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We acknowledge the herbaria that contributed data to this work: HA, FCO, MFU, UNEX, VDB, ASDM, BPI, BRI, CLF, L, LPB, AD, TAES, FEN, FHO, A, ANSM, BCMEX, RB, TRH, AAH, ACOR, AJOU, UI, AK, ALCB, AKPM, EA, AAU, ALU, AMES, AMNH, AMO, ANA, GH, ARAN, ARM, AS, CICY, ASU, BAI, AUT, B, BA, BAA, BAB, BACP, BAF, BAL, COCA, BARC, BBS, BC, BCN, BCRU, BEREA, BG, BH, BIO, BISH, SEV, BLA, BM, MJG, BOL, CVRD, BOLV, BONN, BOUM, BR, BREM, BRLU, BSB, BUT, C, CAMU, CAN, CANB, CAS, CAY, CBG, CBM, CEN, CEPEC, CESJ, CHR, ENCB, CHRB, CIIDIR, CIMI, CLEMS, COA, COAH, COFC, CP, COL, COLO, CONC, CORD, CPAP, CPUN, CR, CRAI, FURB, CU, CRP, CS, CSU, CTES, CTESN, CUZ, DAO, HB, DAV, DLF, DNA, DS, DUKE, DUSS, E, HUA, EAC, ECU, EIF, EIU, GI, GLM, GMNHJ, K, GOET, GUA, EKY, EMMA, HUAZ, ERA, ESA, F, FAA, FAU, UVIC, FI, GZU, H, FLAS, FLOR, HCIB, FR, FTG, FUEL, G, GB, GDA, HPL, GENT, GEO, HUAA, HUJ, CGE, HAL, HAM, IAC, HAMAB, HAS, HAST, IB, HASU, HBG, IBUG, HBR, IEB, HGI, HIP, IBGE, ICEL, ICN, ILL, SF, NWOSU, HO, HRCB, HRP, HSS, HU, HUAL, HUEFS, HUEM, HUSA, HUT, IAA, HYO, IAN, ILLS, IPRN, FCQ, ABH, BAFC, BBB, INPA, IPA, BO, NAS, INB, INEGI, INM, MW, EAN, IZTA, ISKW, ISC, GAT, IBSC, UCSB, ISU, IZAC, JBAG, JE, SD, JUA, JYV, KIEL, ECON, TOYA, MPN, USF, TALL, RELC, CATA, AQP, KMN, KMNH, KOR, KPM, KSTC, LAGU, UESC, GRA, IBK, KTU, KU, PSU, KYO, LA, LOMA, SUU, UNITEC, NAC, IEA, LAE, LAF, GMDRC, LCR, LD, LE, LEB, LI, LIL, LINN, AV, HUCP, MBML, FAUC, CNH, MACF, CATIE, LTB, LISI, LISU, MEXU, LL, LOJA, LP, LPAG, MGC, LPD, LPS, IRVC, MICH, JOTR, LSU, LBG, WOLL, LTR, MNHN, CDBI, LYJB, LISC, MOL, DBG, AWH, NH, HSC, LMS, MELU, NZFRI, M, MA, UU, UBT, CSUSB, MAF, MAK, MB, KUN, MARY, MASS, MBK, MBM, UCSC, UCS, JBGP, OBI, BESA, LSUM, FULD, MCNS, ICESI, MEL, MEN, TUB, MERL, CGMS, FSU, MG, HIB, TRT, BABY, ETH, YAMA, SCFS, SACT, ER, JCT, JROH, SBBG, SAV, PDD, MIN, SJSU, MISS, PAMP, MNHM, SDSU, BOTU, MPU, MSB, MSC, CANU, SFV, RSA, CNS, JEPS, BKF, MSUN, CIB, VIT, MU, MUB, MVFA, SLPM, MVFQ, PGM, MVJB, MVM, MY, PASA, N, HGM, TAM, BOON, MHA, MARS, COI, CMM, NA, NCSC, ND, NU, NE, NHM, NHMC, NHT, UFMA, NLH, UFRJ, UFRN, UFS, ULS, UNL, US, NMNL, USP, NMR, NMSU, XAL, NSW, ZMT, BRIT, MO, NCU, NY, TEX, U, UNCC, NUM, O, OCLA, CHSC, LINC, CHAS, ODU, OKL, OKLA, CDA, OS, OSA, OSC, OSH, OULU, OXF, P, PACA, PAR, UPS, PE, PEL, SGO, PEUFR, PH, PKDC, SI, PMA, POM, PORT, PR, PRC, TRA, PRE, PY, QMEX, QCA, TROM, QCNE, QRS, UH, R, REG, RFA, RIOC, RM, RNG, RYU, S, SALA, SANT, SAPS, SASK, SBT, SEL, SING, SIU, SJRP, SMDB, SNM, SOM, SP, SRFA, SPF, STL, STU, SUVA, SVG, SZU, TAI, TAIF, TAMU, TAN, TEF, TENN, TEPB, TI, TKPM, TNS, TO, TU, TULS, UADY, UAM, UAS, UB, UC, UCR, UEC, UFG, UFMT, UFP, UGDA, UJAT, ULM, UME, UMO, UNA, UNM, UNR, UNSL, UPCB, UPNA, USAS, USJ, USM, USNC, USZ, UT, UTC, UTEP, UV, VAL, VEN, VMSL, VT, W, WAG, WII, WELT, WIS, WMNH, WS, WTU, WU, Z, ZSS, ZT, CUVC, AAS, AFS, BHCB, CHAM, FM, PERTH and SAN. X.F., D.S.P., E.A.N., A.L. and J.R.B. were supported by the University of Arizona Bridging Biodiversity and Conservation Science program. Z.L. was supported by NSFC (41922006) and K. C. Wong Education Foundation. The BIEN working group was supported by the National Center for Ecological Analysis and Synthesis, a centre funded by NSF EF-0553768 at the University of California, Santa Barbara, and the State of California. Additional support for the BIEN working group was provided by iPlant/Cyverse via NSF DBI-0735191. B.J.E., B.M. and C.M. were supported by NSF ABI-1565118. B.J.E. and C.M. were supported by NSF ABI-1565118 and NSF HDR-1934790. B.J.E., L.H. and P.R.R. were supported by the Global Environment Facility SPARC project grant (GEF-5810). D.D.B. was supported in part by NSF DEB-1824796 and NSF DEB-1550686. S.R.S. was supported by NSF DEB-1754803. X.F. and A.L. were partly supported by NSF DEB-1824796. B.J.E. and D.M.N. were supported by NSF DEB-1556651. M.M.P. is supported by the São Paulo Research Foundation (FAPESP), grant 2019/25478-7. D.M.N. was supported by Instituto Serrapilheira/Brazil (Serra-1912-32082). E.I.N. was supported by NSF HDR-1934712. We thank L. López-Hoffman and L. Baldwin for constructive comments. More

