Ecology

General situation of the research area
The research area was conducted at Liufangzi village, Gongzhuling city, Jilin Province (N43°34′10″, E124°52′55″), as shown in Fig. 8. The area has a continental monsoon climate in the humid area of the middle temperate zone, with an average annual precipitation of 594.8 mm, which is mainly concentrated in June and August. The average annual temperature is 5.6 °C, and the daily average temperature drops to 0 °C in November of each year, with a freezing period of up to five months. Corn is one of the main commodity crops in the area, with a sowing date in early May and a harvest date in early October.
Figure 8

Location of study area (Liufangzi Village, Gongzhuling City, Jilin Province).

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The soil of the site is a silty loam black soil, which had been planted with monoculture corn with no tillage for 5 years. On October 5, 2018 (after the autumn harvest), a flat field was selected to set up the experiment. Soil samples were collected using the zigzag sampling method, and selective physical and chemical properties of soil were determined, including pH (5.48), organic matter (26.4 g kg−1), clay (29.12%), and soil bulk density (1.21 g cm−3 in 5–10 cm and 1.53 g cm−3 in 20–25 cm).
Reagents and instruments
Reagents
The main raw material of the added impervious agent was corn starch and acrylic compound, which was entrusted to Jilin Yida Chemical Co., Ltd. The added urea was an analytical reagent, and the reagents used for analysis included H2SO4, H3PO4, NaOH, NH4OH, NH4Cl, K2S2O8, Na2B4O7, KNO3, KNO2, K2Cr2O7, FeSO4, sulfonamide, and naphthalene ethylenediamine hydrochloride; these were all analytical reagents provided by Beijing Chemical Plant.
Instruments laboratory-built soil leaching column; continuous flow injection analyser (SKALAR SA++, Netherlands).
Test plot setup and agronomic practices
The experimental plots were maintained in the field consisting of (1) CK (no-tillage control treatment, with corn straw removed and soil left under no-till management); (2) ploughing treatment (corn straw was removed and then mouldboard ploughed to a 30 cm depth); (3) straw returning treatment (corn straw (25.32% moist) was incorporated into the soil on October 5, 2018 (after autumn harvest), with an application amount of 1.25 kg m−2. Briefly, corn straw was chopped into small pieces (0.5 cm length), evenly placed on the soil surface, and then incorporated into the soil with ploughing (the depth of 30 cm)); and (4) impervious agent addition treatment (the impervious agent mentioned previously evenly laid on the soil surface at the amount of 15 g m−2 and then incorporated into the 0–30 cm soil by mouldboard ploughing). The abovementioned field operations were conducted after corn harvest in the fall of 2018 with a testing area of 10 m × 50 m for each plot and three replicates for each treatment. In the following spring (2019), grain corn was planted in all treatment plots with a planting density of 65,000 plants ha−1. All plots were managed in the same way with a one-time fertilization application of 200–90-90 kg (N-P-K) ha−1 and 2,4-d spray as weed control.
For all the above treatments (including the control treatment), undisturbed soils (0–30 cm layer) were collected with an undisturbed soil column (refer to Fig. 9) for the leaching experiment on September 25, 2019 (before autumn harvest, after 350 days of straw returning to the field); soil samples of 0–15 cm were collected for determination of soil organic matter and adsorption experiment of nitrogen in the soil; and soil samples of 5–10 cm and 20–25 cm layers were collected for determination of soil bulk density. In addition, for the straw returning treatment, one sampling was added on May 25, 2019 (one month after sowing, 230 days after straw returning), for the determination of soil organic matter content and soil bulk density, nitrogen adsorption and leaching experiment in soil.
Figure 9

Schematic diagram of simulated leaching device of undisturbed soil column. (a) Soil extraction; (b) leaching; (c) physical map of leaching in undisturbed soil column. 1: Handle; 2.3.4: guide port; 5.6: screw port; 7: punching plate. I main body of leaching column; II soil cutter; III leaching solution collector.

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The soil samples used for soil organic matter determination and nitrogen absorption testing were air dried, sieved through a 2-mm sieve and visible plant debris and stones were removed, and then stored.
Experiment of nitrogen adsorption in soil
Ten parts of the soil samples (air-dried,  More

1.
Erős, T., O’Hanley, J. R. & Czeglédi, I. A unified model for optimizing riverscape conservation. J. Appl. Ecol. 55, 1871–1883 (2018).
Google Scholar 
2.
Ruggeri, P., Pasternak, E. & Okamura, B. To remain or leave: Dispersal variation and its genetic consequences in benthic freshwater invertebrates. Ecol. Evol. 9, 12069–12088 (2019).
PubMed  PubMed Central  Google Scholar 

3.
Baguette, M., Blanchet, S., Legrand, D., Stevens, V. M. & Turlure, C. Individual dispersal, landscape connectivity and ecological networks. Biol. Rev. 88, 310–326 (2013).
PubMed  Google Scholar 

4.
Geist, J. Seven steps towards improving freshwater conservation. Aquat. Conserv. Mar. Freshw. Ecosyst. 25, 447–453 (2015).
Google Scholar 

5.
Kujala, H., Lahoz-Monfort, J. J., Elith, J. & Moilanen, A. Not all data are equal: Influence of data type and amount in spatial conservation prioritisation. Methods Ecol. Evol. 9, 2249–2261 (2018).
Google Scholar 

6.
Johnson, J. B., Peat, S. M. & Adams, B. J. Where’s the ecology in molecular ecology?. Oikos 118, 1601–1609 (2009).
Google Scholar 

7.
Janse, J. H. et al. GLOBIO-aquatic, a global model of human impact on the biodiversity of inland aquatic ecosystems. Environ. Sci. Policy 48, 99–114 (2015).
Google Scholar 

8.
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
ADS  PubMed  CAS  Google Scholar 

9.
Moore, D., Cranston, G., Reed, A. & Galli, A. Projecting future human demand on the Earth’s regenerative capacity. Ecol. Indic. 16, 3–10 (2012).
Google Scholar 

10.
Yawson, D. O., Adu, M. O. & Armah, F. A. Impacts of climate change and mitigation policies on malt barley supplies and associated virtual water flows in the UK. Sci. Rep. 10, 1–12 (2020).
Google Scholar 

11.
Naidoo, R. et al. Global mapping of ecosystem services and conservation priorities. Proc. Natl. Acad. Sci. USA 105, 9495–9500 (2008).
ADS  PubMed  CAS  Google Scholar 

12.
Hermoso, V., Villero, D., Clavero, M. & Brotons, L. Spatial prioritisation of EU’s LIFE-Nature programme to strengthen the conservation impact of Natura 2000. J. Appl. Ecol. 55, 1575–1582 (2018).
Google Scholar 

13.
Hermoso, V., Morán-Ordóñez, A., Canessa, S. & Brotons, L. Realising the potential of Natura 2000 to achieve EU conservation goals as 2020 approaches. Sci. Rep. 9, 1–10 (2019).
CAS  Google Scholar 

14.
Lobera, G., Pardo, I., García, L. & García, C. Disentangling spatio-temporal drivers influencing benthic communities in temporary streams. Aquat. Sci. 81, 1–17 (2019).
CAS  Google Scholar 

15.
Richman, N. I. et al. Multiple drivers of decline in the global status of freshwater crayfish (Decapoda: Astacidea). Philos. Trans. R. Soc. B Biol. Sci. 370, 20140060 (2015).

