Ecology

1.Stein, E. D., Cohen, Y. & Winer, A. M. Environmental distribution and transformation of mercury compounds. Crit. Rev. Environ. Sci. Technol. 26, 1–43 (1996).CAS 
Article 

Google Scholar 
2.Ciccarelli, C. et al. Assessment of sampling methods about level of mercury in fish. Ital. J. Food Saf. 8, 153–157 (2019).
Google Scholar 
3.Ditri, F. M. Mercury contamination: What we have learned since Minamata. Environ. Monit. Assess. 19, 165–182 (1991).CAS 
Article 

Google Scholar 
4.Monteiro, L. R. & Furness, R. W. Seabirds as monitors of mercury in the marine environment. Water Air Soil Pollut. 80, 851–870 (1995).CAS 
Article 
ADS 

Google Scholar 
5.Pitter, P. In Hydrochemie 5th edn (ed. Pitter, P.) (VSCHT Praha, 2015).
Google Scholar 
6.Hylander, L. D. & Meili, M. 500 years of mercury production: Global annual inventory by region until 2000 and associated emissions. Sci. Total. Environ. 304, 13–27 (2003).CAS 
Article 
ADS 

Google Scholar 
7.Pacyna, E. G. et al. Global emission of mercury to the atmosphere from anthropogenic sources in 2005 and projections to 2020. Atmos. Environ. 44, 2487–2499 (2010).CAS 
Article 
ADS 

Google Scholar 
8.Pai, P., Niemi, D. & Powers, B. A North American inventory of anthropogenic mercury emissions. Fuel Process. Technol. 65, 101–115 (2000).Article 

Google Scholar 
9.Wang, Q. R., Kim, D., Dionysiou, D. D., Sorial, G. A. & Timberlake, D. Sources and remediation for mercury contamination in aquatic systems: A literature review. Environ. Pollut. 131, 323–336 (2004).Article 

Google Scholar 
10.Buck, D. G. et al. A global-scale assessment of fish mercury concentrations and the identification of biological hotspots. Sci. Total Environ. 687, 956–966 (2019).CAS 
Article 
ADS 

Google Scholar 
11.Gentes, S. et al. Application of European water framework directive: Identification reference sites and bioindicator fish species for mercury in tropical freshwater ecosystems (French Guiana). Ecol. Indic. 106, 105468. https://doi.org/10.1016/j.ecolind.2019.105468 (2019).CAS 
Article 

Google Scholar 
12.Thomas, S. M. et al. Climate and landscape conditions indirectly affect fish mercury levels by altering lake water chemistry and fish size. Environ. Res. 188, 109750. https://doi.org/10.1016/j.envres.2020.109750 (2020).CAS 
Article 
PubMed 

Google Scholar 
13.Zupo, V. et al. Mercury accumulation in freshwater and marine fish from the wild and from aquaculture ponds. Environ. Pollut. 255, 112975. https://doi.org/10.1016/j.envpol.2019.112975 (2019).CAS 
Article 
PubMed 

Google Scholar 
14.Zhang, J. L. et al. Health risk assessment of heavy metals in Cyprinus carpio (Cyprinidae) from the upper Mekong river. Environ. Sci. Pollut. Res. 26, 9490–9499 (2019).CAS 
Article 

Google Scholar 
15.Cerna, M. Opatreni mezinarodnich instituci a Ceske republiky k omezovani rizika znecistovani zivotniho prostredi rtuti. Chem. Listy. 98, 916–921 (2004) ((Article in Czech)).CAS 

Google Scholar 
16.Janouskova, D. & Svehla, J. Mercury concentrations in fish tissues in the water reservoir Rimov, South Bohemia. Crop Sci. 19, 43–48 (2002).
Google Scholar 
17.Purba, J. S., Silalahi, J. & Haro, G. Analysis of mercury in fish, North Sumatera, Indonesia by atomic absorption spectrophotometer. Asian J. Pharm. 8, 21–25 (2020).CAS 
Article 

Google Scholar 
18.Willacker, J. J., Eagles-Smith, C. A. & Blazer, V. S. Mercury bioaccumulation in freshwater fishes of the Chesapeake Bay watershed. Ecotoxicology 29, 459484 (2020).Article 

Google Scholar 
19.Regulation (EU) 2017/852 of European Parliament and of the council of 17 May 2017 on mercury, and repealing Regulation (EC) No 1102/2008. Official Journal of the European Union.20.European Commission. The EU Fish Market. https://www.eumofa.eu/documents/20178/415635/EN_The+EU+fish+market_2020.pdf (2020).21.Nebesky, V., Policar, T., Blecha, M., Kristan, J. & Svacina, P. Trends in import and export of fishery products in the Czech Republic during 2010–2015. Aquacult. Int. 24, 1657–1668 (2016).Article 

Google Scholar 
22.FAO. Fisheries & Aquaculture—National Aquaculture Sector Overview—Czech Republic. http://www.fao.org/fishery/countrysector/naso_czechrepublic/en (accessed April 24 April 2021) (2021).23.Rakmanikhah, Z., Esmaili-Sari, A. & Bahramifar, N. Total mercury and methylmercury concentrations in native and invasive fish species in Shadegan International Wetland, Iran, and health risk assessment. Environ. Sci. Pollut. Res. 27, 6765–6773 (2020).Article 

Google Scholar 
24.Celechovska, O., Svobodova, Z., Zlabek, V. & Macharackova, B. Distribution of metals in tissues of the common carp (Cyprinus carpio L.). Acta Vet. Brno 76, 93–100 (2007).Article 

Google Scholar 
25.Cerveny, D. et al. Fish fin-clips as non-lethal approach for biomonitoring of mercury contamination in aquatic environments and human health risk assessment. Chemosphere 163, 290–295 (2016).CAS 
Article 
ADS 

Google Scholar 
26.WHO. Evaluations of the Joint FAO/WHO Expert Committee on Food Additives (JECFA). https://apps.who.int/food-additives-contaminants-jecfa-database/search.aspx.27.Kannan, K. et al. Distribution of total mercury and methyl mercury in water, sediment, and fish from south Florida estuaries. Arch. Environ. Con. Tox. 34, 109–118 (1998).CAS 
Article 

Google Scholar 
28.US EPA. Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisories Documents. Volume 2: Risk Assessment and Fish Consumption Limits, Third Edition. https://www.epa.gov/fish-tech/guidance-assessing-chemical-contaminant-data-use-fish-advisories-documents (accessed 8 May 2021) (2000).29.Ministry of Agriculture of the Czech Republic. Situacni a vyhledova zprava—Ryby. http://eagri.cz/public/web/file/666957/Ryby_2020_web.pdf (accessed 8 May 2021, in Czech) (2020).30.Novotna, K., Svobodova, Z., Harustiakova, D. & Mikula, P. Spatial and temporal trends in contamination of the Czech part of the Elbe River by mercury between 1991 and 2016. Bull. Environ. Contam. Toxicol. 105, 750–757 (2020).CAS 
Article 

Google Scholar 
31.Raldua, D., Diez, S., Bayona, J. M. & Barcelo, D. Mercury levels and liver pathology in feral fish living in the vicinity of a mercury cell chlor-alkali factory. Chemosphere 66, 1217–1225 (2007).CAS 
Article 
ADS 

Google Scholar 
32.Squadrone, S. et al. Heavy metals distribution in muscle, liver, kidney and gill of European catfish (Silurus glanis) from Italian rivers. Chemosphere 90, 358–365 (2013).CAS 
Article 
ADS 

Google Scholar 
33.Cerveny, D. et al. Contamination of fish in important fishing grounds of the Czech Republic. Ecotoxicol. Environ. Saf. 109, 101–109 (2014).CAS 
Article 

Google Scholar 
34.Marsalek, P., Svobodova, Z. & Randak, T. The content of total mercury and methylmercury in common carp from selected Czech ponds. Aquac. Int. 15, 299–304 (2007).CAS 
Article 

Google Scholar 
35.Vicarova, P., Docekalova, H., Ridoskova, A. & Pelcova, P. Heavy metals in the common carp (Cyprinus carpio L.) from three reservoirs in the Czech Republic. Czech J. Food Sci. 34, 422–428 (2016).CAS 
Article 

Google Scholar 
36.Akerblom, S., Bignert, A., Meili, M., Sonesten, L. & Sundbom, M. Half a century of changing mercury levels in Swedish freshwater fish. Ambio 43, 91–103 (2014).Article 

Google Scholar 
37.Dvorak, P., Andreji, J., Mraz, J. & Dvorakova Liskova, Z. Concentration of heavy and toxic metals in fish and sediments from the Morava river basin, Czech Republic. Neuroendocrinol. Lett. 36, 126–132 (2015).CAS 
PubMed 

Google Scholar 
38.Dusek, L. et al. Bioaccumulation of mercury in muscle tissue of fish in the Elbe River (Czech Republic): Maultispecies monitoring study 1991–1996. Ecotoxicol. Environ. Saf. 61, 256–267 (2005).CAS 
Article 

Google Scholar 
39.Marsalek, P., Svobodova, Z. & Randak, T. Total mercury and methylmercury contamination in fish from various sites along the Elbe River. Acta Vet. Brno. 75, 579–585 (2006).CAS 
Article 

Google Scholar 
40.Wang, X. & Wang, W. X. The three ‘B’ of mercury in China: Bioaccumulation, biodynamics and biotransformation. Environ. Pollut. 250, 216–232 (2019).CAS 
Article 

Google Scholar 
41.Jankovska, I. et al. Importance of fish gender as a factor in environmental monitoring of mercury. Environ. Sci. Pollut. Res. 21, 6239–6242 (2014).CAS 
Article 

Google Scholar 
42.Carrasco, L. et al. Patterns of mercury and methylmercury bioaccumulation in fish species downstream of a long-term mercury-contaminated site in the lower Ebro River (NE Spain). Chemosphere 84, 1642–1649 (2011).CAS 
Article 
ADS 

Google Scholar 
43.Havelkova, M., Dusek, L., Nemethova, D., Poleszczuk, G. & Svobodova, Z. Comparison of mercury distribution between liver and muscle: A biomonitoring of fish from lightly and heavily contaminated localities. Sensors. 8, 4095–4109 (2008).CAS 
Article 
ADS 

Google Scholar 
44.Kruzikova, K. et al. The correlation between fish mercury liver/muscle ratio and high and low levels of mercury contamination in Czech localities. Int. J. Electrochem. Sc. 8, 45–56 (2013).CAS 

Google Scholar 
45.Kensova, R., Kruzikova, K. & Svobodova, Z. Mercury speciation and safety of fish from important fishing locations in the Czech Republic. Czech J. Food Sci. 30, 276–284 (2012).CAS 
Article 

Google Scholar 
46.European Commission. Commission Regulation 1881/2006 Setting Maximum Levels of Certain Contaminants in Foodstuffs. https://eur-lex.europa.eu/ (accessed 2 May 2021) (2006). More

Deer capture and samplingWe captured 100 mule deer (54 females, 46 males) during November 2018–February 2019, avoiding capture and sampling of juveniles. We attempted to distribute captures throughout the ~23 km2 study area described by Miller et al.5 to minimize spatial disparities in comparing contemporary and past data, and to assure marks were widely distributed for December ground counts to estimate deer abundance5,35,36. Field and sampling methods generally followed those used elsewhere5,31,37. Field procedures were reviewed and approved by the CPW Animal Care and Use Committee (file 14–2018).We pursued deer on foot and darted them opportunistically, delivering sedative combinations intramuscularly via projectile syringe. Premixed immobilization drug combinations included either nalbuphine (N; 0.9 mg/kg) or butorphanol (B; 0.5 mg/kg) combined with azaperone (A; 0.2 mg/kg) and medetomidine (M; 0.2 mg/kg)38, with standard total doses for respective combinations based on an estimated mass of 70 kg (average drug volume per animal was 1.3 ml NMA, 1.4 ml BAM). We collected rectal mucosa biopsies to determine CWD infection status37. We also collected whole blood and marked all deer with individually identifiable ear tags and some with telemetry (n = 51) or visual identification (n = 12) collars. Ages were estimated to the nearest year via tooth replacement and wear patterns39; observers used a pocket reference guide in the field to help assure consistency. To antagonize sedation upon completion of handling and sampling, each deer received 5 mg atipamezole/mg M administered, injected intramuscularly.Prion diagnosticsFormalin-fixed tissue biopsies were processed and analyzed by immunohistochemistry (IHC) at the Colorado State University Veterinary Diagnostic Laboratory (Fort Collins, Colorado USA; CSUVDL) for evidence of CWD-associated prion (PrPCWD) accumulations using monoclonal antibody F99/97.6.1 (VMRD Inc., Pullman, Washington, USA)40 and standard IHC methods24,37,41, except that the CSUVDL’s IHC staining machine (Leica Microsystems Inc., Buffalo Grove, Illinois, USA) was different from that used in earlier studies (Ventana Medical Systems, Oro Valley, Arizona, USA). Biopsies were evaluated microscopically and classified as positive (infected) or not detected (negative) based on PrPCWD presence or absence; the same pathologist (T. R. Spraker) read biopsies for both the current and prior5 studies.We included only data from deer with biopsies providing ≥3 lymphoid follicles in analyses involving infection status in order to maintain a relatively high (≥90%) probability of detecting infected individuals24. Two animals with low follicle counts that died shortly after capture were excepted by substituting postmortem IHC results. Limiting the acceptable follicle count excluded seven females (two 225SS, five 225SF) and two males (one 225SS, one 225SF) from some analyses. One male deer was 225FF and one female deer was missing a blood sample and thus not assigned to a PRNP gene group; these two individuals also were excluded from some analyses (e.g., Table 1).
PRNP genotypingWe used DNA extracted from whole blood buffy coat aliquots (n = 99) to screen for the presence of sequences at PRNP gene codon 225 that encode for serine (S) and/or phenylalanine (F) in the mature prion polypeptide, classifying individuals as 225SS, 225SF, or 225FF16,36,42. Methods generally followed those described by Jewell et al.16. Briefly, we extracted DNA using the DNeasy® blood and tissue kit (Qiagen, Valenica, California, USA). We amplified the complete open reading frame (ORF) plus 25 bp of 5′ flanking sequences and 53 bp of 3′ flanking sequences in the PRNP coding region using polymerase chain reaction (PCR). Purified DNA was combined in a 0.2 ml PCR tube containing a puReTag Ready-To-Go PCR bead (illustra™, GE Healthcare Bio-Sciences Corp, Piscataway, New Jersey, USA). Each PCR bead contained 2.5 units puReTag DNA polymerase, 10 mM Tris-HCI, 50 mM KCl, 1.5 mM MgCl2, 200 µM of each deoxynucleoside triphosphate, and stabilizers, including bovine serum albumin. For each PCR assay, 1 μL of each primer at 200 nM, 22 μL of RNase-free water and 1 μL of approximately 100 ng total genomic DNA was added for a final volume of 25 μL. Primers used for amplification were forward (MD582F, 5′-ACATGGGCATATGATGCTGACACC-3′) and reverse (MD1479RC, 5′-ACTACAGGGCTGCAGGTAGATACT-3′) described by Jewell et al.16. Reactions were thermal-cycled in a PTC 100 (MJ Research) at 94 C for 5 min and then 32 cycles of 94 C for 7 s, 62 C for 15 s, 72 C for 30 s and a final cycle of 72 C for 5 min, and kept at 4 C until inspected for successful amplification by agarose gel electrophoresis. As confirmed by LaCava et al.19, the MD582F and MD1479RC primers developed by Jewell et al.16 specifically amplify the functional PRNP gene ORF, thereby excluding confounding effects that could arise from the presence of a processed pseudogene that occurs in a majority of deer (Odocoileus spp.)42.We used EcoRI restriction digestion of the PCR-amplified PRNP region16—a validated assay targeting the singular polymorphism at codon 225 in mule deer—to screen all 99 samples for presence of S or F codons. Aliquots (10 μl) of completed PCR reactions were incubated with 10 U EcoRI (New England Biolabs) in a total volume of 12 μl containing 50 mM NaCl, 100 mM Tris/HCl, 10 mM MgCl2, 0.025% Triton X-100 (pH 7.5) at 37 C for 2–16 h followed by the addition of 2.5 μl 6× concentrate gel loading solution (Sigma- Aldrich) per sample, and the inspection of products by agarose gel electrophoresis for the presence of one 897bp-sized band for 225SS, two bands—one 897 bp and one 719 bp—for 225SF, or one 719 bp-sized band for 225FF. As noted by Jewell et al.16, occurrence of TTC (the F codon) at position 225 creates an EcoRI recognition DNA sequence and cleavage site GAATTC from codons 224–225, whereas TCC (the S codon) creates the sequence GAATCC, which is not cut by EcoRI. When incubated with EcoRI, PCR products with a TTC codon at position 225 yielded cleavage fragments of the predictable sizes listed16. Because no other sites within the PRNP ORF DNA sequence are potentially transformable to GAATTC with one base change, this represents a specific genotyping method for assessing the S225F polymorphism in mule deer16.To confirm findings from EcoRI screening, we examined sequences of the complete PRNP ORF from 20 samples that showed evidence of cleavage indicating 225*F and 6 samples without cleavage identified as 225SS. For DNA sequencing, we used primers 245 (5′-GGTGGTGACTGACTGTGTGTTGCTTGA-3′), 12 (5′-TGGTGGTGACTGTGTGTTGCTTGA-3′) and 3FL1 (5′-GATTAAGAAGATAATGAAAACAGGAAGG-3′; Integrated DNA Technologies). Sanger sequencing was done on purified PCR product by Eurofins Genomics (Louisville, Kentucky, USA). Sequence chromatograms were viewed and DNA sequence alignments and comparisons were made using the MAFFT multiple sequence alignment program v7.450 module, software platform v2020.2.3 of Geneious Prime. Sequencing confirmed the presence of coding for F in all samples identified as 225*F by EcoRI digestion, as well as the absence of such coding in samples identified by EcoRI digestion as 225SS. Moreover, presence of AGC at codon 138 in all sequenced samples reconfirmed that the primers we used had amplified the functional PRNP gene42.Statistics and reproducibilityFor analyses, we tabulated IHC-positive and -negative results to estimate apparent prevalence of prion infection. We also tabulated the number of individuals assigned to PRNP genotypes and to age groupings as described. Age groupings were selected based on relevance to CWD epidemiology in mule deer1,5,8,12,16,17,18,20,24,31,37. Assuming a ~2-year disease course5,8,17 and relative scarcity of end-stage disease in 225SS deer More

NEWS
11 January 2022

Landmark Colombian bird study repeated to right colonial-era wrongs

A re-run of a 100-year-old, US-led bird survey will inform future conservation efforts — but be helmed by local researchers.

