Philippa Kaur

China is the world’s most populous country, with a population of over 1.4 billion people, or 18% of the world human population1. However, China has only about 9% of the 1.4 billion hectares of total arable land in the world2. The question of “who will feed China?” raised by Dr. Lester R. Brown in 1995 is still worthy of consideration today, and ensuring food security remains a top priority for the Chinese government3.Rice is the staple food on dining-tables of over 65% of the population in China; thus, adequate rice production is critical to ensure food security in China4. In order to produce more rice on the limited amount of arable land available, double-season rice cropping systems, which involve successively growing early-season rice (ESR) and late-season rice (LSR) from March to November within a single calendar year, have been extensively developed in southern China5. The development of double-season rice cropping systems has made a considerable contribution toward achieving rice self-sufficiency in China6.Hunan and Jiangxi are the top two double-season rice producing provinces in China7. However, in recent years, the planting area devoted to double-season rice has sharply decreased in the Hunan-Jiangxi region as a result of the conversion from double- to single-season rice (SSR) cropping systems (referred as the rice “double-to-single” phenomenon) (Fig. 1A). A reduced rural labor supply and rising labor wages due to urbanization and economic growth are the key driving forces for the rice “double-to-single” phenomenon11. Fortunately, the rice “double-to-single” phenomenon has not resulted in a decrease in total rice production in the Hunan-Jiangxi region (Fig. 1B). During the most recent 10 years (2011–2020), the total rice production in the Hunan-Jiangxi region has been ranged from 45.3 to 48.7 million tons (Mt) with an average of 46.6 Mt, and the contribution of the Hunan-Jiangxi region to rice production in China has been maintained at ~ 22%.Figure 1(A) Planting areas (million hectares, Mha) for early-season rice (ESR), late-season rice (LSR), and single-season rice (SSR) in the Hunan-Jiangxi region and (B) total rice production (million tons, Mt) in the Hunan-Jiangxi region and the contribution of the Hunan-Jiangxi region to total rice production in China from 2011 to 2020. In (B), the dashed line represents the average rice production during 2011–2020. The rice planting area and total rice production in the Hunan-Jiangxi region were calculated based on data collected from the Hunan Provincial Bureau of Statistics8 and the Jiangxi Provincial Bureau of Statistics9. The contribution of the Hunan-Jiangxi region to rice production in China is the percentage of total rice production in the Hunan-Jiangxi region to the total rice production in China. Data for total rice production in China were collected from the National Bureau of Statistics of China10.Full size imageBecause China’s population is still growing12, China must continue to increase rice production. The domestic demand for rice grain in China is expected to reach 217 Mt by 2030, when the population of China is expected to stabilize6. To meet this demand, the Hunan-Jiangxi region will need to produce 47.7 Mt of rice grains, assuming that the contribution of the Hunan-Jiangxi region to rice production in China remains at the level of the most recent 10 years (~ 22%) (Fig. 1B). This expected rice production (ERP) is 1.1 Mt higher than the average total rice production during the most recent 10 years. In order to avoid the negative effect of the “double-to-single” phenomenon on achieving the ERP in the Hunan-Jiangxi region by 2030, it is necessary to estimate how much planting area of double-season rice will be needed in this region by this point in time.The ERP can be expressed by the following formula: ERP = EPAESR × EGYESR + EPALSR × EGYLSR + EPASSR × EGYSSR, where EGYESR, EGYLSR, and EGYSSR are the estimated grain yields of ESR, LSR, and SSR, respectively; and EPAESR, EPALSR, and EPASSR are the estimated planting areas for ESR, LSR, and SSR, respectively. We assume the following conditions in the Hunan-Jiangxi region by 2030: (1) the total paddy field area will be maintained in the range of 4.57–5.02 million hectares (Mha) that was planted during the years 2011–20208,9; (2) the ratio of EPALSR to EPAESR is the same as the average ratio of planting area of LSR to ESR during 2011–2020 (i.e., 1.07) (Fig. 1A); (3) EPASSR is the difference between the total paddy field area and the EPALSR; and (4) EGYESR, EGYLSR, and EGYSSR are projected under three scenarios: (1) constant yield scenario, (2) 5% yield increase scenario, and (3) 10% yield increase scenario (Fig. 2). The baseline yield for all three scenarios is the average grain yields during 2011–2020. The EPAESR, EPALSR, and EPASSR in the Hunan-Jiangxi region needed to achieve the expected rice production by 2030 were obtained by solving the above formula and are shown in Fig. 3.Figure 2(A) Grain yields from 2011 to 2020 and (B) estimated grain yields by 2030 under three scenarios for early-season rice (ESR), late-season rice (LSR), and single-season rice (SSR) in the Hunan-Jiangxi region. The grain yields from 2011 to 2020 were calculated based on data collected from the Hunan Provincial Bureau of Statistics8 and the Jiangxi Provincial Bureau of Statistics9. In (A), ns denotes non-significant trend at the 0.05 probability level (Statistix 8.0, Analytical software, Tallahassee, FL, USA). In (B), the baseline yield for all three scenarios is the average grain yields during 2011–2020.Full size imageFigure 3Estimated planting areas for early-season rice (ESR), late-season rice (LSR), and single-season rice (SSR) in the Hunan-Jiangxi region that will be required to achieve the expected rice production by 2030 under three scenarios: (A) constant yield scenario, (B) 5% yield increase scenario, and (C) 10% yield increase scenario. Mha is million hectares.Full size imageThe results presented in Fig. 3 provide guidance and models for the government’s decision-making process in the planning planting areas for ESR, LSR, and SSR in the Hunan-Jiangxi region. In brief, farmers will need to plant 2.55–3.18 Mha of ESR, 2.73–3.40 Mha of LSR, and 1.17–2.29 Mha of SSR under the constant yield scenario, 2.09–2.72 Mha of ESR, 2.24–2.91 Mha of LSR, and 1.66–2.78 Mha of SSR under the 5% yield increase scenario, and 1.67–2.31 Mha of ESR, 1.79–2.47 Mha of LSR, and 2.10–3.23 Mha of SSR under the 10% yield increase scenario in the Hunan-Jiangxi region by 2030 depending on the total available paddy field area.One thing to note here is that the actual planting areas for ESR (2.44 Mha) and LSR (2.57 Mha) in 2020 are below the estimated lower limits of planting areas for ESR (2.55 Mha) and LSR (2.73 Mha) that will be needed by 2030 under the constant yield scenario, while the actual planting area for SSR in 2020 (2.42 Mha) is above the estimated upper limit for SSR (2.29 Mha) that will be needed by 2030 under the constant yield scenario (Figs. 1A and 3A). This finding indicates that it is urgent to avoid a further aggravated “double-to-single” phenomenon while maintaining the total paddy field area in the Hunan-Jiangxi region. Because it is not an easy task to maintain the total paddy field area under the projected scenario for urban expansion13, the government should prepare an alternative to reverse the “double-to-single” phenomenon in the Hunan-Jiangxi region. Increasing the mechanized level of farming operation and improving economic returns to farmers are two key aspects for the government to take into account to promote the development of double-season rice.Although the current planting area of double-season rice can fully meet the requirement for achieving the ERP in the Hunan-Jiangxi region by 2030 under both the 5% and 10% yield increase scenarios, there is some difficulty in reaching the yield increase targets. In recent years, the planting area of high-quality rice varieties has been rapidly increased in China14. However, grain yield is generally not very high for high-quality rice varieties, although no genetic linkage has been identified between grain yield and quality in rice15. Hence, great efforts are required to develop rice varieties with both high quality and high yield. In addition, rice yields are determined not only by the variety but also by environments and crop management practices. Soil nutrient deficiencies, unfavorable climatic conditions (e.g., heat, cold, and drought), and pest infestations have always been major yield-limiting factors for rice production in China16. Therefore, great efforts are also required to: (1) improve soil fertility of low- and medium-yielding rice fields and optimize nutrient management practices; (2) develop climate-smart agriculture practices for alleviating climatic stresses; and (3) promote integrated pest management practices. More

Meredith M, Sommerkorn M, Cassotta S, Derksen C, Ekaykin A, Hollowed A. IPCC special report on the ocean and cryosphere in a changing climate In: Pörtner H-O, Roberts D, Masson-Delmotte V, Zhai P, Tignor M, Poloczanska Eea, editors. 2022; chapter 3: https://doi.org/10.1017/9781009157964 (in press).Rozema PD, Venables HJ, van de Poll WH, Clarke A, Meredith MP, Buma AGJ. Interannual variability in phytoplankton biomass and species composition in northern Marguerite Bay (West Antarctic Peninsula) is governed by both winter sea ice cover and summer stratification. Limnol Oceanogr. 2017;62:235–52.Article 

Google Scholar 
Venables HJ, Clarke A, Meredith MP. Wintertime controls on summer stratification and productivity at the western Antarctic Peninsula. Limnol Oceanogr. 2013;58:1035–47.Article 

Google Scholar 
Barnes DKA, Souster T. Reduced survival of Antarctic benthos linked to climate-induced iceberg scouring. Nat Clim Change. 2011;1:365–8.Article 

Google Scholar 
Grange L, Tyler P, Peck L, Cornelius N. Long-term interannual cycles of the gametogenic ecology of the Antarctic brittle star Ophionotus victoriae. Mar Ecol Prog Ser. 2004;278:141–55.Article 

Google Scholar 
Schratzberger M, Ingels J. Meiofauna matters: The roles of meiofauna in benthic ecosystems. J Exp Mar Biol Ecol. 2018;502:12–25.Article 

Google Scholar 
Mayor D, Thornton B, Jenkins H, Felgate S. Microbiota: the living foundation. In: Beninger P, editor. Mudflat ecology. Switzerland AG: Springer Nature 2018. p. 43–61.Fonseca VG, Sinniger F, Gaspar JM, Quince C, Creer S, Power DM, et al. Revealing higher than expected meiofaunal diversity in Antarctic sediments: a metabarcoding approach. Sci Rep. 2017;7:6094.CAS 
Article 

Google Scholar 
Vause BJ, Morley SA, Fonseca VG, Jazdzewska A, Ashton GV, Barnes DKA, et al. Spatial and temporal dynamics of Antarctic shallow soft-bottom benthic communities: ecological drivers under climate change. BMC Ecol. 2019;19:27.Article 

Google Scholar 
Danovaro R, Scopa M, Gambi C, Fraschetti S. Trophic importance of subtidal metazoan meiofauna: evidence from in situ exclusion experiments on soft and rocky substrates. Mar Biol. 2007;152:339–50.Article 

Google Scholar 
Watzin MC. The effects of meiofauna on settling macrofauna: meiofauna may structure macrofaunal communities. Oecologia. 1983;59:163–6.Article 

Google Scholar 
Schmidt JL, Deming JW, Jumars PA, Keil RG. Constancy of bacterial abundance in surficial marine sediments. Limnol Oceanogr. 1998;43:976–82.Article 

Google Scholar 
Whitman WB, Coleman DC, Wiebe WJ. Prokaryotes: the unseen majority. Proc Natl Acad Sci USA. 1998;95:6578–83.CAS 
Article 

Google Scholar 
Burdige DJ. Preservation of organic matter in marine sediments: controls, mechanisms, and an imbalance in sediment organic carbon budgets? Chem Rev. 2007;107:467–85.CAS 
Article 

Google Scholar 
Zou K, Thébault E, Lacroix G, Barot S. Interactions between the green and brown food web determine ecosystem functioning. Funct Ecol. 2016;30:1454–65.Article 

Google Scholar 
Anderson TR, Pond DW, Mayor DJ. The role of microbes in the nutrition of detritivorous invertebrates: a stoichiometric analysis. Front Microbiol. 2016;7:2113.
Google Scholar 
Lacoste E, Piot A, Archambault P, McKindsey CW, Nozais C. Bioturbation activity of three macrofaunal species and the presence of meiofauna affect the abundance and composition of benthic bacterial communities. Mar Environ Res. 2018;136:62–70.CAS 
Article 

Google Scholar 
Bonaglia S, Nascimento FJ, Bartoli M, Klawonn I, Bruchert V. Meiofauna increases bacterial denitrification in marine sediments. Nat Commun. 2014;5:5133.CAS 
Article 

Google Scholar 
Riemann F, Helmke E. Symbiotic relations of sediment-agglutinating nematodes and bacteria in detrital habitats: the enzyme-sharing concept. Mar Ecol. 2002;23:93–113.CAS 
Article 

Google Scholar 
dos Santos GAP, Derycke S, Fonseca-Genevois VG, Coelho LCBB, Correia MTS, Moens T. Differential effects of food availability on population growth and fitness of three species of estuarine, bacterial-feeding nematodes. J Exp Mar Biol Ecol. 2008;355:27–40.Article 

Google Scholar 
Zeppilli D, Sarrazin J, Leduc D, Arbizu PM, Fontaneto D, Fontanier C, et al. Is the meiofauna a good indicator for climate change and anthropogenic impacts? Mar Biodivers. 2015;45:505–35.Article 

Google Scholar 
Moens T, Beninger PG. Meiofauna: an inconspicuous but important player in Mudflat ecology. In: Beninger P, editor. Mudflat ecology aquatic ecology series. 7. Switzerland: Springer; 2018.Webb AL, Hughes KA, Grand MM, Lohan MC, Peck LS. Sources of elevated heavy metal concentrations in sediments and benthic marine invertebrates of the western Antarctic Peninsula. Sci Total Environ. 2020;698:134268.CAS 
Article 

Google Scholar 
Brown KM, Fraser KP, Barnes DK, Peck LS. Links between the structure of an Antarctic shallow-water community and ice-scour frequency. Oecologia. 2004;141:121–9.Article 

Google Scholar 
Stoeck T, Bass D, Nebel M, Christen R, Jones MDM, Breiner H-W, et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol Ecol. 2010;19:21–31.CAS 
Article 

Google Scholar 
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.CAS 
Article 

Google Scholar 
Leray M, Yang JY, Meyer CP, Mills SC, Agudelo N, Ranwez V, et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front Zool. 2013;10:34.Article 

Google Scholar 
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–2.Article 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
Article 

Google Scholar 
Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinform. 2009;10:421.Article 

Google Scholar 
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217.CAS 
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:D590–6.CAS 
Article 

Google Scholar 
Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank. Nucleic Acids Res. 2005;33:D34–8.CAS 
Article 

