Philippa Kaur

1.Klemetsen, A. et al. Atlantic salmon Salmo salar L., brown trout Salmo trutta L. and Arctic charr Salvelinus alpinus (L.): A review of aspects of their life histories. Ecol. Freshw. Fish. 12, 1–59. https://doi.org/10.1034/j.1600-0633.2003.00010.x (2003).Article 

Google Scholar 
2.Elliott, J. M. Quantitative Ecology and the Brown Trout (Oxford University Press, 1994).
Google Scholar 
3.ICES. Baltic Salmon and Trout Assessment Working Group (WGBAST). ICES Sci. Rep. 2(22), 261. https://doi.org/10.17895/ices.pub.5974 (2020).Article 

Google Scholar 
4.Berrebi, P., Horvath, Á., Splendiani, A., Palm, S. & Bernaś, R. Genetic diversity of domestic brown trout stocks in Europe. Aquaculture 544, 737043. https://doi.org/10.1016/j.aquaculture.2021.737043 (2021).CAS 
Article 

Google Scholar 
5.Jonsson, B. & Jonsson, N. Partial migration: Niche shift versus sexual maturation in fishes. Rev. Fish Biol. Fish. 3, 348–365. https://doi.org/10.1007/BF00043384 (1993).Article 

Google Scholar 
6.Jonsson, B. Diadromous and resident Trout Salmo Trutta: Is their difference due to genetics?. Oikos 38, 297–300. https://doi.org/10.2307/3544668 (1982).Article 

Google Scholar 
7.Olsson, I. C., Greenberg, L. A., Bergman, E. & Wysujack, K. Environmentally induced migration: The importance of food. Ecol. Lett. 9, 45–51. https://doi.org/10.1111/j.1461-0248.2006.00909.x (2006).Article 

Google Scholar 
8.Wysujack, K., Greenberg, L. A., Bergman, E. & Olsson, I. C. The role of the environment in partial migration: Food availability affects the adoption of a migratory tactic in brown trout Salmo trutta. Ecol. Freshw. Fish. 18, 52–59. https://doi.org/10.1111/j.1600-0633.2008.00322.x (2009).Article 

Google Scholar 
9.Charles, K., Roussel, J. M. & Cunjak, R. A. Estimating the contribution of sympatric anadromous and freshwater resident brown trout to juvenile production. Mar. Freshw. Res. 55, 185–191. https://doi.org/10.1071/MF03173 (2004).CAS 
Article 

Google Scholar 
10.Youngson, A. F., Mitchell, A. I., Noack, P. T. & Laird, L. M. Carotenoid pigment profiles distinguish anadromous and nonanadromous brown trout (Salmo trutta). Can. J. Fish. Aquat. Sci. 54, 1064–1066. https://doi.org/10.1139/f97-023 (1997).CAS 
Article 

Google Scholar 
11.Eek, D. & Bohlin, T. Strontium in scales verifies that sympatric sea-run and stream-resident brown trout can be distinguished by coloration. J. Fish Biol. 51, 659–661. https://doi.org/10.1111/j.1095-8649.1997.tb01522.x (1997).Article 

Google Scholar 
12.Veinott, G., Northcote, T., Rosenau, M. & Evans, R. D. Concentrations of strontium in the pectoral fin rays of the white sturgeon (Acipenser transmontanus) by laser ablation sampling—inductively coupled plasma—mass spectrometry as an indicator of marine migrations. Can. J. Fish. Aquat. Sci. 56, 1981–1990. https://doi.org/10.1139/f99-120 (1999).CAS 
Article 

Google Scholar 
13.Jardine, T. D., Cartwright, D. F., Dietrich, J. P. & Cunjak, R. A. Resource use by salmonids in riverine, lacustrine and marine environments: Evidence from stable isotope analysis. Environ. Biol. Fishes. 73, 309–319. https://doi.org/10.1007/s10641-005-2259-8 (2005).Article 

Google Scholar 
14.Jones, A. G. & Ardren, W. R. Methods of parentage analysis in natural populations. Mol. Ecol. 12, 2511–2523. https://doi.org/10.1046/j.1365-294X.2003.01928.x (2003).CAS 
Article 
PubMed 

Google Scholar 
15.Goodwin, J. C. A., King, R. A., Jones, J. I., Ibbotson, A. & Stevens, J. R. A small number of anadromous females drive reproduction in a brown trout (Salmo trutta) population in an English chalk stream. Freshw. Biol. 61, 1075–1089. https://doi.org/10.1111/fwb.12768 (2016).Article 

Google Scholar 
16.Charles, K., Guyomard, R., Hoyheim, B., Ombredane, D. & Baglinière, J.-L. Lack of genetic differentiation between anadromous and resident sympatric brown trout (Salmo trutta) in a Normandy population. Aquat. Living Resour. 18, 65–69. https://doi.org/10.1051/alr:2005006 (2005).CAS 
Article 

Google Scholar 
17.Charles, K., Roussel, J.-M., Lebel, J.-M., Bagliniere, J.-L. & Ombredane, D. Genetic differentiation between anadromous and freshwater resident brown trout (Salmo trutta L.): Insights obtained from stable isotope analysis. Ecol. Freshw. Fish. 15, 255–263. https://doi.org/10.1111/j.1600-0633.2006.00149.x (2006).Article 

Google Scholar 
18.Jarry, M. et al. Sea trout (Salmo trutta L.) growth patterns during early steps of invasion in the Kerguelen Islands. Polar Biol. 41, 925–934. https://doi.org/10.1007/s00300-018-2253-1 (2018).Article 