Luke Taylor

Luke Taylor

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Ornithologist Andrés Cuervo takes a selfie of a team of Colombia Resurvey Project researchers during an expedition in Caquetá.Credit: Andrés M. Cuervo

Colombia has more animal and plant species per square kilometre than anywhere else in the world. Pioneering US bird scientist Frank Chapman once said that the country was so rich in biodiversity that when his research team explored the area in the early twentieth century, it could have studied a single mountain range for five years and still not have mapped all of its fauna.More than 100 years later, Colombian researchers are redoing his legendary bird survey, which is a reference for ornithologists the world over. They are surveying the areas that Chapman catalogued between 1911 and 1915, to investigate how a century of war, global warming and industrialization has affected the landscape and its biodiversity.
The world’s species are playing musical chairs: how will it end?
But this project will not snatch birds and whisk them to a museum abroad — as Chapman’s team did. Instead, local scientists will keep specimens in Colombia and engage with local communities during their expeditions, to include them in the momentous endeavour, improve the quality of the research and set an ethical standard for future fieldwork.Chapman and at least 5 other collectors shot many of the nearly 16,000 birds that they hauled back to the American Museum of Natural History in New York City, offering local residents little explanation — or credit. “You wouldn’t like it if I came to your house, surveyed it without permission, took photos and then went back to Colombia without telling you what I had found,” says Nelsy Niño-Rodríguez, the Colombia Resurvey Project’s community-relations coordinator, who is an ornithologist at the Alexander von Humboldt Biological Resources Research Institute in Bogotá. Without local guides knowledgeable about Colombia and its birds, Chapman couldn’t possibly have located and collected so many specimens, says Natalia Ocampo-Peñuela, a research partner on the resurvey project and a conservation ecologist at the University of California, Santa Cruz. Yet Chapman’s logs hardly mention guides; when they are discussed, it’s usually in racist or pejorative terms, she says.“His interest was to feed his curiosity, his scientific intellect and the museum,” she adds, but not to inform the wider population — and definitely not the local populations.A changed landscape Colombian researchers have dreamt of re-running Chapman’s expeditions for decades. But it wasn’t possible until the past few years, because many areas were inaccessible owing to armed conflict. Following a landmark peace deal in 2016, remote regions that had been under the control of the Revolutionary Armed Forces of Colombia (FARC), a left-wing guerrilla group, once again opened to exploration. That, and an infusion of funding from the Colombian government and international donors, meant researchers could attempt a resurvey.

Birds of Colombia: top, many-banded araçari (Pteroglossus pluricinctus); left, pileated finch (Coryphospingus pileatus); right, white-fringed antwren (Formicivora grisea).Credit: Andrés M. Cuervo

Chapman visited Colombia because he thought that its geography made it one of the most biodiverse places in the world. He theorized that the presence of the Andes Mountains, combined with the country’s position bridging South and Central America, made it an evolutionary melting pot.
FARC and the forest: Peace is destroying Colombia’s jungle — and opening it to science
Although Colombia is still home to around 10% of the world’s biodiversity, the forests once explored by Chapman have changed immensely. Pristine jungles have been cleared to create uniform pastures resembling golf courses, says Andrés Cuervo, an ornithologist at the National University of Colombia in Bogotá who is one of the directors of the resurvey project. The dirt tracks that Chapman and his team traversed on mules are now roads. And climate change has pushed birds to higher elevations and altered their migratory patterns.Seeking to understand the effects of these changes on biodiversity, researchers launched the Colombia Resurvey Project in 2019. The main objective is to gather bird specimens, including DNA and tissue samples, to compare the modern population with Chapman’s collection. The team, which includes US researchers as well as local ones, has so far conducted 6 expeditions, visiting 14 of Chapman’s original sites — leaving 60 to go. A useful catalogue The researchers are finding that they have to venture deep into the forest to find birds that were once a stone’s throw from Chapman’s campsites, Cuervo says. And some species are nowhere to be found, including the red-ruffed fruitcrow (Pyroderus scutatus) — almost certainly lost when the trees in its territory were cut down to grow avocados, he adds.

Resurvey project researchers Jessica Diaz (right) and Andrés Sierra (left) record data from a mist net, used to collect birds during expeditions.Credit: Andrés M. Cuervo

The team has also confirmed that birds dependent on unique ecosystems are being replaced by generalist species — which are more adaptable to fragmented forest and a disrupted diet — reducing the country’s biodiversity1. Larger species and fruit eaters seem to have been hit particularly hard over the past century, because they require vast expanses of forest to thrive.
Illegal mining in the Amazon hits record high amid Indigenous protests
The effects of climate and landscape changes on bird populations in the tropics are not well understood, so the project will inform future conservation efforts, researchers say. “It’s almost impossible to imagine all the ways in which this data can potentially be used down the road,” says John Bates, curator and head of life sciences at the Field Museum of Natural History in Chicago, Illinois. Members of the resurvey project hope their catalogue will have as much impact as Chapman’s. It will include resources such as a genomic map illustrating birds’ evolution, generated from DNA samples. “We are collecting the most complete set of specimens that one can imagine so that scientists from now and the future can answer questions that we haven’t thought of,” Ocampo says.Taking chargeThe Colombia Resurvey Project team especially hopes that its anti-colonial approach will resonate with the scientific community. The researchers run workshops before each excursion to inform local communities about why they are planning to kill some birds, and how this is important for conservation and science. They are storing the specimens at the National University of Colombia, where the birds will be digitally catalogued, so that people can view them online, listen to audio of their song and scroll through interactive maps of the expeditions. And the team is creating birdwatching tours at the expedition sites to boost tourism.
Brazilian road proposal threatens famed biodiversity hotspot
Involving communities leads to better results, Niño-Rodríguez says. For instance, even if some Indigenous people do not know the scientific names for birds, they might be able to identify them on sight and know where they are most likely to be found. And community knowledge of how the forests have changed has passed from generation to generation, so local residents are able to fill gaps when satellite data and research logs aren’t available.It’s equally important to the researchers that those leading the project are from Colombia. They say it’s common for local experts to help visiting foreign researchers to find new species and make discoveries, but be excluded from the scientific process and the credit. “We don’t want to be the guys with the permits or the guys who facilitate the logistics of someone else’s research,” Cuervo says. “We want to do our own high-quality research, and we want it to be available for people to use.” This time around, the American Museum of Natural History is a partner on the project, rather than its lead. “Although the Chapman expedition was conducted with help and permissions from the Colombian government, today’s expeditions appropriately look much different than they did in Chapman’s time,” says a museum spokesperson, adding that the museum “is proud of the very active relationship it maintains with Colombia’s scientific institutions through education and research”.
Colombia: after the violence
Meanwhile, project researchers are training curious members of local communities in how to identify birds scientifically, so they can continue to log species with their cameras and mobile phones once the researchers leave the forest. Areas previously ruled by FARC guerrillas are now falling under the control of other armed groups, which might not let outsiders in, so local residents could soon be the only people who have access to some of Colombia’s most biodiverse jungles and the birds that inhabit them.“Hopefully we won’t have to wait another hundred years for scientists to return to these sites and assess their bird fauna,” Cuervo says. “Communities can do it with empowerment and interest in their biodiversity and surroundings.”

Nature 601, 178-179 (2022)
doi: https://doi.org/10.1038/d41586-021-03527-x

References1.Gómez, C., Tenorio, E. A. & Cadena, C. D. Conserv. Biol. 35, 1552–1563 (2021).PubMed 
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1.Olefeldt, D. et al. Circumpolar distribution and carbon storage of thermokarst landscapes. Nat. Commun. 7, 13043 (2016).
Google Scholar 
2.Lindgren, A., Hugelius, G. & Kuhry, P. Extensive loss of past permafrost carbon but a net accumulation into present-day soils. Nature 560, 219–222 (2018).
Google Scholar 
3.Turetsky, M. R. et al. Carbon release through abrupt permafrost thaw. Nat. Geosci. 13, 138–143 (2020).
Google Scholar 
4.Walter Anthony, K. et al. Methane emissions proportional to permafrost carbon thawed in Arctic lakes since the 1950s. Nat. Geosci. 9, 679–682 (2016).
Google Scholar 
5.Schuur, E. A. G. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).
Google Scholar 
6.Gasser, T. et al. Path-dependent reductions in CO2 emission budgets caused by permafrost carbon release. Nat. Geosci. 11, 830–835 (2018).
Google Scholar 
7.McGuire, A. D. et al. Dependence of the evolution of carbon dynamics in the northern permafrost region on the trajectory of climate change. Proc. Natl Acad. Sci. USA 115, 3882–3887 (2018).
Google Scholar 
8.Heffernan, L., Estop-Aragonés, C., Knorr, K. H., Talbot, J. & Olefeldt, D. Long-term impacts of permafrost thaw on carbon storage in peatlands: deep losses offset by surficial accumulation. J. Geophys. Res. Biogeosci. 125, e2019JG005501 (2020).
Google Scholar 
9.Chadburn, S. E. et al. An observation-based constraint on permafrost loss as a function of global warming. Nat. Clim. Chang. 7, 340–344 (2017).
Google Scholar 
10.Bartsch, A. et al. Can C-band synthetic aperture radar be used to estimate soil organic carbon storage in tundra? Biogeosciences 13, 5453–5470 (2016).
Google Scholar 
11.Obu, J. et al. Northern Hemisphere permafrost map based on TTOP modelling for 2000–2016 at 1 km2 scale. Earth Sci. Rev. 193, 299–316 (2019).
Google Scholar 
12.Commane, R. et al. Carbon dioxide sources from Alaska driven by increasing early winter respiration from Arctic tundra. Proc. Natl Acad. Sci. USA 114, 5361–5366 (2017).
Google Scholar 
13.Natali, S. M. et al. Permafrost carbon feedbacks threaten global climate goals. Proc. Natl. Acad. Sci. USA 118, e2100163118 (2021).
Google Scholar 
14.Zona, D. et al. Cold season emissions dominate the Arctic tundra methane budget. Proc. Natl Acad. Sci. USA 113, 40–45 (2016).
Google Scholar 
15.Comyn-Platt, E. et al. Carbon budgets for 1.5 and 2°C targets lowered by natural wetland and permafrost feedbacks. Nat. Geosci. 11, 568–573 (2018).
Google Scholar 
16.Heslop, J. K. K. et al. A synthesis of methane dynamics in thermokarst lake environments. Earth Sci. Rev. 210, 103365 (2020).
Google Scholar 
17.Keuper, F. et al. Carbon loss from northern circumpolar permafrost soils amplified by rhizosphere priming. Nat. Geosci. 13, 560–565 (2020).
Google Scholar 
18.Nitze, I., Grosse, G., Jones, B. M., Romanovsky, V. E. & Boike, J. Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic. Nat. Commun. 9, 5423 (2018).
Google Scholar 
19.Lara, M. J. et al. Local-scale Arctic tundra heterogeneity affects regional-scale carbon dynamics. Nat. Commun. 11, 4925 (2020).
Google Scholar 
20.Walker, X. J. et al. Increasing wildfires threaten historic carbon sink of boreal forest soils. Nature 572, 520–523 (2019).
Google Scholar 
21.Rey, D. M. et al. Wildfire-initiated talik development exceeds current thaw projections: observations and models from Alaska’s continuous permafrost zone. Geophys. Res. Lett. 47, e2020GL087565 (2020).
Google Scholar 
22.Kim, J. S., Kug, J. S., Jeong, S. J., Park, H. & Schaepman-Strub, G. Extensive fires in southeastern Siberian permafrost linked to preceding Arctic Oscillation. Sci. Adv. 6, eaax3308 (2020).
Google Scholar 
23.Vonk, J. E., Tank, S. E. & Walvoord, M. A. Integrating hydrology and biogeochemistry across frozen landscapes. Nat. Commun. 10, 5377 (2019).
Google Scholar 
24.Saunois, M. et al. The global methane budget 2000–2017. Earth Syst. Sci. Data 12, 1561–1623 (2020).
Google Scholar 
25.Williams, J. W., Ordonez, A. & Svenning, J. C. A unifying framework for studying and managing climate-driven rates of ecological change. Nat. Ecol. Evol. 5, 17–26 (2021).
Google Scholar 
26.Schwab, M. S. et al. An abrupt aging of dissolved organic carbon in large Arctic rivers. Geophys. Res. Lett. 47, e2020GL088823 (2020).
Google Scholar 
27.Walter Anthony, K. M. et al. Decadal-scale hotspot methane ebullition within lakes following abrupt permafrost thaw. Environ. Res. Lett. 16, 35010 (2021).
Google Scholar 
28.Goldstein, A. et al. Protecting irrecoverable carbon in Earth’s ecosystems. Nat. Clim. Chang. 10, 287–295 (2020).
Google Scholar 
29.Turner, M. G. et al. Climate change, ecosystems and abrupt change: science priorities. Phil. Trans. R. Soc. B 375, 20190105 (2020).
Google Scholar 
30.Fountain, A. G. et al. The disappearing cryosphere: impacts and ecosystem responses to rapid cryosphere loss. Bioscience 62, 405–415 (2012).
Google Scholar 
31.Gruber, S. Derivation and analysis of a high-resolution estimate of global permafrost zonation. Cryosphere 6, 221–233 (2012).
Google Scholar 
32.Zou, D. et al. A new map of permafrost distribution on the Tibetan Plateau. Cryosphere 11, 2527–2542 (2012).
Google Scholar 
33.Sayedi, S. S. et al. Subsea permafrost carbon stocks and climate change sensitivity estimated by expert assessment. Environ. Res. Lett. 15, 124075 (2020).
Google Scholar 
34.Smith, S. L., O’Neill, H. B., Isaksen, K., Noetzli, J. & Romanovsky, V. E. The changing thermal state of permafrost. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-021-00240-1 (2022).Article 