Google Scholar 
Wentworth CK. A scale of grade and class terms for clastic sediments. J Geol. 1922;30:377–92.Article 

Google Scholar 
Dean WE. Determination of carbonate and organic matter in calcareous sediments and sedimentary rocks by loss on ignition; comparison with other methods. J Sediment Res. 1974;44:242–8.CAS 

Google Scholar 
Tatzber M, Stemmer M, Spiegel H, Katzlberger C, Haberhauer G, Gerzabek MH. An alternative method to measure carbonate in soils by FT-IR spectroscopy. Environ Chem Lett. 2007;5:9–12.CAS 
Article 

Google Scholar 
Hsieh CH, Reiss CS, Hunter JR, Beddington JR, May RM, Sugihara G. Fishing elevates variability in the abundance of exploited species. Nature. 2006;443:859–62.CAS 
Article 

Google Scholar 
Elbrecht V, Braukmann TWA, Ivanova NV, Prosser SWJ, Hajibabaei M, Wright M, et al. Validation of COI metabarcoding primers for terrestrial arthropods. Peer J. 2019;7:e7745–e.Article 

Google Scholar 
Kirse A, Bourlat SJ, Langen K, Fonseca VG. Unearthing the potential of Soil eDNA metabarcoding—towards best practice advice for invertebrate biodiversity assessment. Front. Ecol. Evol. 2021;9:630560.Article 

Google Scholar 
Zhang GK, Chain FJJ, Abbott CL, Cristescu ME. Metabarcoding using multiplexed markers increases species detection in complex zooplankton communities. Evolut Appl. 2018;11:1901–14.CAS 
Article 

Google Scholar 
Marquina D, Andersson AF, Ronquist F. New mitochondrial primers for metabarcoding of insects, designed and evaluated using in silico methods. Mol Ecol Resour. 2019;19:90–104.CAS 
Article 

Google Scholar 
Leasi F, Sevigny JL, Laflamme EM, Artois T, Curini-Galletti M, de Jesus Navarrete A, et al. Biodiversity estimates and ecological interpretations of meiofaunal communities are biased by the taxonomic approach. Commun Biol. 2018;1:112.Article 

Google Scholar 
Giebner H, Langen K, Bourlat SJ, Kukowka S, Mayer C, Astrin JJ, et al. Comparing diversity levels in environmental samples: DNA sequence capture and metabarcoding approaches using 18S and COI genes. Mol Ecol Resour. 2020;20:1333–45.CAS 
Article 

Google Scholar 
Vanhove S, Lee HJ, Beghyn M, Gansbeke DV, Brockington S, Vincx M. The Metazoan Meiofauna in its biogeochemical environment: the case of an Antarctic coastal sediment. J Mar Biol Assoc UK. 1998;78:411–34.Article 

Google Scholar 
Pasotti F, Saravia LA, De Troch M, Tarantelli MS, Sahade R, Vanreusel A. Benthic Trophic Interactions in an Antarctic Shallow Water Ecosystem Affected by Recent Glacier Retreat. PLoS ONE. 2015;10:e0141742.Article 

Google Scholar 
Griffiths JR, Kadin M, Nascimento FJA, Tamelander T, Tornroos A, Bonaglia S, et al. The importance of benthic-pelagic coupling for marine ecosystem functioning in a changing world. Global Change Biology. 2017;23:2179–96.Article 

Google Scholar 
Virta L, Gammal J, Järnström M, Bernard G, Soininen J, Norkko J, et al. The diversity of benthic diatoms affects ecosystem productivity in heterogeneous coastal environments. Ecology. 2019;100:e02765.Article 

Google Scholar 
Malviya S, Scalco E, Audic S, Vincent F, Veluchamy A, Poulain J, et al. Insights into global diatom distribution and diversity in the world’s ocean. Proc Natl Acad Sci USA. 2016;113:E1516–25.CAS 
Article 

Google Scholar 
Forster D, Dunthorn M, Mahe F, Dolan JR, Audic S, Bass D, et al. Benthic protists: the under-charted majority. Fems Microbiol Ecol. 2016;92:fiw120.Article 

Google Scholar 
Fonseca VG, Carvalho GR, Nichols B, Quince C, Johnson HF, Neill SP, et al. Metagenetic analysis of patterns of distribution and diversity of marine meiobenthic eukaryotes. Glob Ecol Biogeogr. 2014;23:1293–302.Article 

Google Scholar 
O’Malley MA. The nineteenth century roots of ‘everything is everywhere’. Nat Rev Microbiol. 2007;5:647–51.Article 

Google Scholar 
Pasotti F, Manini E, Giovannelli D, Wölfl A-C, Monien D, Verleyen E, et al. Antarctic shallow water benthos in an area of recent rapid glacier retreat. Mar Ecol. 2015;36:716–33.Article 

Google Scholar 
Molari M, Janssen F, Vonnahme TR, Wenzhöfer F, Boetius A. The contribution of microbial communities in polymetallic nodules to the diversity of the deep-sea microbiome of the Peru Basin (4130–4198 m depth). Biogeosciences. 2020;17:3203–22.CAS 
Article 

Google Scholar 
Signori CN, Thomas F, Enrich-Prast A, Pollery RCG, Sievert SM. Microbial diversity and community structure across environmental gradients in Bransfield Strait, Western Antarctic Peninsula. Front Microbiol. 2014;5:647.Article 

Google Scholar 
Ozturk RC, Feyzioglu AM, Altinok I. Prokaryotic community and diversity in coastal surface waters along the Western Antarctic Peninsula. Pol Sci. 2021;31:100764.Article 

Google Scholar 
Ghiglione JF, Murray AE. Pronounced summer to winter differences and higher wintertime richness in coastal Antarctic marine bacterioplankton. Environ Microbiol. 2012;14:617–29.CAS 
Article 

Google Scholar 
Luria CM, Ducklow HW, Amaral-Zettler LA. Marine bacterial, archaeal and eukaryotic diversity and community structure on the continental shelf of the western Antarctic Peninsula. Aquat Microbial Ecol. 2014;73:107–21.Article 

Google Scholar 
Cao S, He J, Zhang F, Lin L, Gao Y, Zhou Q. Diversity and community structure of bacterioplankton in surface waters off the northern tip of the Antarctic Peninsula. Pol Res. 2019;38:3491.Article 

Google Scholar 
Walsh EA, Kirkpatrick JB, Rutherford SD, Smith DC, Sogin M, D’Hondt S. Bacterial diversity and community composition from seasurface to subseafloor. ISME J. 2016;10:979–89.Article 

Google Scholar 
Kiko R, Werner I, Wittmann A. Osmotic and ionic regulation in response to salinity variations and cold resistance in the Arctic under-ice amphipod Apherusa glacialis. Pol Biol. 2009;32:393–8.Article 

Google Scholar 
Zeppilli D, Leduc D, Fontanier C, Fontaneto D, Fuchs S, Gooday AJ, et al. Characteristics of meiofauna in extreme marine ecosystems: a review. Mar Biodivers. 2018;48:35–71.Article 

Google Scholar 
Arnosti C, Joergensen BB, Sagemann J, Thamdrup B. Temperature dependence of microbial degradation of organic matter in marine sediments: polysaccharide hydrolysis, oxygen consumption, and sulfate reduction. Mar Ecol Prog Ser. 1998;165:59–70.CAS 
Article 

Google Scholar 
Fabiano M, Danovaro R. Enzymatic activity, bacterial distribution, and organic matter composition in sediments of the ross sea (Antarctica). Appl Environ Microbiol. 1998;64:3838–45.CAS 
Article 

Google Scholar 
Kujawinski EB, Longnecker K, Barott KL, Weber RJM, Kido Soule, MC. Microbial community structure affects marine dissolved organic matter composition. Front Mar Sci. 2016;3:45.Article 

Google Scholar 
Barrett JE, Virginia RA, Hopkins DW, Aislabie J, Bargagli R, Bockheim JG, et al. Terrestrial ecosystem processes of Victoria Land, Antarctica. Soil Biol Biochem. 2006;38:3019–34.CAS 
Article 

Google Scholar 
Ganzert L, Lipski A, Hubberten H-W, Wagner D. The impact of different soil parameters on the community structure of dominant bacteria from nine different soils located on Livingston Island, South Shetland Archipelago, Antarctica. Fems Microbiol Ecol. 2011;76:476–91.CAS 
Article 

Google Scholar 
Rusch A, Huettel M, Reimers CE, Taghon GL, Fuller CM. Activity and distribution of bacterial populations in Middle Atlantic Bight shelf sands. Fems Microbiol Ecol. 2003;44:89–100.CAS 
Article 

Google Scholar 
Hemkemeyer M, Dohrmann AB, Christensen BT, Tebbe CC. Bacterial preferences for specific soil particle size fractions revealed by community analyses. Front Microbiol. 2018;9:149.Article 

Google Scholar 
Giere O. Meiobenthology: the microscopic motile fauna of aquatic sediments. 2nd ed: Springer-Verlag Berlin Heidelberg; 2009. 527 p.Fonseca VG, Carvalho GR, Sung W, Johnson HF, Power DM, Neill SP, et al. Second-generation environmental sequencing unmasks marine metazoan biodiversity. Nat Commun. 2010;1:98.Article 

Google Scholar 
Pitcher RC, Lawton P, Ellis N, Smith SJ, Incze LS, Wei C-L, et al. Exploring the role of environmental variables in shaping patterns of seabed biodiversity composition in regional-scale ecosystems. J Appl Ecol. 2012;49:670–9.Article 

Google Scholar 
Rose A, Ingels J, Raes M, Vanreusel A, Arbizu PM. Long-term iceshelf-covered meiobenthic communities of the Antarctic continental shelf resemble those of the deep sea. Heidelberg: Springer; 2014. 743–62 p.Gonçalves-Araujo R, de Souza MS, Tavano VM, Garcia CAE. Influence of oceanographic features on spatial and interannual variability of phytoplankton in the Bransfield Strait, Antarctica. J Mar Syst. 2015;142:1–15.Article 

Google Scholar 
Learman DR, Henson MW, Thrash JC, Temperton B, Brannock PM, Santos SR, et al. Biogeochemical and microbial variation across 5500 km of Antarctic surface sediment implicates organic matter as a driver of benthic community structure. Front Microbiol. 2016;7:284.Article 

Google Scholar 
Ghiglione JF, Galand PE, Pommier T, Pedros-Alio C, Maas EW, Bakker K, et al. Pole-to-pole biogeography of surface and deep marine bacterial communities. Proc Natl Acad Sci USA. 2012;109:17633–8.CAS 
Article 

Google Scholar 
Rosli N, Leduc D, Rowden A, Probert P. Review of recent trends in ecological studies of deep-sea meiofauna, with focus on patterns and processes at small to regional spatial scales. Mar Biodivers. 2017;48:13–34.Article 

Google Scholar 
Ruff SE, Probandt D, Zinkann A-C, Iversen M, Klaas C, Schwabe L, et al. Indications for algae-degrading benthic microbial communities in deep-sea sediments along the Antarctic Polar Front. Deep Sea Res Part II: Top Stud Oceanogr. 2014;108:6–16.Article 

Google Scholar 
El-Serehy HA, Al-Rasheid KA, Al-Misned FA, Al-Talasat AA, Gewik MM. Microbial-meiofaunal interrelationships in coastal sediments of the Red Sea. Saudi J Biol Sci. 2016;23:327–34.CAS 
Article 

Google Scholar 
Danovaro R, Company JB, Corinaldesi C, D’Onghia G, Galil B, Gambi C, et al. Deep-sea biodiversity in the Mediterranean Sea: the known, the unknown, and the unknowable. PLoS ONE. 2010;5:e11832.Article 

Google Scholar 
Mussmann M, Pjevac P, Kruger K, Dyksma S. Genomic repertoire of the Woeseiaceae/JTB255, cosmopolitan and abundant core members of microbial communities in marine sediments. ISME J. 2017;11:1276–81.CAS 
Article 

Google Scholar 
Hinger I, Pelikan C, Mußmann M. Role of the ubiquitous bacterial family Woeseiaceae for N2O production in marine sediments. Geophys Res Abstracts. 2019;21:17441.
Google Scholar 
Hoffmann K, Bienhold C, Buttigieg PL, Knittel K, Laso-Pérez R, Rapp JZ, et al. Diversity and metabolism of Woeseiales bacteria, global members of marine sediment communities. ISME J. 2020;14:1042–56.CAS 
Article 

Google Scholar 
Mare MF. A study of a marine benthic community with special reference to the microorganisms. J Mar Biol Assoc UK. 1942;25:517–54.Article 

Google Scholar 
Bott TL, Borchardt MA. Grazing of protozoa, bacteria, and diatoms by Meiofauna in lotic epibenthic communities. J North Am Bentholog Soc. 1999;18:499–513.Article 

Google Scholar 
Griffiths HJ. Antarctic marine biodiversity-what do we know about the distribution of life in the Southern Ocean? PLoS ONE. 2010;5:e11683.Article 

Google Scholar 
Convey P, Chown SL, Clarke A, Barnes DKA, Bokhorst S, Cummings V, et al. The spatial structure of Antarctic biodiversity. Ecol Monogr. 2014;84:203–44.Article 

Google Scholar 
Li L, Ma ZS. Species sorting and neutral theory analyses reveal archaeal and bacterial communities are assembled differently in hot springs. Front Bioeng Biotechnol. 2020;8:464.Article 

Google Scholar 
Lee JE, Buckley HL, Etienne RS, Lear G. Both species sorting and neutral processes drive assembly of bacterial communities in aquatic microcosms. Fems Microbiol Ecol. 2013;86:288–302.CAS 
Article 

Google Scholar 
Gansfort B, Fontaneto D, Zhai M. Meiofauna as a model to test paradigms of ecological metacommunity theory. Hydrobiologia. 2020;847:2645–63.Article 

Google Scholar 
Convey P, Peck LS. Antarctic environmental change and biological responses. Sci Adv. 2019;5:eaaz0888.CAS 
Article 

Google Scholar  More

Brown MV, Philip GK, Bunge JA, Smith MC, Bissett A, Lauro FM, et al. Microbial community structure in the North Pacific ocean. ISME J. 2009;3:1374–86.CAS 
Article 

Google Scholar 
Chénard C, Wijaya W, Vaulot D, dos Santos AL, Martin P, Kaur A, et al. Temporal and spatial dynamics of Bacteria, Archaea and protists in equatorial coastal waters. Sci Rep. 2019;9:1–13.Article 