Google Scholar 
19.Brauer, C. J. & Beheregaray, L. B. Recent and rapid anthropogenic habitat fragmentation increases extinction risk for freshwater biodiversity. Evol. Appl. 13, 2857–2869. https://doi.org/10.1111/eva.13128 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
20.Griffiths, A. M., Koizumi, I., Bright, D. & Stevens, J. R. A case of isolation by distance and shortterm temporal stability of population structure in brown trout (Salmo trutta) within the River Dart, southwest England. Evol. Appl. 2, 537–554. https://doi.org/10.1111/j.1752-4571.2009.00092.x (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
21.HELCOM. Sea Trout and Salmon Populations and Rivers in Poland—HELCOM Assessment of Salmon (Salmo salar) and Sea Trout (Salmo trutta) Populations and Habitats in Rivers Flowing to the Baltic Sea. Balt. Sea Environ. Proc. No. 126B. 2011.22.Dębowski, P. Fish assemblages in the Parsęta River drainage basin. Pol. Arch. Hydrobiol. 46, 161–172 (1999).
Google Scholar 
23.Kuligowski, D. R., Ford, M. J. & Berejikian, B. A. Breeding structure of steelhead inferred from patterns of genetic relatedness among nests. Trans. Am. Fish. Soc. 134, 1202–2121. https://doi.org/10.1577/T04-187.1 (2005).Article 

Google Scholar 
24.Dauphin, G., Prévost, E., Adams, C. E. & Boylan, P. Using redd counts to estimate salmonids spawner abundances: A Bayesian modelling approach. Fish. Res. 106, 32–40. https://doi.org/10.1016/j.fishres.2010.06.014 (2010).Article 

Google Scholar 
25.Cairney, M., Taggart, J. B. & Hoyheim, B. Characterization of microsatellite and minisatellite loci in Atlantic salmon (Salmo salar L.) and cross-species amplification in other salmonids. Mol. Ecol. 9, 2175–2178. https://doi.org/10.1046/j.1365-294X.2000.105312.x (2000).CAS 
Article 
PubMed 

Google Scholar 
26.Estoup, A., Presa, P., Krieg, F., Vaiman, D. & Guyomard, R. (CT)n and (GT)n microsatellites: A new class of genetic markers for Salmo trutta L. brown trout. Heredity 71, 488–496. https://doi.org/10.1038/hdy.1993.167 (1993).CAS 
Article 
PubMed 

Google Scholar 
27.O’Reilly, P. T., Hamilton, L. C., McConnell, S. K. & Wright, J. M. Rapid analysis of genetic variation in Atlantic salmon (Salmo salar) by PCR multiplexing of dinucleotide and tetranucleotide microsatellites. Can. J. Fish. Aquat. Sci. 53, 2292–2298. https://doi.org/10.1139/f96-192 (1996).Article 

Google Scholar 
28.Poteaux, C., Bonhomme, F. & Berrebi, P. Microsatellite polymorphism and genetic impact of restocking in Mediterranean brown trout (Salmo trutta L.). Heredity 82, 645–653. https://doi.org/10.1046/j.1365-2540.1999.00519.x (1999).Article 
PubMed 

Google Scholar 
29.Presa, P. & Guyomard, R. Conservation of microsatellites in three species of salmonids. J. Fish Biol. 49, 1326–1329. https://doi.org/10.1111/j.1095-8649.1996.tb01800.x (1996).Article 

Google Scholar 
30.Scribner, K. T., Gust, J. R. & Fields, R. L. Isolation and characterization of novel salmon microsatellite loci: Cross species amplification and population genetics applications. Can. J. Fish. Aquat. Sci. 53, 833–841. https://doi.org/10.1139/cjfas-53-4-833 (1996).CAS 
Article 

Google Scholar 
31.Slettan, A., Olsaker, I. & Lie, O. Atlantic salmon, Salmo salar, microsatellites at the SSOSL25, SSOSL85, SSOSL311, SSOSL417 loci. Anim. Genet. 26, 281–282. https://doi.org/10.1111/j.1365-2052.1995.tb03262.x (1995).CAS 
Article 
PubMed 

Google Scholar 
32.Slettan, A., Olsaker, I. & Lie, O. Polymorphic Atlantic salmon, Salmo salar L., microsatellites at the SSOSL438, SSOSL429 and SSOSL444 loci. Anim. Genet. 27, 57–58 (1996).CAS 
Article 

Google Scholar 
33.Linløkken, A. N., Haugen, T. O., Kent, M. P. & Lien, S. Genetic differences between wild and hatchery-bred brown trout (Salmo trutta L.) in single nucleotide polymorphisms linked to selective traits. Ecol. Evol. 7, 4963–4972. https://doi.org/10.1002/ece3.3070 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
34.Bernaś, R. et al. Genetic differentiation in hatchery and stocked populations of sea trout in the Southern Baltic: Selection evidence at SNP loci. Genes 11, 184. https://doi.org/10.3390/genes11020184 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
35.Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 35: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567. https://doi.org/10.1111/j.1755-0998.2010.02847.x (2010).Article 
PubMed 

Google Scholar 
36.Peakall, R. & Smouse, P. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28, 2537–2539. https://doi.org/10.1093/bioinformatics/bts460 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
37.Kalinowski, S. T. hp-rare 1.0: A computer program for performing rarefaction on measures of allelic richness. Mol. Ecol. Notes 5, 187–189. https://doi.org/10.1111/j.1471-8286.2004.00845.x (2005).CAS 
Article 

Google Scholar 
38.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
Article 

Google Scholar 
39.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software structure: A simulation study. Mol. Ecol. 14, 2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x (2005).CAS 
Article 
PubMed 

Google Scholar 
40.Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: A program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191. https://doi.org/10.1111/1755-0998.12387 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
41.Rice, W. R. Analyzing tables of statistical tests. Evolution 43, 223–225. https://doi.org/10.1111/j.1558-5646.1989.tb04220.x (1989).Article 
PubMed 

Google Scholar 
42.Bernaś, R., Burzyński, A., Dębowski, P., Poćwierz-Kotus, A. & Wenne, R. Genetic diversity within sea trout population from an intensively stocked southern Baltic river, based on microsatellite DNA analysis. Fish. Manage. Ecol. 21, 398–409. https://doi.org/10.1111/fme.12090 (2014).Article 

Google Scholar 
43.Bernaś, R. & Wąs-Barcz, A. Genetic structure of important resident brown trout breeding lines in Poland. J. Appl. Genet. 61, 239–247. https://doi.org/10.1007/s13353-020-00548-6 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
44.Ostergren, J. & Nilsson, J. Importance of life-history and landscape characteristics for genetic structure and genetic diversity of brown trout (Salmo trutta L.). Ecol. Freshw. Fish. 21, 119–133 (2012).Article 