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Evaluating indicatorsAmong the selected parameters the ratio of rural to the urban population directly relates to the per capita renewable water, whereas the population density, internet users, and education index exhibit an inverse relation with the per capita renewable water worldwide. It means the per capita renewable water decreases with decreasing rural to urban population and increasing population density, internet users, and education index. The urban population has increased in developing regions, which feature increasing population density. People’s health is threatened by poor urban sanitary infrastructure leading to disease and social decay. Increasing population density and a reduction in per capita renewable water inflict social harm and disrupt society’s economic growth58. Population density also is positively related to the relative number of elderly and social vulnerability because potential casualties increase with population size40. On the other hand, with the increase of Internet users and education index, the per capita renewable water has increased. As long as the knowledge and awareness of communities improved, the consumption algorithm decreased, leading to a reduction of renewable water per capita. Therefore, the level of literacy and knowledge for a community can be the basis for making the right decisions in agriculture, health, natural resource management, and other activities related to water resources for decision-makers. The latter situation calls for better communication among water users through social media and improved education to learn and develop optimal water management.Evaluating models and developing hydro-social equationsThree soft-computing approaches, namely ANN-LM, ANFIS-SC, and GEP, were applied to develop predictive equations with social indicators worldwide. The ANN-Levenberg–Marquardt (LM) backpropagation algorithm with one hidden layer was applied, and the hidden nodes’ number was determined by trial and error. A hybrid algorithm was combined with the ANFIS-SC models. There is no rule for determining the radii values of the ANFIS-SC models. The final radii values were determined by trial-and-error.The numbers of neurons in the ANN-LM models and the radii values of the ANFIS-SC models are listed in Table 4. The activation functions of the output nodes were linear for all the continents. The activation functions of the hidden nodes of the ANN-LM models for the P1 through P4 indicators were respectively the tangent sigmoid, tangent sigmoid, tangent sigmoid, and logarithm sigmoid for Africa; the activation functions of the proportion of rural to urban population was the tangent sigmoid for all the continents. Table 5 lists the results of the soft computing optimal models’ estimates of the proportion of rural to urban population (PRUP), population density (PD), internet users (IU), and education index (EI), denoted respectively by P1 through P4, during the test period in the world’s continents. Figures 4 and 5 display the characteristics of ANN (the number of neurons and activation functions of hidden and output layers) and ANFIS-SC (radii values) models, respectively. The values of R and RMSE for Africa corresponding to the ANN-LM models were respectively (0.921, 0.981, 0.858, 0.862) and (0.193, 0.058, 0.190, 0.172) associated with the PRUP, PD, IU, and EI parameters, respectively. The values of R and RMSE for Africa corresponding to the ANFIS-SC models equaled respectively (0.933, 0.991, 0.868, 0.891) and (0.130, 0.044, 0.186, 0.156) for the P1 through P4 parameters, respectively. Concerning the GEP models, the root relative squared error (RRSE) was selected as the pressure tree’s fitness function. The values of RMSE for GEP models equaled (0.084, 0.029, 0.178, 0.135), (0.197, 0.056, 0.152, 0.163), (0.151, 0.036, 0.123, 0.210), (0.182, 0.039, 0.148, 0.204) and (0.141, 0.030, 0.226, 0.082) for Africa, America, Asia, Europe, and Oceania, respectively. Table 5 results for the R, RMSE, and MAE values establish the GEP model estimates of PRUP, PD, IU, and EI indicators had the highest R values and the lowest RMSE values. The average R values of the best models (GEP) for all selected social parameters equaled 0.942, 0.909, 0.910, 0.889, and 0.947 for Africa, America, Asia, Europe, and Oceania, respectively. These results indicate the climatic characteristics of the continents influence the performance of the models. The models’ performances for Africa and Oceania associated with the type B dominant Koppen climate classification was the best. The models’ performances for Asia and America that have similar climatic classification were nearly equal. The average model performance for Europe in the type D climate classification was the poorest among the continents.Table 4 The characteristics of ANN (the number of neurons) and ANFIS (radii values) models corresponding to social indicators and continents.Full size tableTable 5 The results of soft computing optimal models corresponding to the testing period in the world’s continents.Full size tableFigure 4The characteristics of optimal ANN models; showing the number of neurons and activation functions of hidden and output layers.Full size imageFigure 5The characteristic of optimal ANFIS-SC model showing the radii values.Full size imageFigures 6, 7, 8, 9 and 10 show the observed and estimated social parameters obtained with the soft-computing models during the test period in Africa, America, Asia, Europe, and Oceania, respectively. Figure 11 compares the R, RMSE, and MAE values from the soft-computing models. The R values for soft-computing models are close to 1, with the quality relations being: RGEP  > RANFIS-SC  > RANN-LM for all social indicators. Figure 11 establishes that the ANFIS-SC model exceeded the ANN-LM models’ performance. Also, the GEP models had better performance than the ANFIS-SC and ANN-LM for estimating the proportion of rural to urban population (PRUP), population density (PD), internet users (IU), and education index (EI) parameters in Africa, America, Asia, Europe, and Oceania.Figure 6Observed and estimated social parameters during the testing period in Africa.Full size imageFigure 7Observed