Google Scholar 
Yeh YC, McNichol J, Needham DM, Fichot EB, Berdjeb L, Fuhrman JA. Comprehensive single‐PCR 16S and 18S rRNA community analysis validated with mock communities, and estimation of sequencing bias against 18S. Environ Microbiol. 2021;23:2340–3250.Article 

Google Scholar 
Needham DM, Fuhrman JA. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat. Microbiol. 2016;1:16005.CAS 
Article 

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

Google Scholar 
Needham DM, Fichot EB, Wang E, Berdjeb L, Cram JA, Fichot CG, et al. Dynamics and interactions of highly resolved marine plankton via automated high-frequency sampling. ISME J. 2018;12:2417.CAS 
Article 

Google Scholar 
Steinberg DK, Carlson CA, Bates NR, Johnson RJ, Michaels AF, Knap AH. Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a decade-scale look at ocean biology and biogeochemistry. Deep Sea Res Part II Top Stud Oceanogr. 2001;48:1405–47.CAS 
Article 

Google Scholar 
Karl DM, Church MJ. Microbial oceanography and the Hawaii Ocean Time-series programme. Nat Rev Microbiol. 2014;12:699–713.CAS 
Article 

Google Scholar 
Mestre M, Höfer J, Sala MM, Gasol JM. Seasonal variation of bacterial diversity along the marine particulate matter continuum. Front Microbiol. 2020;11:1590.Article 

Google Scholar 
Cram JA, Chow C-ET, Sachdeva R, Needham DM, Parada AE, Steele JA, et al. Seasonal and interannual variability of the marine bacterioplankton community throughout the water column over ten years. ISME J. 2015;9:563–80.Article 

Google Scholar 
Berelson WM. The flushing of two deep‐sea basins, southern California borderland. Limnol Oceanogr. 1991;36:1150–66.CAS 
Article 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
Article 

Google Scholar 
Traving SJ, Kellogg CT, Ross T, McLaughlin R, Kieft B, Ho GY, et al. Prokaryotic responses to a warm temperature anomaly in northeast subarctic Pacific waters. Commun Biology. 2021;4:1–12.Article 

Google Scholar 
Butler TM, Wilhelm A-C, Dwyer AC, Webb PN, Baldwin AL, Techtmann SM. Microbial community dynamics during lake ice freezing. Scient Rep. 2019;9:1–11.
Google Scholar 
LeBrun ES, King RS, Back JA, Kang S. Microbial community structure and function decoupling across a phosphorus gradient in streams. Microb Ecol. 2018;75:64–73.CAS 
Article 

Google Scholar 
McNichol J, Berube PM, Biller SJ, Fuhrman JA. Evaluating and improving small subunit rRNA PCR primer coverage for bacteria, archaea, and eukaryotes using metagenomes from global ocean surveys. Msystems. 2021;6:e00565–21.CAS 
Article 

Google Scholar 
De Bie T, De Meester L, Brendonck L, Martens K, Goddeeris B, Ercken D, et al. Body size and dispersal mode as key traits determining metacommunity structure of aquatic organisms. Ecol Lett. 2012;15:740–7.Article 

Google Scholar 
Soininen J, Korhonen JJ, Karhu J, Vetterli A. Disentangling the spatial patterns in community composition of prokaryotic and eukaryotic lake plankton. Limnol. Oceanogr. 2011;56:508–20.Article 

Google Scholar 
Wu W, Lu H-P, Sastri A, Yeh Y-C, Gong G-C, Chou W-C, et al. Contrasting the relative importance of species sorting and dispersal limitation in shaping marine bacterial versus protist communities. ISME J. 2018;12:485–94.Article 

Google Scholar 
Kraemer S, Ramachandran A, Colatriano D, Lovejoy C, Walsh DA. Diversity and biogeography of SAR11 bacteria from the Arctic Ocean. ISME J. 2020;14:79–90.Article 

Google Scholar 
Tsementzi D, Wu J, Deutsch S, Nath S, Rodriguez-R LM, Burns AS, et al. SAR11 bacteria linked to ocean anoxia and nitrogen loss. Nature. 2016;536:179–83.CAS 
Article 

Google Scholar 
Brown MV, Lauro FM, DeMaere MZ, Muir L, Wilkins D, Thomas T, et al. Global biogeography of SAR11 marine bacteria. Mol Syst Biol. 2012;8:595.Article 

Google Scholar 
Giovannoni SJ. SAR11 bacteria: the most abundant plankton in the oceans. Ann Rev Mar Sci. 2017;9:231–55.Article 

Google Scholar 
Thrash JC, Temperton B, Swan BK, Landry ZC, Woyke T, DeLong EF, et al. Single-cell enabled comparative genomics of a deep ocean SAR11 bathytype. ISME J. 2014;8:1440–51.Article 

Google Scholar 
Fernández-Gomez B, Richter M, Schüler M, Pinhassi J, Acinas SG, González JM, et al. Ecology of marine Bacteroidetes: a comparative genomics approach. ISME J. 2013;7:1026–37.Article 

Google Scholar 
Countway PD, Vigil PD, Schnetzer A, Moorthi SD, Caron DA. Seasonal analysis of protistan community structure and diversity at the USC Microbial Observatory (San Pedro Channel, North Pacific Ocean). Limnol Oceanogr. 2010;55:2381–96.Article 

Google Scholar 
Kim DY, Countway PD, Jones AC, Schnetzer A, Yamashita W, Tung C, et al. Monthly to interannual variability of microbial eukaryote assemblages at four depths in the eastern North Pacific. ISME J. 2014;8:515–30.Article 

Google Scholar 
Parris DJ, Ganesh S, Edgcomb VP, DeLong EF, Stewart FJ. Microbial eukaryote diversity in the marine oxygen minimum zone off northern Chile. Front Microbiol. 2014;5:543.Article 

Google Scholar 
Orsi W, Song YC, Hallam S, Edgcomb V. Effect of oxygen minimum zone formation on communities of marine protists. ISME J. 2012;6:1586–601.CAS 
Article 

Google Scholar 
Leibold MA, Holyoak M, Mouquet N, Amarasekare P, Chase JM, Hoopes MF, et al. The metacommunity concept: a framework for multi‐scale community ecology. Ecol Lett. 2004;7:601–13.Article 

Google Scholar 
Fuhrman JA, Hewson I, Schwalbach MS, Steele JA, Brown MV, Naeem S. Annually reoccurring bacterial communities are predictable from ocean conditions. Proc Natl Acad Sci USA. 2006;103:13104–9.CAS 
Article 

Google Scholar 
Chow C-ET, Sachdeva R, Cram JA, Steele JA, Needham DM, Patel A, et al. Temporal variability and coherence of euphotic zone bacterial communities over a decade in the Southern California Bight. ISME J. 2013;7:2259–73.CAS 
Article 

Google Scholar 
Parada AE, Fuhrman JA. Marine archaeal dynamics and interactions with the microbial community over 5 years from surface to seafloor. ISME J. 2017;11:2510–25.Article 

Google Scholar 
Mestre M, Ruiz-González C, Logares R, Duarte CM, Gasol JM, Sala MM. Sinking particles promote vertical connectivity in the ocean microbiome. Proc Natl Acad Sci USA. 2018;115:E6799–E807.CAS 
Article 

Google Scholar 
Mestre M, Ferrera I, Borrull E, Ortega‐Retuerta E, Mbedi S, Grossart HP, et al. Spatial variability of marine bacterial and archaeal communities along the particulate matter continuum. Mol Ecol. 2017;26:6827–40.CAS 
Article 

Google Scholar 
Wilson B, Müller O, Nordmann E-L, Seuthe L, Bratbak G, Øvreås L. Changes in marine prokaryote composition with season and depth over an Arctic polar year. Front Mar Sci. 2017;4:95.
Google Scholar 
Treusch AH, Vergin KL, Finlay LA, Donatz MG, Burton RM, Carlson CA, et al. Seasonality and vertical structure of microbial communities in an ocean gyre. ISME J. 2009;3:1148–63.Article 

Google Scholar 
Zinger L, Amaral-Zettler LA, Fuhrman JA, Horner-Devine MC, Huse SM, Welch DBM, et al. Global patterns of bacterial beta-diversity in seafloor and seawater ecosystems. PLoS ONE. 2011;6:e24570.CAS 
Article 

Google Scholar 
DeLong EF, Preston CM, Mincer T, Rich V, Hallam SJ, Frigaard N-U, et al. Community genomics among stratified microbial assemblages in the ocean’s interior. Science. 2006;311:496–503.CAS 
Article 

Google Scholar 
Agogué H, Lamy D, Neal PR, Sogin ML, Herndl GJ. Water mass‐specificity of bacterial communities in the North Atlantic revealed by massively parallel sequencing. Mol. Ecol. 2011;20:258–74.Article 

Google Scholar 
Walsh EA, Kirkpatrick JB, Rutherford SD, Smith DC, Sogin M, D’Hondt S. Bacterial diversity and community composition from seasurface to subseafloor. ISME J. 2016;10:979–89.Article 

Google Scholar 
Reji L, Tolar BB, Chavez FP, Francis CA. Depth-differentiation and seasonality of planktonic microbial assemblages in the Monterey Bay upwelling system. Front Microbiol. 2020;11:1075.Article 

Google Scholar 
Milici M, Vital M, Tomasch J, Badewien TH, Giebel HA, Plumeier I, et al. Diversity and community composition of particle‐associated and free‐living bacteria in mesopelagic and bathypelagic Southern Ocean water masses: Evidence of dispersal limitation in the Bransfield Strait. Limnol Oceanogr. 2017;62:1080–95.Article 

Google Scholar 
Crespo BG, Pommier T, Fernández‐Gómez B, Pedrós‐Alió C. Taxonomic composition of the particle‐attached and free‐living bacterial assemblages in the Northwest Mediterranean Sea analyzed by pyrosequencing of the 16S rRNA. Microbiologyopen. 2013;2:541–52.CAS 
Article 

Google Scholar 
Ganesh S, Parris DJ, DeLong EF, Stewart FJ. Metagenomic analysis of size-fractionated picoplankton in a marine oxygen minimum zone. ISME J. 2014;8:187–211.CAS 
Article 

Google Scholar 
Ghiglione J-F, Galand PE, Pommier T, Pedrós-Alió C, Maas EW, Bakker K, et al. Pole-to-pole biogeography of surface and deep marine bacterial communities. Proc Natl Acad Sci USA. 2012;109:17633–8.CAS 
Article 

Google Scholar 
Murillo AA, Ramírez-Flandes S, DeLong EF, Ulloa O. Enhanced metabolic versatility of planktonic sulfur-oxidizing γ-proteobacteria in an oxygen-deficient coastal ecosystem. Front Mar Sci. 2014;1:18.Article 

Google Scholar 
Hawley AK, Nobu MK, Wright JJ, Durno WE, Morgan-Lang C, Sage B, et al. Diverse Marinimicrobia bacteria may mediate coupled biogeochemical cycles along eco-thermodynamic gradients. Nat Commun. 2017;8:1–10.CAS 
Article 

Google Scholar 
Santoro AE, Buchwald C, McIlvin MR, Casciotti KL. Isotopic signature of N2O produced by marine ammonia-oxidizing archaea. Science. 2011;333:1282–5.CAS 
Article 

Google Scholar 
Aldunate M, De la Iglesia R, Bertagnolli AD, Ulloa O. Oxygen modulates bacterial community composition in the coastal upwelling waters off central Chile. Deep Sea Res Part II Top Stud Oceanogr. 2018;156:68–79.CAS 
Article 

Google Scholar 
Duret MT, Lampitt RS, Lam P. Prokaryotic niche partitioning between suspended and sinking marine particles. Environ Microbiol Rep. 2019;11:386–400.CAS 
Article 

Google Scholar 
Lindh MV, Sjöstedt J, Andersson AF, Baltar F, Hugerth LW, Lundin D, et al. Disentangling seasonal bacterioplankton population dynamics by high‐frequency sampling. Environ Microbiol. 2015;17:2459–76.Article 

Google Scholar 
Teeling H, Fuchs BM, Bennke CM, Krueger K, Chafee M, Kappelmann L, et al. Recurring patterns in bacterioplankton dynamics during coastal spring algae blooms. elife. 2016;5:e11888.Article 

Google Scholar 
Buchan A, LeCleir GR, Gulvik CA, González JM. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat Rev Microbiol. 2014;12:686–98.CAS 
Article 

Google Scholar 
Giovannoni SJ, Tripp HJ, Givan S, Podar M, Vergin KL, Baptista D, et al. Genome streamlining in a cosmopolitan oceanic bacterium. Science. 2005;309:1242–5.CAS 
Article 

Google Scholar 
Cram JA, Xia LC, Needham DM, Sachdeva R, Sun F, Fuhrman JA. Cross-depth analysis of marine bacterial networks suggests downward propagation of temporal changes. ISME J. 2015;9:2573–86.Article 

Google Scholar 
Salazar G, Cornejo‐Castillo FM, Borrull E, Díez‐Vives C, Lara E, Vaqué D, et al. Particle‐association lifestyle is a phylogenetically conserved trait in bathypelagic prokaryotes. Mol Ecol. 2015;24:5692–706.Article 

Google Scholar 
Mohit V, Archambault P, Toupoint N, Lovejoy C. Phylogenetic differences in attached and free-living bacterial communities in a temperate coastal lagoon during summer, revealed via high-throughput 16S rRNA gene sequencing. Appl Environ Microbiol. 2014;80:2071–83.Article 

Google Scholar 
Rieck A, Herlemann DP, Jürgens K, Grossart H-P. Particle-associated differ from free-living bacteria in surface waters of the Baltic Sea. Front Microbiol. 2015;6:1297.Article 

Google Scholar 
Pachiadaki MG, Brown JM, Brown J, Bezuidt O, Berube PM, Biller SJ, et al. Charting the complexity of the marine microbiome through single-cell genomics. Cell. 2019;179:1623–35. e11.CAS 
Article 

Google Scholar 
Giner CR, Pernice MC, Balagué V, Duarte CM, Gasol JM, Logares R, et al. Marked changes in diversity and relative activity of picoeukaryotes with depth in the world ocean. ISME J. 2020;14:437–49.Article 

Google Scholar 
Countway PD, Gast RJ, Dennett MR, Savai P, Rose JM, Caron DA. Distinct protistan assemblages characterize the euphotic zone and deep sea (2500 m) of the western North Atlantic (Sargasso Sea and Gulf Stream). Environ Microbiol. 2007;9:1219–32.CAS 
Article 