Google Scholar 
45.Lehtonen, P. K., Tonteri, A., Sendek, D., Titov, S. & Primmer, C. R. Spatio-temporal genetic structuring of brown trout (Salmo trutta L.) populations within the River Luga, northwest Russia. Conserv. Genet. 10, 281–289. https://doi.org/10.1007/s10592-008-9577-2 (2009).Article 

Google Scholar 
46.Cross, T. F., Mills, C. P. R. & de CourcyWilliams, M. An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland. J. Fish Biol. 40, 25–32. https://doi.org/10.1111/j.1095-8649.1992.tb02550.x (1992).CAS 
Article 

Google Scholar 
47.Stelkens, R., Jaffuel, G., Escher, M. & Wedekind, C. Genetic and phenotypic population divergence on a microgeographic scale in brown trout. Mol. Ecol. 21, 2896–2915. https://doi.org/10.1111/j.1365-294X.2012.05581.x (2012).Article 
PubMed 

Google Scholar 
48.Hansen, M. M., Limborg, M. T., Ferchaud, A.-L. & Pujolar, J.-M. The effects of Medieval dams on genetic divergence and demographic history in brown trout populations. BMC Evol. Biol. 14, 122. https://doi.org/10.1186/1471-2148-14-122 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
49.Kohlmann, K. & Wüstemann, O. Tracing the genetic origin of brown trout (Salmo trutta) re-colonizing the Ecker reservoir in the Harz National Park, Germany. Environ. Biotechnol. 8, 39–44 (2012).
Google Scholar 
50.Dellefors, C. & Faremo, U. Early sexual maturation in males of wild sea trout, Salmo trutta L. inhibits smoltification. J. Fish Biol. 33, 741–749. https://doi.org/10.1111/j.1095-8649.1988.tb05519.x (1988).Article 

Google Scholar 
51.Jonsson, B. & Jonsson, N. Differences in growth between offspring of anadromous and freshwater brown trout Salmo trutta. J. Fish Biol. 20, 1–7. https://doi.org/10.1111/jfb.14693 (2021).Article 

Google Scholar  More

1.Schimel J, Schaeffer S. Microbial control over carbon cycling in soil. Front Microbiol. 2012;3:1–11.
Google Scholar 
2.Liang C, Schimel JP, Jastrow JD. The importance of anabolism in microbial control over soil carbon storage. Nat Microbiol. 2017;2:17105.CAS 
PubMed 

Google Scholar 
3.Malik AA, Martiny JBH, Brodie EL, Martiny AC, Treseder KK, Allison SD. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J. 2020;14:1–9.CAS 
PubMed 

Google Scholar 
4.Xu X, Schimel JP, Janssens IA, Song X, Song C, Yu G, et al. Global pattern and controls of soil microbial metabolic quotient. Ecol Monogr. 2017;87:429–41.
Google Scholar 
5.Chen L, Liu L, Mao C, Qin S, Wang J, Liu F, et al. Nitrogen availability regulates topsoil carbon dynamics after permafrost thaw by altering microbial metabolic efficiency. Nat commun. 2018;9:3951.PubMed 
PubMed Central 

Google Scholar 
6.Wang C, Qu L, Yang L, Liu D, Morrissey E, Miao R, et al. Large-scale importance of microbial carbon use efficiency and necromass to soil organic carbon. Global Change Biol. 2021;27:2039–48.
Google Scholar 
7.Wieder WR, Bonan GB, Allison SD. Global soil carbon projections are improved by modelling microbial processes. Nat Clim Change. 2013;3:909–12.CAS 

Google Scholar 
8.Sinsabaugh RL, Manzoni S, Moorhead DL, Richter A. Carbon use efficiency of microbial communities: stoichiometry, methodology and modelling. Ecol Lett. 2013;16:930–9.PubMed 

Google Scholar 
9.Xu M, Li X, Cai X, Gai J, Li X, Christie P, et al. Soil microbial community structure and activity along a montane elevational gradient on the Tibetan Plateau. Eur J Soil Biol. 2014;64:6–14.
Google Scholar 
10.Banerjee S, Walder F, Büchi L, Meyer M, Held AY, Gattinger A, et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. 2019;13:1722–36.PubMed 
PubMed Central 

Google Scholar 
11.Malik AA, Swenson T, Weihe C, Morrison EW, Martiny JBH, Brodie EL, et al. Drought and plant litter chemistry alter microbial gene expression and metabolite production. ISME J. 2020;14:2236–47.CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Crowther TW, van den Hoogen J, Wan J, Mayes MA, Keiser AD, Mo L, et al. The global soil community and its influence on biogeochemistry. Science. 2019;365:eaav0550.CAS 

Google Scholar 
13.Nottingham AT, Bååth E, Reischke S, Salinas N, Meir P. Adaptation of soil microbial growth to temperature: Using a tropical elevation gradient to predict future changes. Global Change Biol. 2019;25:827–38.
Google Scholar 
14.Feng J, Wei K, Chen Z, Lü X, Tian J, Wang C, et al. Coupling and decoupling of soil carbon and nutrient cycles across an aridity gradient in the drylands of northern China: evidence from ecoenzymatic stoichiometry. Global Biogeochem Cycles. 2019;33:559–69.CAS 

Google Scholar 
15.Allison S, Weintraub M, Gartner T, & Waldrop M. Evolutionary-economic principles as regulators of soil enzyme production and ecosystem function. In: Shukla G, Varma A., editors Soil enzymology. Soil Biology, vol 22. Berlin, Germany: Springer Berlin Heidelberg; 2011, pp 229–43.16.Tribelli PM, López NI. Reporting key features in cold-adapted bacteria. Life. 2018;8:8.PubMed Central 

Google Scholar 
17.Allison SD. A trait-based approach for modelling microbial litter decomposition. Ecol Lett. 2012;15:1058–70.CAS 
PubMed 

Google Scholar 
18.Fierer N, Bradford MA, Jackson RB. Toward an ecological classification of soil bacteria. Ecology. 2007;88:1354–64.PubMed 
PubMed Central 