and estimated social parameters during the testing period in America.Full size imageFigure 8Observed and estimated social parameters during the testing period in Asia.Full size imageFigure 9Observed and estimated social parameters during the testing period in Europe.Full size imageFigure 10Observed and estimated social indicators during the testing period in Oceania.Full size imageFigure 11Comparison of R, RMSE and MAE values corresponding to the soft computing methods.Full size imageThe main advantage of the GEP over other soft computing methods (e.g., ANFIS and ANN) is in producing predictive equations. The equations obtained with the optimal models for the social indicators (i.e., the proportion of rural to urban population (PRUP), population density (PD), internet users (IU), and education index (EI) in Africa, America, Asia, Europe, and Oceania) are listed in Table 6. The equations that the GEP model discovers as a structure do not necessarily correspond to reality. The equations listed in Table 6 merely show the optimal equations extracted from the model after the evolution, for all indicators and in all basins (considering renewable water per capita as a decision variable).Table 6 Mathematical equations governing hydro-social indicators.Full size tableThe performance of the GEP models in estimating the social indicators in three ranges of values, namely, 20% of the maximum estimated values (20%max), 60% of median estimated values (60%mid or 20%min to 20%max), and 20% of minimum estimated values (20%min), during the test period for the proportion of rural to urban population (PRUP), population density (PD), internet users (IU) and the education index (EI) parameters of Africa, America, Asia, Europe, and Oceania are listed in Table 7. Table 7’s results indicate there is not a regular rule to determine the best-cited ranges performances. The education index and the population density have the lowest and highest R values among the other parameters in the three different ranges (20%max, 60%mid, and 20%min) in Africa, America, Asia, Europe, and Oceania. Therefore, the results indicate a strong pattern of association between the population density parameter and water resources status in all continents of the world.Table 7 The performance of GEP models with respect to selected ranges.Full size tableFigure 12 depicts the distribution of estimated data values of the social parameters (i = 1, 2, 3, 4) and their comparison through the continents. The box plots are a graphic display integrating multiple numerical relations. One approach to understanding the distribution or dispersion of data is through the box diagram, which is based on the “minimum,” “first quartile-Q1(0.25%)”, “median (0.50%)”, “third quartile-Q3(0.75%)” and “maximum” statistical indicators. Figure 12 shows Oceania and Africa exhibit the smallest and largest values of the rural to urban population, respectively. America has the lowest values of the first to the third quartile. The estimated population density value in Europe has the most values in the third quartile (0.75%). The median values of estimated internet users have the smallest and largest values in Africa and Europe, respectively. America has the lowest values of the first quartile, median, third quartile, and maximum values associated with the estimated education index values among the continents.Figure 12Distribution of estimated data values of social indicators (Pi, i = 1, 2, …, 4).Full size imageThe summary of hydro-social equations performance is listed in Table 8, where it is seen the best models’, performances are such that PD  > PRUP  > EI  > IU, PD  > IU  > EI  > PRUP, PD  > IU  > PRUP  > EI, PD  > PRUP  > IU  > EI and PD  > EI  > IU  > PRUP for Africa, America, Asia, Europe, and Oceania, respectively.Table 8 Summary of hydro-social equations performance.Full size tableThis paper’s results indicate the pattern of association between social parameters and water resources is complex. Renewable water per capita was estimated using social indicators PRUP, PD, IU, and EI based on gene expression programming. The results of GEP to estimate RWPC corresponding to the testing period in the world’s continents as listed in Table 9. The values of RMSE for optimal GEP models equaled 0.089, 0.058, 0.042, 0.049, and 0.036 for Africa, America, Asia, Europe, and Oceania, respectively. Figure 13 displays the observed and estimated RWPC parameter during the test period in the world’s continents. The equations obtained with the optimal models for the renewable water per capita in Africa, America, Asia, Europe, and Oceania are listed in Table 10. The fitted equations can be applied at variable spatial and temporal scales. The derived equations imply that water resources in Africa and Oceania are governed by the PRUP, PD, IU, and EI indicators. Also, the PRUP, PD, and IU indicators in Europe and PD and IU indicators in America and Asia have the most influence on their water resources status. The association between social parameters and water resources in all continents is variable. The linking of these social indicators with the per capita renewable water is a function of the countries’ cultural and economic conditions, thus bearing on the future management and policymaking across continents. This study’s results concerning hydro-social indicators are consistent with the findings by Forouzani et al.2, Carey et al.15, Lima et al.25, Pande et al.7, Diep et al.26, and Diaz et al.22.Table 9 The results of GEP estimating RWPC corresponding to the testing period in the world’s continents.Full size tableFigure 13Observed and estimated RWPC parameters during the test period in the world’s continents.Full size imageTable 10 Mathematical equations governing hydro-social indicators.Full size tableThis paper’s results establish the importance of examining the interactions between climate, the status of water resources, and social indicators. The state and social conditions of a country reflect the status of its water resources. Therefore, this study has shown how significant an impact the management and planning of a country can have on its water resources. Each successful water resources project rests on a successful social setting. More