Google Scholar 
35.Strauss, J. et al. Deep Yedoma permafrost: a synthesis of depositional characteristics and carbon vulnerability. Earth Sci. Rev. 172, 75–86 (2017).
Google Scholar 
36.Strauss, J. et al. The deep permafrost carbon pool of the Yedoma region in Siberia and Alaska. Geophys. Res. Lett. 40, 6165–6170 (2013).
Google Scholar 
37.Elder, C. D. et al. Greenhouse gas emissions from diverse Arctic Alaskan lakes are dominated by young carbon. Nat. Clim. Chang. 8, 166–171 (2018).
Google Scholar 
38.Martens, J. et al. Remobilization of old permafrost carbon to Chukchi Sea sediments during the end of the last deglaciation. Glob. Biogeochem. Cycles 33, 2–14 (2019).
Google Scholar 
39.Vonk, J. E. et al. Reviews and syntheses: effects of permafrost thaw on Arctic aquatic ecosystems. Biogeosciences 12, 7129–7167 (2015).
Google Scholar 
40.Turetsky, M. R. et al. Permafrost collapse is accelerating carbon release. Nature 569, 32–24 (2019).
Google Scholar 
41.Wild, B. et al. Rivers across the Siberian Arctic unearth the patterns of carbon release from thawing permafrost. Proc. Natl Acad. Sci. USA 116, 10280–10285 (2019).
Google Scholar 
42.Mishra, U. et al. Spatial heterogeneity and environmental predictors of permafrost region soil organic carbon stocks. Sci. Adv. 7, 5236–5260 (2021).
Google Scholar 
43.Treat, C. C. et al. Tundra landscape heterogeneity, not interannual variability, controls the decadal regional carbon balance in the Western Russian Arctic. Global Chang. Biol. 24, 5188–5204 (2018).
Google Scholar 
44.Siewert, M. B., Lantuit, H., Richter, A. & Hugelius, G. Permafrost causes unique fine-scale spatial variability across tundra soils. Glob. Biogeochem. Cycles 35, e2020GB006659 (2021).
Google Scholar 
45.Niittynen, P. et al. Fine-scale tundra vegetation patterns are strongly related to winter thermal conditions. Nat. Clim. Chang. 10, 1143–1148 (2020).
Google Scholar 
46.Hope, C. & Schaefer, K. Economic impacts of carbon dioxide and methane released from thawing permafrost. Nat. Clim. Chang. 6, 56–59 (2016).
Google Scholar 
47.Farquharson, L. M. et al. Climate change drives widespread and rapid thermokarst development in very cold permafrost in the Canadian High Arctic. Geophys. Res. Lett. 46, 6681–6689 (2019).
Google Scholar 
48.Biskaborn, B. K. et al. Permafrost is warming at a global scale. Nat. Commun. 10, 264 (2019).
Google Scholar 
49.Hood, E., Battin, T. J., Fellman, J., O’Neel, S. & Spencer, R. G. M. Storage and release of organic carbon from glaciers and ice sheets. Nat. Geosci. 8, 91–96 (2015).
Google Scholar 
50.Tanski, G. et al. Rapid CO2 release from eroding permafrost in seawater. Geophys. Res. Lett. 46, 11244–11252 (2019).
Google Scholar 
51.Liljedahl, A. K., Gädeke, A., O’Neel, S., Gatesman, T. A. & Douglas, T. A. Glacierized headwater streams as aquifer recharge corridors, subarctic Alaska. Geophys. Res. Lett. 44, 6876–6885 (2017).
Google Scholar 
52.Yumashev, D. et al. Climate policy implications of nonlinear decline of Arctic land permafrost and other cryosphere elements. Nat. Commun. 10, 1900 (2019).
Google Scholar 
53.Woodcroft, B. J. et al. Genome-centric view of carbon processing in thawing permafrost. Nature 560, 49–54 (2018).
Google Scholar 
54.Nauta, A. L. et al. Permafrost collapse after shrub removal shifts tundra ecosystem to a methane source. Nat. Clim. Chang. 5, 67–70 (2015).
Google Scholar 
55.Anthony, K. W. et al. 21st-century modeled permafrost carbon emissions accelerated by abrupt thaw beneath lakes. Nat. Commun. 9, 3262 (2018).
Google Scholar 
56.Hugelius, G. et al. Large stocks of peatland carbon and nitrogen are vulnerable to permafrost thaw. Proc. Natl Acad. Sci. USA 117, 201916387 (2020).
Google Scholar 
57.Christensen, T. R., Arora, V. K., Gauss, M., Höglund-Isaksson, L. & Parmentier, F. J. W. Tracing the climate signal: mitigation of anthropogenic methane emissions can outweigh a large Arctic natural emission increase. Sci. Rep. 9, 1146 (2019).
Google Scholar 
58.United Nations Framework Convention on Climate Change. Total aggregate greenhouse gas emissions of individual nations, annex 1. World Resources Institute https://www.wri.org/resources/data-sets/climate-watch-cait-unfccc-annex-i-ghg-emissions-data (2008).59.Lewkowicz, A. G. & Way, R. G. Extremes of summer climate trigger thousands of thermokarst landslides in a High Arctic environment. Nat. Commun. 10, 1329 (2019).
Google Scholar 
60.Knoblauch, C., Beer, C., Liebner, S., Grigoriev, M. N. & Pfeiffer, E. M. Methane production as key to the greenhouse gas budget of thawing permafrost. Nat. Clim. Chang. 8, 309–312 (2018).
Google Scholar 
61.Jones, B. M. et al. Lake and drained lake basin systems in lowland permafrost regions. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-021-00238-9 (2022).62.Matthews, E., Johnson, M. S., Genovese, V., Du, J. & Bastviken, D. Methane emission from high latitude lakes: methane-centric lake classification and satellite-driven annual cycle of emissions. Sci. Rep. 10, 12465 (2020).
Google Scholar 
63.Lamontagne-Hallé, P., McKenzie, J. M., Kurylyk, B. L. & Zipper, S. C. Changing groundwater discharge dynamics in permafrost regions. Environ. Res. Lett. 13, 084017 (2018).
Google Scholar 
64.Nitzbon, J. et al. Fast response of cold ice-rich permafrost in northeast Siberia to a warming climate. Nat. Commun. 11, 2201 (2020).
Google Scholar 
65.Jeong, S. J. et al. Accelerating rates of Arctic carbon cycling revealed by long-term atmospheric CO2 measurements. Sci. Adv. 4, eaao1167 (2018).
Google Scholar 
66.Disher, B. S., Connon, R. F., Haynes, K. M., Hopkinson, C. & Quinton, W. L. The hydrology of treed wetlands in thawing discontinuous permafrost regions. Ecohydrology 14, e2296 (2021).
Google Scholar 
67.Parazoo, N. C. et al. Detecting regional patterns of changing CO2 flux in Alaska. Proc. Natl Acad. Sci. USA 113, 7733–7738 (2016).
Google Scholar 
68.Silva, J. L. A., Souza, A. F., Caliman, A., Voigt, E. L. & Lichston, J. E. Weak whole-plant trait coordination in a seasonally dry South American stressful environment. Ecol. Evol. 8, 4–12 (2018).
Google Scholar 
69.Ward, C. P. & Cory, R. M. Chemical composition of dissolved organic matter draining permafrost soils. Geochim. Cosmochim. Acta 167, 63–79 (2015).
Google Scholar 
70.Johnston, E. R. et al. Responses of tundra soil microbial communities to half a decade of experimental warming at two critical depths. Proc. Natl Acad. Sci. USA 116, 15096–15105 (2019).
Google Scholar 
71.Stein, L. Y. The long-term relationship between microbial metabolism and greenhouse gases. Trends Microbiol. 28, 500–511 (2020).
Google Scholar 
72.Feng, J. et al. Warming-induced permafrost thaw exacerbates tundra soil carbon decomposition mediated by microbial community. Microbiome 8, 3 (2020).
Google Scholar 
73.Estop-Aragonés, C. et al. Assessing the potential for mobilization of old soil carbon after permafrost thaw: a synthesis of 14C measurements from the northern permafrost region. Glob. Biogeochem. Cycles 34, e2020GB006672 (2020).
Google Scholar 
74.Wik, M., Varner, R. K., Anthony, K. W., MacIntyre, S. & Bastviken, D. Climate-sensitive northern lakes and ponds are critical components of methane release. Nat. Geosci. 9, 99–105 (2016).
Google Scholar 
75.Schaefer, K., Lantuit, H., Romanovsky, V. E., Schuur, E. A. G. & Witt, R. The impact of the permafrost carbon feedback on global climate. Environ. Res. Lett. 9, 085003 (2014).
Google Scholar 
76.Xue, K. et al. Tundra soil carbon is vulnerable to rapid microbial decomposition under climate warming. Nat. Clim. Chang. 6, 595–600 (2016).
Google Scholar 
77.Bay, S. K. et al. Trace gas oxidizers are widespread and active members of soil microbial communities. Nat. Microbiol. 6, 246–256 (2021).
Google Scholar 
78.Singleton, C. M. et al. Methanotrophy across a natural permafrost thaw environment. ISME J. 12, 2544–2558 (2018).
Google Scholar 
79.Kwon, M. J. et al. Plants, microorganisms, and soil temperatures contribute to a decrease in methane fluxes on a drained Arctic floodplain. Glob. Chang. Biol. 23, 2396–2412 (2017).
Google Scholar 
80.Jin, X.-Y. et al. Impacts of climate-induced permafrost degradation on vegetation: a review. Adv. Clim. Chang. Res. 12, 29–47 (2020).
Google Scholar 
81.Song, X. et al. Soil moisture as a key factor in carbon release from thawing permafrost in a boreal forest. Geoderma 357, 113975 (2020).
Google Scholar 
82.Zhu, Y. et al. Disproportionate increase in freshwater methane emissions induced by experimental warming. Nat. Clim. Chang. 10, 685–690 (2020).
Google Scholar 
83.Watts, J. D., Kimball, J. S., Bartsch, A. & McDonald, K. C. Surface water inundation in the boreal-Arctic: potential impacts on regional methane emissions. Environ. Res. Lett. 9, 075001 (2014).
Google Scholar 
84.Thompson, R. L. et al. Methane fluxes in the high northern latitudes for 2005–2013 estimated using a Bayesian atmospheric inversion. Atmos. Chem. Phys. 17, 3553–3572 (2017).
Google Scholar 
85.Oh, Y. et al. Reduced net methane emissions due to microbial methane oxidation in a warmer Arctic. Nat. Clim. Chang. 10, 317–321 (2020).
Google Scholar 
86.Street, L. E. et al. Plant carbon allocation drives turnover of old soil organic matter in permafrost tundra soils. Glob. Chang. Biol. 26, 4559–4571 (2020).
Google Scholar 
87.Natali, S. M. et al. Large loss of CO2 in winter observed across the northern permafrost region. Nat. Clim. Chang. 9, 852–857 (2019).
Google Scholar 
88.Hu, Y., Fernandez-Anez, N., Smith, T. E. L. & Rein, G. Review of emissions from smouldering peat fires and their contribution to regional haze episodes. Int. J. Wildland Fire 27, 293–312 (2018).
Google Scholar 
89.Abbott, B. W. et al. Biomass offsets little or none of permafrost carbon release from soils, streams, and wildfire: an expert assessment. Environ. Res. Lett. 11, 034014 (2016).
Google Scholar 
90.Mack, M. C. et al. Carbon loss from boreal forest wildfires offset by increased dominance of deciduous trees. Science 372, 280–283 (2021).
Google Scholar 
91.Holloway, J. E. et al. Impact of wildfire on permafrost landscapes: a review of recent advances and future prospects. Permafr. Periglac. Process. 31, 371–382 (2020).
Google Scholar 
92.McCarty, J. L., Smith, T. E. L. & Turetsky, M. R. Arctic fires re-emerging. Nat. Geosci. 13, 658–660 (2020).
Google Scholar 
93.Scholten, R. C., Jandt, R., Miller, E. A., Rogers, B. M. & Veraverbeke, S. Overwintering fires in boreal forests. Nature 593, 399–404 (2021).
Google Scholar 
94.Koven, C. D. et al. A simplified, data-constrained approach to estimate the permafrost carbon-climate feedback. Phil. Trans. R. Soc. A 373, 20140423 (2015).
Google Scholar 
95.MacDougall, A. H. & Knutti, R. Projecting the release of carbon from permafrost soils using a perturbed parameter ensemble modelling approach. Biogeosciences 13, 2123–2136 (2016).
Google Scholar 
96.Cooper, M. D. A. et al. Limited contribution of permafrost carbon to methane release from thawing peatlands. Nat. Clim. Chang. 7, 507–511 (2017).
Google Scholar 
97.Andresen, C. G. et al. Soil moisture and hydrology projections of the permafrost region–a model intercomparison. Cryosphere 14, 445–459 (2020).
Google Scholar 
98.Bartsch, A., Pointner, G., Ingeman-Nielsen, T. & Lu, W. Towards circumpolar mapping of Arctic settlements and infrastructure based on Sentinel-1 and Sentinel-2. Remote Sens. 12, 2368 (2020).
Google Scholar 
99.Swingedouw, D. et al. Early warning from space for a few key tipping points in physical, biological, and social-ecological systems. Surv. Geophys. 41, 1237–1284 (2020).
Google Scholar 
100.Elder, C. D. et al. Airborne mapping reveals emergent power law of Arctic methane emissions. Geophys. Res. Lett. 47, e2019GL085707 (2020).
Google Scholar 
101.Byrne, B. et al. Improved constraints on northern extratropical CO2 fluxes obtained by combining surface-based and space-based atmospheric CO2 measurements. J. Geophys. Res. Atmos. 125, e2019JD032029 (2020).
Google Scholar 
102.Karlson, M. et al. Delineating northern peatlands using Sentinel-1 time series and terrain indices from local and regional digital elevation models. Remote Sens. Environ. 231, 111252 (2019).
Google Scholar 
103.Cusworth, D. H. et al. Synthesis of methane observations across scales: strategies for deploying a multitiered observing network. Geophys. Res. Lett. 47, e2020GL087869 (2020).
Google Scholar 
104.Bale, N. J. et al. Fatty acid and hopanoid adaption to cold in the methanotroph methylovulum psychrotolerans. Front. Microbiol. 10, 589 (2019).
Google Scholar 
105.Mackelprang, R. et al. Microbial survival strategies in ancient permafrost: insights from metagenomics. ISME J. 11, 2305–2318 (2017).
Google Scholar 
106.Siliakus, M. F., van der Oost, J. & Kengen, S. W. M. Adaptations of archaeal and bacterial membranes to variations in temperature, pH and pressure. Extremophiles 21, 651–670 (2017).
Google Scholar 
107.Johnson, S. S. et al. Ancient bacteria show evidence of DNA repair. Proc. Natl Acad. Sci. USA 104, 14401–14405 (2007).
Google Scholar 
108.Hueffer, K., Drown, D., Romanovsky, V. & Hennessy, T. Factors contributing to anthrax outbreaks in the circumpolar north. Ecohealth 17, 174–180 (2020).
Google Scholar 
109.Miner, K. R. et al. Emergent biogeochemical risks from Arctic permafrost degradation. Nat. Clim. Chang. 11, 809–819 (2021).
Google Scholar 
110.Perron, G. G. et al. Functional characterization of bacteria isolated from ancient Arctic soil exposes diverse resistance mechanisms to modern antibiotics. PLoS ONE 10, e0069533 (2015).
Google Scholar 
111.MacKelprang, R. et al. Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw. Nature 480, 368–371 (2011).
Google Scholar 
112.Burkert, A., Douglas, T. A., Waldrop, M. P. & Mackelprang, R. Changes in the active, dead, and dormant microbial community structure across a pleistocene permafrost chronosequence. Appl. Environ. Microbiol. 85, e02646-18 (2019).
Google Scholar 
113.Jansson, J. K. & Hofmockel, K. S. Soil microbiomes and climate change. Nat. Rev. Microbiol. 18, 35–46 (2020).
Google Scholar 
114.Hultman, J. et al. Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes. Nature 521, 208–212 (2015).
Google Scholar 
115.Schadel, C. et al. Potential carbon emissions dominated by carbon dioxide from thawed permafrost soils. Nat. Clim. Chang. 6, 950–953 (2016).
Google Scholar 
116.Lee, H. et al. A spatially explicit analysis to extrapolate carbon fluxes in upland tundra where permafrost is thawing. Glob. Chang. Biol. 17, 1379–1393 (2011).
Google Scholar 
117.Euskirchen, E. S., Edgar, C. W., Turetsky, M. R., Waldrop, M. P. & Harden, J. W. Differential response of carbon fluxes to climate in three peatland ecosystems that vary in the presence and stability of permafrost. J. Geophys. Res. Biogeosci. 119, 1576–1595 (2014).
Google Scholar 
118.Euskirchen, E. S., Bret-Harte, M. S., Shaver, G. R., Edgar, C. W. & Romanovsky, V. E. Long-term release of carbon dioxide from arctic tundra ecosystems in Alaska. Ecosystems 20, 960–974 (2017).
Google Scholar 
119.Karlsson, J. et al. Carbon emission from Western Siberian inland waters. Nat. Commun. 12, 825 (2021).
Google Scholar 
120.Schuur, E. A. G. et al. Tundra underlain by thawing permafrost persistently emits carbon to the atmosphere over 15 years of measurements. J. Geophys. Res. Biogeosci. 126, e2020JG006044 (2021).
Google Scholar 
121.Oechel, W. C. et al. Acclimation of ecosystem CO2 exchange in the Alaskan Arctic in response to decadal climate warming. Nature 406, 978–981 (2000).
Google Scholar 
122.Heijmans, M. M. P. D. et al. Tundra vegetation change and impacts on permafrost. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-021-00233-0 (2022).Article 