Google Scholar 
19.Li H, Yang S, Semenov MV, Yao F, Ye J, Bu R, et al. Temperature sensitivity of SOM decomposition is linked with a K-selected microbial community. Global Change Biol. 2021;27:2763–79.
Google Scholar 
20.Arce E, Archaimbault V, Mondy CP, Usseglio-Polatera P. Recovery dynamics in invertebrate communities following water-quality improvement: taxonomy- vs trait-based assessment. Freshw Sci. 2014;33:1060–73. 1014
Google Scholar 
21.Bench SR, Ilikchyan IN, Tripp HJ, Zehr JP. Two strains of crocosphaera watsonii with highly conserved genomes are distinguished by strain-specific features. Front Microbiol. 2011;2:261–261.PubMed 
PubMed Central 

Google Scholar 
22.Ma B, Wang H, Dsouza M, Lou J, He Y, Dai Z, et al. Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China. ISME J. 2016;10:1891–901.CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Du B, Kang H, Pumpanen J, Zhu P, Yin S, Zou Q, et al. Soil organic carbon stock and chemical composition along an altitude gradient in the Lushan Mountain, subtropical China. Ecol Res. 2014;29:433–9.CAS 

Google Scholar 
24.Yao T, Thompson L, Yang W. Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nat Clim Change. 2012;2:663–7.
Google Scholar 
25.Zhuo G, Ciren B, Wang J, Lan X. Analysis of regional climate characteristics of Tibetan herbal products growing on Mt. Seqilha. Resour Sci. 2010;32:1452–61.
Google Scholar 
26.Chen L, Flynn DFB, Zhang X, Gao X, Lin L, Luo J, et al. Divergent patterns of foliar δ13C and δ15N in Quercus aquifolioides with an altitudinal transect on the Tibetan Plateau: an integrated study based on multiple key leaf functional traits. J Plant Ecol. 2014;8:303–12.
Google Scholar 
27.Xu M, Wang G, Li X, Cai X, Li X, Christie P, et al. The key factor limiting plant growth in cold and humid alpine areas also plays a dominant role in plant carbon isotope discrimination. Front Plant Sci. 2015;3:961.
Google Scholar 
28.Du J, Gao R, Ma PF, Liu YM, Zhou KS. Analysis of stereoscopic climate features on Mt. Seqiha, Tibet. Plateau Mt Meteorol Res. 2009;19:14–18.
Google Scholar 
29.Hu Q-W, Wu Q, Cao G-M, Li D, Long R-J, Wang Y-S. Growing season ecosystem respirations and associated component fluxes in two alpine meadows on the Tibetan Plateau. J Integr Plant Biol. 2008;50:271–9.CAS 
PubMed 

Google Scholar 
30.IUSS Working Group. World reference base for soil resources 2006, first update 2007. World soil resources reports no.103. in World soil resources reports no. 103. Rome, Italy: FAO; 2007.31.Walkley A. A critical examination of a rapid method for determining organic carbon in soils-effect of variations in digestion conditions and of inorganic soil constituents. Soil Sci. 1947;63:251–64.CAS 

Google Scholar 
32.Bray RH, Kurtz L. Determination of total, organic, and available forms of phosphorus in soils. Soil Sci. 1945;59:39–46.CAS 

Google Scholar 
33.Olsen SR, Cole CV, Watanabe FS. Estimation of available phosphorus in soils by extraction with sodium bicarbonate. Washington, DC: United States Department of Agriculture; 1954.34.Liu YR, Delgado-Baquerizo M, Wang JT, Hu HW, Yang Z, He JZ. New insights into the role of microbial community composition in driving soil respiration rates. Soil Biol Biochem. 2018;118:35–41.CAS 

Google Scholar 
35.Yao Q, Liu J, Yu Z, Li Y, Jin J, Liu X, et al. Three years of biochar amendment alters soil physiochemical properties and fungal community composition in a black soil of northeast China. Soil Biol Biochem. 2017;110:56–67.CAS 

Google Scholar 
36.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–D596.PubMed 
PubMed Central 

Google Scholar 
37.Marx M-C, Wood M, Jarvis S. A microplate fluorimetric assay for the study of enzyme diversity in soils. Soil Biol Biochem. 2001;33:1633–40.CAS 

Google Scholar 
38.Moorhead DL, Sinsabaugh RL, Hill BH, Weintraub MN. Vector analysis of ecoenzyme activities reveal constraints on coupled C, N and P dynamics. Soil Biol Biochem. 2016;93:1–7.CAS 

Google Scholar 
39.Wardle DA, Ghani A. A critique of the microbial metabolic quotient (qCO2) as a bioindicator of disturbance and ecosystem development. Soil Biol Biochem. 1995;27:1601–10.CAS 

Google Scholar 
40.Wang Q, Liu S, Tian P. Carbon quality and soil microbial property control the latitudinal pattern in temperature sensitivity of soil microbial respiration across Chinese forest ecosystems. Glob Change Biol. 2018;24:2841–9.
Google Scholar 
41.Xu M, Li X, Kuyper TW, Xu M, Zhang J. High microbial diversity stabilizes the responses of soil organic carbon decomposition to warming in the subsoil on the Tibetan Plateau. Global Change Biol. 2021;27:2061–75.
Google Scholar 
42.Li Y, Lv W, Jiang L, Zhang L, Wang S, Wang Q, et al. Microbial community responses reduce soil carbon loss in Tibetan alpine grasslands under short-term warming. Global Change Biol. 2019;25:3438–49.
Google Scholar 
43.Vance E, Brookes P, Jenkinson D. An extraction method for measuring soil microbial biomass C. Soil Biol Biochem. 1987;19:703–7.CAS 

Google Scholar 
44.Sinsabaugh RL, Shah JJF. Ecoenzymatic stoichiometry and ecological theory. Annu Rev Ecol Evol S. 2012;43:313–43.
Google Scholar 
45.Cui Y, Wang X, Zhang X, Ju W, Duan C, Guo X, et al. Soil moisture mediates microbial carbon and phosphorus metabolism during vegetation succession in a semiarid region. Soil Biol Biochem. 2020;147:107814.CAS 