16.
Manenti, R. et al. Causes and consequences of crayfish extinction: Stream connectivity, habitat changes, alien species and ecosystem services. Freshw. Biol. 64, 284–293 (2019).
Google Scholar 

17.
Kozák, P., Füreder, L., Kouba, A., Reynolds, J. & Souty-Grosset, C. Current conservation strategies for European crayfish. Knowl. Manag. Aquat. Ecosyst. 01, https://doi.org/10.1051/kmae/2011018 (2011).

18.
Pârvulescu, L. Introducing a new Austropotamobius crayfish species (Crustacea, Decapoda, Astacidae): A miocene endemism of the Apuseni Mountains, Romania. Zool. Anz. 279, 94–102 (2019).
Google Scholar 

19.
Kouba, A., Petrusek, A. & Kozák, P. Continental-wide distribution of crayfish species in Europe: Update and maps. Knowl. Manag. Aquat. Ecosyst. 413, 05–31 (2014).
Google Scholar 

20.
Pârvulescu, L. et al. A journey on plate tectonics sheds light on European crayfish phylogeography. Ecol. Evol. 9, 1957–1971 (2019).
PubMed  PubMed Central  Google Scholar 

21.
Pârvulescu, L. & Zaharia, C. Current limitations of the stone crayfish distribution in Romania: Implications for its conservation status. Limnologica 43, 143–150 (2013).
Google Scholar 

22.
Klobučar, G. I. V. et al. Role of the Dinaric Karst (western Balkans) in shaping the phylogeographic structure of the threatened crayfish Austropotamobius torrentium. Freshw. Biol. 58, 1089–1105 (2013).
Google Scholar 

23.
Qian, S. S., Cuffney, T. F., Alameddine, I., McMahon, G. & Reckhow, K. H. On the application of multilevel modeling in environmental and ecological studies. Ecology 91, 355–361 (2010).
PubMed  Google Scholar 

24.
Manning, P. et al. Redefining ecosystem multifunctionality. Nat. Ecol. Evol. 2, 427–436 (2018).
PubMed  Google Scholar 

25.
Koizumi, I., Usio, N., Kawai, T., Azuma, N. & Masuda, R. Loss of genetic diversity means loss of geological information: The endangered Japanese crayfish exhibits remarkable historical footprints. PLoS ONE 7, e33986 (2012).
ADS  PubMed  PubMed Central  CAS  Google Scholar 

26.
McNyset, K. M. Use of ecological niche modelling to predict distributions of freshwater fish species in Kansas. Ecol. Freshw. Fish 14, 243–255 (2005).
Google Scholar 

27.
Henrys, P. A. & Jarvis, S. G. Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition. Ecol. Evol. 9, 8104–8112 (2019).
PubMed  PubMed Central  Google Scholar 

28.
Pârvulescu, L., Zaharia, C., Satmari, A. & Drăguţ, L. Is the distribution pattern of the stone crayfish in the Carpathians related to karstic refugia from Pleistocene glaciations?. Freshw. Sci. 32, 1410–1419 (2013).
Google Scholar 

29.
Longshaw, M. & Stebbing, P. Biology and Ecology of Crayfish. (CRC Press, 2015).

30.
Chucholl, C. The bad and the super-bad: Prioritising the threat of six invasive alien to three imperilled native crayfishes. Biol. Invasions 18, 1967–1988 (2016).
Google Scholar 

31.
Chucholl, C. & Schrimpf, A. The decline of endangered stone crayfish (Austropotamobius torrentium) in southern Germany is related to the spread of invasive alien species and land-use change. Aquat. Conserv. Mar. Freshw. Ecosyst. 26, 44–56 (2016).
Google Scholar 

32.
Pârvulescu, L. et al. Flash-flood potential: A proxy for crayfish habitat stability. Ecohydrology 9, 1507–1516 (2016).
Google Scholar 

33.
Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, RG2004 (2007).

34.
Şandric, I. et al. Integrating catchment land cover data to remotely assess freshwater quality: A step forward in heterogeneity analysis of river networks. Aquat. Sci. 81, 26 (2019).
Google Scholar 

35.
Burkhard, B., Kroll, F., Nedkov, S. & Müller, F. Mapping ecosystem service supply, demand and budgets. Ecol. Indic. 21, 17–29 (2012).
Google Scholar 

36.
Zeller, K. A., McGarigal, K. & Whiteley, A. R. Estimating landscape resistance to movement: A review. Landsc. Ecol. 27, 777–797 (2012).
Google Scholar 

37.
Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Google Scholar 

38.
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2017).

39.
Freeman, E. A. & Moisen, G. G. A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecol. Modell. 217, 48–58 (2008).
Google Scholar 

40.
Iorgu, E. I., Popa, O. P., Petrescu, A.-M. & Popa, L. O. Cross-amplification of microsatellite loci in the endangered stone-crayfish Austropotamobius torrentium (Crustacea: Decapoda). Knowl. Manag. Aquat. Ecosyst. 08, https://doi.org/10.1051/kmae/2011021 (2011).

41.
Peakall, R. & Smouse, P. E. genalex 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).

42.
Goudet, J. FSTAT (Version 1.2): A computer program to calculate F-statistics. J. Hered. 86, 485–486 (1995).

43.
Rousset, F. genepop’007: A complete re-implementation of the genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).
PubMed  Google Scholar 

44.
Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. & Shipley, P. micro-checker: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).
Google Scholar 

45.
Dempster, A. P., Laird, N. M. & Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B 39, 1–22 (1977).
MathSciNet  MATH  Google Scholar 

46.
Chapuis, M. P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631 (2007).
PubMed  CAS  Google Scholar 

47.
Weir, B. S. & Cockerham, C. C. Estimating F‐statistics for the analysis of population structure. Evolution (N. Y). 38, 1358–1370 (1984).

48.
Hammer, D. A. T., Ryan, P. D., Hammer, Ø. & Harper, D. A. T. Past: Paleontological Statistics Software Package for Education and Data Analysis. Palaeontologia Electronica vol. 4 https://palaeo-electronica.orghttp//palaeo-electronica.org/2001_1/past/issue1_01.htm. (2001).

49.
Nei, M., Tajima, F. & Tateno, Y. Accuracy of estimated phylogenetic trees from molecular data. J. Mol. Evol. 19, 153–170 (1983).
ADS  PubMed  CAS  Google Scholar 

50.
Langella, O. Populations, 1.2. 30. https://bioinformatics.org/~tryphon/populations (1999).

51.
Pritchard, J. K., Stephens, M., Rosenberg, N. A. & Donnelly, P. Association mapping in structured populations. Am. J. Hum. Genet. 67, 170–181 (2000).
PubMed  PubMed Central  CAS  Google Scholar 

52.
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).
PubMed  CAS  Google Scholar 

53.
Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: A program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191 (2015).
PubMed  PubMed Central  CAS  Google Scholar 

54.
Vähä, J. P. & Primmer, C. R. Efficiency of model-based Bayesian methods for detecting hybrid individuals under different hybridization scenarios and with different numbers of loci. Mol. Ecol. 15, 63–72 (2005).
Google Scholar 

55.
Bergl, R. A. & Viglant, L. Genetic analysis reveals population structure and recent migration within the highly fragmented range of the Cross River gorilla (Gorilla gorilla diehli). Mol. Ecol. 16, 501–516 (2006).
Google Scholar 

56.
Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet. 11, 1–15 (2010).
Google Scholar 

57.
Paetkau, D., Calvert, W., Stirling, I. & Strobeck, C. Microsatellite analysis of population structure in Canadian polar bears. Mol. Ecol. 4, 347–354 (1995).
PubMed  CAS  Google Scholar 

58.
Duchesne, P. & Turgeon, J. FLOCK Provides Reliable Solutions to the ‘“Number of Populations”’ Problem. https://doi.org/10.1093/jhered/ess038.