Google Scholar 
123.Kanevskiy, M. et al. Patterns and rates of riverbank erosion involving ice-rich permafrost (yedoma) in northern Alaska. Geomorphology 253, 370–384 (2016).
Google Scholar 
124.Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).
Google Scholar 
125.Schimel, D. & Schneider, F. D. Flux towers in the sky: global ecology from space. New Phytol. 224, 570–584 (2019).
Google Scholar 
126.Humphrey, V. et al. Soil moisture–atmosphere feedback dominates land carbon uptake variability. Nature 592, 65–69 (2021).
Google Scholar 
127.Jammet, M. et al. Year-round CH4 and CO2 flux dynamics in two contrasting freshwater ecosystems of the subarctic. Biogeosciences 14, 5189–5216 (2017).
Google Scholar 
128.Kohnert, K., Serafimovich, A., Metzger, S., Hartmann, J. & Sachs, T. Strong geologic methane emissions from discontinuous terrestrial permafrost in the Mackenzie Delta, Canada. Sci. Rep. 7, 5828 (2017).
Google Scholar 
129.Sayres, D. S. et al. Arctic regional methane fluxes by ecotope as derived using eddy covariance from a low-flying aircraft. Atmos. Chem. Phys. 17, 8619–8633 (2017).
Google Scholar 
130.Ueyama, M. et al. Upscaling terrestrial carbon dioxide fluxes in Alaska with satellite remote sensing and support vector regression. J. Geophys. Res. Biogeosci. 118, 1266–1281 (2013).
Google Scholar 
131.Davidson, S. J. et al. Upscaling CH4 fluxes using high-resolution imagery in Arctic tundra ecosystems. Remote Sens. 9, 1227 (2017).
Google Scholar 
132.Peltola, O. et al. Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations. Earth Syst. Sci. Data 11, 1263–1289 (2019).
Google Scholar 
133.Chang, R. Y. W. et al. Methane emissions from Alaska in 2012 from CARVE airborne observations. Proc. Natl Acad. Sci. USA 111, 16694–16699 (2014).
Google Scholar 
134.Saeki, T. et al. Carbon flux estimation for Siberia by inverse modeling constrained by aircraft and tower CO2 measurements. J. Geophys. Res. Atmos. 118, 1100–1122 (2013).
Google Scholar 
135.Kim, J. et al. Impact of Siberian observations on the optimization of surface CO2 flux. Atmos. Chem. Phys. 17, 2881–2899 (2017).
Google Scholar 
136.O’Shea, S. J. et al. Methane and carbon dioxide fluxes and their regional scalability for the European Arctic wetlands during the MAMM project in summer 2012. Atmos. Chem. Phys. 14, 13159–13174 (2014).
Google Scholar 
137.Gottwald, M. & Bovensmann, H. SCIAMACHY — Exploring the Changing Earth’s Atmosphere (Springer, 2011).138.Siewert, M. B. High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment. Biogeosciences 15, 1663–1682 (2018).
Google Scholar 
139.Arndt, K. A. et al. Arctic greening associated with lengthening growing seasons in Northern Alaska. Environ. Res. Lett. 14, 125018 (2019).
Google Scholar 
140.Widhalm, B., Bartsch, A. & Heim, B. A novel approach for the characterization of tundra wetland regions with C-band SAR satellite data. Int. J. Remote Sens. 36, 5537–5556 (2015).
Google Scholar 
141.Varon, D. J. et al. High-frequency monitoring of anomalous methane point sources with multispectral Sentinel-2 satellite observations. Atmos. Meas. Tech. 14, 2771–2785 (2021).
Google Scholar 
142.Bartsch, A., Hofler, A., Kroisleitner, C. & Trofaier, A. M. Land cover mapping in northern high latitude permafrost regions with satellite data: achievements and remaining challenges. Remote Sens. 8, 979 (2016).
Google Scholar 
143.Flato, G. et al. in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Ch. 9 (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).144.Kivimäki, E. et al. Evaluation and analysis of the seasonal cycle and variability of the trend from GOSAT methane retrievals. Remote Sens. 11, 882 (2019).
Google Scholar 
145.Lindqvist, H. et al. Does GOSAT capture the true seasonal cycle of carbon dioxide? Atmos. Chem. Phys. 15, 13023–13040 (2015).
Google Scholar 
146.Chadburn, S. et al. Carbon stocks and fluxes in the high latitudes: using site-level data to evaluate Earth system models. Biogeosciences 14, 5143–5169 (2017).
Google Scholar 
147.Graven, H. D. et al. Enhanced seasonal exchange of CO2 by northern ecosystems since 1960. Science 341, 1085–1089 (2013).
Google Scholar 
148.Aas, K. S. et al. Thaw processes in ice-rich permafrost landscapes represented with laterally coupled tiles in a land surface model. Cryosphere 13, 591–609 (2019).
Google Scholar 
149.Westermann, S. et al. Transient modeling of the ground thermal conditions using satellite data in the Lena River delta, Siberia. Cryosphere 11, 1441–1463 (2017).
Google Scholar 
150.Houweling, S. et al. An intercomparison of inverse models for estimating sources and sinks of CO2 using GOSAT measurements. J. Geophys. Res. 120, 5253–5266 (2015).
Google Scholar 
151.Houweling, S. et al. Global inverse modeling of CH4 sources and sinks: an overview of methods. Atmos. Chem. Phys. 17, 235–256 (2017).
Google Scholar 
152.Tsuruta, A. et al. Global methane emission estimates for 2000–2012 from CarbonTracker Europe-CH4 v1.0. Geosci. Model Dev. 10, 1261–1289 (2017).
Google Scholar 
153.Virkkala, A. M., Abdi, A. M., Luoto, M. & Metcalfe, D. B. Identifying multidisciplinary research gaps across Arctic terrestrial gradients. Environ. Res. Lett. 14, 124061 (2019).
Google Scholar 
154.Hakkarainen, J., Ialongo, I., Maksyutov, S. & Crisp, D. Analysis of four years of global XCO2 anomalies as seen by Orbiting Carbon Observatory-2. Remote Sens. 11, 850 (2019).
Google Scholar 
155.Fisher, J. B. et al. Missing pieces to modeling the Arctic-Boreal puzzle. Environ. Res. Lett. 13, 020202 (2018).
Google Scholar 
156.McGuire, A. D. et al. An assessment of the carbon balance of Arctic tundra: comparisons among observations, process models, and atmospheric inversions. Biogeosciences 9, 3185–3204 (2012).
Google Scholar 
157.Lenton, T. M. & Williams, H. T. P. On the origin of planetary-scale tipping points. Trends Ecol. Evol. 28, 380–382 (2013).
Google Scholar 
158.Lenton, T. M. Arctic climate tipping points. Ambio 41, 10–22 (2012).
Google Scholar 
159.Lenton, T. M. et al. Climate tipping points — too risky to bet against. Nature 575, 592–595 (2019).
Google Scholar 
160.Fleisher, A. J., Long, D. A., Liu, Q., Gameson, L. & Hodges, J. T. Optical measurement of radiocarbon below unity fraction modern by linear absorption spectroscopy. J. Phys. Chem. Lett. 8, 4550–4556 (2017).
Google Scholar 
161.Genoud, G. et al. Laser spectroscopy for monitoring of radiocarbon in atmospheric samples. Anal. Chem. 91, 12315–12320 (2019).
Google Scholar 
162.Levin, I. et al. Observations and modelling of the global distribution and long-term trend of atmospheric 14CO2. Tellus B Chem. Phys. Meteorol. 62, 26–46 (2010).
Google Scholar 
163.Voigt, C. et al. Warming of subarctic tundra increases emissions of all three important greenhouse gases — carbon dioxide, methane, and nitrous oxide. Glob. Chang. Biol. 23, 3121–3138 (2017).
Google Scholar 
164.Mu, C. C. et al. Permafrost collapse shifts alpine tundra to a carbon source but reduces N2O and CH4 release on the northern Qinghai-Tibetan Plateau. Geophys. Res. Lett. 44, 8945–8952 (2017).
Google Scholar 
165.Krogh, S. A., Pomeroy, J. W. & Marsh, P. Diagnosis of the hydrology of a small Arctic basin at the tundra-taiga transition using a physically based hydrological model. J. Hydrol. 550, 685–703 (2017).
Google Scholar 
166.Burke, E. J., Zhang, Y. & Krinner, G. Evaluating permafrost physics in the Coupled Model Intercomparison Project 6 (CMIP6) models and their sensitivity to climate change. Cryosphere 14, 3155–3174 (2020).
Google Scholar 
167.Treat, C. C., Bloom, A. A. & Marushchak, M. E. Nongrowing season methane emissions — a significant component of annual emissions across northern ecosystems. Glob. Chang. Biol. 24, 3331–3343 (2018).
Google Scholar 
168.Kelley, J. J., Weaver, D. F. & Smith, B. P. The variation of carbon dioxide under the snow in the Arctic. Ecology 49, 358–361 (1968).
Google Scholar 
169.Du, J. et al. Assessing global surface water inundation dynamics using combined satellite information from SMAP, AMSR2 and Landsat. Remote Sens. Environ. 213, 1–17 (2018).
Google Scholar 
170.Webb, E. E. et al. Increased wintertime CO2 loss as a result of sustained tundra warming. J. Geophys. Res. Biogeosci. 121, 249–265 (2016).
Google Scholar 
171.Grosse, G., Goetz, S., McGuire, A. D., Romanovsky, V. E. & Schuur, E. A. G. Changing permafrost in a warming world and feedbacks to the Earth system. Environ. Res. Lett. 11, 040201 (2016).
Google Scholar 
172.Kleinen, T. & Brovkin, V. Pathway-dependent fate of permafrost region carbon. Environ. Res. Lett. 13, 094001 (2018).
Google Scholar 
173.Anthony, K. M. W. et al. A shift of thermokarst lakes from carbon sources to sinks during the Holocene epoch. Nature 511, 452–456 (2014).
Google Scholar 
174.Crichton, K. A., Bouttes, N., Roche, D. M., Chappellaz, J. & Krinner, G. Permafrost carbon as a missing link to explain CO2 changes during the last deglaciation. Nat. Geosci. 9, 683–686 (2016).
Google Scholar 
175.Tesi, T. et al. Massive remobilization of permafrost carbon during post-glacial warming. Nat. Commun. 7, 13653 (2016).
Google Scholar 
176.McClain, M. E. et al. Biogeochemical hot spots and hot moments at the interface of terrestrial and aquatic ecosystems. Ecosystems 6, 301–312 (2003).
Google Scholar 
177.Bernhardt, E. S. et al. Control points in ecosystems: moving beyond the hot spot hot moment concept. Ecosystems 20, 665–682 (2016).
Google Scholar 
178.Kuze, A. et al. Update on GOSAT TANSO-FTS performance, operations, and data products after more than 6 years in space. Atmos. Meas. Tech. 9, 2445–2461 (2016).
Google Scholar 
179.Eldering, A. et al. The Orbiting Carbon Observatory-2 early science investigations of regional carbon dioxide fluxes. Science 358, eaam5745 (2017).
Google Scholar 
180.Yang, D. et al. First global carbon dioxide maps produced from TanSat measurements. Adv. Atmos. Sci. 35, 621–623 (2018).
Google Scholar 
181.Glumb, R., Davis, G. & Lietzke, C. in IEEE International Geoscience and Remote Sensing Symposium 1238–1240 (IEEE, 2014).182.Lorente, A. et al. Methane retrieved from TROPOMI: improvement of the data product and validation of the first 2 years of measurements. Atmos. Meas. Tech. 14, 665–684 (2021).
Google Scholar 
183.Ehret, G. et al. MERLIN: a French–German space lidar mission dedicated to atmospheric methane. Remote Sens. 9, 1052 (2017).
Google Scholar 
184.Bousquet, P. et al. Error budget of the MEthane Remote LIdar missioN and its impact on the uncertainties of the global methane budget. J. Geophys. Res. Atmos. 123, 11,766–11,785 (2018).
Google Scholar 
185.Bezy, J.-L. et al. in IEEE International Geoscience and Remote Sensing Symposium 8400–8403 (IEEE, 2019).186.Ingmann, P. et al. Requirements for the GMES atmosphere service and ESA’s implementation concept: Sentinels-4/-5 and -5p. Remote Sens. Environ. 120, 58–69 (2012).
Google Scholar 
187.Nassar, R. et al. The atmospheric imaging mission for northern regions: AIM-North. Can. J. Remote Sens. 45, 423–442 (2019).
Google Scholar 
188.Polonsky, I. N., O’Brien, D. M., Kumer, J. B., O’Dell, C. W. & the geoCARB Team. Performance of a geostationary mission, geoCARB, to measure CO2, CH4 and CO column-averaged concentrations. Atmos. Meas. Tech. 7, 959–981 (2014).
Google Scholar 
189.Chahine, M. T. et al. Improving weather forecasting and providing new data on greenhouse gases. Bull. Am. Meteorol. Soc. 87, 911–926 (2006).
Google Scholar 
190.Clerbaux, C. et al. Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder. Atmos. Chem. Phys. 9, 6041–6054 (2009).
Google Scholar 
191.Han, Y. et al. Suomi NPP CrIS measurements, sensor data record algorithm, calibration and validation activities, and record data quality. J. Geophys. Res. Atmos. 118, 734–12,748 (2013).
Google Scholar 
192.Zou, C. Z. et al. The reprocessed Suomi NPP satellite observations. Remote Sens. 12, 2891 (2020).
Google Scholar 
193.Obu, J. et al. ESA Permafrost Climate Change Initiative (Permafrost_CCI): permafrost climate research data package v1 (CEDA, 2020).194.Voigt, C. et al. Nitrous oxide emissions from permafrost-affected soils. Nat. Rev. Earth Environ. 1, 420–434 (2020).
Google Scholar 
195.Arctic Climate Impact Assessment. Impacts of a Warming Arctic: Arctic Climate Impact Assessment (Cambridge Univ. Press, 2004). More