Google Scholar 
Ollison GA, Hu SK, Mesrop LY, DeLong EF, Caron DA. Come rain or shine: depth not season shapes the active protistan community at station ALOHA in the North Pacific Subtropical Gyre. Deep Sea Res Part I Oceanogr Res Pap. 2021;170:103494.Article 

Google Scholar 
Schnetzer A, Moorthi SD, Countway PD, Gast RJ, Gilg IC, Caron DA. Depth matters: microbial eukaryote diversity and community structure in the eastern North Pacific revealed through environmental gene libraries. Deep Sea Res Part I Oceanogr Res Pap. 2011;58:16–26.Article 

Google Scholar 
Martin P, Allen JT, Cooper MJ, Johns DG, Lampitt RS, Sanders R, et al. Sedimentation of acantharian cysts in the Iceland Basin: strontium as a ballast for deep ocean particle flux, and implications for acantharian reproductive strategies. Limnol Oceanogr. 2010;55:604–14.CAS 
Article 

Google Scholar 
Lampitt R, Salter I, Johns D. Radiolaria: Major exporters of organic carbon to the deep ocean. Glob Biogeochem Cycle. 2009;23:GB1010.Article 

Google Scholar 
Skovgaard A, Massana R, Balague V, Saiz E. Phylogenetic position of the copepod-infesting parasite Syndinium turbo (Dinoflagellata, Syndinea). Protist. 2005;156:413–23.CAS 
Article 

Google Scholar 
Bachvaroff TR, Kim S, Guillou L, Delwiche CF, Coats DW. Molecular diversity of the syndinean genus Euduboscquella based on single-cell PCR analysis. Appl Environ Microbiol. 2012;78:334–45.CAS 
Article 

Google Scholar 
Guillou L, Viprey M, Chambouvet A, Welsh R, Kirkham A, Massana R, et al. Widespread occurrence and genetic diversity of marine parasitoids belonging to Syndiniales (Alveolata). Environ Microbiol. 2008;10:3349–65.CAS 
Article 

Google Scholar 
Berdjeb L, Parada A, Needham DM, Fuhrman JA. Short-term dynamics and interactions of marine protist communities during the spring–summer transition. ISME J. 2018;12:1907.Article 

Google Scholar 
Hu SK, Connell PE, Mesrop LY, Caron DA. A hard day’s night: diel shifts in microbial eukaryotic activity in the north pacific subtropical gyre. Front Mar Sci. 2018;5:351.Article 

Google Scholar 
Clarke LJ, Bestley S, Bissett A, Deagle BE. A globally distributed Syndiniales parasite dominates the Southern Ocean micro-eukaryote community near the sea-ice edge. ISME J. 2019;13:734–7.CAS 
Article 

Google Scholar 
De Vargas C, Audic S, Henry N, Decelle J, Mahé F, Logares R, et al. Eukaryotic plankton diversity in the sunlit ocean. Science. 2015;348:1261605.Article 

Google Scholar 
Pernice MC, Giner CR, Logares R, Perera-Bel J, Acinas SG, Duarte CM, et al. Large variability of bathypelagic microbial eukaryotic communities across the world’s oceans. ISME J. 2016;10:945–58.Article 

Google Scholar 
Crutsinger GM, Collins MD, Fordyce JA, Gompert Z, Nice CC, Sanders NJ. Plant genotypic diversity predicts community structure and governs an ecosystem process. Science. 2006;313:966–8.CAS 
Article 

Google Scholar 
Hawkins BA, Porter EE. Does herbivore diversity depend on plant diversity? The case of California butterflies. Am Nat. 2003;161:40–9.Article 

Google Scholar 
Scherber C, Eisenhauer N, Weisser WW, Schmid B, Voigt W, Fischer M, et al. Bottom-up effects of plant diversity on multitrophic interactions in a biodiversity experiment. Nature. 2010;468:553–6.CAS 
Article 

Google Scholar 
Yang JW, Wu W, Chung C-C, Chiang K-P, Gong G-C, Hsieh C-h Predator and prey biodiversity relationship and its consequences on marine ecosystem functioning—interplay between nanoflagellates and bacterioplankton. ISME J. 2018;12:1532–42.Article 

Google Scholar 
Fuhrman JA, Comeau DE, Hagström Å, Chan AM. Extraction from natural planktonic microorganisms of DNA suitable for molecular biological studies. Appl Environ Microbiol. 1988;54:1426–9.CAS 
Article 

Google Scholar 
Lie AA, Kim DY, Schnetzer A, Caron DA. Small-scale temporal and spatial variations in protistan community composition at the San Pedro Ocean Time-series station off the coast of southern California. Aquat Microb Ecol. 2013;70:93–110.Article 

Google Scholar 
Yeh Y-C, Needham DM, Sieradzki ET, Fuhrman JA. Taxon disappearance from microbiome analysis reinforces the value of mock communities as a standard in every sequencing run. MSystems. 2018;3:e00023–18.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. Nucl Acids Res. 2013;41:D590–D6.CAS 
Article 

Google Scholar 
Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucl Acids Res. 2013;41:D579–D604.
Google Scholar 
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
Article 

Google Scholar 
Decelle J, Romac S, Stern RF, Bendif EM, Zingone A, Audic S, et al. Phyto REF: a reference database of the plastidial 16S rRNA gene of photosynthetic eukaryotes with curated taxonomy. Mol Ecol Resour. 2015;15:1435–45.CAS 
Article 

Google Scholar 
Oksanen J, Blanchet F, Friendly M, Kindt R, Legendre P, McGlinn D, et al. Vegan: community ecology package. R package version 2.5-7. 2020. https://CRAN.R-project.org/package=vegan.Wickham H. ggplot2-elegant graphics for data analysis. Cham, Switzerland: Springer International Publishing; 2016.Kolde R. Pheatmap: pretty heatmaps. R Package Version. 2012;1:726.
Google Scholar 
Schloerke B, Crowley J, Cook D. Package ‘GGally’. Extension to ‘ggplot2’See. 2018;713. More

Franco FP, Túler AC, Gallan DZ, Gonçalves FG, Favaris AP, Peñaflor MFGV, et al. Fungal phytopathogen modulates plant and insect responses to promote its dissemination. ISME J. 2021;15:3522–33.CAS 

Google Scholar 
Huang H, Ren L, Li H, Schmidt A, Gershenzon J, Lu Y, et al. The nesting preference of an invasive ant is associated with the cues produced by actinobacteria in soil. PLoS Pathog. 2020;16:e1008800.CAS 

Google Scholar 
Angleró-Rodríguez YI, Blumberg BJ, Dong Y, Sandiford SL, Pike A, Clayton AM, et al. A natural Anopheles-associated Penicillium chrysogenum enhances mosquito susceptibility to Plasmodium infection. Sci Rep. 2016;6:34084.
Google Scholar 
Davis TS, Landolt PJ. A survey of insect assemblages responding to volatiles from a ubiquitous fungus in an agricultural landscape. J Chem Ecol. 2013;39:860–8.CAS 

Google Scholar 
Flury P, Vesga P, Dominguez-Ferreras A, Tinguely C, Ullrich CI, Kleespies RG, et al. Persistence of root-colonizing Pseudomonas protegens in herbivorous insects throughout different developmental stages and dispersal to new host plants. ISME J. 2018;13:860–72.
Google Scholar 
Kandasamy D, Gershenzon J, Andersson MN, Hammerbacher A. Volatile organic compounds influence the interaction of the Eurasian spruce bark beetle (Ips typographus) with its fungal symbionts. ISME J. 2019;13:1788–800.CAS 

Google Scholar 
Keesey IW, Koerte S, Khallaf MA, Retzke T, Guillou A, Grosse-Wilde E, et al. Pathogenic bacteria enhance dispersal through alteration of Drosophila social communication. Nat Commun. 2017;8:265.
Google Scholar 
Paul GB, Gerhard F, Elżbieta R, Alexandra S, Arne H, Sébastien L, et al. Yeast, not fruit volatiles mediate Drosophila melanogaster attraction, oviposition and development. Funct Ecol. 2012;26:1365–2435.
Google Scholar 
Ganter PF. Yeast and invertebrate associations. In: Gábor P, Carlos R, editors. Biodiversity and ecophysiology of yeasts. Berlin, Heidelberg: Springer; 2006. pp 303–70.Anagnostou C, Legrand EA, Rohlfs M. Friendly food for fitter flies?—Influence of dietary microbial species on food choice and parasitoid resistance in Drosophila. Oikos. 2010;119:533–41.
Google Scholar 
Günther CS, Knight SJ, Jones R, Goddard MR. Are Drosophila preferences for yeasts stable or contextual? Ecol Evol. 2019;9:8075–86.
Google Scholar 
Luo Y, Johnson JC, Chakraborty TS, Piontkowski A, Gendron CM, Pletcher SD. Yeast volatiles double starvation survival in Drosophila. Sci Adv. 2021;7:eabf8896.CAS 

Google Scholar 
Fogleman S. Coadaptation of Drosophila and yeasts in their natural habitat. J Chem Ecol. 1986;12:1037–55.
Google Scholar 
Droby S, Eick A, Macarisin D, Cohen L, Rafael G, Stange R, et al. Role of citrus volatiles in host recognition, germination and growth of Penicillium digitatum and Penicillium italicum. Postharvest Biol Tec. 2008;49:386–96.CAS 

Google Scholar 
Stensmyr MC, Dweck HK, Farhan A, Ibba I, Strutz A, Mukunda L, et al. A conserved dedicated olfactory circuit for detecting harmful microbes in Drosophila. Cell. 2012;151:1345–57.CAS 

Google Scholar 
Melo N, Wolff GH, Costa-da-Silva AL, Arribas R, Triana MF, Gugger M, et al. Geosmin attracts Aedes aegypti mosquitoes to oviposition sites. Curr Biol. 2020;30:127–34.CAS 

Google Scholar 
Wei DD, He W, Lang N, Miao ZQ, Xiao LF, Dou W, et al. Recent research status of Bactrocera dorsalis: Insights from resistance mechanisms and population structure. Arch Insect Biochem. 2019;102:e21601.CAS 

Google Scholar 
Han P, Wang X, Niu CY, Dong YC, Zhu JQ, Desneux N. Population dynamics, phenology, and overwintering of Bactrocera dorsalis (Diptera: Tephritidae) in Hubei Province, China. J Pest Sci. 2011;84:289–95.
Google Scholar 
Duyck PF, David P, Quilici S. A review of relationships between interspecific competition and invasions in fruit flies (Diptera: Tephritidae). Ecol Entomol. 2004;29:511–20.
Google Scholar 
Wen T, Zheng L, Dong S, Gong Z, Sang M, Long X, et al. Rapid detection and classification of citrus fruits infestation by Bactrocera dorsalis (Hendel) based on electronic nose. Postharvest Biol Tec. 2019;147:156–65.
Google Scholar 
Li X, Yang H, Wang T, Wang J, Wei H. Life history and adult dynamics of Bactrocera dorsalis in the citrus orchard of Nanchang, a subtropical area from China: implications for a control timeline. ScienceAsia. 2019;45:212–20.
Google Scholar 
Chalupowicz D, Veltman B, Droby S, Eltzov E. Evaluating the use of biosensors for monitoring of Penicillium digitatum infection in citrus fruit. Sens Actuat B-Chem. 2020;311:127896.CAS 

Google Scholar 
Turlings TC, Lengwiler UB, Bernasconi ML, Wechsler D. Timing of induced volatile emissions in maize seedlings. Planta. 1998;207:146–52.CAS 

Google Scholar 
Wang B, Dong W, Li H, D’Onofrio C, Bai P, Chen R, et al. Molecular basis of (E)-β-farnesene-mediated aphid location in the predator Eupeodes corollae. Curr Biol. 2022;32:951–62.CAS 

Google Scholar 
Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2− ΔΔCT method. Methods. 2001;25:402–8.CAS 

Google Scholar 
Cellar NA, De Nison JE, Seipelt CT, Twohig M, Burgess JA. Title of subordinate document. In: Dramatic improvements in assay reproducibility for water-soluble vitamins using ACQUITY UPLC and the Ultra-Sensitive Xevo TQ-S Mass Spectrometer. 2013. https://www.waters.com/webassets/cms/library/docs/720004690en.pdf.Ren FR, Sun X, Wang TY, Yan JY, Yao YL, Li CQ, et al. Pantothenate mediates the coordination of whitefly and symbiont fitness. ISME J. 2021;15:1655–67.CAS 

Google Scholar 
Batta YA. Quantitative postharvest contamination and transmission of Penicillium expansum (Link) conidia to nectarine and pear fruit by Drosophila melanogaster (Meig.) adults. Postharvest Biol Tec. 2006;40:190–6.
Google Scholar 
Rohlfs M. Clash of kingdoms or why Drosophila larvae positively respond to fungal competitors. Front Zool. 2005;2:2.
Google Scholar 
Becher PG, Bengtsson M, Hansson BS, Witzgall P. Flying the fly: long-range flight behavior of Drosophila melanogaster to attractive odors. J Chem Ecol. 2010;36:599–607.CAS 

Google Scholar 
Dionigi C, Ahten T, Wartelle L. Effects of several metals on spore, biomass, and geosmin production by Streptomyces tendae and Penicillium expansum. J Ind Microbiol Biot. 1996;17:84–88.CAS 

Google Scholar 
Jin S, Zhou X, Gu F, Zhong G, Yi X. Olfactory plasticity: variation in the expression of chemosensory receptors in Bactrocera dorsalis in different physiological states. Front Physiol. 2017;8:672.
Google Scholar 
Li H, Ren L, Xie M, Gao Y, He M, Hassan B, et al. Egg-surface bacteria are indirectly associated with oviposition aversion in Bactrocera dorsalis. Curr Biol. 2020;30:4432–40.CAS 

Google Scholar 
Liu Y, Cui Z, Si P, Liu Y, Zhou Q, Wang G. Characterization of a specific odorant receptor for linalool in the Chinese citrus fly Bactrocera minax (Diptera: Tephritidae). Insect Biochem Molec. 2020;122:103389.CAS 