Google Scholar 
46.Breiman L. Random forests. Mach Learn. 2001;45:5–32.
Google Scholar 
47.Benjamini Y, Krieger AM, Yekutieli D. Adaptive linear step-up procedures that control the false discovery rate. Biometrika. 2006;93:491–507.
Google Scholar 
48.Delgado-Baquerizo M, Reith F, Dennis PG, Hamonts K, Powell JR, Young A, et al. Ecological drivers of soil microbial diversity and soil biological networks in the Southern Hemisphere. Ecology. 2018;99:583–96.PubMed 

Google Scholar 
49.Cui Y, Moorhead DL, Guo X, Peng S, Wang Y, Zhang X, et al. Stoichiometric models of microbial metabolic limitation in soil systems. Glob Ecol Biogeogr. 2021;30:2297–311.
Google Scholar 
50.Nedwell DB. Effect of low temperature on microbial growth: lowered affinity for substrates limits growth at low temperature. Fems Microbiol Ecol. 1999;30:101–11.CAS 
PubMed 

Google Scholar 
51.Weinstein RN, Montiel PO, Johnstone K. Influence of growth temperature on lipid and soluble carbohydrate synthesis by fungi isolated from fellfield soil in the maritime Antarctic. Mycologia. 2000;92:222–9.CAS 

Google Scholar 
52.Varin T, Lovejoy C, Jungblut AD, Vincent WF, Corbeil J. Metagenomic analysis of stress genes in microbial mat communities from antarctica and the high arctic. Appl Environ Microb. 2012;78:549–59.
Google Scholar 
53.Nichols CM, Bowman JP, Guezennec J. Effects of incubation temperature on growth and production of exopolysaccharides by an antarctic sea ice bacterium grown in batch culture. Appl Environ Microbiol. 2005;71:3519–23.CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Ren C, Zhang W, Zhong Z, Han X, Yang G, Feng Y, et al. Differential responses of soil microbial biomass, diversity, and compositions to altitudinal gradients depend on plant and soil characteristics. Sci Total Environ. 2018;610-1:750–8.
Google Scholar 
55.Kumar S, Suyal DC, Yadav A, Shouche Y, Goel R. Microbial diversity and soil physiochemical characteristic of higher altitude. PLoS ONE. 2019;14:e0213844.CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Ruuskanen MO, Colby G, St Pierre KA, St Louis VL, Aris-Brosou S, Poulain AJ. Microbial genomes retrieved from high arctic lake sediments encode for adaptation to cold and oligotrophic environments. Limnol Oceanogr. 2020;65:S233–S247.CAS 

Google Scholar 
57.Feng L-j, Jia R, Sun J-y, Wang J, Lv Z-h, Mu J, et al. Response of performance and bacterial community to oligotrophic stress in biofilm systems for raw water pretreatment. Biodegradation. 2017;28:231–44.CAS 
PubMed 

Google Scholar 
58.Robinson CH. Cold adaptation in Arctic and Antarctic fungi. New Phytol. 2001;151:341–53.CAS 

Google Scholar 
59.Shahryari Z, Fazaelipoor M, Ghasemi Y, Lennartsson P, Taherzadeh M. Amylase and xylanase from edible fungus neurospora intermedia: production and characterization. Molecules. 2019;24:721.CAS 
PubMed Central 

Google Scholar 
60.Turner BC, Perkins DD, Fairfield A. Neurospora from natural populations: a global study. Fungal Genet Biol. 2001;32:67–92.CAS 
PubMed 

Google Scholar 
61.Malik AA, Puissant J, Buckeridge KM, Goodall T, Jehmlich N, Chowdhury S, et al. Land use driven change in soil pH affects microbial carbon cycling processes. Nat Commun. 2018;9:3591.PubMed 
PubMed Central 

Google Scholar  More

1.Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Chang. 6, 166–171 (2016).ADS 

Google Scholar 
2.Oki, T. & Kanae, S. Global hydrological cycles and world water resources. Science 313, 1068–1072 (2006).CAS 
PubMed 
ADS 

Google Scholar 
3.Nunn, C. L., Thrall, P. H., Leendertz, F. H. & Boesch, C. The spread of fecally transmitted parasites in socially-structured populations. PLoS ONE 6, e21677 (2011).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
4.Vicente, J., Fernández De Mera, I. G. & Gortazar, C. Epidemiology and risk factors analysis of elaphostrongylosis in red deer (Cervus elaphus) from Spain. Parasitol. Res. 98, 77–85 (2006).PubMed 

Google Scholar 
5.Paull, S. H. et al. From superspreaders to disease hotspots: linking transmission across hosts and space. Front. Ecol. Environ. 10, 75–82 (2012).PubMed 

Google Scholar 
6.Leach, C. B., Webb, C. T. & Cross P. C. When environmentally persistent pathogens transform good habitat into ecological traps. R. Soc. Open Sci. 3, 160051 (2016).7.Dougherty, E. R., Seidel, D. P., Carlson, C. J., Spiegel, O. & Getz, W. M. Going through the motions: incorporating movement analyses into disease research. Ecol. Lett. 21, 588–604 (2018).PubMed 

Google Scholar 
8.Valeix, M., Fritz, H., Chamaillé-Jammes, S., Bourgarel, M. & Murindagomo, F. Fluctuations in abundance of large herbivore populations: insights into the influence of dry season rainfall and elephant numbers from long-term data. Anim. Conserv. 11, 391–400 (2008).
Google Scholar 
9.Western, D. Water availability and its influence on the structure and dynamics of a savannah large mammal community. Afr. J. Ecol. 13, 265–286 (1975).
Google Scholar 
10.Sutherland, K., Ndlovu, M. & Pérez-Rodríguez, A. Use of artificial waterholes by animals in the Southern Region of the Kruger National Park, South Africa. Afr. J. Wildl. Res. 48, 023003 (2018).
Google Scholar 
11.Chamaillé-Jammes, S., Fritz, H., Valeix, M., Murindagomo, F. & Clobert, J. Resource variability, aggregation and direct density dependence in an open context: the local regulation of an African elephant population. J. Anim. Ecol. 77, 135–144 (2008).PubMed 