59.
Janes, J. K. et al. The K = 2 conundrum. Mol. Ecol. 26, 3594–3602 (2017).
PubMed  Google Scholar 

60.
Funk, S. M. et al. Major inconsistencies of inferred population genetic structure estimated in a large set of domestic horse breeds using microsatellites. Ecol. Evol. 10, 4261–4279 (2020).
PubMed  PubMed Central  Google Scholar 

61.
Berger, C., Štambuk, A., Maguire, I., Weiss, S. & Füreder, L. Integrating genetics and morphometrics in species conservation—A case study on the stone crayfish, Austropotamobius torrentium. Limnologica 69, 28–38 (2018).
Google Scholar 

62.
Iojă, C. I. et al. The efficacy of Romania’s protected areas network in conserving biodiversity. Biol. Conserv. 143, 2468–2476 (2010).
Google Scholar 

63.
Rabăgia, T. & Maţenco, L. Tertiary tectonic and sedimentological evolution of the South Carpathians foredeep: Tectonic vs eustatic control. Mar. Pet. Geol. 16, 719–740 (1999).

64.
Rãdoane, M., Rãdoane, N. & Dumitriu, D. Geomorphological evolution of longitudinal river profiles in the Carpathians. Geomorphology 50, 293–306 (2003).
ADS  Google Scholar 

65.
Helms, B., Loughman, Z. J., Brown, B. L. & Stoeckel, J. Recent advances in crayfish biology, ecology, and conservation. Freshw. Sci. 32, 1273–1275 (2013).
Google Scholar 

66.
Svobodová, J. et al. The relationship between water quality and indigenous and alien crayfish distribution in the Czech Republic: Patterns and conservation implications. Aquat. Conserv. Mar. Freshw. Ecosyst. 22, 776–786 (2012).
Google Scholar 

67.
Pöckl, M. & Streissl, F. Austropotamobius torrentium as an indicator for habitat quality in running waters? Bull. Français la Pêche la Piscic. 743–758, https://doi.org/10.1051/kmae:2005030 (2005).

68.
Magyar, I. et al. Progradation of the paleo-Danube shelf margin across the Pannonian Basin during the Late Miocene and Early Pliocene. Glob. Planet. Change 103, 168–173 (2013).
ADS  Google Scholar 

69.
Zhang, Y., Luan, P., Ren, G., Hu, G. & Yin, J. Estimating the inbreeding level and genetic relatedness in an isolated population of critically endangered Sichuan taimen (Hucho Bleekeri) using genome-wide SNP markers. Ecol. Evol. 10, 1390–1400 (2020).
PubMed  PubMed Central  Google Scholar 

70.
Hoarau, G. et al. Low effective population size and evidence for inbreeding in an overexploited flatfish, plaice (Pleuronectes platessa L.). Proc. Biol. Sci. 272, 497–503 (2005).

71.
Jourdan, J. et al. Reintroduction of freshwater macroinvertebrates: Challenges and opportunities. Biol. Rev. https://doi.org/10.1111/brv.12458 (2018).
Article  PubMed  Google Scholar 

72.
Oidtmann, B., Heitz, E., Rogers, D. & Hoffmann, R. Transmission of crayfish plague. Dis. Aquat. Organ. 52, 159–167 (2002).
PubMed  Google Scholar 

73.
Rusch, J. C. et al. Simultaneous detection of native and invasive crayfish and Aphanomyces astaci from environmental DNA samples in a wide range of habitats in Central Europe. NeoBiota (2020).

74.
Hall, Q. A., Curtis, J. M., Williams, J. & Stunz, G. W. The importance of newly-opened tidal inlets as spawning corridors for adult Red Drum (Sciaenops ocellatus). Fish. Res. 212, 48–55 (2019).
Google Scholar 

75.
Stewart, F. E. C., Darlington, S., Volpe, J. P., McAdie, M. & Fisher, J. T. Corridors best facilitate functional connectivity across a protected area network. Sci. Rep. 9, 10852 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

76.
Strauss, A., White, A. & Boots, M. Invading with biological weapons: The importance of disease-mediated invasions. Funct. Ecol. 26, 1249–1261 (2012).
Google Scholar 

77.
Clavero, M. & García-Berthou, E. Invasive species are a leading cause of animal extinctions. Trends Ecol. Evol. 20, 110 (2005).
PubMed  Google Scholar 

78.
Nunes, A. L., Tricarico, E., Panov, V. E., Cardoso, A. C. & Katsanevakis, S. Pathways and gateways of freshwater invasions in Europe. Aquat. Invasions 10, 359–370 (2015).
Google Scholar 

79.
Zeng, Y. & Yeo, D. C. J. Assessing the aggregated risk of invasive crayfish and climate change to freshwater crabs: A Southeast Asian case study. Biol. Conserv. 223, 58–67 (2018).
Google Scholar 

80.
Alonso, F., Temino, C. & Diéguez-Uribeondo, J. Status of the white-clawed crayfish, Austropotamobius pallipes (Lereboullet, 1858), in Spain: Distribution and legislation. 31–53 (2000).

81.
Van Dyck, H. & Baguette, M. Dispersal behaviour in fragmented landscapes: Routine or special movements?. Basic Appl. Ecol. 6, 535–545 (2005).
Google Scholar 

82.
Rodrigues, A. S. L., Pilgrim, J. D., Lamoreux, J. F., Hoffmann, M. & Brooks, T. M. The value of the IUCN Red List for conservation. Trends Ecol. Evol. 21, 71–76 (2006).
PubMed  Google Scholar 

83.
Füreder, L., Gherardi, F. & Souty-Grosset, C. Austropotamobius torrentium. The IUCN Red List of Threatened Species 2010 e.T2431A9439449 https://doi.org/10.2305/IUCN.UK.2010-3.RLTS.T2431A9439449.en (2010). More

NEWS
10 September 2020

Fires are releasing record levels of carbon dioxide, partly because they are burning ancient peatlands that have been a carbon sink.

Alexandra Witze

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Northern fires (like the one shown here in the Novosibirsk Region of south Siberia) released record-setting amounts of carbon dioxide this year.Credit: Kirill Kukhmar/TASS/Getty

Wildfires blazed along the Arctic Circle this summer, incinerating tundra, blanketing Siberian cities in smoke and capping the second extraordinary fire season in a row. By the time the fire season waned at the end of last month, the blazes had emitted a record 244 megatonnes of carbon dioxide — that’s 35% more than last year, which also set records. One culprit, scientists say, could be peatlands that are burning as the top of the world melts.
Peatlands are carbon-rich soils that accumulate as waterlogged plants slowly decay, sometimes over thousands of years. They are the most carbon-dense ecosystems on Earth; a typical northern peatland packs in roughly ten times as much carbon as a boreal forest. When peat burns, it releases its ancient carbon to the atmosphere, adding to the heat-trapping gases that cause climate change.