Google Scholar 
Ju JF, Bing XL, Zhao DS, Guo Y, Hong XY. Wolbachia supplement biotin and riboflavin to enhance reproduction in planthoppers. ISME J. 2019;14:1–12.
Google Scholar 
Liu F, Wickham JD, Cao Q, Lu M, Sun J. An invasive beetle–fungus complex is maintained by fungal nutritional-compensation mediated by bacterial volatiles. ISME J. 2020;14:2829–42.CAS 

Google Scholar 
Douglas AE. The B vitamin nutrition of insects: the contributions of diet, microbiome and horizontally acquired genes. Curr Opin Insect Sci. 2017;23:65–69.
Google Scholar 
Honda K, Ômura H, Hayashi N, Abe F, Yamauchi T. Conduritols as oviposition stimulants for the danaid butterfly, Parantica sita, identified from a host plant, Marsdenia tomentosa. J Chem Ecol. 2004;30:2285–96.CAS 

Google Scholar 
Soldano A, Alpizar YA, Boonen B, Franco L, Lopez-Requena A, Liu G, et al. Gustatory-mediated avoidance of bacterial lipopolysaccharides via TRPA1 activation in Drosophila. Elife. 2016;5:e13133.
Google Scholar 
Hussain A, Üçpunar HK, Zhang M, Loschek LF, Grunwald Kadow IC. Neuropeptides modulate female chemosensory processing upon mating in Drosophila. PLoS Biol. 2016;14:e1002455.
Google Scholar 
Stötefeld L, Holighaus G, Schütz S, Rohlfs M. Volatile-mediated location of mutualist host and toxic non-host microfungi by Drosophila larvae. Chemoecology. 2015;5:271–83.
Google Scholar 
Gou B, Liu Y, Guntur A, Stern U, Yang HC. Mechanosensitive neurons on the internal reproductive tract contribute to egg-laying-induced acetic acid attraction in Drosophila. Cell Rep. 2014;9:522–30.CAS 

Google Scholar 
Mezzera C, Brotas M, Gaspar M, Pavlou HJ, Goodwin SF, Vasconcelos ML. Ovipositor extrusion promotes the transition from courtship to copulation and signals female acceptance in Drosophila melanogaster. Curr Biol. 2020;30:3736–48.CAS 

Google Scholar 
Teimoori-Boghsani Y, Ganjeali A, Cernava T, Müller H, Asili J, Berg G. Endophytic fungi of native Salvia abrotanoides plants reveal high taxonomic diversity and unique profiles of secondary metabolites. Front Microbiol. 2020;10:3013–20.
Google Scholar 
Holden JT, Furman C, Snell EE. D-alanine and the vitamin B6 content of microorganisms. J Biol Chem. 1949;178:789–97.CAS 

Google Scholar 
Michalkova V, Benoit JB, Weiss BL, Attardo GM, Aksoy S. Vitamin B6 generated by obligate symbionts is critical for maintaining proline homeostasis and fecundity in tsetse flies. Appl Environ Micro. 2014;80:5844–53.
Google Scholar 
Ren FR, Sun X, Wang TY, Yao YL, Huang YZ, Zhang X, et al. Biotin provisioning by horizontally transferred genes from bacteria confers animal fitness benefits. ISME J. 2020;14:2542–53.CAS 

Google Scholar 
Salem H, Bauer E, Strauss AS, Vogel H, Marz M, Kaltenpoth M. Vitamin supplementation by gut symbionts ensures metabolic homeostasis in an insect host. Proc Biol Sci. 2014;281:20141838.
Google Scholar  More

Gross, M. R. Alternative reproductive strategies and tactics: diversity within sexes. Trend. Ecol. Evol. 11, 92–98 (1996).CAS 

Google Scholar 
Oliveira, R. F., Taborsky, M., and Brockmann, H. J. Alternative reproductive tactics: An integrative approach. (Cambridge University Press, 2008).Schradin, C. & Lindholm, A. K. Relative fitness of alternative male reproductive tactics in a mammal varies between years. J. Anim. Ecol. 80, 908–917 (2011).PubMed 

Google Scholar 
Riehl, C. & Strong, M. J. Social parasitism as an alternative reproductive tactic in a cooperatively breeding cuckoo. Nature 567, 96–99 (2019).CAS 
PubMed 

Google Scholar 
Ferrari, M., Lindholm, A. K. & König, B. Fitness consequences of female alternative reproductive tactics in house mice (Mus musculus domesticus). Am. Natural. 193, 106–124 (2019).
Google Scholar 
Eggert, A.-K. & Müller, J. K. Joint breeding in female burying beetles. Behav. Ecol. Sociobiol. 31, 237–242 (1992).
Google Scholar 
Komdeur, J. Importance of habitat saturation and territory quality for evolution of cooperative breeding in the seychelles warbler. Nature 358, 493–495 (1992).
Google Scholar 
Hayes, D. L. et al. Fitness consequences of group living in the degu Octodon degus, a plural breeder rodent with communal care. Anim. Behav. 78, 131–139 (2009).
Google Scholar 
Scott, M. P. & Williams, S. M. Comparative reproductive success of communally breeding burying beetles as assessed by PCR with randomly amplified polymorphic DNA. Proc. Natl. Acad. Sci. USA 90, 2242–2245 (1993).CAS 
PubMed 
PubMed Central 

Google Scholar 
Chantrey, D. F. & Jenkins, B. Sensory processes in the discrimination of pups by female mice (Mus musculus). Anim. Behav.30, 881–885 (1982).
Google Scholar 
König, B. Kin recognition and maternal care under restricted feeding in house mice (Mus domesticus). Ethology 82, 328–343 (1989).
Google Scholar 
Ferrari, M. & Lindholm, A. K. The risk of exploitation during communal nursing in house mice, Mus musculus domesticus. Anim. Behav. 110, 133–143 (2015).
Google Scholar 
Sayler, A. & Salmon, M. An ethological analysis of communal nursing by the house mouse (Mus musculus). Behaviour 40, 62–85 (1971).
Google Scholar 
Manning, C. J., Dewsbury, D. A., Wakeland, E. K. & Potts, W. K. Communal nesting and communal nursing in house mice, Mus musculus domesticus. Anim. Behav. 50, 741–751 (1995).
Google Scholar 
König, B. Non-offspring nursing in mammals: General implications from a case study on house mice. In Peter M. Kappeler & Carel P. van Schaik, editor, Cooperation in Primates and Humans. Mechanisms and Evolution, pages 191–205. Springer Verlag, Berlin Heidelberg, 2006.Mumme, R. L., Koenig, W. D., & Pitelka, F. A. Costs and benefits of joint nesting in the Acorn Woodpecker. Am. Natural. 131, 654–677 (1988).Packer, C., Lewis, S. & Pusey, A. A comparative analysis of non-offspring nursing. Anim. Behav. 43, 265–281 (1992).
Google Scholar 
Bourke, A. F. & Heinze, J. The ecology of communal breeding: the case of multiple-queen leptothoracine ants. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 345, 359–372 (1994).
Google Scholar 
Marin, G. & Pilastro, A. Communally breeding dormice, glis glis, are close kin. Anim. Behav. 47, 1485–1487 (1994).
Google Scholar 
Hayes, D. L. To nest communally or not to nest communally: a review of rodent communal nesting and nursing. Anim. Behav.59, 677–688 (2000).CAS 
PubMed 

Google Scholar 
König, B. Components of lifetime reproductive success in communally and solitarily nursing house mice: A laboratory study. Behav. Ecol. Sociobiol. 34, 275–283 (1994).
Google Scholar 
Auclair, Y., König, B. & Lindholm, A. K. Socially mediated polyandry: a new benefit of communal nesting in mammals. Behav. Ecol. 25, 1476–1473 (2014).
Google Scholar 
Palanza, P., Della Seta, D., Ferrari, P. F. & Parmigiani, S. Female competition in wild house mice depends upon timing of female/male settlement and kinship between females. Anim. Behav. 69, 1259–1271 (2005).
Google Scholar 
Schmidt, J. et al. Reproductive asynchrony and infanticide in house mice breeding communally. Anim. Behav. 101, 201–211 (2015).
Google Scholar 
Dobson, F. S., Jacquot, C. & Baudoin, C. An experimental test of kin association in the house mouse. Can. J. Zool. 78, 1806–1812 (2000).
Google Scholar 
König, B. et al. A system for automatic recording of social behavior in a free-living wild house mouse population. Anim. Biotelemetry 3, 1–15 (2015).
Google Scholar 
Mathot, K. J. & Giraldeau, L.-A. Within-group relatedness can lead to higher levels of exploitation: a model and empirical test. Behav. Ecol. 21, 843–850 (2010).
Google Scholar 
Harrison, N., Lindholm, A. K., Dobay, A., Halloran, O., Manser, A., & König, B. Female nursing partner choice in a population of wild house mice (Musmusculusdomesticus). Front. Zool. 15, 4 (2018).König, B., Riester, J. & Markl, H. Maternal care in house mice (Mus musculus): II. The energy cost of lactation as a function of litter size. J. Zool. 216, 195–210 (1988).
Google Scholar 
Hurst, J. L. Behavioural variation in wild house mice mus domesticus rutty: A quantitative assessment of female social organization. Anim. Behav. 35, 1846–1857 (1987).Weidt, A., Lindholm, A. K. & König, B. Communal nursing in wild house mice is not a by-product of group living: Females choose. Naturwissenschaften 101, 73–76 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lidicker, W. Z. Social behaviour and density regulation in house mice living in large enclosures. J. Anim. Ecol. 45, 677–697 (1976).
Google Scholar 
Southwick, C. H. The population dynamics of confined house mice supplied with unlimited food. Ecology 36, 212–225 (1955).
Google Scholar 
König, B. & Lindholm, A. K. The complex social environment of female house mice (Mus domesticus). In Macholàn, M., Baird, S. J. E., Mundlinger, P., and Piàlek, J., editors, Evolution of the House Mouse, pages 114–134. Cambridge University Press, Cambridge, UK, 2012.Hestbeck, J. B., Nichols, J. D. & Malecki, R. A. Estimates of movement and site fidelity using mark-resight data of wintering canada geese. Ecology 72, 523–533 (1991).
Google Scholar 
Lebreton, J.-D., Burnham, K. P., Clobert, J. & Anderson, D. R. Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies. Ecol. Monogr. 62, 67–118 (1992).
Google Scholar 
Lebreton, J., Nichols, J. D., Barker, R. J., Pradel, R., & Spendelow, J. A. Chapter 3 modeling individual animal histories with multistate capture–recapture models. In Caswell, H., editor, Advances in Ecological Research, volume 41, pages 87–173. Academic Press, 2009.Caswell, H. Matrix Population Models: Construction, Analysis, and Interpretation. Matrix Population Models: Construction, Analysis, and Interpretation. Sinauer Associates, 2001.Runge, J.-N. & Lindholm, A. K. Carrying a selfish genetic element predicts increased migration propensity in free-living wild house mice. Proc. R. Soc. B: Biol. Sci. 285, 20181333 (2018).
Google Scholar 
Oli, M. K., Slade, N. A. & Dobson, F. S. Effect of density reduction on Uinta ground squirrels: analysis of life table response experiments. Ecology 82, 1921–1929 (2001).
Google Scholar 
Descamps, S., Boutin, S., Berteaux, D., McAdam, A. G. & Gaillard, J.-M. Cohort effects in red squirrels: the influence of density, food abundance and temperature on future survival and reproductive success. J. Anim. Ecol. 77, 305–314 (2008).PubMed 

Google Scholar 
Gaines, M. S. & McClenaghan Jr, L. R. Dispersal in small mammals. Ann. Rev. Ecol. Sys. 11, 163–196 (1980).
Google Scholar 
Matthysen, E. Density-dependent dispersal in birds and mammals. Ecography 28, 403–416 (2005).
Google Scholar 
Wolff, J. O. Population regulation in mammals: an evolutionary perspective. J. Anim. Ecol. 66, 1–13 (1997).
Google Scholar 
Pocock, M. J. O., Hauffe, H. C. & Searle, J. B. Dispersal in house mice. Biol. J. Linnean Soc. 84, 565–583 (2005).
Google Scholar 
Clutton-Brock, T., Major, M., Albon, S. & Guinness, F. Early development and population dynamics in red deer. i. density-dependent effects on juvenile survival. J. Anim. Ecol. 56, 53–67 (1987).
Google Scholar 
Gerlach, G. & Bartmann, S. Reproductive skew, costs, and benefits of cooperative breeding in female wood mice (Apodemus sylvaticus). Behav. Ecol. 13, 408–418 (2002).
Google Scholar 
Festa-Bianchet, M., Gaillard, J. & Jorgenson, J. T. Mass- and density-dependent reproductive success and reproductive costs in a capital breeder. Am. Natural. 152, 367–379 (1998).CAS 

Google Scholar 
Tavecchia, G. et al. Predictors of reproductive cost in female soay sheep. J. Anim. Ecol. 74, 201–213 (2005).
Google Scholar 
McNamara, J. M. & Houston, A. I. State-dependent life histories. Nature 380, 215 (1996).CAS 
PubMed 

Google Scholar 
Taborsky, M., Oliveira, R. F., & Brockmann, H. J. The evolution of alternative reproductive tactics: concepts and questions. In Oliveira, R. F., Brockmann, H. J., and Taborsky, M., editors, Alternative Reproductive Tactics: An Integrative Approach. Cambridge University Press, Cambridge, UK, 2008.Stearns, S. C. Life-history tactics: a review of the ideas. Q. Rev. Biol 51, 3–47 (1976).CAS 
PubMed 

Google Scholar 
McShea, W. J. & Madison, D. M. Communal nesting between reproductively active females in a spring population of Microtus pennsylvanicus. Can. J. Zool. 62, 344–346 (1984).
Google Scholar 
Hill, D. L., Pillay, N. & Schradin, C. Alternative reproductive tactics in female striped mice: heavier females are more likely to breed solitarily than communally. J. Anim. Ecol. 84, 1497–1508 (2015).PubMed 

Google Scholar 
Krebs, C. J., Chitty, D., Singleton, G. & Boonstra, R. Can changes in social behaviour help to explain house mouse plagues in Australia? Oikos 73, 429–434 (1995).
Google Scholar 
Bult, C. J., Eppig, J. T., Kadin, J. A., Richardson, J. E. & Blake, J. A. The mouse genome database (MGD): mouse biology and model systems. Nucleic Acids Res. 36, D724–D728 (2007).PubMed 
PubMed Central 

