, Germany) at 800 rpm for 15 min, decanted into a 50 mL tube, and centrifuged at 15,000 × g and 6 °C for 15 min. The supernatant was removed with a sterile pipette, leaving 15 mL of liquid at the bottom of the tube. Subsequently, 33 mL of ethanol and 1.5 mL of 3 M sodium acetate were added to each 50 mL tube, and the mixtures were stored at −20 °C for at least one night. The tubes were then centrifuged at 15,000 × g and 6 °C for 15 min, and the supernatants were discarded. Then, 720 µL of ATL buffer from a DNeasy Blood & Tissue Extraction Kit (Qiagen, Hilden, Germany) was added. The tubes were vortexed, and the supernatants were transferred to 2 mL tubes containing 20 µL proteinase K. The tubes were then incubated at 56 °C for 2 h. DNA extraction was performed using a NucleoSpin Soil kit (Macherey-Nagel GmbH, Düren, Germany) starting from step six of the manufacturer’s instructions. Elution was performed by adding 100 µL of SE buffer twice. After the DNA extraction, the samples were tested for inhibition by qPCR following the protocol in ref. 57. Briefly, quantitative PCR was performed in duplicate for each sample. If at least one of the replicates showed a different Ct (Cycle threshold) than expected (at least 2 Cts), the sample was considered inhibited and diluted 5-fold before the amplification.For the fish, the “teleo” primers58 (forward: 3ʹ-ACACCGCCCGTCACTCT-5ʹ; reverse: 3ʹ-CTTCCGGTACACTTACCATG-5ʹ) were used as they efficiently discriminated local fish species43,45. For the mammals, the 12S-V5 vertebrate marker59 (forward: 3ʹ-TAGAACAGGCTCCTCTAG-5ʹ; reverse: 3ʹ-TTAGATACCCCACTATGC-5ʹ) was used as it also effectively distinguishes local mammal species44,60. The DNA amplifications were performed in a final volume of 25 μL containing 1 U AmpliTaq Gold DNA Polymerase (Applied Biosystems, Foster City, CA, USA), 0.2 μM of each primer, 10 mM Tris-HCl, 50 mM KCl, 2.5 mM MgCl2, 0.2 mM of each dNTP, and 3 μL DNA template. Human blocking primer was added to the mixture for the “teleo”58 (5′-ACCCTCCTCAAGTATACTTCAAAGGAC-C3-3′) and the “12S-V5” primers61 (5′-CTATGCTTAGCCCTAAACCTCAACAGTTAAATCAACAAAACTGCT-C3-3′) at final concentrations of 4 μM and 0.2 μg/μL bovine serum albumin (BSA; Roche Diagnostics, Basel, Switzerland). Twelve PCR replicates were performed per field sample. The forward and reverse primer tags were identical within each PCR replicate. The PCR mixture was denatured at 95 °C for 10 min, followed by 50 cycles of 30 s at 95 °C, 30 s at 55 °C for the “teleo” primers and 50 °C for the 12S-V5 primers, 1 min at 72 °C, and a final elongation step at 72 °C for 7 min. This step was conducted in a dedicated room for DNA amplification that is kept under negative air pressure and is physically separated from the DNA extraction rooms maintained under positive air pressure. The purified PCR products were pooled in equal volumes to achieve an expected sequencing depth of 500,000 reads per sample before DNA library preparation.For the fish analyses, 10 libraries were prepared using a PCR-free library protocol (https://www.fasteris.com/metafast) at Fasteris, Geneva, Switzerland. Four libraries were sequenced on an Illumina HiSeq 2500 (2 × 125 bp) (Illumina, San Diego, CA, USA) with a HiSeq SBS Kit v4 (Illumina), three were sequenced on a MiSeq (2 × 125 bp) (Illumina) with a MiSeq Flow Cell Kit Version3 (Illumina), and three libraries were sequenced on a NextSeq (2 × 150 bp + 8) (Illumina) with a NextSeq Mid kit (Illumina). The libraries run on the NextSeq were equally distributed in four lanes. Sequencing was performed according to the manufacturer’s instructions at Fasteris. For the mammal analyses, eight libraries were prepared using a PCR-free library protocol (https://www.fasteris.com/metafast) at Fasteris. Two libraries were sequenced on an Illumina HiSeq 2500 (2 × 125 bp) (Illumina) using a HiSeq Rapid Flow Cell v2 and a HiSeq Rapid SBS Kit v2 (Illumina), three libraries were prepared on a MiSeq (2 × 125 bp) (Illumina) with a MiSeq Flow Cell Kit Version3 (Illumina), and three libraries were prepared using a NextSeq (2 × 150 bp + 8) (Illumina) and a NextSeq Mid