Nearly half the world’s peatland-stored carbon lies between 60 and 70 degrees north, along the Arctic Circle. The problem with this is that historically frozen carbon-rich soils are expected to thaw as the planet warms, making them even more vulnerable to wildfires and more likely to release large amounts of carbon. It’s a feedback loop: as peatlands release more carbon, global warming increases, which thaws more peat and causes more wildfires. A study published last month1 shows that northern peatlands could eventually shift from being a net sink for carbon to a net source of carbon, further accelerating climate change.
The unprecedented Arctic wildfires of 2019 and 2020 show that transformational shifts are already under way, says Thomas Smith, an environmental geographer at the London School of Economics and Political Science. “Alarming is the right term.”
Zombie fires
The fire season in the Arctic kicked off unusually early this year: as early as May, there were fires blazing north of the tree line in Siberia, which normally wouldn’t happen until around July. One reason is that temperatures in winter and spring were warmer than usual, priming the landscape to burn. It’s also possible that peat fires had been smouldering beneath the ice and snow all winter and then emerged, zombie-like, in the spring as the snow melted. Scientists have shown that this kind of low-temperature, flameless combustion can burn in peat and other organic matter, such as coal, for months or even years.
Because of the early start, individual Arctic wildfires have been burning for longer than usual, and “they’re starting much farther north than they used to — in landscapes that we thought were fire-resistant rather than fire-prone”, says Jessica McCarty, a geographer at Miami University in Oxford, Ohio.

Sources: Copernicus Atmosphere Monitoring Service/European Centre for Medium-Range Weather Forecasts; Hugelius, G. et al. Proc. Natl. Acad. Sci. USA 117, 20438–20446 (2020)

Researchers are now assessing just how bad this Arctic fire season was. The Russian Wildfires Remote Monitoring System catalogued 18,591 separate fires in Russia’s two easternmost districts, with a total of nearly 14 million hectares burnt, says Evgeny Shvetsov, a fire specialist at the Sukachev Institute of Forest, which is part of the Russian Academy of Sciences in Krasnoyarsk. Most of the burning happened in permafrost zones, where the ground is normally frozen year-round.
To estimate the record carbon dioxide emissions, scientists with the European Commission’s Copernicus Atmosphere Monitoring Service used satellites to study the wildfires’ locations and intensity, and then calculated how much fuel each had probably burnt. Yet even that is likely to be an underestimate, says Mark Parrington, an atmospheric scientist at the European Centre for Medium-Range Weather Forecasts in Reading, UK, who was involved in the analysis. Fires that burn in peatland can be too low-intensity for satellite sensors to capture.
The problem with peat
How much this year’s Arctic fires will affect global climate over the long term depends on what they burnt. That’s because peatlands, unlike boreal forest, do not regrow quickly after a fire, so the carbon released is permanently lost to the atmosphere.
Smith has calculated that about half of the Arctic wildfires in May and June were on peatlands — and that in many cases, the fires went on for days, suggesting that they were fuelled by thick layers of peat or other soil rich in organic matter.

And the August study1 found that there are nearly four million square kilometres of peatlands in northern latitudes. More of that than previously thought is frozen and shallow — and therefore vulnerable to thawing and drying out, says Gustaf Hugelius, a permafrost scientist at Stockholm University who led the investigation. He and his colleagues also found that although peatlands have been helping to cool the climate for thousands of years, by storing carbon as they accumulate, they will probably become a net source of carbon being released into the atmosphere — which could happen by the end of the century.
Fire risk in Siberia is predicted to increase as the climate warms2, but by many measures, the shift has already arrived, says Amber Soja, an environmental scientist who studies Arctic fires at the US National Institute of Aerospace in Hampton, Virginia. “What you would expect is already happening,” she says. “And in some cases faster than we would have expected.”

doi: 10.1038/d41586-020-02568-y

References

1.
Hugelius, G. et al. Proc. Natl Acad. Sci. USA 117, 20438–20446 (2020).

2.
Sherstyukov, B. G. & Sherstyukov, A. B. Russian Meteorol. Hydrol. 39, 292–301 (2014).

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IPBES. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES secretariat, 2019).
2.
Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).
PubMed  Google Scholar 

3.
Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).
Google Scholar 

4.
Mehrabi, Z., Ellis, E. C. & Ramankutty, N. The challenge of feeding the world while conserving half the planet. Nat. Sustain. 1, 409–412 (2018).
Google Scholar 

5.
Maxwell, S. L., Fuller, R. A., Brooks, T. M. & Watson, J. E. M. Biodiversity: the ravages of guns, nets and bulldozers. Nature 536, 143–145 (2016).
ADS  CAS  PubMed  Google Scholar 

6.
Tittensor, D. P. et al. A mid-term analysis of progress toward international biodiversity targets. Science 346, 241–244 (2014).
ADS  CAS  PubMed  Google Scholar 

7.
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
ADS  CAS  PubMed  Google Scholar 

8.
Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73–81 (2017).
ADS  CAS  PubMed  Google Scholar 

9.
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
ADS  CAS  PubMed  Google Scholar 

10.
Steffen, W. et al. Planetary boundaries: guiding human development on a changing planet. Science 347, 1259855 (2015).
PubMed  Google Scholar 

11.
Chaplin-Kramer, R. et al. Global modeling of nature’s contributions to people. Science 366, 255–258 (2019).
ADS  CAS  PubMed  Google Scholar 

12.
Van Vuuren, D. P. et al. Pathways to achieve a set of ambitious global sustainability objectives by 2050: explorations using the IMAGE integrated assessment model. Technol. Forecast. Soc. Change 98 303–323 (2015).
Google Scholar 

13.
Wilson, E. O. Half-Earth: Our Planet’s Fight for Life (Liveright, 2016).

14.
Tebaldi, C. & Knutti, R. The use of the multi-model ensemble in probabilistic climate projections. Phil. Trans. R. Soc. A 365, 2053–2075 (2007).
ADS  MathSciNet  PubMed  Google Scholar 

15.
IPBES. Summary for Policymakers of the Methodological Assessment of Scenarios and Models of Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES secretariat, 2016).

16.
Popp, A. et al. Land-use futures in the shared socio-economic pathways. Glob. Environ. Change 42, 331–345 (2017).
Google Scholar 

17.
Efron, B. & Tibshirani, R. Statistical data analysis in the computer age. Science 253, 390–395 (1991).
ADS  CAS  PubMed  Google Scholar 

18.
Briscoe, N. J. et al. Forecasting species range dynamics with process-explicit models: matching methods to applications. Ecol. Lett. 22, 1940–1956 (2019).
PubMed  Google Scholar 

19.
McRae, L., Deinet, S. & Freeman, R. The diversity-weighted living planet index: controlling for taxonomic bias in a global biodiversity indicator. PLoS ONE 12, e0169156 (2017).
PubMed  PubMed Central  Google Scholar 

20.
Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353, 288–291 (2016).
ADS  CAS  PubMed  Google Scholar 

21.
Newbold, T., Sanchez-Ortiz, K., De Palma, A., Hill, S. L. L. & Purvis, A. Reply to ‘The biodiversity intactness index may underestimate losses’. Nat. Ecol. Evol. 3, 864–865 (2019).
PubMed  Google Scholar 

22.
Martin, P. A., Green, R. E. & Balmford, A. The biodiversity intactness index may underestimate losses. Nat. Ecol. Evol. 3, 862–863 (2019).
PubMed  Google Scholar 

23.
Phalan, B. et al. How can higher-yield farming help to spare nature? Science 351, 450–451 (2016).
ADS  CAS  PubMed  Google Scholar 

24.
Lambin, E. F. & Meyfroidt, P. Global land use change, economic globalization, and the looming land scarcity. Proc. Natl Acad. Sci. USA 108, 3465–3472 (2011).
ADS  CAS  PubMed  Google Scholar 

25.
Springmann, M. et al. Options for keeping the food system within environmental limits. Nature 562, 519–525 (2018).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

26.
Pimm, S. L., Jenkins, C. N. & Li, B. V. How to protect half of Earth to ensure it protects sufficient biodiversity. Sci. Adv. 4, eaat2616 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

27.
Mouquet, N. et al. Predictive ecology in a changing world. J. Appl. Ecol. 52, 1293–1310 (2015).
Google Scholar 

28.
Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).
PubMed  PubMed Central  Google Scholar 

29.
Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

30.
Eker, S., Rovenskaya, E., Obersteiner, M. & Langan, S. Practice and perspectives in the validation of resource management models. Nat. Commun. 9, 5359 (2018).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

31.
Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).
Google Scholar 

32.
Fricko, O. et al. The marker quantification of the Shared Socioeconomic Pathway 2: a middle-of-the-road scenario for the 21st century. Glob. Environ. Change 42, 251–267 (2017).
Google Scholar 

33.
Leclère, D. et al. Supporting material for the article entitled “Bending the curve of terrestrial biodiversity needs an integrated strategy” [Data Collection]. http://dare.iiasa.ac.at/57/ (2020).