Google Scholar 
12.Vanderwaal, K., Gilbertson, M., Okanga, S., Allan, B. F. & Craft, M. E. Seasonality and pathogen transmission in pastoral cattle contact networks. R. Soc. Open Sci. 4, 170808 (2017).PubMed 
PubMed Central 

Google Scholar 
13.Hayward, M. W. & Hayward, M. D. Waterhole use by African fauna. South Afr. J. Wildl. Res. 42, 117–127 (2012).
Google Scholar 
14.Valeix, M., Fritz, H., Matsika, R., Matsvimbo, F. & Madzikanda, H. The role of water abundance, thermoregulation, perceived predation risk and interference competition in water access by African herbivores. Afr. J. Ecol. 46, 402–410 (2008).
Google Scholar 
15.Crosmary, W.-G., Valeix, M., Fritz, H., Madzikanda, H. & Côté, S. D. African ungulates and their drinking problems: hunting and predation risks constrain access to water. Anim. Behav. 83, 145–153 (2012).
Google Scholar 
16.Payne, A., Philipon, S., Hars, J., Dufour, B. & Gilot-Fromont, E. Wildlife interactions on baited places and waterholes in a French area infected by bovine tuberculosis. Front. Vet. Sci. 3, 16 (2017).
Google Scholar 
17.Wright, A. N. & Gompper, M. E. Altered parasite assemblages in raccoons in response to manipulated resource availability. Oecologia 144, 148–156 (2005).PubMed 
ADS 

Google Scholar 
18.Morgan, E. R., Milner-Gulland, E. J., Torgerson, P. R. & Medley, G. F. Ruminating on complexity: macroparasites of wildlife and livestock. Trends Ecol. Evol. 19, 181–188 (2004).PubMed 

Google Scholar 
19.Anderson, R. M. & May, R. M. Regulation and stability of host-parasite population interactions: I. regulatory processes. J. Anim. Ecol. 47, 219–247 (1978).
Google Scholar 
20.Hudson, P. J., Dobson, A. P. & Lafferty, K. D. Is a healthy ecosystem one that is rich in parasites? Trends Ecol. Evol. 21, 381–385 (2006).PubMed 

Google Scholar 
21.Charlier, J., van der Voort, M., Kenyon, F., Skuce, P. & Vercruysse, J. Chasing helminths and their economic impact on farmed ruminants. Trends Parasitol. 30, 361–367 (2014).PubMed 

Google Scholar 
22.Kaplan, R. M. & Vidyashankar, A. N. An inconvenient truth: Global worming and anthelmintic resistance. Vet. Parasitol. 186, 70–78 (2012).PubMed 

Google Scholar 
23.WHO Expert Committee. Prevention and control of schistosomiasis and soil-transmitted helminthiasis. World Heal. Organ Tech. Rep. Ser. 912, 1–57 (2002).
Google Scholar 
24.Ezenwa, V. O. Interactions among host diet, nutritional status and gastrointestinal parasite infection in wild bovids. Int. J. Parasitol. 34, 535–542 (2004).PubMed 

Google Scholar 
25.Froy, H. et al. Senescence in immunity against helminth parasites predicts adult mortality in a wild mammal. Science 365, 1296–1298 (2019).CAS 
PubMed 
ADS 

Google Scholar 
26.Brearley, G. et al. Wildlife disease prevalence in human-modified landscapes. Biol. Rev. 88, 427–442 (2013).PubMed 

Google Scholar 
27.Bradley, C. A. & Altizer, S. Urbanization and the ecology of wildlife diseases. Trends Ecol. Evol. 22, 95–102 (2007).PubMed 

Google Scholar 
28.Mignatti, A., Boag, B. & Cattadori, I. M. Host immunity shapes the impact of climate changes on the dynamics of parasite infections. Proc. Natl Acad. Sci. USA 113, 2970–2975 (2016).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
29.Anderson, R. C. Nematode Parasites of Vertebrates: Their Development and Transmission, Second Edi (CABI Publishing, 2000).30.Stromberg, B. E. Environmental Factors Influencing Transmission in Veterinary Parasitology 247–264 (Elsevier, 1997).31.Knapp-Lawitzke, F., Küchenmeister, F., Küchenmeister, K., von Samson-Himmelstjerna, G. & Demeler, J. Assessment of the impact of plant species composition and drought stress on survival of strongylid third-stage larvae in a greenhouse experiment. Parasitol. Res. 113, 4123–4131 (2014).PubMed 

Google Scholar 
32.Nunn, C. L., Thrall, P. H. & Kappeler, P. M. Shared resources and disease dynamics in spatially structured populations. Ecol. Modell. 272, 198–207 (2014).
Google Scholar 
33.Ogada, D. L., Torchin, M. E., Kinnaird, M. F. & Ezenwa, V. O. Effects of vulture declines on facultative scavengers and potential implications for mammalian disease transmission. Conserv. Biol. 26, 453–460 (2012).CAS 
PubMed 

Google Scholar 
34.Round, M. C. Check List of the Helminth Parasites of African Mammals of the Orders Carnivora, Tubulidentata, Proboscidea, Hyra-coidea, Artiodactyla and Perissodactyla (Farnham Royal, Commonwealth Agricultural Bureaux, 1968).35.Wells, K. et al. Global spread of helminth parasites at the human–domestic animal–wildlife interface. Glob. Chang. Biol. 24, 3254–3265 (2018).PubMed 
ADS 

Google Scholar 
36.VanderWaal, K., Omondi, G. P. & Obanda, V. Mixed-host aggregations and helminth parasite sharing in an East African wildlife-livestock system. Vet. Parasitol. 205, 224–232 (2014).PubMed 

Google Scholar 
37.Walker, J. G., Plein, M., Morgan, E. R. & Vesk, P. A. Uncertain links in host-parasite networks: lessons for parasite transmission in a multi-host system. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 372, 20160095 (2017).PubMed 
PubMed Central 

Google Scholar 
38.Bull, J. J. Virulence. Evolution 48, 1423–1437 (1994).CAS 
PubMed 