kit (Illumina). The libraries run on the NextSeq were equally distributed in four lanes. As different sequencing platforms were used (MiSeq and NextSeq for the Maroni and HiSeq 2500 and MiSeq for the Oyapock; Supplementary Fig. 6 and Supplementary Data 3), the possible influences of the platforms on the sequencing results were verified. To this end, we compared the differences in species numbers between the sample replicates assigned to the same platform (accounting for replicate effect only) against those of the sample replicates assigned to different platforms (accounting for replicate and platform effects). As there were more sites with their two replicates sequenced with the same platform than sites with their replicates sequenced with different platforms (see Supplementary Fig. 6), sites with replicates on the same platform were randomly selected for the comparisons. We repeated this procedure 50 times. The number of species between replicates sequenced on the same platform and those sequenced on different platforms did not differ for >98.5% of all fish and mammal samples (Supplementary Fig. 7 and Supplementary Note 2). Similar to these results, a previous study on 16 S rRNA amplicon has shown that the samples were not influenced by the Illumina sequencing platform used62.To monitor for contaminants, 13 negative extraction controls were performed for each of the primers (“teleo” and “12S-V5”); one control was amplified twice. All of them were amplified and sequenced by the same methods as the samples and in parallel to them. Therefore, for the negative extraction controls, 168 amplifications were prepared with the “teleo” primers (13 negative controls; one amplified and sequenced twice) and 156 amplifications with the “12S-V5” primers (13 negative controls). Fourteen negative PCR controls (ultrapure water; 12 replicates) were amplified and sequenced in parallel to the samples. Eight were amplified with the “teleo” primers and six were amplified with the “12S-V05” primers. Thus, for the PCR negative controls, there were 96 amplifications with the “teleo” primers and 72 amplifications with the Vert01 primers. Sequencing information for the controls is shown in Supplementary Data 3c.An updated version of the reference database from ref. 43 was used. There were 265 Guianese species for the fish analyses (ref. 47). The GenBank nucleotide database was consulted, but it contained little information on the Guianese fish species. Most of the sequences were derived from ref. 43. For the mammal analyses, the vertebrate database was built using ecoPCR software63 from the releases 134 and 138 of the European Nucleotide Archive (ENA), for the Maroni and Oyapock river samples, respectively. The two releases were compared, and it was established that the new mammal species added to each version did not originate from French Guiana. Hence, the results were not influenced by the EMBL release number. The relevant metabarcoding fragment was extracted from this database with ecoPCR63 and OBITools64. Therefore, the reference database comprised the local database of French Guianese mammals60, which references 576 specimens from 164 species as well as all available vertebrate species in EMBL.The sequence reads were analyzed with the OBITools package according to the protocol described by Valentini et al.58. Briefly, the forward and reverse reads were assembled with the illuminapairedend programme. The ngsfilter programme was then used to assign the sequences to each sample. A separate dataset was created for each sample by splitting the original dataset into several files with obisplit. Sequences shorter than 20 bp or occurring less than 10 times per sample were discarded. The obiclean program was used to identify amplicon sequence variants (ASVs) that have likely arisen due to PCR or sequencing errors. It uses the information of sequence counts and sequence similarities to classify whether a sequence is a variant (“internal”) of a more abundant (“head”) ASV64. After this step, we matched the ASV with the reference database to obtain the taxonomic assignation for each