34.
van Vuuren, D. P. et al. Energy, land-use and greenhouse gas emissions trajectories under a green growth paradigm. Glob. Environ. Change 42, 237–250 (2017).
Google Scholar 

35.
IUCN & UNEP-WCMC. The World Database on Protected Areas (WDPA). https://www.protectedplanet.net/ (UNEP-WCMC, accessed October 2017).

36.
Key Biodiversity Area Partnership World Database of Key Biodiversity Areas. http://www.keybiodiversityareas.org/site/requestgis (BirdLife International, accessed 5 October 2017).

37.
Allan, J. R., Venter, O. & Watson, J. E. M. Temporally inter-comparable maps of terrestrial wilderness and the last of the wild. Sci. Data 4, 170187 (2017).
PubMed  PubMed Central  Google Scholar 

38.
Scholes, R. J. & Biggs, R. A biodiversity intactness index. Nature 434, 45–49 (2005).
ADS  CAS  PubMed  Google Scholar 

39.
Hudson, L. N. et al. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project. Ecol. Evol. 7, 145–188 (2017).
PubMed  Google Scholar 

40.
Hurtt, G. et al. Harmonization of global land-use change and management for the period 850–2100. Preprint at https://doi.org/10.5194/gmd-2019-360 (2020).

41.
IUCN. Red List of Threatened Species. version 2017.3 http://www.iucnredlist.org (2017).

42.
BirdLife International & Handbook of the Birds of the World. Bird Species Distribution Maps of the World. version 7.0. http://datazone.birdlife.org/species/requestdis (2017).

43.
Harfoot, M. et al. Integrated assessment models for ecologists: the present and the future. Glob. Ecol. Biogeogr. 23, 124–143 (2014).
Google Scholar 

44.
Fujimori, S., Masui, T. & Matsuoka, Y. AIM/CGE [basic] Manual. Discussion Paper Series No. 2012-01 (Center for Social and Environmental Systems Research, NIES, 2012).

45.
Hasegawa, T., Fujimori, S., Ito, A., Takahashi, K. & Masui, T. Global land-use allocation model linked to an integrated assessment model. Sci. Total Environ. 580, 787–796 (2017).
ADS  CAS  PubMed  Google Scholar 

46.
Havlík, P. et al. Climate change mitigation through livestock system transitions. Proc. Natl Acad. Sci. USA 111, 3709–3714 (2014).
ADS  PubMed  Google Scholar 

47.
Stehfest, E. et al. Integrated Assessment of Global Environmental Change with IMAGE 3.0: Model Description and Policy Applications. https://www.pbl.nl/en/publications/integrated-assessment-of-global-environmental-change-with-IMAGE-3.0 (Netherlands Environmental Assessment Agency (PBL), 2014).

48.
Woltjer, G. et al. The MAGNET Model: Module Description. https://edepot.wur.nl/310764 (LEI, part of Wageningen University and Research Centre, The Hague, 2014).

49.
Popp, A. et al. Land-use protection for climate change mitigation. Nat. Clim. Change 4, 1095–1098 (2014).
ADS  CAS  Google Scholar 

50.
Brooks, T. M. et al. Analysing biodiversity and conservation knowledge products to support regional environmental assessments. Sci. Data 3, 160007 (2016).
PubMed  PubMed Central  Google Scholar 

51.
Klein Goldewijk, K., Beusen, A., van Drecht, G. & de Vos, M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. Glob. Ecol. Biogeogr. 20, 73–86 (2011).
Google Scholar 

52.
Ohashi, H. et al. Biodiversity can benefit from climate stabilization despite adverse side effects of land-based mitigation. Nat. Commun. 10, 5240 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

53.
Visconti, P. et al. Projecting global biodiversity indicators under future development scenarios. Conserv. Lett. 9, 5–13 (2016).
Google Scholar 

54.
Rondinini, C. & Visconti, P. Scenarios of large mammal loss in Europe for the 21st century. Conserv. Biol. 29, 1028–1036 (2015).
PubMed  Google Scholar 

55.
Spooner, F. E. B., Pearson, R. G. & Freeman, R. Rapid warming is associated with population decline among terrestrial birds and mammals globally. Glob. Change Biol. 24, 4521–4531 (2018).
ADS  Google Scholar 

56.
Ferrier, S., Manion, G., Elith, J. & Richardson, K. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Divers. Distrib. 13, 252–264 (2007).
Google Scholar 

57.
Di Marco, M. et al. Projecting impacts of global climate and land-use scenarios on plant biodiversity using compositional-turnover modelling. Glob. Change Biol. 25, 2763–2778 (2019).
ADS  Google Scholar 

58.
Hoskins, A. J. et al. BILBI: supporting global biodiversity assessment through high-resolution macroecological modelling. Environ. Model. Softw. 104806 (2020).

59.
Chaudhary, A. & Brooks, T. M. National Consumption and Global Trade Impacts on Biodiversity. World Dev. 121, 178–187 (2017).
Google Scholar 

60.
UNEP & SETAC. Global Guidance for Life Cycle Impact Assessment Indicators, vol. 1 (United Nations Environment Programme, 2016).

61.
Chaudhary, A., Verones, F., de Baan, L. & Hellweg, S. Quantifying land use impacts on biodiversity: combining species–area models and vulnerability indicators. Environ. Sci. Technol. 49, 9987–9995 (2015).
ADS  CAS  PubMed  Google Scholar 

62.
Alkemade, R. et al. GLOBIO3: a framework to investigate options for reducing global terrestrial biodiversity loss. Ecosystems 12, 374–390 (2009).
Google Scholar 

63.
De Palma, A. et al. Annual changes in the Biodiversity Intactness Index in tropical and subtropical forest biomes, 2001–2012. Preprint at https://doi.org/10.1101/311688 (2018).

64.
Hill, S. L. L. et al. Worldwide impacts of past and projected future land-use change on local species richness and the Biodiversity Intactness Index. Preprint at https://doi.org/10.1101/311787 (2018).