Google Scholar 
39.R. W. Ashford, W. Crewe, Parasites of Homo sapiens: An Annotated Checklist of the Protozoa, Helminths, and Arthropods for which We Are Home, 2nd edn (Taylor & Francis, 2003).40.Loarie, S. R., Van Aarde, R. J. & Pimm, S. L. Fences and artificial water affect African savannah elephant movement patterns. Biol. Conserv. 142, 3086–3098 (2009).
Google Scholar 
41.Kay, R. N. B. Responses of African livestock and wild herbivores to drought. J. Arid Environ. 37, 683–694 (1997).ADS 

Google Scholar 
42.Chamaillé-Jammes, S., Mtare, G., Makuwe, E. & Fritz, H. African elephants adjust speed in response to surface-water constraint on foraging during the dry-season. PLoS ONE 8, e59164 (2013).43.Redfern, J. V., Grant, R., Biggs, H. & Getz, W. M. Surface-water constraints on herbivore foraging in the Kruger National Park, South Africa. Ecology 84, 2092–2107 (2003).
Google Scholar 
44.Titcomb, G. C., Amooni, G., Mantas, J. N. & Young, H. S. The effects of herbivore aggregations at water sources on savanna plants differ across soil and climate gradients. Ecol. Appl. 31, e02422 (2021).PubMed 

Google Scholar 
45.Smit, I. P. J., Grant, C. C. & Devereux, B. J. Do artificial waterholes influence the way herbivores use the landscape? Herbivore distribution patterns around rivers and artificial surface water sources in a large African savanna park. Biol. Conserv. 136, 85–99 (2007).
Google Scholar 
46.Estes, R. D. The Behavior Guide to African Mammals 1st edn (University of California Press, 2012).47.Ezenwa, V. O. Selective defecation and selective foraging: antiparasite behavior in wild ungulates? Ethology 110, 851–862 (2004).
Google Scholar 
48.Valeix, M. et al. How key habitat features influence large terrestrial carnivore movements: Waterholes and African lions in a semi-arid savanna of north-western Zimbabwe. Landsc. Ecol. 25, 337–351 (2010).
Google Scholar 
49.Sinclair, A. R. E., Mduma, S. & Brashares, J. S. Patterns of predation in a diverse predator–prey system. Nature 425, 288–290 (2003).CAS 
PubMed 
ADS 

Google Scholar 
50.Ford, A. T. et al. Large carnivores make savanna tree communities less thorny. Science 346, 346–349 (2014).CAS 
PubMed 
ADS 

Google Scholar 
51.Thrash, I. & Derry, J. F. Review of literature on the nature and modelling of piospheres. Koedoe 42, 73–94 (1999).
Google Scholar 
52.Rohr, J. R. et al. Frontiers in climate change-disease research. Trends Ecol. Evol. 26, 270–277 (2011).PubMed 
PubMed Central 

Google Scholar 
53.Ogutu, J. O. et al. Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: what are the causes? PLoS ONE 11, e0163249 (2016).PubMed 
PubMed Central 

Google Scholar 
54.Adhikari, U., Nejadhashemi, A. & Matthew, R. A review of climate change impacts on water resources in east. Afr. Trans. Am. Soc. Agric. Biol. Eng. 58, 1493–1507 (2015).
Google Scholar 
55.Funk, C. et al. Warming of the Indian Ocean threatens eastern and southern African food security but could be mitigated by agricultural development. Proc. Natl Acad. Sci. USA 105, 11081–11086 (2008).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
56.IPCC. In Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds. Pachauri, R., Meyer, L.) (IPCC, 2014).57.de Wit, M. & Stankiewicz, J. Changes in surface water supply across africa with predicted climate change. Science 311, 1917–1921 (2006).PubMed 
ADS 

Google Scholar 
58.Obanda, V., Iwaki, T., Mutinda, N. M. & Gakuya, F. Gastrointestinal parasites and associated pathological lesions in starving free-ranging african elephants. South Afr. J. Wildl. Res. 41, 167–172 (2011).
Google Scholar 
59.Hawkins, J. A. Economic benefits of parasite control in cattle. Vet. Parasitol. 46, 159–173 (1993).CAS 
PubMed 

Google Scholar 
60.Weinstein, S. B., Buck, J. C. & Young, H. S. A landscape of disgust. Science 359, 1213–1214 (2018).CAS 
PubMed 
ADS 

Google Scholar 
61.Buck, J. C., Weinstein, S. B. & Young, H. S. Ecological and evolutionary consequences of parasite avoidance. Trends Ecol. Evol. 33, 619–632 (2018).CAS 
PubMed 

Google Scholar 
62.Ndlovu, M. et al. Water for African elephants (Loxodonta africana): faecal microbial loads affect use of artificial waterholes. Biol. Lett. 14, 20180360 (2018).PubMed 
PubMed Central 

Google Scholar 
63.Amoroso, C. R., Kappeler, P. M., Fichtel, C. & Nunn, C. L. Fecal contamination, parasite risk, and waterhole use by wild animals in a dry deciduous forest. Behav. Ecol. Sociobiol. 73, 1–11 (2019).
Google Scholar 
64.Thurber, M. I. et al. Effects of rainfall, host demography, and musth on strongyle fecal egg counts in African elephants (Loxodonta Africana) in Namibia. J. Wildl. Dis. 47, 172–181 (2011).CAS 
PubMed 

Google Scholar 
65.Cizauskas, C. A., Turner, W. C., Pitts, N. & Getz, W. M. Seasonal patterns of hormones, macroparasites, and microparasites in wild African ungulates: the interplay among stress, reproduction, and disease. PLoS ONE 10, e0120800 (2015).PubMed 
PubMed Central 

Google Scholar 
66.Pelletier, N. & Tyedmers, P. Forecasting potential global environmental costs of livestock production 2000-2050. Proc. Natl Acad. Sci. USA 107, 18371–18374 (2010).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
67.Shorrocks, B. The Biology of African Savannahs (Oxford University Press Inc., 2007).68.Barda, B. D. et al. Mini-FLOTAC, an innovative direct diagnostic technique for intestinal parasitic infections: experience from the field. PLoS Negl. Trop. Dis. 7, e2344 (2013).PubMed 
PubMed Central 

Google Scholar 
69.Azian, M. Y. et al. Detection of helminth infections in dogs and soil contamination in rural and urban areas. Southeast Asian J. Trop. Med. Public Health 39, 205–212 (2008).PubMed 