ASV. Sequences labelled by the obiclean programme as ‘internal’’ and probably corresponding to PCR errors were discarded. The ecotag programme was then used for taxonomic assignment of molecular operational taxonomic units (MOTUs). The taxonomic assignments from ecotag were corrected to avoid overconfidence in assignments. Species-level assignments were validated only for ≥98% sequence identity with the reference database. Sequences below this threshold were discarded.Measuring disturbance intensity using GIS dataIn riverine systems, the disturbances may accumulate because of hydrologic connectivity, which is the downstream transfer of matter and pollutants4. Hence, the upstream sub-basin drainage network was considered to determine the size of the upstream sub-basin affecting local biodiversity (Fig. 1). The sub-basins were delineated by applying a flow accumulation algorithm to the SRTM global 30 m digital elevation model65. Deforestation was measured over 14 upstream spatial extents with radii of 0.5, 1.5, 3, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, and 90 km for each sampling site. Then, these 14 upstream spatial extents were intersected with the sub-basin drainage network. In addition, mammals and fish can also be affected by disturbances other than those mediated by hydrologic connectivity. Thus, deforestation was also measured upstream and downstream from the eDNA sampling sites using the same foregoing 14 radii.At each sampling site, deforestation intensity was quantified for each of the 14 spatial extents. We summed upstream (only accounting for disturbances mediated by river hydrologic connectivity) or upstream and downstream (not only considering disturbances mediated by hydrologic connectivity) deforested surfaces from Landsat satellite image datasets. Forest loss surfaces were obtained from the Global Forest Change dataset66. The Global Forest Change dataset identifies areas deforested between 2001 and 2017 on a 30 m spatial scale. To incorporate deforested areas prior to 2000, tree canopy cover data for that year were also used. Except for river courses, all pixels with More

We express our gratitude to Lukáš Falteisek, Richard Dorrell, Jan Petrášek, Stanislav Volsobě, Kateřina Schwarzerová and Jana Krtková for constructive discussions. English has been kindly corrected by William Bourland. Furthermore, we thank to Dovilė Barcytė, William Bourland, Antonio Calado, Dora Čertnerová, Yana Eglit, Ivan Fiala, Martina Hálová, Miroslav Hyliš, Dagmar Jirsová, Petr Kaštánek, Viktorie Kolátková, Alena Kubátová, Alexander Kudryavtsev, Frederik Leliaert, Julius Lukeš, Jan Mach, Joost Mansour, Jan Mourek, Yvonne Němcová, Fabrice Not, Vladimír Scholtz, Alastair Simpson, Pavel Škaloud, Jan Šťastný, Róbert Šuťák, Daria Tashyreva, Dana Savická, Jan Šobotník, Zdeněk Verner, Jan Votýpka for kindly providing cultures and taxonomic identifications. More

Yuuka Fukui
Yuuka Fukui received Ph.D. degree from Keio University in 2012 under the supervision of Prof. Keiji Fujimoto. She was a JSPS research fellow (DC2) from 2010 to 2012. She joined the laboratory of Prof. Keiji Fujimoto at Keio university as a research associate in 2012 and was promoted to an assistant professor in 2017. Her research interests focus on the design and synthesis of polymeric materials (particles, membranes, porous structures) and organic-inorganic hybrid materials inspired from biological systems.About the award article: The authors reported a new technique to prepare nanoparticles from biomass-derived polymers, which will be utilized as an eco-friendly alternative to synthetic particulate plastics. Nanosized agarose gel particles were produced via sol-to-gel transition of agarose inside water nanodroplets prepared by W/O miniemulsion method. Subsequently, the water evaporation was carried out to generate xerogel nanoparticles (AgarX). The morphologies and crystal structure of AgarX were controlled by changing the pressure and temperature during the water evaporation. The resultant AgarX possessed high crystallinity and exhibited a water dispersibility and a water resistance.