65.
Purvis, A. et al. Modelling and projecting the response of local terrestrial biodiversity worldwide to land use and related pressures. Adv. Ecol. Res. 58, 201–241 (2018).
Google Scholar 

66.
R Core Team. R: A Language and Environment for Statistical Computing. http://www.R-project.org/ (R Foundation for Statistical Computing, 2019). More

Affiliations

Department of Biology, University of Miami, Coral Gables, FL, USA
K. J. Feeley, C. Bravo-Avila, B. Fadrique & T. M. Perez

Fairchild Tropical Botanic Garden, Coral Gables, FL, USA
K. J. Feeley, C. Bravo-Avila & T. M. Perez

Universidad Nacional de Colombia Sede Medellín, Medellín, Colombia
D. Zuleta

Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Washington DC, DC, USA
D. Zuleta

Authors
K. J. Feeley

C. Bravo-Avila

B. Fadrique

T. M. Perez

D. Zuleta

Corresponding author
Correspondence to K. J. Feeley. More

Properties of permafrost soils
All sites contained syngenetic permafrost in which the active layer and the uppermost permafrost have experienced numerous freeze-thaw cycles since formation during the Holocene22. Permafrost organic matter radiocarbon ages ranged from 7850 ± 30 to 830 ± 20 y B.P. (Supplementary Data), with the western sites containing the oldest SOM and the northern Hudson Bay peatlands containing the youngest SOM.
For organic layers, permafrost soil C:N atomic ratios (14.50 [12.61–19.67], median [25th–75th]) were lower and H:C atomic ratios (0.13 [0.13–0.14]) were greater relative to the active layer, 23.89 [19.33–29.50] and 0.14 [0.12–0.15], respectively (Supplementary Fig. 1). Similarly for mineral layers, permafrost C:N ratios were lower (12.0 [3.23–18.09]) and H:C ratios higher (0.24 [0.15–0.86]) compared to the active layer, 16.21 [13.67–18.87] and 0.17 [0.15–0.21], respectively. For both thermal layers, in organic layers C:N ratios were higher and H:C ratios were lower than in mineral layers.
These stoichiometric properties are typical of boreal and tundra soils (Supplementary Fig. 1)23. The higher C:N and very low H:C ratios of the organic layers relative to mineral layers suggest higher contents of condensed aromatic structures originating from peat24. Permafrost layers displayed lower C:N properties suggesting different SOM composition (e.g., lignin, tannins, lipids, sugars or amino acids) and an enrichment in microbial biomass relative to the active layer24. The absence of a downward trend of C:N and H:C within the permafrost (Supplementary Fig. 1), except at Daring Lake, indicates that soil development and microbial processing were effectively halted soon after permafrost aggradation23.
Active layer and permafrost yields of DOC and nitrogen
DOC content correlated with soil C content in both active layer (r2 [log–log] = 0.748, P  More

1.
Hoekstra, A. Y. & Wiedmann, T. O. Humanity’s unsustainable environmental footprint. Science 344, 1114–1117. https://doi.org/10.1126/science.1248365 (2014).
ADS  CAS  Article  PubMed  Google Scholar 
2.
Steffen, W. et al. Planetary boundaries: guiding human development on a changing planet. Science 347, 736–746. https://doi.org/10.1126/science.1259855 (2015).
ADS  CAS  Article  Google Scholar 

3.
Wang, H. et al. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388, 1459–1544. https://doi.org/10.1016/S0140-6736(16)31012-1 (2016).
Article  Google Scholar 

4.
Grizzetti, B. et al. Human pressures and ecological status of European rivers. Sci. Rep. 7, 205. https://doi.org/10.1038/s41598-017-00324-3 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

5.
Hoekstra, A. Y. & Mekonnen, M. M. The water footprint of humanity. Proc. Natl. Acad. Sci. 109, 3232–3237. https://doi.org/10.1073/pnas.1109936109 (2012).
ADS  Article  PubMed  Google Scholar 

6.
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  PubMed  Google Scholar 

7.
Millennium Ecosystem Assessment. Ecosystems and Human Well-being. A Framework for Assessment https://pdf.wri.org/ecosystems_human_wellbeing.pdf (2003).

8.
Carpenter, S. R., Stanley, E. H. & Vander Zanden, M. J. State of the world’s freshwater ecosystems: physical, chemical, and biological changes. Annu. Rev. Environ. Resour. 36, 75–99. https://doi.org/10.1146/annurev-environ-021810-094524 (2011).
Article  Google Scholar 

9.
Richmond, E. K. et al. A diverse suite of pharmaceuticals contaminates stream and riparian food webs. Nat. Commun. 9, 4491. https://doi.org/10.1038/s41467-018-06822-w (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

10.
Maes, J. et al. An indicator framework for assessing ecosystem services in support of the EU Biodiversity Strategy to 2020. Ecosyst. Serv. 17, 14–23. https://doi.org/10.1016/j.ecoser.2015.10.023 (2016).
Article  Google Scholar 

11.
Anzaldua, G. et al. Getting into the water with the ecosystem services approach: the DESSIN ESS evaluation framework. Ecosyst. Serv. 30, 318–326. https://doi.org/10.1016/j.ecoser.2017.12.004 (2018).
Article  Google Scholar 

12.
Van Vliet, M. T. H., Florke, M. & Wada, Y. Quality matters for water scarcity. Nat. Geosci. 10, 800–802. https://doi.org/10.1038/NGEO3047 (2017).
ADS  Article  Google Scholar 

13.
Bernhardt, E. S., Rosi, E. J. & Gessner, M. O. Synthetic chemicals as agents of global change. Front. Ecol. Environ. 15, 84–90. https://doi.org/10.1002/fee.1450 (2017).
Article  Google Scholar 

14.
Global Chemicals Outlook II—from legacies to innovative solutions: implementing the 2030 agenda for sustainable development. Synthesis report https://wedocs.unep.org/bitstream/handle/20.500.11822/28113/GCOII.pdf?sequence=1&isAllowed=y (2019).

15.
Birk, S. et al. Impacts of multiple stressors on freshwater biota across spatial scales and ecosystems. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-020-1216-4 (2020).
Article  PubMed  Google Scholar 

16.
A guide to SDG interactions. From sciene to implementation https://council.science/publications/a-guide-to-sdg-interactions-from-science-to-implementation/ (2017).

17.
EC. Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH). Off. J. Eur. Union L 396, 1–848 https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:02006R01907-20140410&from=EN (2006).

18.
Geiser, K. Chemicals Without Harm. Policies for a Sustainable World (MIT Press, Cambridge, 2015).
Google Scholar 

19.
Escher, B. I., Stapleton, H. M. & Schymanski, E. L. Tracking complex mixtures of chemicals in our changing environment. Science 367, 388–392. https://doi.org/10.1126/science.aay6636 (2020).
ADS  CAS  Article  PubMed  Google Scholar 

20.
UNESCO. Solving the puzzle: the ecosystem approach and biosphere reserves https://unesdoc.unesco.org/ark:/48223/pf0000119790 (2000).

21.
Nõges, P., van de Bund, W., Cardoso, A. C., Solimini, A. G. & Heiskanen, A. S. Assessment of the ecological status of European surface waters: a work in progress. Hydrobiologia 633, 197–211. https://doi.org/10.1007/s10750-009-9883-9 (2009).
CAS  Article  Google Scholar 

22.
Tsakiris, G. The status of the European waters in 2015: a review. Environ. Process. 2, 543–557. https://doi.org/10.1007/s40710-015-0079-1 (2015).
Article  Google Scholar 

23.
Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Off. J. Eur. Commun. L 327, 1–72 https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ:L:2000:2327:TOC (2000).

24.
Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674. https://doi.org/10.1038/s41586-019-1684-3 (2019).
ADS  CAS  Article  PubMed  Google Scholar 

25.
Rockström, J. et al. A safe operating space for humanity. Nature 461, 472–475. https://doi.org/10.1038/461472a (2009).
ADS  CAS  Article  PubMed  Google Scholar 

26.
Clean Water Rule: Definition of “Waters of the United States”. Federal Register 80, 37054–37127, Monday, June 29, 2015/Rules https://www.govinfo.gov/content/pkg/FR-2015-06-29/pdf/2015-13435.pdf (2015).