Google Scholar 
Teschke, M., Mukabayire, O., Wiehe, T. & Tautz, D. Identification of selective sweeps in closely related populations of the house mouse based on microsatellite scans. Genetics 180, 1537–1545 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kalinowski, S. T., Taper, M. L. & Marshall, T. C. Revising how the computer program cervus accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 16, 1099–1106 (2007).PubMed 

Google Scholar 
Arnason, A. N. Parameter estimates from mark-recapture experiments on two populations subject to migration and death. Res Popul. Ecol. 13, 97–113 (1972).
Google Scholar 
Arnason, A. N. The estimation of population size, migration rates and survival in a stratified population. Res. Popul. Ecol. 15, 1–8 (1973).
Google Scholar 
White, G. C. & Burnham, K. P. Program mark: survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).
Google Scholar 
Laake, J. RMark: An r interface for analysis of capture-recapture data with MARK. AFSC Processed Rep. 2013-01, Alaska Fish. Sci. Cent., NOAA, Natl. Mar. Fish. Serv., (Seattle, WA, 2013).Burnham, K. P. and Anderson, D. R. Model Selection and Multimodel Inference. (Springer, 1998).Stubben, C. J. and Milligan, B. G. Estimating and analyzing demographic models using the popbio package in r. J. Stat. Soft. 22, 1–23 (2007).Burnham, K. P., Anderson, D. R. & Huyvaert, K. P. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav. Ecol. Sociobiol. 65, 23–35 (2011).
Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. lme4: Linear mixed-effects models using Eigen and S4. J. Stat. Soft. 67, 1–48 (2014).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2015. More

Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
MacDicken, K. G. Global forest resources assessment 2015: what, why and how? For. Ecol. Manag. 352, 3–8 (2015).
Google Scholar 
Li, M.-M. et al. An overview of the “Three-North” Shelterbelt project in China. Forestry Stud. China 14, 70–79 (2012).ADS 

Google Scholar 
Zhang, P. et al. China’s forest policy for the 21st century. Science 288, 2135–2136 (2000).CAS 
PubMed 

Google Scholar 
Chen, Y. et al. Balancing green and grain trade. Nat. Geosci. 8, 739–741 (2015).ADS 

Google Scholar 
Xu, J., Yin, R., Li, Z. & Liu, C. China’s ecological rehabilitation: unprecedented efforts, dramatic impacts, and requisite policies. Ecol. Econ. 57, 595–607 (2006).
Google Scholar 
Piao, S., Fang, J., Liu, H. & Zhu, B. NDVI-indicated decline in desertification in China in the past two decades. Geophys. Res. Lett. 32, L06402 (2005).ADS 

Google Scholar 
Wang, X., Chen, F., Hasi, E. & Li, J. Desertification in China: an assessment. Earth Sci. Rev. 88, 188–206 (2008).ADS 

Google Scholar 
Ouyang, Z. et al. Improvements in ecosystem services from investments in natural capital. Science 352, 1455–1459 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Bryan, B. A. et al. China’s response to a national land-system sustainability emergency. Nature 559, 193–204 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Feng, X. et al. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat. Clim. Chang. 6, 1019–1022 (2016).ADS 

Google Scholar 
Cao, S., Zhang, J., Chen, L. & Zhao, T. Ecosystem water imbalances created during ecological restoration by afforestation in China, and lessons for other developing countries. J. Environ. Manag. 183, 843–849 (2016).
Google Scholar 
Liu, Y. et al. Recent trends in vegetation greenness in China significantly altered annual evapotranspiration and water yield. Environ. Res. Lett. 11, 094010 (2016).ADS 

Google Scholar 
Yao, Y. et al. The effect of afforestation on soil moisture content in Northeastern China. PLoS ONE 11, e0160776 (2016).PubMed 
PubMed Central 

Google Scholar 
An, W. et al. Exploring the effects of the “Grain for Green” program on the differences in soil water in the semi-arid Loess Plateau of China. Ecol. Eng. 107, 144–151 (2017).
Google Scholar 
Li, Y. et al. Divergent hydrological response to large-scale afforestation and vegetation greening in China. Sci. Adv. 4, eaar4182 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Global Wetland Outlook: State of the World’s Wetlands and their Services to People (Ramsar Convention Secretariat, 2018).Baumgartner, R. J. Sustainable development goals and the forest sector—a complex relationship. Forests 10, 152 (2019).
Google Scholar 
15-year Comprehensive Plan for Ecological System Protection and Recovery Work (National Development and Reform Commission, 2020).Prigent, C., Jimenez, C. & Bousquet, P. Satellite-derived global surface water extent and dynamics over the last 25 years (GIEMS-2). J. Geophys. Res. Atmos. 125, e2019JD030711 (2020).ADS 

Google Scholar 
Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cy. 19, GB1015 (2005).ADS 

Google Scholar 
Tootchi, A. Development of a global wetland map and application to describe hillslope hydrology in the ORCHIDEE land surface model. Sorbonne Université, https://www.metis.upmc.fr/~ducharne/documents/These_Tootchi_revised_11Sep2019.pdf (2019).Beven, K. J. & Kirkby, M. J. A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol. Sci. B. 24, 43–69 (1979).
Google Scholar 
Stocker, B. D., Spahni, R. & Joos, F. DYPTOP: a cost-efficient TOPMODEL implementation to simulate sub-grid spatio-temporal dynamics of global wetlands and peatlands. Geosci. Model Dev. 7, 3089–3110 (2014).ADS 

Google Scholar 
Xi, Y., Peng, S., Ciais, P. & Chen, Y. Future impacts of climate change on inland Ramsar wetlands. Nat. Clim. Chang. 11, 45–51 (2021).ADS 

Google Scholar 
Kim, H. Global soil wetness project phase 3 atmospheric boundary conditions (Experiment 1). Data Integration and Analysis System (DIAS). (2017).Cucchi, M. et al. WFDE5: bias-adjusted ERA5 reanalysis data for impact studies. Earth Syst. Sci. Data 12, 2097–2120 (2020).ADS 

Google Scholar 
Donchyts, G. et al. Earth’s surface water change over the past 30 years. Nat. Clim. Chang. 6, 810–813 (2016).ADS 

Google Scholar 
Zhu, Q. et al. Climate-driven increase of natural wetland methane emissions offset by human-induced wetland reduction in China over the past three decades. Sci. Rep. 6, 38020 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mao, D. et al. Remote observations in China’s Ramsar Sites: wetland dynamics, anthropogenic threats, and implications for sustainable development goals. J. Remote Sens. 2021, 9849343 (2021).ADS 

Google Scholar 
Budyko, M. I. Climate and Life (Academic Press, 1974).Zhang, L., Dawes, W. R. & Walker, G. R. Response of mean annual evapotranspiration to vegetation changes at catchment scale. Water Resour. Res. 37, 701–708 (2001).ADS 

Google Scholar 
Woodward, C., Shulmeister, J., Larsen, J., Jacobsen, G. E. & Zawadzki, A. The hydrological legacy of deforestation on global wetlands. Science 346, 844–847 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Zhang, Z., Zimmermann, N. E., Kaplan, J. O. & Poulter, B. Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties. Biogeosciences 13, 1387–1408 (2016).ADS 

Google Scholar 
Ringeval, B. et al. Modelling sub-grid wetland in the ORCHIDEE global land surface model: evaluation against river discharges and remotely sensed data. Geosci. Model Dev. 5, 941 (2012).ADS 

Google Scholar 
Tootchi, A., Jost, A. & Ducharne, A. Multi-source global wetland maps combining surface water imagery and groundwater constraints. Earth Syst. Sci. Data 11, 189–220 (2019).ADS 

Google Scholar 
List of Protected Wetlands in China. http://www.zrbhq.cn/web/confirm.html (National Forestry and Grassland Administration, 2011).Lehner, B. & Grill, G. Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrol. Process. 27, 2171–2186 (2013).ADS 

Google Scholar 
Lu, F. et al. Effects of national ecological restoration projects on carbon sequestration in China from 2001 to 2010. Proc. Natl Acad. Sci. USA 115, 4039–4044 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Warszawski, L. et al. The inter-sectoral impact model intercomparison project (ISI–MIP): project framework. Proc. Natl Acad. Sci. USA 111, 3228–3232 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Levia, D. F. et al. Homogenization of the terrestrial water cycle. Nat. Geosci. 13, 656–658 (2020).ADS 
CAS 

Google Scholar 
Zhang, J., Fu, B., Stafford-Smith, M., Wang, S. & Zhao, W. Improve forest restoration initiatives to meet sustainable development goal 15. Nat. Ecol. Evol. 5, 10–13 (2020).
Google Scholar 
Zeng, Z. et al. Impact of earth greening on the terrestrial water cycle. J. Clim. 31, 2633–2650 (2018).ADS 

Google Scholar 
Lewis, S. L., Wheeler, C. E., Mitchard, E. T. A. & Koch, A. Restoring natural forests is the best way to remove atmospheric carbon. Nature 568, 25–28 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Meier, R. et al. Empirical estimate of forestation-induced precipitation changes in Europe. Nat. Geosci. 14, 473–478 (2021).ADS 
CAS 

Google Scholar 
Bosch, J. M. & Hewlett, J. D. A review of catchment experiments to determine the effect of vegetation changes on water yield and evapotranspiration. J. Hydrol. 55, 3–23 (1982).ADS 

Google Scholar 
Teuling, A. J. & Hoek van Dijke, A. J. Forest age and water yield. Nature 578, E16–E18 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Doelman, J. C. et al. Afforestation for climate change mitigation: Potentials, risks and trade-offs. Glob. Change Biol. 26, 1576–1591 (2020).ADS 

Google Scholar 
Peng, S. et al. Afforestation in China cools local land surface temperature. Proc. Natl Acad. Sci. USA 111, 2915–2919 (2014).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Seddon, N., Turner, B., Berry, P., Chausson, A. & Girardin, C. A. J. Grounding nature-based climate solutions in sound biodiversity science. Nat. Clim. Chang. 9, 84–87 (2019).ADS 

Google Scholar 
Brown, I. Challenges in delivering climate change policy through land use targets for afforestation and peatland restoration. Environ. Sci. Policy 107, 36–45 (2020).
Google Scholar 
The 2nd – 9th National Forest Resource Inventory Report (State Forestry Administration of the People’s Republic of China, 1973–2018).Fang, J. et al. Forest biomass carbon sinks in East Asia, with special reference to the relative contributions of forest expansion and forest growth. Glob. Change Biol. 20, 2019–2030 (2014).ADS 

Google Scholar 
Hou, X. Vegetation atlas of China. Chinese Academy of Science, the editorial board of vegetation map of China (2001).Xi, Y. et al. Contributions of climate change, CO2, land-use change, and human activities to changes in river flow across 10 Chinese Basins. J. Hydrometeorol. 19, 1899–1914 (2018).ADS 

Google Scholar 
Song, X.-P. et al. Global land change from 1982 to 2016. Nature 560, 639–643 (2018).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Klein Goldewijk, K., Beusen, A., Doelman, J. & Stehfest, E. Anthropogenic land use estimates for the Holocene – HYDE 3.2. Earth Syst. Sci. Data 9, 927–953 (2017).ADS 

Google Scholar 
Fluet-Chouinard, E., Lehner, B., Rebelo, L.-M., Papa, F. & Hamilton, S. K. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sens. Environ. 158, 348–361 (2015).ADS 

Google Scholar 
Herold, M., Van Groenestijn, A., Kooistra, L., Kalogirou, V. & Arino, O. Land cover CCI, product user guide version 2.0. https://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (2015).Pekel, J. F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).ADS 
CAS 

Google Scholar 
Zhou, G. et al. Global pattern for the effect of climate and land cover on water yield. Nat. Commun. 6, 5918 (2015).ADS 
CAS 
PubMed 

Google Scholar 
Yang, H. et al. Changing retention properties of catchments and their influence on runoff under climate change. Environ. Res. Lett. 13, 094019 (2018).ADS 

Google Scholar 
Berghuijs, W. R., Larsen, J. R., van Emmerik, T. H. M. & Woods, R. A. A global assessment of runoff sensitivity to changes in precipitation, potential evaporation, and other factors. Water Resour. Res. 53, 8475–8486 (2017).ADS 

Google Scholar 
Piao, S. et al. Changes in climate and land use have a larger direct impact than rising CO2 on global river runoff trends. Proc. Natl Acad. Sci. USA 104, 15242 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Guimberteau, M. et al. Testing conceptual and physically based soil hydrology schemes against observations for the Amazon Basin. Geosci. Model Dev. 7, 1115–1136 (2014).ADS 

Google Scholar 
Traore, A. K. et al. Evaluation of the ORCHIDEE ecosystem model over Africa against 25 years of satellite-based water and carbon measurements. J. Geophys. Res. Biogeosci. 119, 1554–1575 (2014).
Google Scholar 
de Rosnay, P. & Polcher, J. Impact of a physically based soil water flow and soil‐plant interaction representation for modeling large‐scale land surface processes. J. Geophys. Res. Atmos. 107, ACL 3-1–ACL 3-19 (2002).
Google Scholar 
Campoy, A. et al. Influence of soil bottom hydrological conditions on land surface fluxes and climate in a general circulation model. J. Geophys. Res. Atmos. 118, 10725–10739 (2013).ADS 

Google Scholar 
Guimberteau, M. et al. Discharge simulation in the sub-basins of the Amazon using ORCHIDEE forced by new datasets. Hydrol. Earth Syst. Sci. 16, 11171–11232 (2012).
Google Scholar 
Boucher, O. et al. Presentation and evaluation of the IPSL-CM6A-LR climate model. J. Adv. Model. Earth Sy. 12, e2019MS002010 (2020).ADS 

Google Scholar 
Fan, Y. et al. Hillslope hydrology in global change research and earth system modeling. Water Resour. Res. 55, 1737–1772 (2019).ADS 

Google Scholar 
Rayner, P. J. et al. Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS). Glob. Biogeochem. Cy. 19, GB2026 (2005).ADS 

Google Scholar 
Ducharne, A. Reducing scale dependence in TOPMODEL using a dimensionless topographic index. Hydrol. Earth Syst. Sci. 13, 2399–2412 (2009).ADS 

Google Scholar 
Niu, G., Yang, Z., Dickinson, R. E. & Gulden, L. E. A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models. J. Geophys. Res. 110, D21106 (2005).ADS 