EDITORIAL
31 August 2021

The world’s scientific panel on biodiversity needs a bigger role

IPBES, the international panel of leading biodiversity researchers, should be consulted on how best to measure species loss.

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A baby green sea turtle in Madagascar, one of the regions where the probability of widespread biodiversity loss is greatest.Credit: Alexis Rosenfeld/Getty

For more than 30 years, the international community has tried and failed to find a path to slow down — and eventually reverse — worldwide declines in the richness of plant and animal species. Next year, it will have another chance. The 15th Conference of the Parties (COP 15) to the United Nations Convention on Biological Diversity, recently delayed for the third time, is now slated to take place in person in Kunming, China, in April and May 2022.Biodiversity is fundamental to Earth’s life-support systems, and humans depend on the services that nature provides. In 2010, countries committed to slowing the overall rate of biodiversity loss by 2020. But just 6 out of the 20 targets that were agreed on that occasion — at COP 10 in Aichi, Japan — have been even partially met, notable among them a commitment to conserve 17% of the world’s land and inland waters.Ahead of the Kunming meeting, policymakers and scientists are discussing a new action plan, called the Global Biodiversity Framework, which they hope to agree next year. The latest draft (published in July; see go.nature.com/3kbvspd) includes a promise to conserve 30% of the world’s land and sea areas by 2030 and reiterates the need to meet earlier targets, including the provision of greater financial support to low-income countries to help them to protect their biodiversity.Missing linkResearchers around the world are advising on the plan, through the UN’s institutions and through universities and various scientific networks. But one piece of the puzzle is missing. In 2012, a host of governments established the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). It periodically reviews the literature and provides summaries of the latest knowledge. However, the countries organizing the COP are not involving IPBES in the action plan in the way that the UN Intergovernmental Panel on Climate Change has been consulted for advice ahead of climate COPs. It is important that IPBES be asked, because policymakers are being presented with a range of ideas that would benefit from the systematic evaluation that a global scientific advisory body would bring.
The world’s species are playing musical chairs: how will it end?
For example, biodiversity terminology is often unfamiliar, and therefore challenging, for most policymakers. The word itself — defined by the biodiversity convention as the variety and variability of life on Earth, at the level of genes, species and ecosystems — is not commonly used, nor well understood beyond the scientific community. The magnitude of biodiversity’s value to the planet and to people, as well as the risks of losing it, are also not widely appreciated.Over the years, various teams of scientists have researched and offered ideas on how to communicate the state of biodiversity both accurately and in a way that is accessible and engages the wider public. Some are advocating a biodiversity equivalent of the 1.5 °C warming target, or of net-zero emissions. One suggestion, published last year, is for the international community to adopt a target for limiting species extinctions. The goal would be to keep extinctions of known species to below 20 per year globally for the next 100 years — a single headline number to represent biodiversity (M. D. A. Rounsevell et al. Science 368, 1193–1195; 2020).A focus on species extinctions as a proxy for biodiversity is not a new idea, and is controversial. However, the authors say that their intention is not to replace biodiversity’s many facets with only one number, but to communicate biodiversity in a way that would resonate with more people.Another group is proposing a composite index — a single score made up of measures of some of biodiversity’s main components, including the health of species and ecosystems, as well as the services that biodiversity provides to people, such as pollination and clean water (C. A. Soto-Navarro et al. Nature Sustain. https://doi.org/gmjs2f; 2021). This would be biodiversity’s equivalent of the UN Human Development Index — first published in 1990 — which amalgamates information on health, education and income into a single number and has been adopted worldwide as a measure of prosperity and well-being.
Fewer than 20 extinctions a year: does the world need a single target for biodiversity?
A third idea, published by the leaders of some of the world’s most influential conservation and environmental science organizations, is called Nature Positive (see go.nature.com/2ydk89n). Its authors are proposing that the UN’s many global environmental agreements should include three common targets: no net loss of nature from 2020 (meaning that although nature might continue to be degraded in some areas, this would be offset by conservation gains elsewhere); some recovery by 2030; and full recovery by 2050. At present, the UN agreements on biodiversity, stopping climate change and combating desertification all have their own processes, occasionally acting together, but more often operating independently. The goal is to get them to sign up to one set of principles.All of these ideas have advantages and risks, which is why they need to be systematically evaluated by researchers. That’s where IPBES’s role is crucial. IPBES comprises a broad community of researchers, and, importantly, it represents voices from under-represented low- and middle-income countries, as well as the world’s Indigenous peoples. The governments involved in organizing the Kunming COP should ask IPBES to evaluate the ideas being put forward for the next biodiversity action plan, so they can be confident that what they decide has the support of a consensus of researchers, particularly in more-biodiverse regions of the world. Although preparations for the Kunming COP are well under way, this could also happen after the COP.Biodiversity loss could be as serious for the planet — and for humanity — as climate change. World leaders have become skilled at organizing complex international meetings and making promises that they then fail to keep. The upcoming biodiversity COP risks being one more such event, which is why researchers offering solutions are right to feel frustrated. They should work with IPBES to review their ideas. A unified voice is powerful, and if scientists can present a united front, policymakers will have fewer excuses to continue with business as usual.