27.
C&L Inventory. Database containing classification and labelling information on notified and registered substances received from manufacturers and importers https://echa.europa.eu/information-on-chemicals/cl-inventory-database (accessed March 4, 2019) (2019).

28.
Posthuma, L., de Zwart, D. & Dyer, S. D. Chemical mixtures affect freshwater species assemblages: from problems to solutions. Curr. Opin. Environ. Sci. Health 11, 78–89. https://doi.org/10.1016/j.coesh.2019.09.002 (2019).
Article  Google Scholar 

29.
The Water Framework Directive and the Floods Directive: Actions towards the ‘good status’ of EU water and to reduce flood risks. Communication from the Commission to the European Parliament and the Council, 9.3.2015. COM(2015) 120 final https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52015DC0120 (2015).

30.
EC. Fitness check of the Water Framework Directive, Groundwater Directive, Environmental Quality Standards Directive and Floods Directive https://ec.europa.eu/environment/water/fitness_check_of_the_eu_water_legislation/documents/Water%20Fitness%20Check%20-%20SWD(2019)439%20-%20web.pdf. 1–176 (2019).

31.
Arle, J., Mohaupt, V. & Kirst, I. Monitoring of surface waters in Germany under the Water Framework Directive—a review of approaches, methods and results. Water 8, 217. https://doi.org/10.3390/w8060217 (2016).
Article  Google Scholar 

32.
Drakvik, E. et al. Statement paper on advancing the assessment of chemical mixtures and their risks for human health and the environment. Environ. Int. 134, 105267. https://doi.org/10.1016/j.envint.2019.105267 (2020).
CAS  Article  PubMed  Google Scholar 

33.
Brack, W. et al. High-resolution mass spectrometry to complement monitoring and track emerging chemicals and pollution trends in European water resources. Environ. Sci. Eur. 31, 62. https://doi.org/10.1186/s12302-019-0230-0 (2019).
Article  Google Scholar 

34.
Van Gils, J. et al. The European Collaborative Project SOLUTIONS developed models to provide diagnostic and prognostic capacity and fill data gaps for chemicals of emerging concern. Environ. Sci. Eur. 31, 72. https://doi.org/10.1186/s12302-019-0248-3 (2019).
Article  Google Scholar 

35.
van Gils, J. et al. Computational material flow analysis for thousands of chemicals of emerging concern in European waters. J. Hazard. Mater. https://doi.org/10.1016/j.jhazmat.2020.122655 (2020).
Article  PubMed  Google Scholar 

36.
Pistocchi, A. et al. Assessment of the effectiveness of reported Water Framework Directive Programmes of Measures. Part III—JRC Pressure Indicators v.2.0: nutrients, urban runoff, flow regime and hydromorphological alteration https://doi.org/10.2760/325451 (2018).

37.
EC. Directive 2013/39/EU of the European Parliament and of the Council of 12 August 2013 amending Directives 2000/60/EC and 2008/105/EC as regards priority substances in the field of water policy. Off. J. Eur. Union L 226, 1–17 https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2013:2226:0001:0017:EN:PDF (2013).

38.
EEA. European waters—assessment of status and pressures https://www.eea.europa.eu/publications/state-of-water (2018).

39.
Dulio, V. et al. Emerging pollutants in the EU: 10 years of NORMAN in support of environmental policies and regulations. Environ. Sci. Eur. 30, 5. https://doi.org/10.1186/s12302-018-0135-3 (2018).
Article  PubMed  PubMed Central  Google Scholar 

40.
Guidelines for the health risk assessment of chemical mixtures Fed. Reg. 51 185, 34014–34025 https://www.epa.gov/sites/production/files/32014-34011/documents/chem_mix_31986.pdf (1986).

41.
Calamari, D. & Vighi, M. A proposal to define quality objectives for aquatic life for mixtures of chemical substances. Chemosphere 25, 531–542. https://doi.org/10.1016/0045-6535(92)90285-Y (1992).
ADS  Article  Google Scholar 

42.
Technical guidance for deriving environmental quality standards. Common Implementation Strategy for the Water framework Directive (2000/60/EC)—Guidance Document No. 27 https://circabc.europa.eu/sd/a/ba6810cd-e611-4f72-9902-f0d8867a2a6b/Guidance%20No%2027%20-%20Deriving%20Environmental%20Quality%20Standards%20-%20version%202018.pdf (2011).

43.
Posthuma, L. & De Zwart, D. Encyclopedia of Toxicology 3rd edn, Vol. 4, 363–368 (Elsevier Inc., Academic Press, 2014).
Google Scholar 

44.
Birk, S. et al. Three hundred ways to assess Europe’s surface waters: an almost complete overview of biological methods to implement the Water Framework Directive. Ecol. Ind. 18, 31–41. https://doi.org/10.1016/j.ecolind.2011.10.009 (2012).
Article  Google Scholar 

45.
De Zwart, D. & Posthuma, L. Complex mixture toxicity for single and multiple species: proposed methodologies. Environ. Toxicol. Chem. 24, 2665–2676. https://doi.org/10.1897/04-639r.1 (2005).
Article  PubMed  Google Scholar 

46.
Lyche Solheim, A. et al. A new broad typology for rivers and lakes in Europe: Development and application for large-scale environmental assessments. Sci. Total Environ. 697, 134043. https://doi.org/10.1016/j.scitotenv.2019.134043 (2019).
ADS  CAS  Article  Google Scholar 

47.
Cade, B. S. & Noon, B. R. A gentle introduction to quantile regression for ecologists. Front. Ecol. Environ. 1, 412–420 https://www.jstor.org/stable/3868138 (2003).

48.
Vermeulen, R., Schymanski, E. L., Barabási, A.-L. & Miller, G. W. The exposome and health: where chemistry meets biology. Science 367, 392–396. https://doi.org/10.1126/science.aay3164 (2020).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

49.
Posthuma, L., van Gils, J., Zijp, M. C., van de Meent, D. & de Zwart, D. Species sensitivity distributions for use in environmental protection, assessment, and management of aquatic ecosystems for 12 386 chemicals. Environ. Toxicol. Chem. 38, 905–917. https://doi.org/10.1002/etc.4373 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

50.
Hoondert, R. P. J., Oldenkamp, R., de Zwart, D., van de Meent, D. & Posthuma, L. QSAR-based estimation of Species Sensitivity Distribution parameters: an exploratory investigation. Environ. Toxicol. Chem. 38, 2764–2770. https://doi.org/10.1002/etc.4601 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

51.
Williams, A. J. et al. The CompTox Chemistry Dashboard: a community data resource for environmental chemistry. J. Chem. Inform. 9, 61. https://doi.org/10.1186/s13321-017-0247-6 (2017).
CAS  Article  Google Scholar 

52.
Blum, C. et al. The concept of sustainable chemistry: key drivers for the transition towards sustainable development. Sustain. Chem. Pharm. 5, 94–104. https://doi.org/10.1016/j.scp.2017.01.001 (2017).
CAS  Article  Google Scholar 

53.
Kostal, J., Voutchkova-Kostal, A., Anastas, P. T. & Zimmerman, J. B. Identifying and designing chemicals with minimal acute aquatic toxicity. Proc. Natl. Acad. Sci. U.S.A. 112, 6289–6294. https://doi.org/10.1073/pnas.1314991111 (2015).
ADS  CAS  Article  PubMed  Google Scholar 

54.
Saouter, E. et al. Environmental footprint: update of Life Cycle Impact Assessment methods—ecotoxicity freshwater, human toxicity cancer, and non-cancer https://doi.org/10.2760/178544 (2018).