Google Scholar 
Xi, Y. et al. Monthly inundated fraction over China for 2000-2015 from GIEMS-2 (Version v1.0). Zenodo https://doi.org/10.5281/zenodo.5750962 (2021).Xi, Y. et al. Code of wetland simulation for trade-off between tree planting and wetland conservation in China (Version v1.0). Zenodo https://doi.org/10.5281/zenodo.4435082 (2021). More

Kovacs, K. M. et al. The endangered Spitsbergen bowhead whales’ secrets revealed after hundreds of years in hiding. Biol. Lett. https://doi.org/10.1098/rsbl.2020.0148 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Cooke, J. & Reeves, R. Balaena mysticetus (East Greenland-Svalbard-Barents Sea subpopulation). The IUCN Red List of Threatened Species 2018, e.T2472A50348144 (2018). https://doi.org/10.2305/IUCN.UK.2018-1.RLTS.T2472A50348144.enAllen, R. C. & Keay, I. Bowhead whales in the eastern Arctic, 1611–1911: Population reconstruction with historical whaling records. Environ. Hist. 12, 89–113 (2006).Article 

Google Scholar 
Reeves, R. R. Spitsbergen bowhead stock: A short review. Mar. Fish. Rev. 42, 65–69 (1980).
Google Scholar 
Shelden, K. E. W. & Rugh, D. J. The Bowhead Whale, Balaena mysticetus: Its Historic and Current Status. Mar. Fish. Rev. 57, 1–20 (1995).
Google Scholar 
Gilg, O. & Born, E. W. Recent sightings of the bowhead whale (Balaena mysticetus) in Northeast Greenland and the Greenland Sea. Polar Biol. 28, 796–801. https://doi.org/10.1007/s00300-005-0001-9 (2005).Article 

Google Scholar 
Boertmann, D., Kyhn, L. A., Witting, L. & Heide-Jørgensen, M. P. A hidden getaway for bowhead whales in the Greenland Sea. Polar Biol. 38, 1315–1319. https://doi.org/10.1007/s00300-015-1695-y (2015).Article 

Google Scholar 
Wiig, Ø., Bachmann, L., Janik, V., Kovac, K. & Lydersen, C. Spitsbergen bowhead whales revisited. Mar. Mamm. Sci. 23, 688–693. https://doi.org/10.1111/j.1748-7692.2007.02373.x (2007).Article 

Google Scholar 
Wiig, Ø., Bachmann, L., Øien, N., Kovacs, K. & Lydersen, C. Observations of bowhead whales (Balaena mysticetus) in the Svalbard area 1940–2009. Polar Biol. 33, 979–984. https://doi.org/10.1007/s00300-010-0776-1 (2010).Article 

Google Scholar 
Lydersen, C. et al. Lost highway not forgotten: Satellite tracking of a bowhead whale (Balaena mysticetus) from the critically endangered Spitsbergen stock. Arctic 65, 76–86. https://doi.org/10.14430/arctic4167 (2012).Article 

Google Scholar 
Vacquié-Garcia, J. et al. Late summer distribution and abundance of ice-associated whales in the Norwegian High Arctic. Endang. Spec. Res. 32, 59–70. https://doi.org/10.3354/esr00791 (2017).Article 

Google Scholar 
Givens, G. H. & Heide-Jørgensen, M. P. Abundance. In The Bowhead Whale: Balaena Mysticetus: Biology and Human Interactions (eds George, J. C. & Thewissen, J. G. M.) 77–86 (Academic Press, 2020).
Google Scholar 
Rooney, A. P., Honeycutt, R. L. & Derr, J. N. Historical population size change of bowhead whales inferred from DNA sequence polymorphism data. Evolution 55, 1678–1685. https://doi.org/10.1111/j.0014-3820.2001.tb00687.x (2001).CAS 
Article 
PubMed 

Google Scholar 
Borge, T., Bachmann, L., Bjørnstad, G. & Wiig, Ø. Genetic variation in Holocene bowhead whales from Svalbard. Mol. Ecol. 16, 2223–2235. https://doi.org/10.1111/j.1365-294X.2007.03287.x (2007).CAS 
Article 
PubMed 

Google Scholar 
LeDuc, R. G. et al. Genetic analyses (mtDNA and microsatellites) of Okhotsk and Bering/Chukchi/Beaufort Seas populations of bowhead whales. J. Cetacean Res. Manag. 7, 107–111 (2005).
Google Scholar 
Meschersky, I. G., Chichkina, A. N., Shpak, O. V. & Rozhnov, V. V. Molecular genetic analysis of the Shantar Summer Group of bowhead whales (Balaena mysticetus L.) in the Okhotsk Sea. Russ. J. Genet. 50, 395–405. https://doi.org/10.1134/S1022795414040097 (2014).CAS 
Article 

Google Scholar 
Bachmann, L. et al. Mitogenomics and the genetic differentiation of contemporary Balaena mysticetus (Cetacea) from Svalbard. Zool. J. Linn. Soc. 191, 1192–1203. https://doi.org/10.1093/zoolinnean/zlaa082 (2021).Article 

Google Scholar 
Grond, J., Płecha, M., Hahn, C., Wiig, Ø. & Bachmann, L. Mitochondrial genomes of ancient bowhead whales (Balaena mysticetus) from Svalbard. Mitochondrial DNA Part B 4, 4152–4154. https://doi.org/10.1080/23802359.2019.1693284 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Nyhus, E. S. et al. Mitogenomes of contemporary Spitsbergen stock bowhead whales (Balaena mysticetus). Mitochondrial DNA Part B 1, 898–900. https://doi.org/10.1080/23802359.2016.1258345 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Keane, M. et al. Insights into the evolution of longevity from the bowhead whale genome. Cell Rep. 10, 112–122. https://doi.org/10.1016/j.celrep.2014.12.008) (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993. https://doi.org/10.1093/bioinformatics/btr509 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158. https://doi.org/10.1093/bioinformatics/btr330 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ortiz, E. M. vcf2phylip v2.0: Convert a VCF matrix into several matrix formats for phylogenetic analysis. zenodo.org, https://zenodo.org/record/2540861#.YDUOKy1Q0f0 (2019).Huson, D. H. & Bryant, D. Application of phylogenetic networks in evolutionary studies. Mol. Biol. Evol. 23, 254–267. https://doi.org/10.1093/molbev/msj030 (2006).CAS 
Article 
PubMed 

Google Scholar 
Purcell, S. et al. PLINK: A tool set for whole-genome and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–576. https://doi.org/10.1086/519795 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (2020).Knaus, B. J. & Grunwald, N. J. VcfR: An R package to manipulate and visualize VCF format data. bioRxiv, 041277 (2016). https://doi.org/10.1101/041277Jombart, T. & Ahmed, I. adegenet 1.3–1: New tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071. https://doi.org/10.1093/bioinformatics/btr521 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hanghøj, K., Moltke, I., Alstrup Andersen, P., Manica, A. & Korneliussen, T. S. Fast and accurate relatedness estimation from high-throughput sequencing data in the presence of inbreeding. GigaScience 8, giz034. https://doi.org/10.1093/gigascience/giz034 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: Analysis of next generation sequencing data. BMC Bioinform. 15, 356. https://doi.org/10.1186/s12859-014-0356-4 (2014).Article 

Google Scholar 
Renaud, G., Hanghøj, K., Korneliussen, T. S., Willerslev, E. & Orlando, L. Joint estimates of heterozygosity and runs of homozygosity for modern and ancient samples. Genetics 212, 587–614. https://doi.org/10.1534/genetics.119.302057 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Grabherr, M. G. et al. Genome-wide synteny through highly sensitive sequence alignment: Satsuma. Bioinformatics 26, 1145–1151. https://doi.org/10.1093/bioinformatics/btq102 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079. https://doi.org/10.1093/bioinformatics/btp352 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Westbury, M. V. et al. Extended and continuous decline in effective population size results in low genomic diversity in the world’s rarest hyena species, the brown hyena. Mol. Biol. Evol. 35, 1225–1237. https://doi.org/10.1093/molbev/msy037 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Li, H. & Durbin, R. Inference of human population history from whole genome sequence of a single individual. Nature 475, 493–496. https://doi.org/10.1038/nature10231 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Westbury, M. V., Petersen, B., Garde, E., Heide-Jørgensen, M. P. & Lorenzen, E. D. Narwhal genome reveals long-term low genetic diversity despite current large abundance size. iScience 15, 592–599. https://doi.org/10.1016/j.isci.2019.03.023 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Taylor, B. et al. Synthesis of lines of evidence for population structure for bowhead whales in the Bering-Chukchi-Beaufort region. Paper SC/59/BRG35 presented to the IWC Scientific Committee, Anchorage, Alaska (2007).Phillips, C. D. et al. Molecular insights into the historic demography of bowhead whales: Understanding the evolutionary basis of contemporary management practices. Ecol. Evol. 3, 18–37. https://doi.org/10.1002/ece3.374 (2012).CAS 
Article 
PubMed 

Google Scholar 
Liu, X. & Fu, Y. X. Stairway Plot 2: Demographic history inference with folded SNP frequency spectra. Genome Biol. 21, 280. https://doi.org/10.1186/s13059-020-02196-9 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Westbury, M. V. et al. Speciation in the face of gene flow within the toothed whale superfamily Delphinoidea. bioRxiv, https://doi.org/10.1101/2020.10.23.352286 (2020).Westbury, M. V. et al. Ecological specialisation and evolutionary reticulation in extant Hyaenidae. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msab055 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
IWC. Report of the Scientific Committee Virtual Meeting, 11–26 May 2020. J. Cetacean Res. Manag. (Supplement) 22, 1–122 (2021).Jonsgård, Å. A right whale (Balaena sp.), in all probability a Greenland right whale (Balaena mysticetus) observed in the Barents Sea. Norsk Hvalfangst-Tidende 53, 311–313 (1964).
Google Scholar 
De Jong, C. The hunt of the Greenland whale: A short history and statistical sources. Rep. Int. Whaling Comm. Spec. Issue 5, 83–106 (1983).
Google Scholar 
Weslawski, J. M., Hacquebord, L., Stempniewicz, L. & Malinga, M. Greenland whales and walruses in the Svalbard food web before and after exploitation. Oceanologia 2, 37–56 (2000).
Google Scholar 
George, J. C. et al. Age and growth estimates of bowhead whales (Balaena mysticetus) via aspartic acid racemization. Can. J. Zool. 77, 571–580. https://doi.org/10.1139/z99-015 (1999).Article 

Google Scholar 
de Jager, D. et al. High diversity, inbreeding and a dynamic Pleistocene demographic history revealed by African buffalo genomes. Sci. Rep. 11, 4540. https://doi.org/10.1038/s41598-021-83823-8 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Belikov, S. E., Gorbunov, Y. A. & Shil’nikov, V. I. Distribution of pinnipedia and cetacea in Soviet arctic seas and the Bering Sea in winter. Sov. J. Marine Biology 15, 251–257 (1989).
Google Scholar 
Gavrilo, M. V. Status of the bowhead whale Balaena mysticetus in the waters of Franz Josef Land Archipelago. Paper SC/66a/BRG20 Presented to the IWC Scientific Committee, May 2015, San Diego, USA (2015).Heide-Jorgensen, M. P., Hansen, R. G. & Shpak, O. V. Distribution, migrations, and ecology of the Atlantic and the Okhotsk Sea Populations. In The Bowhead Whale: Balaena Mysticetus: Biology and Human Interactions (eds George, J. C. & Thewissen, J. G. M.) 57–75 (Academic Press, 2020).
Google Scholar 
Petrov, S. A. et al. The results of marine mammal countins during the four expeditions in the Arctic in 2014 and 2015. Collection of scientific papers 9th International Conference ‘Marine mammals of the Holarctic’, Astrakhan, Russia, 2016. 91–102 (2018).Gavrilo, M. V. & Tretiakov V. Y. Observation of bowhead whales (Balaena mysticetus) in the East-Siberian Sea during 2007 season with record-low ice cover – Marine mammals of the Holarctic. In: Collection of Scientific Papers. Odessa, 191–194 (2008).Citta, J. J., Quakenbush, L. & George, J. C. Distribution and behavior of Bering-Chukchi-Beaufort bowhead whales as inferred by telemetry. In The Bowhead Whale: Balaena Mysticetus: Biology and Human Interactions (eds George, J. C. & Thewissen, J. G. M.) 31–56 (Academic Press, 2021). https://doi.org/10.1016/B978-0-12-818969-6.00004-2.Chapter 

Google Scholar 
Arnason, Ú., Lammers, F., Kumar, V., Nilsson, M. A. & Janke, A. Whole-genome sequencing of the blue whale and other rorquals finds signatures for introgressive gene flow. Sci. Adv. 4, eaap9873. https://doi.org/10.1126/sciadv.aap9873 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Bazin, E., Glémin, S. & Galtier, N. Population size does not influence mitochondrial genetic diversity in animals. Science 312, 570–572. https://doi.org/10.1126/science.1122033 (2006).CAS 
Article 
PubMed 

Google Scholar 
Corbett-Detig, R., Hartl, D. L. & Sackton, T. B. Natural selection constrains neutral diversity across a wide range of species. PLoS Biol. 13, e1002112. https://doi.org/10.1371/journal.pbio.1002112 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Vachon, F., Whitehead, H. & Frasier, T. R. What factors shape genetic diversity in cetaceans?. Ecol. Evol. 8, 1554–1572. https://doi.org/10.1002/ece3.3727 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Kumar, S. & Subramanian, S. Mutation rates in mammalian genomes. Proc. Natl. Acad. Sci. U.S.A. 99, 803–808. https://doi.org/10.1073/pnas.022629899 (2002).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bininda-Emonds, O. R. P. Fast genes and slow clades: Comparative rates of molecular evolution in mammals. Evol. Bioinf. 3, 59–85. https://doi.org/10.1177/117693430700300008 (2007).CAS 
Article 

Google Scholar 
Jackson, J. A. et al. Big and slow: Phylogenetic estimates of molecular evolution in baleen whales (Suborder Mysticeti). Mol. Biol. Evol. 26, 2427–2440. https://doi.org/10.1093/molbev/msp169 (2009).CAS 
Article 
PubMed 

Google Scholar 
Foote, A. D. et al. Ancient DNA reveals that bowhead whale lineages survived Late Pleistocene climate change and habitat shifts. Nat. Commun. 4, 1667. https://doi.org/10.1038/ncomms2714 (2013).CAS 
Article 