Study areaThe North Sea is a continental sea connected to the Atlantic Ocean through the English Channel in the southwest and between northern Shetland along the 61° latitude parallel to Norway in the north (Fig. 1). It is bordered by Norway, Denmark, Germany, the Netherlands, Belgium, France and the UK, and has a surface of 570,000 km2. The North Sea has an average depth of 95 m, yet maximum depths of ca. 700 m are found in the Norwegian Trench. The maximum tidal amplitude of the North Sea can reach up to 8 m, average winter sea surface temperatures are ca. 6 °C and average summer temperatures reach ca. 17°C33. The English Channel encompasses the marine strait between the UK and France. It covers 75,000 km2, has an average depth of 63 m, a maximum depth of 174 m and can reach a maximum tidal amplitude up to 12 m. The average winter and summer sea surface temperatures in the English Channel are ca. 5 and 20 °C, respectively54.TaggingIn total, 320 silver eels were tagged with pop-off archival tags (Table 1; Supplementary Table S2). In Belgium, 238 eels were caught and tagged at a drainage system upstream of the Yser Estuary (hereafter referred to as the Belgian eels) in 2018–2020 via nets that were attached to gravitational discharge sluice gates (coordinates: 51.127 N, 2.761 E) in October, November and December (n2018 = 102, n2019 = 60 and n2020 = 76). In Germany, 82 eels were tagged in 2011 and 2012. In early December 2011, seven eels were caught at Lake Plön (coordinates: 54.137 N, 10.334 E) with fyke nets. During September, October and November 2012, eels were caught in the Rivers Eider (n = 30; coordinates: 54.190 N, 9.093 E) and Havel (n = 45; coordinates: 52.419 N, 12.571 E) with fyke and stow nets, respectively.Upon capture, the eels were anaesthetized with 0.3 ml/L clove oil (Belgium), 0.4 ml/L ethylene glycol monophenyl ether (Germany 2011) or 120 mg/L MS-222 (Germany 2012), and various morphometric characteristics were measured to identify the life stage55: total length (to the nearest mm), weight (to the nearest g), horizontal and vertical eye diameter (to the nearest 0.01 mm in Belgium and to the nearest 0.1 mm in Germany) and pectoral fin length (to the nearest 0.01 mm and 0.1 mm in Belgium and Germany, respectively). Given that their total body length was  > 450 mm, all eels were considered female55. According to the morphometrics, five Belgian eels could be considered in the premigratory stage (FIII); however, based on visual inspection, they were considered silver eels (i.e. silver-coloured abdomen, dark grey on the dorsal side, jaw hinge not proceeding beyond the eye, enlarged eyes and dark coloured pectoral fins). The other 315 eels identified as silver eels based on both morphometry and visual inspection (201 FIV stage and 114 FV stage).Eels weighing ≥ 550 g were externally fitted with a G5 PDST (CEFAS Technology Ltd, UK), which log temperature and pressure (providing information on depth). They were attached applying the three-point Westerberg attachment method56. Two tag types were used: one with a separate tag and pop-off mechanism (Germany) and one where both mechanisms were integrated (Belgium). The flotation collar of the PDSTs was painted bright red, contained contact information and a cash reward to stimulate retrieval by the general public (e.g. beach combers and fishermen). The seven eels caught in 2011 in Germany (minimum 1220 g) were fitted with PSATs (X-Tag, Microwave Telemetry Inc., USA), also using the Westerberg-method56. Like the PDSTs, the PSATs record temperature and pressure. After release, they drift to the surface and transmit the data to the user via the ARGOS satellite system (www.argos-system.org). For the specifications of the different tags, we refer to Supplementary Table S3.Upon recovery from the anaesthetic, eels tagged with PDSTs were released close to their capture locations in the rivers Eider (coordinates 2011: 54.381 N, 9.009 E; coordinates 2012: 54.379 N, 9.013 E), Elbe (coordinates 1: 53.793 N, 9.402 E; coordinates 2: 53.569 N, 9.700 E; coordinates 3: 53.396 N, 10.171 E) and Yser (coordinates: 51.135 N, 2.757 E) (Table 1). The seven eels captured for PSAT tagging in 2011 were held for several weeks in the Thünen Institute of Fisheries Ecology, then tagged and released the same day; others were tagged in the field.PreprocessingOnce downloaded, the temperature and pressure data obtained from the PDSTs was subsampled to 1-min (Belgian eels) or 2-min (German eels) intervals to reduce the datasets and improve geolocation calculation time; this discrepancy is due to the minimum logging rate of the tags (Supplementary Table S3). Linear regression was applied to correct for pressure sensor drift over time. Indeed, pressure values increased over time even if the tag was kept at atmospheric pressure level. The regression was applied between 15 min before release and the moment the tag popped off and reached the surface, since the tag was then considered at sea level and hence to be under zero pressure.The PSAT data were retrieved through the ARGOS satellite system as a subset with 15-min intervals and converted to values of pressure and temperature. Contemporaneous values of temperature and depth were not always transmitted due to the transmission method. As a consequence of the tag release programming, the transmission of the first position for one of the tags was only received five days after the tag reached the sea surface.GeolocationThe daily movements of each electronically tagged European eel were reconstructed using an adapted version of the tidal geolocation model of Pedersen et al.57. The geolocation model uses a novel Fokker–Planck based method to combine the tidal location method of Metcalfe and Arnold58 with a hidden Markov model (HMM), such that an individual’s daily location d is modelled conditionally on its previous location (d − 1), its inferred behavioural state ds, where behaviour is defined by a single diffusivity parameter (i.e. the maximum amount of movement permitted in a given day), and the observations made between d and d − 1. In this case, observations consisted of the recorded depth (m; D1, …, Dn) and temperature (°C; T1, …, Tn), where n is the number of measurements made per day (the HMM down-samples to 10-min intervals, hence 144 measurements per day), and any hydrostatic (tidal) data which are derived from the sinusoidal pressure cycle recorded in the depth data when a fish is at rest on the seafloor. In addition to bathymetry and tidal amplitude with phase, the model was developed to include sea surface temperature (SST), which can provide additional validation when fish are swimming at or near the surface (i.e. depth ≤ 20 m)59,60, and temperature at depth, which can provide additional validation when fish remain at depths well below the sea surface61,62.The model was run in three different configurations for each recovered dataset: (i) using the tidal location model only (as for Pedersen et al.57), hereafter termed TLM geolocation; (ii) using the TLM plus sea surface temperature (as for Wright et al.60), hereafter termed SST geolocation, and (iii) using temperature at the surface and sub-surface, hereafter termed 3D geolocation (Supplemental Fig. S3). The final trajectory output for the PDST Belgian eels and PSAT German eels was obtained via 3D geolocation, while SST geolocation was used for the PDST German eels. The reason for this discrepancy is that the German PDST eels stayed closer to the coast and in shallower water. Consequently, the 3D geolocation results were more prone to error due to coastal influences on water temperature. As a result, we used the SST geolocation method for these datasets to obtain more reliable results.Data for the model were derived from publicly available resources. Gridded global bathymetry data were obtained from the general bathymetric chart of the oceans (Gebco; British Oceanographic Data Centre, Liverpool, United Kingdom, 2009). Tidal constituents were obtained from the Oregon State University Tidal Prediction model, as described in Egbert and Erofeeva63. Sea surface temperature data were sourced from OSTIA64, while temperature at depth data were sourced from the operational Mercator global ocean analysis and forecast system65. These datasets were downloaded from the Copernicus Marine Environmental Model Service (CMEMS: documented here http://resources.marine.copernicus.eu/documents/PUM/CMEMS-GLO-PUM-001-024.pdf). Data were sourced so as to fit the spatial scale of the model (30°N to 80°N and from 110°W to 60°E) and coarsened to reduce model run-time by modifying the spatial grid to a 1/10th of a degree resolution. The output of the model is a nonparametric probability distribution of the geographical position from which a most probable location, for each day at liberty, and a most probable movement path can be estimated.Prior to running the model, a number of constraints and input parameters were defined to ensure that the model ran effectively. The recapture information was either set as (a) the latitude and longitude where the tag was recaptured, with a high confidence ( 200 m) and hence did not exhibit diel vertical migrations, the input estimates of longitude were based on a simple linear interpolation from release to estimated pop-up. However, for eels that did reach oceanic depths, the time of local noon was estimated (based on the timing of significant diel vertical migrations, as for Righton et al.16), and used to estimate longitude. Geolocation was conducted with MATLAB software66.Migration routesOnly datasets containing ≥ 100 km of net tracking distance were included for further analysis, leading to 54 datasets from the 96 retrieved tags and 320 tagged eels. The net tracking distance was identified as the distance along the reconstructed trajectory between the release of the tagged eel and the pop-off event. When an eel was ingested by a predator, leading to the tag tracking the predator rather than the eel, the data were excluded from the day the eel was predated. The 100 km cut-off point was arbitrarily chosen to select migration paths of sufficient length for further analysis (e.g. migration direction); tracks had a minimum deployment duration of 4 days.Migration speedTo exclude a size-effect, we first applied an independent two-sample t-test to confirm eel sizes (i.e. weight) did not differ between Belgian and German eels. The assumptions of normality (Shapiro–Wilk test), homogeneity of variances (F-test) and independence were met (weight measurements are individual-specific and therefore independent).Next, an independent two-sample t-test was conducted to test if the total migration speeds (i.e. the ground speed along the reconstructed trajectory between the release of the tagged eel and the pop-off or predation event) differed between Belgian and German eels. The assumptions were tested and met as described above.Finally, we tested if the daily migration speed (i.e. the ground speed along the reconstructed trajectory per day) differed according to the eel’s position (i.e. modelled latitude and longitude) via a linear mixed effects model. The tag IDs were implemented as a random effect to account for autocorrelation. Since the two-sample t-test showed a significant difference between Belgian and German total migration speeds, we performed a separate analysis on eels from both countries. Assumptions of normality, homogeneity of variances and independence were tested and met.The migration speed analyses were conducted in R (version 3.6.3)67. The packages ‘lme4’ and ‘nlme’ were used to conduct the linear mixed effects model.Ethical statementEels were tagged using approved protocols by trained and individually licensed scientists working under national project authority in accordance with institutional and national guides for the care and use of laboratory animals. These guidelines are consistent with Institutional Review Board/Institutional Animal Care and Use Committee guidelines. Tagging in Belgium was carried out in accordance with the Belgian national and regional regulations for animal welfare and treatment (Permit ID: EC INBO-011). Tagging in Germany followed German legislation concerning care and use of laboratory animals, and ethical permission for the experiments was given by the Ministry of Energy, Agriculture, the Environment, and Rural Areas of the federal state Schleswig–Holstein (reference numbers V312-72241.123-34 (90-8/11) and V311-7224.123.3 (93-6/12) for tagging in 2011 and 2012 respectively). More