55.
Rapport, D. & Friend, A. Towards a comprehensive framework for environmental statistics. A stress-response approach https://www.worldcat.org/title/towards-a-comprehensive-framework-for-environmental-statistics-a-stress-response-approach/oclc/21772350 (1979).

56.
Kaika, M. & Page, B. The EU Water Framework Directive: part 1. European policy-making and the changing topography of lobbying. Eur. Environ. 13, 314–327. https://doi.org/10.1002/eet.331 (2003).
Article  Google Scholar 

57.
Page, B. & Kaika, M. The EU Water Framework Directive: part 2. Policy innovation and the shifting choreography of governance. Eur. Environ. 13, 328–343. https://doi.org/10.1002/eet.332 (2003).
Article  Google Scholar 

58.
Elosegi, A., Gessner, M. O. & Young, R. G. River doctors: learning from medicine to improve ecosystem management. Sci. Total Environ. 595, 294–302. https://doi.org/10.1016/j.scitotenv.2017.03.188 (2017).
ADS  CAS  Article  PubMed  Google Scholar 

59.
Kortenkamp, A. & Faust, M. Regulate to reduce chemical mixture risk. Science 361, 224–226. https://doi.org/10.1126/science.aat9219 (2018).
ADS  CAS  Article  PubMed  Google Scholar 

60.
Voulvoulis, N., Arpon, K. D. & Giakoumis, T. The EU Water Framework Directive: from great expectations to problems with implementation. Sci. Total Environ. 575, 358–366. https://doi.org/10.1016/j.scitotenv.2016.09.228 (2017).
ADS  CAS  Article  PubMed  Google Scholar 

61.
Giakoumis, T. & Voulvoulis, N. The transition of EU water policy towards the Water Framework Directive’s integrated river basin management paradigm. Environ. Manag. 62, 819–831. https://doi.org/10.1007/s00267-018-1080-z (2018).
ADS  Article  Google Scholar 

62.
Suter, G. W., Traas, T. P. & Posthuma, L. In Species Sensitivity Distributions in Ecotoxicology, Ch 21 (eds Posthuma, L. et al.) 437–474 (CRC Press, Boca Raton, 2002).
Google Scholar 

63.
Kortenkamp, A. et al. Common assessment framework for HRA and ERA higher tier assessments including fish and drinking water and multi-species ERA via SSD, population-level ERA via IBM and food web vulnerability ERA. SOLUTIONS Deliverable D18.1 https://www.solutions-project.eu/wp-content/uploads/2018/11/D18.1_SOLUTIONS-D18_1-after-peer-review-clean-V2_Kortenkamp_chm_with_annex.pdf (2018).

64.
Posthuma, L., De Zwart, D., Keijzers, R. & Postma, J. Water systems analysis with the ecological key factor ‘toxicity’. Part 2. Calibration. Toxic pressure and ecological effects on macrofauna in the Netherlands (in Dutch) https://www.stowa.nl/sites/default/files/assets/PUBLICATIES/Publicaties%202016/STOWA%202016-15/STOWA%202016-15B.pdf (STOWA, Amersfoort, the Netherlands, 2016).

65.
Posthuma, L. & De Zwart, D. Predicted effects of toxicant mixtures are confirmed by changes in fish species assemblages in Ohio, USA, rivers. Environ. Toxicol. Chem. 25, 1094–1105. https://doi.org/10.1897/05-305r.1 (2006).
CAS  Article  PubMed  Google Scholar 

66.
Posthuma, L. & De Zwart, D. Predicted mixture toxic pressure relates to observed fraction of benthic macrofauna species impacted by contaminant mixtures. Environ. Toxicol. Chem. 31, 2175–2188. https://doi.org/10.1002/etc.1923 (2012).
CAS  Article  PubMed  Google Scholar 

67.
Berger, E., Haase, P., Oetken, M. & Sundermann, A. Field data reveal low critical chemical concentrations for river benthic invertebrates. Sci. Total Environ. 544, 864–873. https://doi.org/10.1016/j.scitotenv.2015.12.006 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

68.
Posthuma, L. et al. Mixtures of chemicals are important drivers of impacts on ecological status in European surface waters. Environ. Sci. Eur. 31, 71. https://doi.org/10.1186/s12302-019-0247-4 (2019).
Article  Google Scholar 

69.
Zijp, M. C., Posthuma, L. & Van de Meent, D. Definition and applications of a versatile chemical pollution footprint methodology. Environ. Sci. Technol. 48, 10588–10597. https://doi.org/10.1021/es500629f (2014).
ADS  CAS  Article  PubMed  Google Scholar 

70.
Bjørn, A., Diamond, M., Birkved, M. & Hauschild, M. Z. Chemical footprint method for improved communication of freshwater ecotoxicity impacts in the context of ecological limits. Environ. Sci. Technol. 48, 13253–13262. https://doi.org/10.1021/es503797d (2014).
ADS  CAS  Article  PubMed  Google Scholar 

71.
Kapo, K. E. et al. iSTREEM®: an approach for broad-scale in-stream exposure assessment of “down-the-drain” chemicals. Integr. Environ. Assess. Manag. 12, 782–792. https://doi.org/10.1002/ieam.1793 (2016).
Article  PubMed  Google Scholar 

72.
Donnelly, C., Arheimer, B., Capell, R., Dahne, J. & Stromqvist, J. Regional overview of nutrient load in Europe—challenges when using a large-scale model approach, E-HYPE. Understanding fresh-water quality problems in a changing world https://iahs.info/uploads/dms/15569.361%2049-58.pdf (2013).

73.
Posthuma, L., Suter, G. W. I. & Traas, T. P. Species Sensitivity Distributions in Ecotoxicology (CRC-Press, Boca Raton, 2002).
Google Scholar 

74.
Drescher, K. & Bödeker, W. Assessment of the combined effects of substances—the relationship between concentration addition and independent action. Biometrics 51, 716–730. https://doi.org/10.2307/2532957 (1995).
MathSciNet  Article  MATH  Google Scholar 

75.
EEA. WISE WFD database at https://www.eea.europa.eu/data-and-maps/data/wise-wfd-3 (2012).

76.
Globevnik, L., Koprivsek, M. & Snoj, L. Metadata to the MARS spatial database. Freshw. Metadata J. 21, 1–7. https://doi.org/10.15504/fmj.2017.21 (2017).
Article  Google Scholar 

77.
Birk, S. et al. Intercalibrating classifications of ecological status: Europe’s quest for common management objectives for aquatic ecosystems. Sci. Total Environ. 454–455, 490–499. https://doi.org/10.1016/j.scitotenv.2013.03.037 (2013).
ADS  CAS  Article  PubMed  Google Scholar 

78.
Zijp, M. C., Huijbregts, M. A. J., Schipper, A. M., Mulder, C. & Posthuma, L. Identification and ranking of environmental threats with ecosystem vulnerability distributions. Sci. Rep. 7, 9298. https://doi.org/10.1038/s41598-017-09573-8 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar  More

Genome structure of DhNV
The circular genome of DhNV is 119,754 bp and contains 106 hypothetical ORFs, with 56 on the positive strand and 50 on the negative strand (Fig. 1). Fifty-nine of the ORFs met our comparative e-value threshold of  More