Google Scholar 
Wiig, Ø., Bachmann, L. & Hufthammer, A. K. Late Pleistocene and Holocene occurrence of bowhead whales (Balaena mysticetus) along the coasts of Norway. Polar Biol. 42, 645–656. https://doi.org/10.1007/s00300-019-02460-0 (2018).Article 

Google Scholar 
Alter, S. E. et al. Gene flow on ice: The role of sea ice and whaling in shaping Holarctic genetic diversity and population differentiation in bowhead whales (Balaena mysticetus). Ecol. Evol. 2, 2895–2911. https://doi.org/10.1093/zoolinnean/zlaa082 (2012).Article 

Google Scholar  More

Karr, J.R., & Chu, E.W. Introduction: sustaining living rivers. In Assessing the Ecological Integrity of Running Waters, Developments in Hydrobiology, vol 149 (eds. Jungwirth, M., Muhar, S., & S. Schmutz, S.) 1–14. (Springer: Dordrecht, 2000).Lu, S., Dai, W., Tang, Y. & Guo, M. A review of the impact of hydropower reservoirs on global climate change. Sci. Total Environ. 711, 134996 (2020).ADS 
CAS 
Article 

Google Scholar 
Liu, C., Ahn, C. R., An, X. & Lee, S. H. Life-cycle assessment of concrete dam construction: comparison of environmental impact of rock-filled and conventional concrete. J. Constr. Eng. Manage. 20139(12), A4013009. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000752 (2013).Article 

Google Scholar 
Maavara, T. et al. River dam impacts on biogeochemical cycling. Nat. Rev. Earth Environ. 1, 103–116 (2020).ADS 
Article 

Google Scholar 
Grigg, N. S. Global water infrastructure: state of the art review. Int. J. Water Resour. Dev. 35(2), 181–205. https://doi.org/10.1080/07900627.2017.1401919 (2019).Article 

Google Scholar 
European Environment Agency. European waters: Assessment of status and pressures 2018. https://www.eea.europa.eu/publications/state-of-water (Publications Office of the European Union (2018).Belletti, B. et al. More than one million barriers fragment Europe’s rivers. Nature 588, 436–441 (2020).ADS 
CAS 
Article 

Google Scholar 
Grill, G. et al. An index-based framework for assessing patterns and trends in river fragmentation and flow regulation by global dams at multiple scales. Environ. Res. Lett. 10(1), 015001 (2015).ADS 
Article 

Google Scholar 
Kim, J. & An, K. G. Integrated ecological river health assessments, based on water chemistry, physical habitat quality and biological integrity. Water 7(11), 6378–6403. https://doi.org/10.3390/w7116378 (2015).ADS 
CAS 
Article 

Google Scholar 
Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561. https://doi.org/10.1038/nature09440 (2010).ADS 
CAS 
Article 

Google Scholar 
McCartney, M. Living with dams: managing the environmental impacts. Water Policy 11(S1), 121–139 (2009).MathSciNet 
Article 

Google Scholar 
Van Cappellen, P. & Maavara, T. Rivers in the Anthropocene: global scale modifications of riverine nutrient fluxes by damming. Ecohydrol. Hydrobiol. 16(2), 106–111 (2016).Article 

Google Scholar 
Drouineau, H. et al. Freshwater eels: a symbol of the effects of global change. Fish Fish 19(5), 903–930 (2018).Article 

Google Scholar 
Jones, J. et al. A comprehensive assessment of stream fragmentation in Great Britain. Sci. Total Environ. 673, 756–762. https://doi.org/10.1016/j.scitotenv.2019.04.125 (2019).ADS 
CAS 
Article 

Google Scholar 
Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).Article 

Google Scholar 
Hermoso, V., Clavero, M., Blanco-Garrido, F. & Prenda, J. Invasive species and habitat degradation in Iberian streams: an analysis of their role in freshwater fish diversity loss. Ecol. Appl. 21(1), 175–188 (2011).Article 

Google Scholar 
Maceda-Veiga, A. Towards the conservation of freshwater fish: Iberian Rivers as an example of threats and management practices. Rev. Fish Biol. Fish. 23(1), 1–22 (2013).Article 

Google Scholar 
Sánchez-Pérez, A. et al. Seasonal use of fish passes in a modified Mediterranean river: first insights of the LIFE+ Segura-Riverlink. FiSHMED 008, 3. https://doi.org/10.29094/FiSHMED.2016.008 (2016).Article 

Google Scholar 
Schiermeir, Q. Dam removal restores rivers. Nature 557, 290–291. https://doi.org/10.1038/d41586-018-05182-1 (2018).ADS 
CAS 
Article 

Google Scholar 
Benjankar, R. et al. Dam operations may improve aquatic habitat and offset negative effects of climate change. J. Environ. Manage. 213, 126–134. https://doi.org/10.1016/j.jenvman.2018.02.066 (2018).Article 

Google Scholar 
Tupiño Salinas, C. E., Pinto Vidal de Oliveira, V., Brito, L., Ferreira, A. V. & de Araújo, J. C. Social impacts of a large-dam construction: the case of Castanhão, Brazil. Water Int. 44(8), 871–885. https://doi.org/10.1080/02508060.2019.1677303 (2019).Article 

Google Scholar 
Opperman, J. J. et al. Valuing Rivers: How the diverse benefits of healthy rivers underpin economies. WWF Global Science (2018).Kellner, E. Social acceptance of a multi-purpose reservoir in a recently deglaciated landscape in the Swiss Alps. Sustainability 11, 3819. https://doi.org/10.3390/su11143819 (2019).Article 

Google Scholar 
Boyé, H., & de Vivo, M. The environmental and social acceptability of dams. Field Actions Sci. Rep. http://journals.openedition.org/factsreports/4055 (2016).Wiejaczka, Ł, Piróg, D. & Fidelus-Orzechowska, J. Cost-benefit analysis of dam projects: the perspectives of resettled and non-resettled communities. Water Resour. Manag. 34(1), 343–357 (2020).Article 

Google Scholar 
Rodeles, A. A., Galicia, D. & Miranda, R. Recommendations for monitoring freshwater fishes in river restoration plans: a wasted opportunity for assessing impact. Aquat. Conserv. 27(4), 880–885. https://doi.org/10.1002/aqc.2753 (2017).Article 

Google Scholar 
Birnie-Gauvin, K., Tummers, J. S., Lucas, M. C. & Aarestrup, K. Adaptive management in the context of barriers in European freshwater ecosystems. J. Environ. Manag. 204, 436–441. https://doi.org/10.1016/j.jenvman.2017.09.023 (2017).Article 

Google Scholar 
Yousefi-Sahzabi, A. et al. Turkish challenges for low-carbon society: current status, government policies and social acceptance. Renew. Sustain. Energy Rev. 68, 596–608. https://doi.org/10.1016/j.rser.2016.09.090 (2017).Article 

Google Scholar 
Jiang, H., Lin, P. & Qiang, M. Public-opinion sentiment analysis for large hydro projects. J. Construct. Eng. Manage. 142(2), 05015013. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001039 (2016).Article 

Google Scholar 
Schulz, C., Martin-Ortega, J. & Glenk, K. Understanding public views on a dam construction boom: the role of values. Water Resour. Manage. 33, 4687–4700. https://doi.org/10.1007/s11269-019-02383-9 (2019).Article 

Google Scholar 
Kirchherr, J., Pohlner, H. & Charles, K. J. Cleaning up the big muddy: A meta-synthesis of the research on the social impact of dams. Environ. Impact Assess. Rev. 60, 115–125. https://doi.org/10.1016/j.eiar.2016.02.007 (2016).Article 

Google Scholar 
Piróg, D., Fidelus-Orzechowska, J., Wiejaczka, L. & Łajczak, A. Hierarchy of factors affecting the social perception of dam reservoirs. Environ. Impact Assess. Rev. 79, 106301. https://doi.org/10.1016/j.eiar.2019.106301 (2019).Article 

Google Scholar 
Arboleya, E., Fernandez, S., Clusa, L., Dopico, E. & Garcia-Vazquez, E. River connectivity is crucial for safeguarding biodiversity but may be socially overlooked. Insights from Spanish University students. Front. Environ. Sci. 9, 643820. https://doi.org/10.3389/fenvs.2021.643820 (2021).Article 

Google Scholar 
Gilg, A., & Barr, S. Behavioural attitudes towards water saving? Evidence from a study of environmental actions. Ecol. Econ. 57(3), 400–414. doi:https://doi.org/10.1016/j.ecolecon.2005.04.010 (2006)Schapper, A., Unrau, C., & Killoh, S. Social mobilization against large hydroelectric dams: a comparison of Ethiopia, Brazil, and Panama. Sustain. Develop. 28, 413–423. doi:https://doi.org/10.1002/sd.1995 (2020)Flaminio, S., Piégay, H., & Le Lay, Y-F. To dam or not to dam in an age of anthropocene: insights from a genealogy of media discourses. Anthropocene. 36, 100312, doi:https://doi.org/10.1016/j.ancene.2021.100312 (2021)Bellmore, J. R. et al. Conceptualizing ecological responses to dam removal: If you remove it, what’s to come?. Bioscience 69(1), 26–39. https://doi.org/10.1093/biosci/biy152 (2019).Article 

Google Scholar 
Heberlein, T. A. Navigating environmental attitudes. Conserv. Biol. 26(4), 583–585. https://doi.org/10.1111/j.1523-1739.2012.01892.x (2012).Article 

Google Scholar 
Lewandowsky, S., Gignac, G. E. & Vaughan, S. The pivotal role of perceived scientific consensus in acceptance of science. Nat. Clim. Change. 3, 399–404. https://doi.org/10.1038/NCLIMATE1720 (2013).ADS 
Article 

Google Scholar 
Schuldt, J. P., Roh, S. & Schwarz, N. Questionnaire design effects in climate change surveys: Implications for the partisan divide. Ann. Am. Acad. Pol. Soc. Sci. 658(1), 67–85. https://doi.org/10.1177/0002716214555066 (2015).Article 

Google Scholar 
Bowden, V., Nyberg, D. & Wright, C. Planning for the past: local temporality and the construction of denial in climate change adaptation. Glob. Environ. Change 57, 101939. https://doi.org/10.1016/j.gloenvcha.2019.101939 (2019).Article 

Google Scholar 
Venus, T. E., Hinzmann, M., Bakken, T. H., Gerdes, H., Nunes Godinho, F., Hansen, B., Pinheiro, A., & Sauer, J. The public’s perception of run-of-the-river hydropower across Europe. Energy Policy. 140, 111422. doi:https://doi.org/10.1016/j.enpol.2020.111422 (2020)Schober, M. F. The future of face-to-face interviewing. Qual. Assur. Educ. 26(2), 290–302. https://doi.org/10.1108/QAE-06-2017-0033 (2018).MathSciNet 
Article 

Google Scholar 
Couper, M. P. The future of modes of data collection. Public Opin. Q. 75, 889–908. https://doi.org/10.1093/poq/nfr046 (2011).Article 

Google Scholar 
Zhang, X., Kuchinke, L., Woud, M. L., Velten, J. & Margraf, J. Survey method matters: Online/offline questionnaires and face-to-face or telephone interviews differ. Comput. Hum. Behav. 71, 172–180. https://doi.org/10.1016/j.chb.2017.02.006 (2017).Article 

Google Scholar 
Garcia de Leaniz, C., Berkhuysen, A., & Belletti, B. Beware small dams, they can do damage, too. Nature 570, 164–164; doi:https://doi.org/10.1038/d41586-019-01826-y (2019).Belletti, B., et al. Small isn’t beautiful: the impact of small barriers on longitudinal connectivity of European rivers. Geophys. Res. Abst. 20: EGU2018-PREVIEW (2018).Hophmayer-Tokich, S. & Krozer, Y. Public participation in rural area water management: experiences from the North Sea countries in Europe. Water Int. 33(2), 243–257. https://doi.org/10.1080/02508060802027604 (2008).Article 

Google Scholar 
San-Martín, E., Larraz, B. & Gallego, M. S. When the river does not naturally flow: a case study of unsustainable management in the Tagus River (Spain). Water Int. 45(3), 189–221. https://doi.org/10.1080/02508060.2020.1753395 (2020).Article 

Google Scholar 
Dunlap, R. E. Environmental concern. The Wiley‐Blackwell Encyclopedia of Globalization. (Wiley, Amsterdam, 2012).European Commission Ethics for researchers. Facilitating Research Excellence in FP7. https://doi.org/10.2777/7491 (Publications Office of the European Union, 2013).Jenner, B. M. & Myers, K. C. Intimacy, rapport, and exceptional disclosure: a comparison of in-person and mediated interview contexts. Int. J. Soc. Res. Methodol. 22(2), 165–177. https://doi.org/10.1080/13645579.2018.1512694 (2019).Article 

Google Scholar 
Given, L. M. 100 questions (and answers) about qualitative research (Sage, 2015).
Google Scholar 
Saris, W. E. & Gallhofer, I. N. Design, evaluation, and analysis of questionnaires for survey research (Wiley, 2014).Book 

Google Scholar 
Avella, J. R. Delphi panels: research design, procedures, advantages, and challenges. IJDS 11(1), 305–321. https://doi.org/10.28945/3561 (2016).Article 

Google Scholar 
Vandenplas, C. & Loosveldt, G. Modeling the weekly data collection efficiency of face-to-face surveys: six rounds of the European social survey. J. Surv. Stat. Methodol. 5(2), 212–232. https://doi.org/10.1093/jssam/smw034 (2017).Article 

Google Scholar 
Barbero-García, M. I., Vila-Abad, E. & Holgado-Tello, F. P. Tests adaptation in cross-cultural comparative studies. Acción Psicol. 5, 7–16. https://doi.org/10.5944/ap.5.2.454 (2008).Article 

Google Scholar 
Flick, U. Triangulation in data collection. The SAGE Handbook of Qualitative Data Collection. (Sage, London, 2018).Heesen, R., Bright, L. K. & Zucker, A. Vindicating methodological triangulation. Synthese 196(8), 3067–3081. https://doi.org/10.1007/s11229-016-1294-7 (2019).MathSciNet 
Article 

Google Scholar 
DeVellis, R. F. Scale development: Theory and applications (Sage, 2012).
Google Scholar 
Hammer, Ø., Harper, D.A.T., & Ryan, P.D. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Elect. 4(1), 9. http://palaeo-electronica.org/2001_1/past/issue1_01.htm (2001). More