Plant defence traitsWe compiled species level data for five plant traits: wood density (WD), leaf and stem spinescence, latex production, and leaf size, for tropical and extra-tropical South and Central American woody species (i.e., the Neotropical biogeographic realm). WD was obtained for 2577 species from ref. 44. We only used wood density data from Zanne et al.44, because this study used WD measured in stems, whereas most other studies with available data used WD measured in branches. Leaf size data were obtained for 2660 woody species from Wright et al.37. We did not include leaf size from herbaceous species because herbaceous and woody species are influence by different megafauna guilds, suggesting distinct mechanisms, and because this dataset37 only included data for 253 Neotropical herbaceous species. The presence or absence of stem (and/or branch) spines (mostly thorns, but also prickles) were obtained from Dantas and Pausas45 for Neotropical savanna and forest species (1004 species) and complemented with other literature sources for other ecoregions (listed in the supplementary materials) using the names of the species for which we had WD and Leaf Size data. Our final stem spines dataset included 2843 woody species. We also compiled data on the presence of latex in plant stems and leaves for all the species for which we had data on other traits (3160 species; references in the supplementary materials). Finally, we also compiled data on leaf spines. While we managed to find leaf spine data for a total of 2173 woody species, we found spinescence in leaves to be especially concentrated in the palm Family (Arecaceae; 198 out of 221 species with leaf spines). Moreover, out of the non-palm species, all but three species also presented stem spines, indicating that, for other taxa, leaf spines might be dependent on the presence of stem spines at the region (in palms, 51% have stem spines). Thus, we only used leaf spinescence data of palm species (694 species) from the global Palm Traits Database 1.046.For wood density and leaf size, we often had more than one trait value per species (1005 and 831 species with more than one trait value, respectively). Thus, we computed the species mean trait value. This rarely occurred for binary traits (spinescence and latex) and, when occurred, the maximum value was used (0 for absence and 1 for presence). This later decision was based in the assumption that omitting the presence of spines or latex is more likely than incorrectly reporting the presence when it is absent. Moreover, some of these traits can be plastic18.From species to ecoregionsWe searched for geographical distribution data (coordinates) from the Global Biodiversity Information Facility (GBIF) for all of the species in each species-trait dataset (Data available from GBIF using the following doi: WD: https://doi.org/10.15468/dl.3vua3x; Stem spines: https://doi.org/10.15468/dl.ar5ddj; Latex: https://doi.org/10.15468/dl.m8dzjd; Leaf spines: https://doi.org/10.15468/dl.vv8gw4; Leaf size: https://doi.org/10.15468/dl.k98nxc). For this search, we used tools provided by the “rgbif” package for R in which species names are updated to the most recent classification and the returned occurrences also include those associated with synonyms (i.e., the “backbone” method). We labelled the obtained geographical coordinates according to their ecoregion and biogeographical realm (following Dinerstein et al.47) and cropped out occurrences falling outside of the Neotropical realm. Since occurrence data was not available to all the species in our initial trait dataset, the number of species used to calculate ecoregion level means was reduced to 2110 species, for wood density, 2133, for leaf size, 2629, for stem spines, 2714, for latex, and 657, for leaf spines. A detailed evaluation of the representativity of this data in relation to ecoregion- and Neotropical- level patterns can be found in the Supplementary Methods. Based on the occurrence data and their ecoregion label, we built a species abundance (columns) by ecoregion (rows) matrix for each trait.We obtained ecoregion scale abundance-weighted means for continuous traits (WD and Leaf Size) by: (1) Multiplying species abundance in each grid cell of the ecoregion by the mean species value; (2) Summing up the row values; (3) dividing the resulting row sum by the total species abundance (row sum prior to trait multiplication), and (4) calculating the ecoregions’ means (across all of the grid cells). For Stem Spines and Latex (binary traits), we used a similar procedure, but the maximum (0 for absence and 1 for presence) value was used instead of the mean in step (1), and step (2) was directly used to calculate the number of presences (i.e., 1 s). Moreover, instead of the steps (3) and (4), we calculated the number of absences as the difference between the total abundance (row sums before trait multiplication) and the values obtained in step (2). This process resulted in weighted means for WD and stem spinescence for 173 ecoregions, and Leaf Size and Latex for 174, out of the 179 Neotropical ecoregions. For leaf spinescence, we used a similar approach, although, because of the fewer species, the abundance estimate from GBIF was less reliable. Thus, we transformed the ecoregion species abundance to presence/absence before multiplying the trait values (0/1 for absence/presence). We obtained leaf spinescence data for 159 out of the 179 Neotropical ecoregions. The species- and ecoregion- level data is provided in the Supplementary Data and in ref. 47.Historical megafauna distributionWe obtained data on historical distribution of megafauna species from the MegaPast2Future/PHYLACINE_1.2 dataset24, a dataset containing distribution maps (96.5 km of spatial resolution) and functional traits for mammal species of the last 130,000 years. From this dataset, we obtained the probable past distribution of extinct large mammal herbivore (hereafter, “megafauna”) species, if these species were still alive today (“Present Natural” scenario; see details below). The “Present Natural” distribution of extinct species in this database is based on the estimated historical distribution (i.e., preceding anthropogenic range modifications) of extant species that are known (from the fossil record) to have coexisted with the extinct species. In this approach, an extinct species is considered to have been present in a given grid cell if at least 50% of the extant species that were found coexisting with the extinct species in the fossil (and subfossil) record was predicted to have occurred in the same cell prior to anthropogenic range modifications24,48. This approach assumes that, since extant and extinct species coexisted in the same locations, they must have had similar ecological requirements. It also assumes that megafauna extinction had anthropogenic causes, instead of causes related to climate change49, which is largely accepted in the literature50.We extracted the “Present Natural” distribution of extinct mammal (coded “EP” for IUCN status; i.e., “extinct in prehistory”, meaning before 1500 CE) whose body mass was higher than 50 kg (megafauna), and for which at least 90% of their diet consisted of plants (i.e., strict herbivores). For each Ecoregion, we began by calculating two megafauna-related metrics: extinct megafauna species richness (Mrich) and their mean body mass (Mbm). For this, we cropped the distribution maps of the megafauna species (containing 1 for presence and 0 for absence of each species) to the Neotropical realm. To calculate Mrich, we (1) counted species presences within each of the grid cells in the global grid (i.e., calculated the cell’s megafauna richness); (2) assigned the corresponding ecoregion label to the resulting richness grid cells, subset the richness cell values corresponding to the Neotropical region; and (3) calculated the mean for each Neotropical ecoregion. For Mbm, we replaced the presences of the megafauna species in the initial raster object (grid cell map of each megafauna species) by their body masses and calculated the grid cell-level mean body mass, before calculating the ecoregion-level means. We also calculated megafauna density and secondary productivity based on allometric equations that relate these metrics to megafauna body mass. However, we did not used megafauna density and secondary productivity because they were strongly correlated to megafauna richness (Supplementary Fig. 3). More details on how these metrics were calculated can be found in the Supplementary Methods.We also obtained diet preference information from the literature for most megafauna species that occurred in the Neotropical region (details and references in the Supplementary Material). Based on these information, we calculated the richness of large browser (MBrich for megabrowser richness), grazer (MGrich for megagrazer richness), and mixed-feeder (MMfrich for mega mixed-feeder richness) species by sub setting the megafauna species by grid cell array before the richness calculation in order to select only species that were classified within the correspondent subgroup.Extant herbivore mammal distributionWe also compiled data on the distribution, body mass and diet of extant and recently extinct (i.e., extinct after 1500 CE) herbivore mammal species (for simplicity, called ‘extant’ species in this study). As with megafauna maps, the distributions used represented reconstructions for periods preceding anthropogenic reduction of extant herbivores ranges (“Present Natural” scenario), based on abiotic, biotic and geographic variables48, rather than the currently observed distribution. This scenario was used because modern anthropogenic range reductions are too recent to produce substantial geographic effects at this spatial scale. These data were obtained by sub setting the MegaPast2Future/PHYLACINE_1.2 dataset to exclude species that were coded “EP” for IUCN status and that were not strict herbivores (at least 90% of the diet constituting of plants). We subsequently associated diet information to these species using data from ref. 51 and excluded all species that did not feed mainly on aboveground vegetative plant tissues (i.e., species that fed mostly on fruits, seed, roots were excluded). This later filtering was because the number of herbivores that feed mostly on seed and fruit increase with decreasing size (and this dataset included small mammals). We subsequently calculated the same metrics as for the extinct megafauna species (except for the richness of mixed-feeders as our source for diets50 labelled species according to dominant feeding pattern). For this, we used the same approach described for extinct megafauna species. We did not use a size threshold for extant species because there were only 13 extant mammal herbivore species with over 50 kg in the Neotropical region, most of which were grazers (9 species; 4 species were mixed-feeders and none were browsers). Therefore, we relied on the mean body mass metric calculated for extant mammals to detect potential size-related effects.Climate, soil, fire, insularity, and hurricanesFor each Ecoregion, we obtained data on climate (mean annual precipitation and temperature, and rainfall seasonality) and soil (sand content, pH, and cation exchange capacity) variables. Climate data was obtained from WorldClim 2.1 (10 min spatial resolution) and was based on climate data from 1970 to 200052. Soil data were obtained from SoilGrids (5 km of spatial resolution)53, and consisted of mean values for two depths, 0.05 and 2 m. We calculated Ecoregion level means for all of the soil and climate variables after intersecting the climate and soil grid maps with the ecoregion map.We obtained the number (a proxy for frequency) and intensity of wildfires per ecoregion area using the MODIS active fire location product (MCD14ML)54. We only considered fires (i.e., hotspots) with detection confidence of 95% or higher occurring from November 2000 to December 2019 (both included). To ensure that only wildfires were considered, we associated each fire pixel with a land cover type (300 m of spatial resolution) from ref. 55 for a buffer area of 1000 m surrounding the fire pixel centroid. We excluded all of the fires occurring in areas in which more than 10% of the surrounding land cover pixels corresponded to agricultural, urban and water classes. We calculated the number of wildfires per ecoregion area by dividing the fire count of each Ecoregion by the ecoregion area, and multiplying the resulting value by the proportion of vegetated land cover pixels (same classes used to exclude fires in anthropogenic areas and water bodies above). Fire intensity was estimated as the average fire radiative power across all detected MODIS hotspots in the ecoregion. Ecoregions lacking large preserved vegetated areas (criteria above) were excluded from subsequent analyses.Using the ecoregion map, we also classified ecoregions into insular (1), when most of the ecoregion area was located in islands, vs. continental (0), otherwise. This was performed because island biogeography theory predicts that, in island, species richness should be low due to low colonization and high extinction rates. Insularity has also been shown to reduce megafauna body size (i.e., the island rule), even though the mechanisms are not fully understood56. We also compiled data on hurricane activity, as woody density was suggested to confer resistance against this disturbance57. We used data from 1990 to 2019 from the HURDAT2 dataset58, containing six-hourly information about the location of all of the known tropical and subtropical cyclones (0.1° latitude/longitude). We used the sum of hurricane occurrences per ecoregions divided by ecoregion area as an indicator of hurricane activity.Statistical analysesTo understand megafauna patterns, we began by fitting (multiple) regression models with habitat-related (fire, climate, soil) and geographical (insularity) variables as predictors. We expected that megafauna richness in general was higher under savanna conditions (arid nutrient-rich or mesic nutrient-poor environments with frequent fires)1,22. We also expected that megafauna richness and body mass were affected negatively by insularity (i.e., following the island biogeography theory and island rule). Before the analyses, we tested the correlations among all of the variables that would eventually be entered as predictors in the same model for both the megafauna and trait models (Supplementary Table 1), in order to avoid multicollinearity associated with highly correlated variables (here, r ≥ 0.60). Since mean annual precipitation and soil pH were strongly positively correlated (r = −0.78), for all of the analyses (including the analyses with functional traits, described below), model selection was performed separately for these two variables (i.e., two different model selection procedures, one containing each of the two variables among the initial set of predictors). We selected the best among the two resulting models as that with the lowest AIC (differences higher than two points in all of the cases). To make sure that no multicollinearity remained we also calculated the Variation Inflation Factor (VIF) for all of the predictor variables as 1/tolerance, where tolerance is calculated as 1 minus the R2 of all of the model regressing a predictive variable against all of the other predictors. In all of the models, VIF was 3.33 or smaller (i.e., a tolerance of 0.30 or higher), indicating absence of multicollinearity.Model simplification was carried interactively using stepwise (both forward and backward) searching for the model with the lowest AIC (using R’s “step” function) and subsequently retaining only the significant variables (p ≤ 0.05). We calculated the Pearson r statistics as a measure of effect size for the selected variables as well as the associated confidence intervals, using the packages “parameters” and “effectsize” for R. The average contribution of each predictor variable was also calculated, using the package “dominanceanalysis”, as the mean difference in R2 before and after removing the target variable from models containing all of the possible subset combinations of the selected predictor variables, including the full selected model.For testing whether the studied plant functional traits were related to our megafauna indicators, we fit linear models to WD and leaf size, and generalized linear models (GLM; binomial family) for spinescence and latescence, using ecoregion as the unit. For spinescence and latescence, we used the matrix containing the count of spiny/latex and non-spiny/non-latex plants (species abundance; for stem spines and latex) or number of species with or without spines (for leaf spines; see above) as response variables. The predictor variables included the animal indicators for extinct megafauna and extant herbivores, as well as climate, soil, and fire predictors (and, for WD, hurricane counts). Because total, as well as megagrazer, megabrowser, and mega mixed-feeder species richness were strongly positively correlated (Supplementary Table 1), we used the richness difference between grazers and browsers to evaluate the effect of diet (Supplementary Fig. 1). For consistency, we used the same diet variable for extant and extinct species. Since we did not identify strong correlations among extinct megafauna and extant herbivore indicators (Supplementary Table 1), these variables were all entered simultaneously in the same initial models. As with the analyses of the megafauna indices, we also used r as effect size and calculated the average predictor contribution in terms of R2 for these models. For the later, we used the MacFadden Pseudo-R2 in the GLM models as implemented in the “pscl” and “dominanceanalysis” packages for R, as this statistic is the most comparable with R2 from linear multiple regression (Maximum Likelihood and Cragg and Uhler’s Pseudo-R2 were also calculated for the logistic models), and adjusted R2 for continuous traits. Islands were not included in these models, as island plants were expected to respond differently due to the effects of insularity on animal species richness, precluding megafauna and extant mammal richness from being accurate proxies for consumer abundance. For stem spines, we always included a quadratic term to both megafauna and extant mammal herbivore body mass, as evidence suggest that medium-size herbivores (i.e., approximately 250 kg) are important selective drivers of this trait12. If a significant relationship with our herbivory indicators (both extant and extinct) were significant but not indicative of a selective effect by herbivores (for more defended plants), this relationship was discarded (along with related variables, such as diet); this happened only once, for leaf size, which increased with extant herbivore richness (Supplementary Table 8).For all of the general linear regression models, assumptions of normality, homoscesticity and lack of spatial autocorrelation in the residuals were checked using the Kolmogorov–Smirnov, Breusch–Pagan and Moran’s I tests, respectively. For the later, ecoregions were considered neighbours when they were adjacent and non-neighbour otherwise. In some cases, heteroscesticity was detected and, thus, the significance of the coefficients was tested using heteroskedasticity-consistent covariance matrix estimation. If one or more variable lost their significance they were stepwise removed from the final model, beginning by the least significant, until all remaining variables had a significant effect. Overdispersion in the generalized linear model was also detected and dealt with using overdispersed binomial logit models, as implemented in the “dispmod” package for R, in which weights are interactively calculated and used to maintain the residual deviance lower than the degrees of freedom. To confirm that the detected associations between megafauna indices and plant traits were robust, we also tested the coefficient significance using randomization of the plant species by ecoregion matrices (see Supplementary Methods for details).To test the prediction that Neotropical ecoregions could be broadly classified into the three hypothesised antiherbiomes, we used hierarchical clustering on principal component axes of the ecoregion by trait matrix (five plant traits, standardized to zero mean and unit variance). We selected the number of clusters associated with the highest loss of inertia (within group variability) when progressively increasing the number of clusters, using the R package “FactoMineR”. This procedure allowed the recognition of large regions characterised by specific patterns of defence strategies (‘antiherbiomes’). We subsequently tested for axes score, megafauna and environmental differences among the resulting antiherbiomes to verify whether and how trait, climate and soil patterns matched those described for African ecosystems, and to understand the megafaunal differences among the antiherbiomes. For these comparisons, we used Kruskal-Wallis and post-hoc pairwise Dunn tests, using the Benjamini & Hochberg59 (1995) correction of P-values for multiple comparisons in both cases, and exclusively included continental ecoregions. For spines, we used the proportion of spinescent plants/species (rather than the number of “yes” and “no” used on previous analyses) in the principal component analysis. Because palms were missing from 20 ecoregions, we completed the values for these ecoregions using predicted model probabilities. To better understand these associations between traits and the environmental and megafauna variables, we also regressed the PCA axes against the same predictors used for traits.We also developed a framework to identify forest ecoregions most likely to have experienced a biome shift after megafauna extinction using antiherbiome, biome and megafauna distribution data. Ecoregions likely to have experienced a savanna-to-forest shift since the Pleistocene are those that: (1) are currently forest-dominated; (2) are classified in antiherbiomes analogous to African arid nutrient-rich or mesic nutrient-poor savannas; and (3) were megafauna- and, especially, megagrazer- rich during the Pleistocene (richness equal or greater than the 0.75 quantile: 14 species for Mrich, and 3 for exclusively grazing species; MGrich). We validated the distribution of these areas with fossil evidence (22 sites) from the Last Glacial Maximum and mid-Holocene (see Supplementary Methods and Supplementary Table 9). For this, we also used information about the present dominant vegetation type in the fossil sites, extracted from the reference sources (see Supplementary Table 9), to segregate savanna-forest shifts from data coming from stable savanna patches within forest or long-term savanna regions. We also contrasted the predicted patterns with the present location of savanna patches within the Amazon Forest region from ref. 60.All statistical analyses and data handling were carried out in the R (v.4.0.2) environment, using the previously mentioned packages, in addition to FSA, gridExtra, grid, lattice, lmtest, latticeExtra, olsrr, raster, rgdal, rgeos, sandwich, spatialreg, spdep and vegan, using codes provided in ref. 47.Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.Gaston, K. J., Jackson, S. F., Cantú-Salazar, L. & Cruz-Piñón, G. The ecological performance of protected areas. Annu. Rev. Ecol. Evol. Syst. 39, 93–113 (2008).
Google Scholar 
2.Staver, A. C., Abraham, J. O., Hempson, G. P., Karp, A. T. & Faith, J. T. The past, present, and future of herbivore impacts on savanna vegetation. J. Ecol. 109, 2804–2822 (2021).
Google Scholar 
3.Bond, W. J. Keystone species. In Biodiversity and Ecosystem Function (eds Schulze, E. D. & Mooney, H. A.) 237–253 (Springer, 1994).
Google Scholar 
4.Cole, M. M. The influence of soils, geomorphology and geology on the distribution of plant communities in savanna ecosystems. In Ecology of Tropical Savannas (eds Huntley, B. J. & Walker, B. H.) 145–174 (Springer, 1982).
Google Scholar 
5.Huntley, B. J. & Walker, B. H. (eds) Ecology of Tropical Savannas Vol. 42 (Springer, 1982).
Google Scholar 
6.Frost, P. et al. Responses of Savannas to Stress and Disturbance: A Proposal for a Collaborative Programme of Research (Biology International 10, 1985).
Google Scholar 
7.Medina, E. & Silva, J. F. Savannas of northern South America: A steady state regulated by water–ire interactions on a background of low nutrient availability. J. Biogeogr. 17, 403–413 (1990).
Google Scholar 
8.Fensham, R. J., Fairfax, R. J. & Archer, S. R. Rainfall, land use and woody vegetation cover change in semi-arid Australian savanna. J. Ecol. 93, 596–606 (2005).
Google Scholar 
9.Mucina, L. & Rutherford, M. C. The Vegetation of South Africa, Lesotho and Swaziland (South African National Biodiversity Institute, 2006).
Google Scholar 
10.Staver, A. C., Botha, J. & Hedin, L. Soils and fire jointly determine vegetation structure in an African savanna. New Phytol. 216, 1151–1160 (2017).CAS 
PubMed 

Google Scholar 
11.Jakubka, D. et al. Effects of climate, habitat and land use on the cover and diversity of the savanna herbaceous layer in Burkina-Faso, West Africa. Folia Geobot. 52, 129–142 (2017).
Google Scholar 
12.Bond, W. J. Open Ecosystems: Ecology and Evolution Beyond the Forest Edge (Oxford University Press, 2019).
Google Scholar 
13.O’Connor, T. G. Composition and population responses of an African savanna grassland to rainfall and grazing. J. Appl. Ecol. 31, 155–171 (1994).
Google Scholar 
14.Walker, B. & Langridge, J. Predicting savanna vegetation structure on the basis of plant available moisture (PAM) and plant available nutrients (PAN): A case study from Australia. J. Biogeogr. 24, 813–825 (1997).
Google Scholar 
15.Sankaran, M. et al. Determinants of woody cover in African savannas. Nature 438, 846–849 (2005).CAS 
ADS 
PubMed 

Google Scholar 
16.Bucini, G. & Hanan, N. P. A continental-scale analysis of tree cover in African savannas. Glob. Ecol. Biogeogr. 16, 593–605 (2007).
Google Scholar 
17.Venter, F. J., Scholes, R. J. & Eckhardt, H. C. The abiotic template and its associated vegetation pattern. In The Kruger Experience: Ecology and Management of Savanna Heterogeneity (eds du Toit, J. T. et al.) 83–129 (Island Press, Washington, D.C., 2003).18.Valeix, M. et al. Vegetation structure and ungulate abundance over a period of increasing elephant abundance in Hwange National Park, Zimbabwe. J. Trop. Ecol. 23, 87–93 (2007).
Google Scholar 
19.Asner, G. P. et al. Large-scale impacts of herbivores on the structural diversity of African savannas. Proc. Natl Acad. Sci. USA 106, 4947–4952 (2009).CAS 
ADS 
PubMed 
PubMed Central 

Google Scholar 
20.Archibald, S. & Hempson, G. P. Competing consumers: Contrasting the patterns and impacts of fire and mammalian herbivory in Africa. Philos. Trans. R. Soc. B 371, 20150309 (2016).
Google Scholar 
21.Archibald, S., Bond, W. J., Stock, W. D. & Fairbanks, D. H. K. Shaping the landscape: Fire–grazer interactions in an African savanna. Ecol. Appl. 15, 96–109 (2005).
Google Scholar 
22.Staver, A. C. & Bond, W. J. Is there a ‘browse trap’? Dynamics of herbivore impacts on trees and grasses in an African savanna. J. Ecol. 102, 595–602 (2014).
Google Scholar 
23.Jeltsch, F., Weber, G. E. & Grimm, V. Ecological buffering mechanisms in savannas: A unifying theory of long-term tree-grass coexistence. Plant Ecol. 105, 161–171 (2000).
Google Scholar 
24.February, E. C., Higgins, S. I., Bond, W. J. & Swemmer, L. Influence of competition and rainfall manipulation on the growth responses of savanna trees and grasses. Ecology 94, 1155–1164 (2013).PubMed 