Nature 597, 7-8 (2021)
doi: https://doi.org/10.1038/d41586-021-02339-3

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CORRESPONDENCE
31 August 2021

Boost for Africa’s research must protect its biodiversity

Nils Chr. Stenseth

 ORCID: http://orcid.org/0000-0002-1591-5399

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Sebsebe Demissew

 ORCID: http://orcid.org/0000-0002-0123-9596

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Nils Chr. Stenseth

University of Oslo, Norway.

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Sebsebe Demissew

Addis Ababa University, Ethiopia.

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We write on behalf of 209 scientists (see go.nature.com/3sa16p9) to endorse a new initiative by the African Research Universities Alliance and the Guild of European Research-Intensive Universities (see go.nature.com/3b364hj). This calls for greater investment by the African Union and the European Union in Africa’s universities, to help them address global challenges such as public health, climate change and good governance. We strongly encourage expansion of the initiative to encompass environmental and biodiversity issues that are crucial to the continent’s future.Safeguarding Africa’s extraordinary natural resources and biodiversity — the backbone of much of its economy and livelihood — demands a new generation of African scientists trained in environmental sciences. Experts are needed in conservation science and environmental economics, as well as in the collection, curation and analysis of biological data.As Julius Nyerere, the former president of Tanzania, put it 60 years ago in a speech now known as the Arusha Manifesto: “The conservation of wildlife and wild places calls for specialist knowledge, trained manpower and money, and we look to other nations to co-operate with us in this important task — the success or failure of which not only affects the continent of Africa but the rest of the world as well.”

Nature 597, 31 (2021)
doi: https://doi.org/10.1038/d41586-021-02356-2

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The authors declare no competing interests.