Mikihiro Hayashi
Mikihiro Hayashi received his Ph.D. degree from Nagoya University (Prof. Yushu Matsushita group) in 2015. During his doctor course, he had been selected as a JSPS research fellow (DC2) and experienced researches in ESPCI Paris-Tech (Prof. Ludwik Leibler) and in Shanghai Jiao Tong University (Prof. Xinyuan Zhu). He then re-joined Ludwik Leibler’s group as a postdoc, and experienced another postdoc in Prof. Masatoshi Tokita in Tokyo institute of technology. In 2017, he became an assistant professor in Prof. Akinori Takasu group (Nagoya institute of technology), and currently manages his own laboratory as a PI. His research interest is the design of functional cross-linked materials.About the award article: the authors reported a preparation vitrimer-like elastomers with dynamic bond-exchangeable cross-links. A poly(ethyl acrylate)-based copolymer bearing random pyridine groups was synthesized, which was cross-linked by quaternization reaction with dibromo cross-linkers. In this system, the bond exchange was operated via trans-N-alkylation of the quaternized pyridine groups, showing useful sustainable functions, such as reprocessability, recyclability, and dissolution ability in some selective solvents.

Ryohei Ishige
Ryohei Ishige received his Ph.D. from Tokyo Institute of Technology in 2011 under the supervision of Prof. Junji Watanabe. He joined Prof. Atsushi Takahara’s laboratory at Kyushu University (2011–2013) and Prof. Yoshinobu Tsujii’s laboratory at Kyoto University (2013–2014). From 2014, he joined Prof. Shinji Ando’s laboratory at Tokyo Institute of Technology as an assistant professor and was promoted to an associate professor in 2021. His research interests are liquid-crystalline aromatic polymers and those structure-property relationships.About the award article: the authors developed a novel analytical technique integrating spectroscopies (infrared pMAIRS, and spectroscopic ellipsometry) and scattering methods (GI-WAXS), applied to the process where thin film polyimide, PI, is generated from linear poly(amic ester), PAE, precursors whose backbone consists of para-linkage. They revealed that PAE-based thin PI films form heterogeneous structure composed of non-oriented amorphous region and oriented ordered region which includes anisotropic nanopores causing structural birefringence. This method enables comprehensive evaluation of the evolution in complex hierarchical structures following chemical reactions for every noncrystalline thin film polymers.

Ryohei Kakuchi
Ryohei Kakuchi received his Ph.D. degree from the Hokkaido University in 2009 with a JSPS (Japan Society for Promotion of Science) research fellowship. After the Ph.D., he has made postdoctoral works in Germany from 2009 to 2014 and joined Kanazawa University as a research assistant professor in 2014. Based on the Leading Initiative for Excellent Young Researchers program, he was then appointed as an assistant professor (PI) at Gunma University in 2017. His research interests are the novel polymer synthesis based on unique organic transformation reactions including multicomponent reactions.About the award article: The authors proposed a new synthetic strategy to utilize wood-biomass sourced compounds in a green fashion. To achieve sustainable material chemistry, the intrinsic reactivity of lignin-derived poly(methacrylated vanillin) (PMV) was spotlighted because many multicomponent reactions employ aldehydes as a reactant. First, the Passerini three-component reaction (Passerini-3CR) of the PMV was revealed to proceed with >90% aldehyde conversions. Taking advantage of this high reactivity of the PMV, its immobilized cellulose fabric, a wood-biomass sourced organic hybrid, was revealed to accept the surface Passerini-3CR with amino acid derivatives, thereby demonstrating a fully bio-based material fabrication. More