Google Scholar 
25.Savadogo, P., Tiveau, D., Sawadogo, L. & Tigabu, M. Herbaceous species responses to long-term effects of prescribed fire, grazing and selective tree cutting in the savanna-woodlands of West Africa. Perspect. Plant Ecol. Evol. Syst. 10, 179–195 (2008).
Google Scholar 
26.Smith, M. D. et al. Long-term effects of fire frequency and season on herbaceous vegetation in savannas of the Kruger National Park, South Africa. J. Plant Ecol. 6, 71–83 (2013).
Google Scholar 
27.Thrash, I. Impact of water provision on herbaceous vegetation in Kruger National Park, South Africa. J. Arid Environ. 38, 437–450 (1998).ADS 

Google Scholar 
28.Staver, A. C., Bond, W. J., Stock, W. D., van Rensburg, S. J. & Waldram, M. S. Browsing and fire interact to suppress tree density in an African savanna. Ecol. Appl. 19, 1909–1919 (2009).PubMed 

Google Scholar 
29.Smit, I. P. J. & Ferreira, S. M. Management intervention affects river-bound spatial dynamics of elephants. Biol. Conserv. 143, 2172–2181 (2010).
Google Scholar 
30.Loarie, S. R., van Aarde, R. J. & Pimm, S. L. Elephant seasonal vegetation preferences across dry and wet savannas. Biol. Conserv. 142, 3099–3107 (2009).
Google Scholar 
31.Young, K. D., Ferreira, S. M. & Van Aarde, R. J. Elephant spatial use in wet and dry savannas of southern Africa. J. Zool. 278, 189–205 (2009).
Google Scholar 
32.Codron, J. et al. Elephant (Loxodonta africana) diets in Kruger National Park, South Africa: spatial and landscape differences. J. Mammal. 87, 27–34 (2006).
Google Scholar 
33.Timberlake, J. Colophospermum mopane: Annotated Bibliography and Review (Zimbabwe Bulletin of Forestry Research No. 11, 1995).
Google Scholar 
34.Pyšek, P. et al. Into the great wide open: Do alien plants spread from rivers to dry savanna in the Kruger National Park?. NeoBiota 60, 61–77 (2020).
Google Scholar 
35.Eckhardt, H. C., van Wilgen, B. W. & Biggs, H. C. Trends in woody vegetation cover in the Kruger National Park, South Africa, between 1940 and 1998. Afr. J. Ecol. 38, 108–115 (2000).
Google Scholar 
36.Groen, T. A. Spatial Matters: How Spatial Patterns and Processes Affect Savanna Dynamics. PhD Thesis, Wageningen University, The Netherlands (2007).37.MacFadyen, S., Hui, C., Verburg, P. H. & Van Teeffelen, A. J. A. Quantifying spatiotemporal drivers of environmental heterogeneity in Kruger National Park, South Africa. Landsc. Ecol. 31, 2013–2029 (2016).
Google Scholar 
38.Munyati, C. & Ratshibvumo, T. Differentiating geological fertility derived vegetation zones in Kruger National Park, SouthAfrica, using Landsat and MODIS imagery. J. Nat. Conserv. 18, 169–179 (2010).
Google Scholar 
39.Colgan, M. S., Asner, G. P., Levick, S. R., Martin, R. E. & Chadwick, O. A. Topo-edaphic controls over woody plant biomass in South African savannas. Biogeosciences 9, 1809–1821 (2012).ADS 

Google Scholar 
40.Walter, H. & Burnett, J. H. Ecology of Tropical and Subtropical Vegetation Vol. 539 (Oliver and Boyd, 1971).
Google Scholar 
41.Scholes, R. J., Bond, W. J. & Eckhardt, H. C. Vegetation dynamics in the Kruger ecosystem. In The Kruger Experience: Ecology and Management of Savanna Heterogeneity (eds du Toit, J. T. et al.) 243–262 (Island Press, 2003).
Google Scholar 
42.Knoop, W. T. & Walker, B. H. Interactions of woody and herbaceous vegetation in southern African savanna. J. Ecol. 73, 235–253 (1985).
Google Scholar 
43.Tilman, D. The resource-ratio hypothesis of plant succession. Am. Nat. 125, 827–852 (1985).
Google Scholar 
44.Scholes, R. J. & Archer, S. R. Tree–grass interactions in savannas. Annu. Rev. Ecol. Syst. 28, 517–544 (1997).
Google Scholar 
45.Muvengwi, J., Davies, A. B., Parrini, F. & Witkowski, E. T. F. Contrasting termite diversity and assemblages on granitic and basaltic African savanna landscapes. Insect. Soc. 65, 25–35 (2018).
Google Scholar 
46.Trollope, W. S. W., Potgieter, A. L. F. & Zambatis, N. Assessing veld condition in the Kruger National Park using key grass species. Koedoe 32, 67–93 (1989).
Google Scholar 
47.Ehleringer, J. R. & Monson, R. K. Evolutionary and ecological aspects of photosynthetic pathway variation. Annu. Rev. Ecol. Syst. 24, 411–439 (1993).
Google Scholar 
48.Chytrý, M., Tichý, L. & Roleček, J. Local and regional patterns of species richness in Central European vegetation types along the pH/calcium gradient. Folia Geobot. 38, 429–442 (2003).
Google Scholar 
49.Owen-Smith, N., Page, B., Teren, G. & Druce, D. J. Megabrowser impacts on woody vegetation in savannas. In Savanna Woody Plants and Large Herbivores (eds Scogings, P. F. & Sankaran, M.) 585–611 (Wiley, 2019).
Google Scholar 
50.Nasseri, N. A., McBrayer, L. D. & Schulte, B. A. The impact of tree modification by African elephant (Loxodonta africana) on herpetofaunal species richness in northern Tanzania. Afr. J. Ecol. 49, 133–140 (2010).
Google Scholar 
51.Asner, G. P. & Levick, S. R. Landscape-scale effects of herbivores on treefall in African savannas. Ecol. Lett. 15, 1211–1217 (2012).PubMed 

Google Scholar 
52.Haynes, G. Elephants (and extinct relatives) as earth-movers and ecosystem engineers. Geomorphology 157, 99–107 (2012).ADS 

Google Scholar 
53.Kruger, L. M., Coetzee, J. A. & Vickers, K. The Impacts of Elephants on Woodlands and Associated Biodiversity (Summary report to South African National Parks, Organization for Tropical Studies, 2007).54.Guy, P. R. The influence of elephants and fire on a Brachystegia julbernardia woodland in Zimbabwe. J. Trop. Ecol. 5, 215–226 (1989).
Google Scholar 
55.Laws, R. M., Parker, I. S. C. & Johnstone, R. C. B. Elephants and Their Habitats: The Ecology of Elephants in North Bunyoro, Uganda (Clarendon Press, 1975).
Google Scholar 
56.Thompson, P. J. The role of elephants, fire and other agents in the decline of Brachystegia woodlands. J. S. Afr. Wildl. Manag. Assoc. 5, 11–18 (1975).
Google Scholar 
57.Barnes, M. E. Effects of large herbivores and fire on the regeneration of Acacia erioloba woodlands in Chobe National Park, Botswana. Afr. J. Ecol. 39, 340–350 (2001).
Google Scholar 
58.Scogings, P. F., Johansson, T., Hjältén, J. & Kruger, J. Responses of woody vegetation to exclusion of large herbivores in semi-arid savannas. Austral Ecol. 37, 56–66 (2012).
Google Scholar 
59.Verweij, R. J. T., Higgins, S. I., Bond, W. J. & February, E. C. Water sourcing by trees in a mesic savanna: Responses to severing deep and shallow roots. Environ. Exp. Bot. 74, 229–236 (2011).
Google Scholar 
60.Smit, I. et al. Effects of fire on woody vegetation structure in African savanna. Ecol. Appl. 20, 1865–1875 (2010).PubMed 

Google Scholar 
61.MacFadyen, S., Hui, C., Verburg, P. H. & Van Teeffelen, A. J. A. Spatiotemporal distribution dynamics of elephants in response to density, rainfall, rivers and fire in Kruger National Park, South Africa. Divers. Distrib. 25, 880–894 (2019).
Google Scholar 
62.O’Connor, T. G., Goodman, P. S. & Clegg, B. A functional hypothesis of the threat of local extirpation of woody plant species by elephant in Africa. Biol. Conserv. 136, 329–345 (2007).
Google Scholar 
63.Didan, K. MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006. NASA EOSDIS Land Processes DAAC. Accessed 2021-06-08 from https://doi.org/10.5067/MODIS/MOD13Q1.006 (2015).64.Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Natl Acad. Sci. 104, 5925–5930 (2007).CAS 
ADS 
PubMed 
PubMed Central 

Google Scholar 
65.Bohdalková, E., Toszogyova, A., Šímová, I. & Storch, D. Universality in biodiversity patterns: variation in species-temperature and species-productivity relationships reveals a prominent role of productivity in diversity gradients. Ecography 44, 1366–1378 (2021).
Google Scholar 
66.Knight, R. S., Crowe, T. M. & Siegfried, W. R. Distribution and species richness of trees in southern Africa. J. S. Afr. Bot. 48, 455–480 (1982).
Google Scholar 
67.Gaylard, A., Owen-Smith, N. & Redfern, J. Surface water availability: implications for heterogeneity and eco-system processes. In The Kruger Experience: Ecology and Management of Savanna Heterogeneity (eds du Toit, J. T. et al.) 171–188 (Island Press, 2003).
Google Scholar 
68.Venter, F. J. A Classification of Land for Management Planning in the Kruger National Park. PhD Thesis, University of South Africa (1990).69.du Toit, J. T. et al. (eds) The Kruger Experience: Ecology and Management of Savanna Heterogeneity (Island Press, 2003).
Google Scholar 
70.Smit, I. P. J., Smit, C. F., Govender, N., van der Linde, M. & MacFadyen, S. Rainfall, geology and landscape position generate large-scale spatiotemporal fire pattern heterogeneity in an African savanna. Ecography 36, 447–459 (2013).
Google Scholar 
71.Obermeijer, A. A. A preliminary list of plants found in the Kruger National Park. Ann. Transvaal Mus. 17, 185–227 (1937).
Google Scholar 
72.van der Schijff, H. P. ‘n Ekologiese Studie van die Flora van die Nasionale Krugerwildtuin. D.Sc. Thesis, Potchefstroom University (1957).73.van der Schijff, H. P. The affinities of the flora of the Kruger National Park. Kirkia 7, 109–120 (1968).
Google Scholar 
74.Coetzee, B. J. Phytosociology, Vegetation Structure and Landscapes of the Central District. Kruger National Park, South Africa. Dissertationes Botanicae, J. Cramer, Vaduz (1983).75.Gertenbach, W. P. D. Landscapes of the Kruger National Park. Koedoe 26, 9–121 (1983).
Google Scholar 
76.Siebert, F. & Eckhardt, H. C. The vegetation and floristics of the Nkhuhlu exclosures, Kruger National Park. Koedoe 50, 126–144 (2008).
Google Scholar 
77.Siebert, F., Eckhardt, H. C. & Siebert, S. J. The vegetation and floristics of the Letaba exclosures, Kruger National Park, South Africa. Koedoe 52, 1–12 (2010).
Google Scholar 
78.Wigley, B. J., Fritz, H., Coetsee, C. & Bond, W. J. Herbivores shape woody plant communities in the Kruger National Park: Lessons from three long-term exclosures. Koedoe 56, 1–12 (2014).
Google Scholar 
79.Enslin, B. W., Potgieter, A. L. F., Biggs, H. C. & Biggs, R. Long term effects of fire frequency and season on the woody vegetation dynamics of the Sclerocarya birrea/Acacia nigrescens savanna of the Kruger National Park. Koedoe 43, 27–37 (2000).
Google Scholar 
80.Brits, J., Van Rooyen, M. W. & Van Rooyen, N. Ecological impact of large herbivores on the woody vegetation at selected watering points on the eastern basaltic soils in the Kruger National Park. Afr. J. Ecol. 40, 53–60 (2002).
Google Scholar 
81.Todd, S. W. Gradients in vegetation cover, structure and species richness of Nama-Karoo shrublands in relation to distance from livestock watering points. J. Appl. Ecol. 43, 293–304 (2006).
Google Scholar 
82.Foxcroft, L. C., Henderson, L., Nichols, G. R. & Martin, B. W. A revised list of alien plants for the Kruger National Park. Koedoe 46, 21–44 (2003).
Google Scholar 
83.Foxcroft, L. C., Richardson, D. M. & Wilson, J. R. Ornamental plants as invasive aliens: Problems and solutions in Kruger National Park, South Africa. Environ. Manag. 41, 32–51 (2008).ADS 

Google Scholar 
84.Mueller-Dombois, D. & Ellenberg, H. Aims and Methods of Vegetation Ecology (Wiley, 1974).
Google Scholar 
85.van der Maarel, E. Transformation of cover-abundance values in phytosociology and its effects on community similarity. Vegetatio 38, 97–114 (1979).
Google Scholar 
86.Magurran, A. E. Ecological Diversity and Its Measurement (Croom Helm, 1988).
Google Scholar 
87.Pooley, E. Wildflowers of Kwazulu-Natal and the Eastern Region (Natal Flora Publications Trust, 1998).
Google Scholar 
88.Schmidt, E., Lötter, M. & McCleland, W. Trees and Shrubs of Mpumalanga and Kruger National Park (Jacana Media, 2002).
Google Scholar 
89.van der Walt, R. Wild Flowers of the Limpopo Valley (Retha van der Walt, 2009).
Google Scholar 
90.Oudtshoorn, F. V. Guide to Grasses of Southern Africa 3rd edn. (Briza Publications, 2018).
Google Scholar 
91.R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2013).
Google Scholar 
92.Lenth, R. Emmeans: Estimated Marginal Means, aka Least-Square Means. R package version 1.2.1. https://CRAN.R-project.org/package=emmeans (2018).93.Lepš, J. & Šmilauer, P. Multivariate Analysis of Ecological Data Using CANOCO 5 (Cambridge University Press, 2014).MATH 

Google Scholar 
94.Dray, S., Legendre, P. & Peres-Neto, P. R. Spatial modelling: A comprehensive framework 562 for principal coordinate analysis of neighbour matrices (PCNM). Ecol. Modell. 196, 483–563 (2006).
Google Scholar 
95.Šmilauer, P. & Lepš, J. Multivariate Analysis of Ecological Data Using Canoco 5 2nd edn. (Cambridge University Press, 2014).MATH 

Google Scholar  More

1.Bastviken, D. Methane. in Encyclopedia of Inland Waters (ed. Likens, G. E.) 783–805 (Elsevier, 2009). https://doi.org/10.1016/B978-012370626-3.00117-42.Hanson, R. S. & Hanson, T. E. Methanotrophic bacteria. Microbiol. Rev. 60, 439–471 (1996).CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Thottathil, S. D., Reis, P. C. J., del Giorgio, P. A. & Prairie, Y. T. The extent and regulation of summer methane oxidation in Northern Lakes. J. Geophys. Res. Biogeosciences 123, 3216–3230 (2018).CAS 
ADS 

Google Scholar 
4.Kankaala, P., Taipale, S., Nykänen, H. & Jones, R. I. Oxidation, efflux, and isotopic fractionation of methane during autumnal turnover in a polyhumic, boreal lake. J. Geophys. Res. Biogeosciences 112, 1–7 (2007).
Google Scholar 
5.Kankaala, P., Huotari, J., Peltomaa, E., Saloranta, T. & Ojala, A. Methanotrophic activity in relation to methane efflux and total heterotrophic bacterial production in a stratified, humic, boreal lake. Limnol. Oceanogr. 51, 1195–1204 (2006).CAS 
ADS 

Google Scholar 
6.Bastviken, D., Ejlertsson, J., Sundh, I. & Tranvik, L. Methane as a source of carbon and energy for lake pelagic food webs. Ecology 84, 969–981 (2003).
Google Scholar 
7.Kankaala, P., Lopez Bellido, J., Ojala, A., Tulonen, T. & Jones, R. I. Variable production by different pelagic energy mobilizers in Boreal Lakes. Ecosystems 16, 1152–1164 (2013).CAS 