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We retrieved 684 Lokiarchaeota and 31 Heimdallarchaeota near-full-length 16S rRNA sequences from sequence libraries generated from sediment sampled at 27 m water depth in 5 cm intervals between 0 and 40 cm below seafloor (cm.b.s.f) in Aarhus Bay (Supplementary Information). The maximum relative read abundance of Lokiarchaeota was 1.6% at 15–20 cm.b.s.f. and 0.1% for Heimdallarchaeota at 10–15 cm.b.s.f. (Fig. 1). The sequences were grouped into 58 Loki- and 3 Heimdallarchaeota operational taxonomic units (OTUs) using a 98% sequence identity threshold and formed three distinct Lokiarchaeota clades and one monophyletic Heimdallarchaeota cluster (Fig. 1). The primer-free sequencing of RNA extracts enabled us to broadly sample the Asgard archaeal diversity in Aarhus Bay sediments and provided a solid database to design oligonucleotide probes for their visualization.Fig. 1: Phylogenetic analysis and depth distribution of Loki- and Heimdallarchaeota 16S rRNA sequences from Aarhus Bay sediments.A Maximum likelihood phylogeny of Loki- and Heimdallarchaeota operational taxonomic units (OTUs) and related sequences selected from the SILVA database (v. 132). Specificities of FISH probes and the number of sequences constituting each OTU are also depicted. TACK archaea were selected as outgroup. Bar: 0.1 substitutions per nucleotide position. B Heatmap and relative abundances of Loki- and Heimdallarchaeota sequences at different sediment depths.Full size imageBased on the newly retrieved full-length sequences, we designed four novel oligonucleotide probes specifically targeting Loki- and Heimdallarchaeota 16S rRNA with high coverage (Fig. 1, Supplementary Table 1). Probe LOK1183 targets almost all sequences in Lokiarchaeota Clade A, which contains 92% of the retrieved Lokiarchaeota sequences from Aarhus Bay sediments, while probe LOK1378 targets 85% of the sequences in all three Lokiarchaeota clades. Probe HEIM329 and HEIM529 each target >97% of the retrieved Heimdallarchaeota sequences. All designed probes cover >89% sequences in their target groups in the SILVA database (v. 132). The two Lokiarchaeota probes match 5 and 10 different non-target sequences in the SILVA database (v. 132), respectively, while the Heimdallarchaeota probes have no match outside their target group. The broad coverage and high specificity suggest that our probes can also be used to detect Loki- and Heimdallarchaeota in other habitats. Furthermore, designing two probes for each phylum enabled us to identify Lokiarchaeota clade A and Heimdallarchaeota cells via double hybridizations with two distinct dyes and thus confidently distinguish true- and false-positive signals (Supplementary Fig. 1). The general archaeal probe ARC915 also targets Lokiarchaeota and thereby provided yet another control for specific hybridization of the two Lokiarchaeota-specific probes, while the non-sense probe NON338 served as the negative control. We also designed competitor probes to minimize the theoretical false-positive hybridizations with the most frequent one and two mismatches [11] in the SILVA database (v. 132) and helper probes to facilitate probe binding [12]. This comprehensive experimental design with appropriate controls enabled reliable detection of low-abundant Loki- and Heimdallarchaeota cells in Aarhus Bay sediments.We used both confocal laser scanning microscopy (CLSM) and three-dimensional super-resolution structured illumination (SR-SIM) microscopy for detailed imaging of dual-labeled Loki- and Heimdallarchaeota signals. Loki- and Heimdallarchaeota cells featured coccoid shapes and often formed clusters (Fig. 2) (Supplementary Fig. 2). Based on SR-SIM imaging, Lokiarchaeota cells (n = 18) were 1.27 ± 0.24 µm in diameter and 1.43 ± 0.25 µm in length, while the width and the length of Heimdallarchaeota cells (n = 11) were 1.30 ± 0.20 µm and 1.37 ± 0.21 µm, respectively (Supplementary Table 2). In addition, we observed a few large ( >3 µm) ovoid and filamentous cells, resembling some of the Lokiarchaeota morphotypes reported from lake sediment [9]; however, we never detected these cell types in double hybridizations with two probes (Supplementary Fig. 1P–R), and therefore consider them false-positives.Fig. 2: Visualization of Loki- and Heimdallarchaeota cells in Aarhus Bay sediments by CARD-FISH.Probe names and the dyes are indicated for each panel. Representative cell morphotypes were imaged in a super-resolution structured illumination microscope (SR-SIM; panels (A), (B), (D), (E)) and confocal laser scanning microscope (CLSM; panels (C) and (F)). For SR-SIM images, single slices from the center of the focal plane are shown. For CLSM images, three-dimensional (3D) surface reconstructions are depicted. All z-stack images taken in CLSM are included in Supplementary Fig. 2. 