Site descriptionThe respective sites were classified into five climatic regions in California, containing San Cruz and San Rose in region 1, Saint Helena and San Jose in region 2, Livermore and Cloverdale in region 3, Davis, Lodi and Fontana in region 4, Fresno and Bakerfield in region 5 (Fig. 1). There were differences in annual mean temperature among five climatic regions, ranging from 14.3°C to 18.6°C. In each region, the GHDs, quality of musts and wines, and wine tasting notes were recorded for 148 cultivars from 1935 to 1941. Meanwhile, in region 2, namely in Napa, the GHDs and must sugar content (in °Brix) were recorded for four representative cultivars (Cabernet Sauvignon, Chardonnay, Merlot and Sauvignon Blanc) during 1991–2018.Fig. 1The locations of five climatic regions for wine grape classed by Winkler index in California. The insert plot represents the distinct Winkler index (WI) during 1935–1941 in five climatic regions.Full size imageClimate dataThe climate data was collected from five stations for over one hundred year-period (1911–2018), including daily average, maximum and minimum temperature (Table 1). Climate data was retrieved from the National Oceanic and Atmospheric Administration (NOAA)’s National Centers for Environmental Information (NCEI). The database from which the data was retrieved was the “Global Historical Climatology Network – Daily (GHCN-Daily), Version 3” (https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/by_station/)25,26. Table 1 showed the search codes and names of five stations in the website. The climate data of region 1 and region 5 were for the periods of 1911–2011 and 1938–2018, respectively.Table 1 Description of weather stations and time-span in five climatic regions.Full size tableBioclimatic indicesHere, we presented seven temperature-related indices to explore the changing climate in five climatic regions during the last 100 years. We compared the changes of these indices between the past (1935–1941) and current climate conditions (1991–2018). Thereafter, four indices were chosen to describe annual changes, including average, maximum, minimum temperature and diurnal temperature range (DTR). Furthermore, other indices were used to analyse growing season temperature (GST), Winkler index (WI) and Huglin index (HI) for the grape-growing season5,27,28. The equations used to calculate the bioclimatic indices of grape-growing season are:$$GST=frac{{sum }_{Apr1}^{Oct31}frac{{T}_{max}+{T}_{min}}{2}}{n}$$
(1)
$$WI={sum }_{Apr1}^{Oct31}left(frac{{T}_{max}+{T}_{min}}{2}-10right)$$
(2)
$$HI={sum }_{Apr1}^{Sep30}left(frac{{T}_{max}+{T}_{ave}}{2}-10right)times K$$
(3)
where Tmax, Tmin and Tave represent daily maximum, minimum and average temperatures, respectively. K is a length of day coefficient ranging from 1.02 to 1.06 between 40 and 50 of latitude in the northern hemisphere.Sample collection, harvest dates, quality of musts and wines measurementSample collection, harvest dates, quality of musts and wines measurement were detailed in the report of Amerine and Winkler24. Briefly, grape berries (22–220 kg) were picked in the morning from representative vines of variety collections or commercial vineyards by Amerine and Winkler24, as well as numerous vineyard owners. The harvest dates were recorded after picking. All grapes picked were crushed within 24 hours except for a few samples in 1935. The clear juice was taken after the coarse sediment had settled, in order to measure total soluble solids (°Brix), total acid (grams per 100 cc), and pH of must. The must was placed in an open oak fermenting tank. After fermentation, it was completed in a closed oak container. Then, the alcohol (percent by volume), extract (grams per 100 cc), tannin (grams per 100 cc), and fixed acid (grams per 100 cc) of wine were measured. The must °Brix was measured with a Brix hydrometer floating in a cylinder, must total acid was determined by titration with sodium hydroxide to a phenolphthalein end point, and must pH was measured with a quinhydrone electrode or a Beckman pH meter. In addition, wine alcohol was measured by the hydrometer and reported as percentage by volume, the extract and tannin of wine were measured by means of a special 0° to 8° Balling hydrometer and the Association of Official Agricultural Chemists method24. Note that the fixed acid of wine are equal to total acid minus volatile acid, where the total acid was measured by titration with phenolphthalein as an indicator while the volatile acid was determined also by titration with pretreated wines by method II of the Association of Official Agricultural Chemists24.Wine tasting notesThe purpose of wine tasting was to evaluate the cultivars based on the merits and defects of wine. The descriptive terms used for recording the results of the organoleptic examination contained appearance, color, odors, volatile acidity, total acidity, dryness, body, taste, smoothness and astringency, and general quality. More