Google Scholar 
8.Morana, C. et al. Methanotrophy within the water column of a large meromictic tropical lake (Lake Kivu, East Africa). Biogeosciences 12, 2077–2088 (2015).ADS 

Google Scholar 
9.Grey, J. The incredible lightness of being methane-fuelled: stable isotopes reveal alternative energy pathways in aquatic ecosystems and beyond. Front. Ecol. Evol. 4, 1–14 (2016).
Google Scholar 
10.Jones, R. I. & Grey, J. Biogenic methane in freshwater food webs. Freshw. Biol. 56, 213–229 (2011).CAS 

Google Scholar 
11.Kankaala, P., Taipale, S. & Grey, J. Experimental d13C evidence for a contribution of methane to pelagic food webs in lakes. Limnol. Oceanogr. 51, 2821–2827 (2006).CAS 
ADS 

Google Scholar 
12.Guérin, F. & Abril, G. Significance of pelagic aerobic methane oxidation in the methane and carbon budget of a tropical reservoir. J. Geophys. Res. 112, 1–14 (2007).
Google Scholar 
13.Rasilo, T., Hutchins, R. H. S., Ruiz-González, C. & del Giorgio, P. A. Transport and transformation of soil-derived CO2, CH4 and DOC sustain CO2 supersaturation in small boreal streams. Sci. Total Environ. 579, 902–912 (2017).CAS 
PubMed 
ADS 

Google Scholar 
14.Soued, C. & Prairie, Y. T. The carbon footprint of a Malaysian tropical reservoir: measured versus modeled estimates highlight the underestimated key role of downstream processes. Biogeosciences 17, 515–227 (2020).CAS 
ADS 

Google Scholar 
15.Del Giorgio, P. A. & Gasol, J. M. Physiological structure and single-cell activity in marine bacterioplankton. in Microbial Ecology of the Oceans: Second Edition (ed. Kirchman, D. L.) 243–298 (John Wiley & Sons, Inc, 2008). https://doi.org/10.1002/9780470281840.ch816.Reis, P. C. J., Ruiz-González, C., Soued, C., Crevecoeur, S. & Prairie, Y. T. Rapid shifts in methanotrophic bacterial communities mitigate methane emissions from a tropical hydropower reservoir and its downstream river. Sci. Total Environ. 748, 141374 (2020).CAS 
PubMed 
ADS 

Google Scholar 
17.Thottathil, S. D., Reis, P. C. J. & Prairie, Y. T. Methane oxidation kinetics in northern freshwater lakes. Biogeochemistry 143, 105–116 (2019).CAS 

Google Scholar 
18.Milucka, J. et al. Methane oxidation coupled to oxygenic photosynthesis in anoxic waters. ISME J. 9, 1991–2002 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Zigah, P. K. et al. Methane oxidation pathways and associated methanotrophic communities in the water column of a tropical lake. Limnol. Oceanogr. 60, 553–572 (2015).ADS 

Google Scholar 
20.Mayr, M. J. et al. Growth and rapid succession of methanotrophs effectively limit methane release during lake overturn. Commun. Biol. 3, 1–9 (2020).
Google Scholar 
21.Bussmann, I., Rahalkar, M. & Schink, B. Cultivation of methanotrophic bacteria in opposing gradients of methane and oxygen. FEMS Microbiol. Ecol. 56, 331–344 (2006).CAS 
PubMed 

Google Scholar 
22.Kankaala, P., Eller, G. & Jones, R. I. Could bacterivorous zooplankton affect lake pelagic methanotrophic activity? Fundam. Appl. Limnol. / Arch. f.ür. Hydrobiol. 169, 203–209 (2007).
Google Scholar 
23.Khmelenina, V. N. et al. Structural and functional features of methanotrophs from hypersaline and alkaline lakes. Microbiology 79, 472–482 (2010).CAS 

Google Scholar 
24.Westfall, C. S. & Levin, P. A. Bacterial cell size: multifactorial and multifaceted. Annu. Rev. Microbiol. 71, 499–517 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Chien, A. C., Hill, N. S. & Levin, P. A. Cell size control in bacteria. Curr. Biol. 22, R340–R349 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Velimirov, B. Nanobacteria, ultramicrobacteria and starvation forms: a search for the smallest metabolizing bacterium. Microbes Environ. 16, 67–77 (2001).
Google Scholar 
27.Reis, P. C. J., Thottathil, S. D., Ruiz-González, C. & Prairie, Y. T. Niche separation within aerobic methanotrophic bacteria across lakes and its link to methane oxidation rates. Environ. Microbiol. 22, 738–751 (2020).CAS 
PubMed 

Google Scholar 
28.Garcia-Chaves, M. C., Cottrell, M. T., Kirchman, D. L., Ruiz-González, C. & del Giorgio, P. A. Single-cell activity of freshwater aerobic anoxygenic phototrophic bacteria and their contribution to biomass production. Isme J. 10, 1579–1588 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
29.Jürgens, K. & Matz, C. Predation as a shaping force for the phenotypic and genotypic composition of planktonic bacteria. Antonie van Leeuwenhoek. Int. J. Gen. Mol. Microbiol. 81, 413–434 (2002).
Google Scholar 
30.Rautio, M. & Vincent, W. F. Benthic and pelagic food resources for–zooplankton in shallow high-latitude lakes and ponds. Freshw. Biol. 51, 1038–1052 (2006).CAS 

Google Scholar 
31.Rissanen, A. J. et al. Gammaproteobacterial methanotrophs dominate methanotrophy in aerobic and anaerobic layers of boreal lake waters. Aquat. Microb. Ecol. 81, 257–276 (2018).
Google Scholar 
32.Zimmermann, M. et al. Microbial methane oxidation efficiency and robustness during lake overturn. Limnol. Oceanogr. Lett. 6, 320–328 (2021).CAS 

Google Scholar 
33.Puri, A. W. et al. Genetic tools for the industrially promising methanotroph Methylomicrobium buryatense. Appl. Environ. Microbiol. 81, 1775–1781 (2015).PubMed 
PubMed Central 
ADS 

Google Scholar 
34.Strong, P. J., Kalyuzhnaya, M., Silverman, J. & Clarke, W. P. A methanotroph-based biorefinery: potential scenarios for generating multiple products from a single fermentation. Bioresour. Technol. 215, 314–323 (2016).CAS 
PubMed 

Google Scholar 
35.Oswald, K. et al. Aerobic gammaproteobacterial methanotrophs mitigate methane emissions from oxic and anoxic lake waters. Limnol. Oceanogr. 61, S101–S118 (2016).
Google Scholar 
36.Smith, E. M. & Prairie, Y. T. Bacterial metabolism and growth efficiency in lakes: the importance of phosphorus availability. Limnol. Oceanogr. 49, 137–147 (2004).CAS 
ADS 

Google Scholar 
37.Del Giorgio, P. A., Cole, J. J., Caraco, N. F. & Peters, R. H. Linking planktonic biomass and metabolism to net gas fluxes in northern temperate lakes. Ecology 80, 1422–1431 (1999).
Google Scholar 
38.Kellerman, A. M., Dittmar, T., Kothawala, D. N. & Tranvik, L. J. Chemodiversity of dissolved organic matter in lakes driven by climate and hydrology. Nat. Commun. 5, 3804 (2014).CAS 
PubMed 
ADS 

Google Scholar 
39.Sun, L., Perdue, E. M., Meyer, J. L. & Weis, J. Use of elemental composition to predict bioavailability of dissolved organic matter in a Georgia river. Limnol. Oceanogr. 42, 714–721 (1997).CAS 
ADS 

Google Scholar 
40.Kellerman, A. M., Kothawala, D. N., Dittmar, T. & Tranvik, L. J. Persistence of dissolved organic matter in lakes related to its molecular characteristics. Nat. Geosci. 8, 454–457 (2015).CAS 
ADS 

Google Scholar 
41.Guillemette, F. & del Giorgio, P. A. Reconstructing the various facets of dissolved organic carbon bioavailability in freshwater ecosystems. Limnol. Oceanogr. 56, 734–748 (2011).CAS 
ADS 

Google Scholar 
42.Logue, J. B. et al. Experimental insights into the importance of aquatic bacterial community composition to the degradation of dissolved organic matter. ISME J. 10, 533–545 (2016).CAS 
PubMed 

Google Scholar 
43.Salcher, M. M., Posch, T. & Pernthaler, J. In situ substrate preferences of abundant bacterioplankton populations in a prealpine freshwater lake. ISME J. 7, 896–907 (2013).CAS 
PubMed 

Google Scholar 
44.Sobek, S., Tranvik, L. J., Prairie, Y., Kortelainen, P. & Cole, J. J. Patterns and regulation of dissolved organic carbon: an analysis of 7,500 widely distributed lakes. Limnol. Oceanogr. 52, 1208–1219 (2007).CAS 
ADS 

Google Scholar 
45.Kalyuzhnaya, M. G. et al. Highly efficient methane biocatalysis revealed in a methanotrophic bacterium. Nat. Commun. 4, 2785 (2013).CAS 
PubMed 
ADS 

Google Scholar 
46.Oshkin, I. Y. et al. Methane-fed microbial microcosms show differential community dynamics and pinpoint taxa involved in communal response. ISME J. 9, 1119–1129 (2014).PubMed 
PubMed Central 

Google Scholar 
47.Chistoserdova, L. & Kalyuzhnaya, M. G. Current trends in methylotrophy. Trends Microbiol 26, 703–714 (2018).CAS 
PubMed 

Google Scholar 
48.Martinez-Cruz, K. et al. Anaerobic oxidation of methane by aerobic methanotrophs in sub-Arctic lake sediments. Sci. Total Environ. 607–608, 23–31 (2017).PubMed 
ADS 

Google Scholar 
49.Samad, M. S. & Bertilsson, S. Seasonal variation in abundance and diversity of bacterial methanotrophs in five temperate lakes. Front. Microbiol. 8, 1–12 (2017).
Google Scholar 
50.Ricão Canelhas, M., Denfeld, B. A., Weyhenmeyer, G. A., Bastviken, D. & Bertilsson, S. Methane oxidation at the water-ice interface of an ice-covered lake. Limnol. Oceanogr. 61, S78–S90 (2016).ADS 

Google Scholar 
51.Houser, J. N. Water color affects the stratification, surface temperature, heat content, and mean epilimnetic irradiance of small lakes. Can. J. Fish. Aquat. Sci. 63, 2447–2455 (2006).
Google Scholar 
52.Caplanne, S. & Laurion, I. Effect of chromophoric dissolved organic matter on epilimnetic stratification in lakes. Aquat. Sci. 70, 123–133 (2008).CAS 

Google Scholar 
53.Oswald, K. et al. Light-dependent aerobic methane oxidation reduces methane emissions from seasonally stratified lakes. PLoS ONE 10, e0132574 (2015).PubMed 
PubMed Central 

Google Scholar 
54.Savvichev, A. S. et al. Light-dependent methane oxidation is the major process of the methane cycle in the water column of the Bol’shie Khruslomeny Polar Lake. Microbiology 88, 370–374 (2019).CAS 

Google Scholar 
55.Baines, S. B. & Pace, M. L. The production of dissolved organic matter by phytoplankton and its importance to bacteria: patterns across marine and freshwater systems. Limnol. Oceanogr. 36, 1078–1090 (1991).ADS 

Google Scholar 
56.Cole, J. J., Likens, G. E. & Strayer, D. L. Photosynthetically produced dissolved organic carbon: an important carbon source for planktonic bacteria. Limnol. Oceanogr. 27, 1080–1090 (1982).CAS 
ADS 

Google Scholar 
57.Dumestre, J. et al. Influence of light intensity on methanotrophic bacterial activity in Petit Saut reservoir, French Guiana. Appl. Environ. Microbiol. 65, 534–539 (1999).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
58.Murase, J. & Sugimoto, A. Inhibitory effect of light on methane oxidation in the pelagic water column of a mesotrophic lake (Lake Biwa, Japan). Limnol. Oceanogr. 50, 1339–1343 (2005).CAS 
ADS 

Google Scholar 
59.Moran, M. A. & Hodson, R. E. Bacterial production on humic and nonhumic components of dissolved organic carbon. Limnol. Oceanogr. 35, 1744–1756 (1990).CAS 
ADS 

Google Scholar 
60.Azam, F. et al. The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. 10, 257–263 (1983).ADS 

Google Scholar 
61.Roulet, N. & Moore, T. R. Browning the waters. Nature 444, 283–284 (2006).CAS 
PubMed 
ADS 

Google Scholar 
62.Weyhenmeyer, G. A., Prairie, Y. T. & Tranvik, L. J. Browning of boreal freshwaters coupled to carbon-iron interactions along the aquatic continuum. PLoS ONE 9, e88104 (2014).PubMed 
PubMed Central 
ADS 

Google Scholar 
63.O’Reilly, C. M. et al. Rapid and highly variable warming of lake surface waters around the globe. Geophys. Res. Lett. 42, 10773–10781 (2015).ADS 

Google Scholar 
64.Yamamoto, S., Alcauskas, J. B. & Crozier, T. E. Solubility of methane in distilled water and seawater. J. Chem. Eng. Data 21, 78–80 (1976).CAS 

Google Scholar 
65.Cantin, A., Beisner, B. E., Gunn, J. M., Prairie, Y. T. & Winter, J. G. Effects of thermocline deepening on lake plankton communities. Can. J. Fish. Aquat. Sci. 68, 260–276 (2011).
Google Scholar 
66.Smith, D. C. & Azam, F. A simple, economical method for measuring bacterial protein synthesis rates in seawater using 3H-leucine. Mar. Microb. Food Webs 6, 107–114 (1992).
Google Scholar 
67.Del Giorgio, P. A., Pace, M. L. & Fischer, D. Relationship of bacterial growth efficiency to spatial variation in bacterial activity in the Hudson River. Aquat. Microb. Ecol. 45, 55–67 (2006).
Google Scholar 
68.Eller, G., Stubner, S. & Frenzel, P. Group-specific 16S rRNA targeted probes for the detection of type I and type II methanotrophs by fluorescence in situ hybridisation. FEMS Microbiol. Lett. 198, 91–97 (2001).CAS 
PubMed 

Google Scholar 
69.Zeder, M. ACME tool3. (2014).70.Callieri, C. et al. Bacteria, Archaea, and Crenarchaeota in the epilimnion and hypolimnion of a deep holo-oligomictic lake. Appl. Environ. Microbiol. 75, 7298–7300 (2009).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
71.Lew, S. & Glińska-Lewczuk, K. Environmental controls on the abundance of methanotrophs and methanogens in peat bog lakes. Sci. Total Environ. 645, 1201–1211 (2018).CAS 
PubMed 
ADS 

Google Scholar 
72.Fagerbakke, K. M., Heldal, M. & Norland, S. Content of carbon, nitrogen, oxygen, sulfur and phosphorus in native aquatic and cultured bacteria. Aquat. Microb. Ecol. 10, 15–27 (1996).
Google Scholar 
73.Read J. S. et al. Derivation of lake mixing and stratification indices from high-resolution lake buoy data. Environmental Modelling and Software. 26, 1325–1336 (2011).
Google Scholar 
74.R Core Team. R: a language and environment for statistical computing. (2019). https://www.R-project.org/.75.RStudio Team. RStudio: Integrated Development for R. RStudio, Inc., Boston, MAURL. (2018). http://www.rstudio.com/.76.Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A Grammar of Data Manipulation. R package version 0.8.3. (2019). https://cran.r-project.org/package=dplyr.77.Sievert, C. Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC Florida. (2020).78.Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag, New York, 2016). https://ggplot2.tidyverse.org.79.Wilke, C.O. cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’. R package version 1.0.0. (2019). https://CRAN.R-project.org/package=cowplot.80.Auguie, B. gridExtra: Miscellaneous Functions for ‘Grid’ Graphics. R package version 2.3. (2017). https://CRAN.R-project.org/package=gridExtra.81.Reis, P. C. J., Thottathil, S. D. & Prairie, Y. T. Dataset: the role of methanotrophy in the microbial carbon metabolism of temperate lakes. (1.0.0) [Data set]. Zenodo. (2021). https://doi.org/10.5281/zenodo.5737277. More