360° rotation of 3D reconstructed images are also provided in Supplementary Video. Negative and positive controls are shown in Supplementary Fig. 1 together with large ovoid and filamentous false-positive signals. Images are representative of dual labeled Lokiarchaeota (n = 72) and Heimdallarchaeota (n = 70) cells in five individual experiments using two different sediment cores taken from the same sampling site. The scale bar is 1 µm.Full size imageThe DNA stain (4′,6-diamidino-2-phenylindole; DAPI) in the FISH-identified Loki- and Heimdallarchaeota cells was consistently confined to a single spherical central or lateral position (Fig. 2), corroborating the signal pattern suggested for some of the Asgard archaeal cells in lake sediments [9]. Using SR-SIM, we could image a clear gap, which separated the DNA from the ribosome-originated FISH signals with an average width of 0.18 ± 0.07 µm in Heimdallarchaeota and 0.16 ± 0.13 µm in Lokiarchaeota cells (Supplementary Table 2). The spatial separation of DNA and ribosomes in Loki- and Heimdallarchaeota cells represents an unusual observation since DAPI and FISH signals generally overlap partially or completely in prokaryotic cells [13]. Accordingly, SR-SIM imaging of benthic bacteria in Aarhus Bay sediments demonstrated the prevalence of this overlapping signal pattern (Supplementary Fig. 3). Also, the separated DNA signal observed in Loki- and Heimdallarchaeota cells appeared different from the condensed DNA formation previously described, for example, in Escherichia coli cells [14] and the Thaumarcheota Cenarcheum symbiosum [15] and Nitrosopumilus maritimus [16]. To corroborate this, we performed SR-SIM imaging of CARD-FISH-labeled E.coli and N. maritimus cells. Although their DNA was condensed in particular cellular locations, their FISH and DAPI signals always overlapped, indicating that their DNA and ribosomes are partially co-localized and not fully separated (Supplementary Fig. 4).To assess whether the gap between DAPI and FISH signals was indicative of an internal membrane, we tried various dyes to stain membranes of the CARD-FISH-labeled Asgard archaeal cells (Supplementary Information). However, none of these stainings was successful, not even for the outer cell membrane, most likely because cell membranes were disintegrated during the CARD-FISH protocol. We then used wheat germ agglutinin (WGA), a lectin primarily binding to N-acetyl-D-glucosamine but also other glycoconjugates and oligosaccharides [17] to at least be able to visualize the surfaces of Loki- and Heimdallarchaeota cells. WGA consistently decorated a cell surface that enclosed the proximal FISH and DAPI signals, suggesting that both signals originated from the same single cell (Supplementary Fig. 5). The WGA staining also demonstrated extracellular structures connected to some Heimdallarchaeota cells (Supplementary Fig. 5). These structures appear different than the membrane protrusions in the first cultured Lokiarchaeon “Ca. P. syntrophicum”, which has a considerably smaller cell size (550 nm in diameter) and does not possess the separated DNA and ribosome signals [5]. Our observations therefore indicate diverse cellular organizations and morphotypes within Asgard archaea superphylum.Our combined results suggest that genomic material is condensed and spatially distinct from the riboplasm within the detected Loki- and Heimdallarchaeota cells. Considering the anticipated role of Asgard archaea in eukaryogenesis, in particular the presence of ESPs potentially involved in dynamic cytoskeleton formation [18] and membrane remodeling [4, 19], the separation of DNA- and ribosome-derived signals might be indicative of cellular compartmentalization. Alternatively, the observed pattern could be the result of a membrane invagination to form a nucleoid region, similar to membrane organizations for example in Planctomycetes cells [20] or Atribacter laminatus [21].Our study demonstrates the first visualization of diverse Loki- and Heimdallarchaeota cells in the marine environment and provides a protocol for reliable in situ imaging of rare microorganisms in environmental samples. Future research should address the ultrastructure of Asgard archaeal cells using electron microscopy. This would help to elucidate the cell biology of Asgard archaea and provide insights into the emergence of subcellular complexity of the eukaryotic cell. More