Google Scholar 
70.Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models (2020).71.Anderson, R. M. & May, R. M. Infectious Disease of Humans: Dynamics and Control (Oxford University Press, 1991).72.Franz, T. E., Caylor, K. K., Nordbotten, J. M., Rodríguez-Iturbe, I. & Celia, M. A. An ecohydrological approach to predicting regional woody species distribution patterns in dryland ecosystems. Adv. Water Resour. 33, 215–230 (2010).ADS 

Google Scholar 
73.K. K. Caylor, J. Gitonga, D. J. Martins, Mpala Research Centre Meterorological and Hydrological Dataset (2017).74.R Core Team. R: A Language and Environment for Statistical Computing (2016).75.Titcomb, G. Herbivore dung and parasite counts, Ol Pejeta Conservancy and Mpala Research Centre, Kenya (2015–2018). Environ. Data Initiat. https://doi.org/10.6073/pasta/2728d61f10b767814b5d95fbd69137fa (2021). More

1.Evariste, L. et al. Gut microbiota of aquatic organisms: A key endpoint for ecotoxicological studies. Environ. Pollut. 248, 989–999 (2019).CAS 
PubMed 

Google Scholar 
2.Guo, G., Yumvihoze, E., Poulain, A. J. & Chan, H. M. Monomethylmercury degradation by the human gut microbiota is stimulated by protein amendments. J. Toxicol. Sci. 43, 717–725 (2018).CAS 
PubMed 

Google Scholar 
3.Dempsey, J. L., Little, M. & Cui, J. Y. Gut microbiome: An intermediary to neurotoxicity. Neurotoxicology 75, 41–69 (2019).PubMed 
PubMed Central 

Google Scholar 
4.Breton, J. Ô. et al. Gut microbiota limits heavy metals burden caused by chronic oral exposure. Toxicol. Lett. 222, 132–138 (2013).CAS 
PubMed 

Google Scholar 
5.Claus, S. P., Guillou, H. & Ellero-Simatos, S. The gut microbiota: A major player in the toxicity of environmental pollutants?. NPJ Biofilms Microbiomes 2, 16003 (2016).PubMed 
PubMed Central 

Google Scholar 
6.Nakamura, I., Hosokawa, K., Tamura, H. & Miura, T. Reduced mercury excretion with feces in germfree mice after oral administration of methyl mercury chloride. Bull. Environ. Contam. Toxicol. 17, 528–533 (1977).CAS 
PubMed 

Google Scholar 
7.Rowland, I. R., Davies, M. J. & Evans, J. G. Tissue content of mercury in rats given methylmercuric chloride orally: Influence of intestinal flora. Arch. Environ. Health 35, 155–160 (1980).CAS 
PubMed 

Google Scholar 
8.Seko, Y., Miura, T., Takahashi, M. & Koyama, T. Methyl mercury decomposition in mice treated with antibiotics. Acta Pharmacol. Toxicol. (Copenh) 49, 259–265 (1981).CAS 

Google Scholar 
9.Lapanje, A., Zrimec, A., Drobne, D. & Rupnik, M. Long-term Hg pollution-induced structural shifts of bacterial community in the terrestrial isopod (Porcellio scaber) gut. Environ. Pollut. 158, 3186–3193 (2010).CAS 
PubMed 

Google Scholar 
10.Ruan, Y. et al. High doses of copper and mercury changed cecal microbiota in female mice. Biol. Trace Elem. Res. 189, 134–144 (2019).CAS 
PubMed 

Google Scholar 
11.Desforges, J.-P.W. et al. Immunotoxic effects of environmental pollutants in marine mammals. Environ. Int. 86, 126–139 (2016).CAS 
PubMed 

Google Scholar 
12.Dietz, R. et al. What are the toxicological effects of mercury in Arctic biota?. Sci. Total Environ. 443, 775–790 (2013).ADS 
CAS 
PubMed 

Google Scholar 
13.Amstrup, S. C., Feldhamer, G. A., Thompson, B. C. & Chapman, J. A. The polar bear-Ursus maritimus biology, management, and conservation. Wild Mammals North Am. Biol. Manag. Conserv. 2, 587–610 (2003).
Google Scholar 
14.McKinney, M. A., Atwood, T. C., Iverson, S. J. & Peacock, E. Temporal complexity of southern Beaufort Sea polar bear diets during a period of increasing land use. Ecosphere 8, e01633 (2017).
Google Scholar 
15.Bourque, J., Atwood, T. C., Divoky, G. J., Stewart, C. & McKinney, M. A. Fatty acid-based diet estimates suggest ringed seal remain the main prey of southern Beaufort Sea polar bears despite recent use of onshore food resources. Ecol. Evol. 10, 2093–2103 (2020).PubMed 
PubMed Central 

Google Scholar 
16.Routti, H. et al. Contaminants in Polar Bears from the Circumpolar Arctic State of Knowledge and Further Recommendations for Monitoring and Research-Action #42 of the Circumpolar Action Plan for polar Bear Conservation (2019).17.Letcher, R. J. et al. Exposure and effects assessment of persistent organohalogen contaminants in Arctic wildlife and fish. Sci. Total Environ. 408, 2995–3043 (2010).ADS 
CAS 
PubMed 

Google Scholar 
18.Dietz, R. et al. Trends in mercury in hair of Greenlandic polar bears (Ursus maritimus) during 1892–2001. Environ. Sci. Technol. 40, 1120–1125 (2006).ADS 
CAS 
PubMed 

Google Scholar 
19.Borgå, K., Fisk, A. T., Hoekstra, P. F. & Muir, D. C. G. Biological and chemical factors of importance in the bioaccumulation and trophic transfer of persistent organochlorine contaminants in Arctic marine food webs. Environ. Toxicol. Chem. 23, 2367 (2004).PubMed 

Google Scholar 
20.Hoekstra, P. F. et al. Trophic transfer of persistent organochlorine contaminants (OCs) within an Arctic marine food web from the southern Beaufort-Chukchi Seas. Environ. Pollut. 124, 509–522 (2003).CAS 
PubMed 

Google Scholar 
21.Ley, R. E. et al. Evolution of mammals and their gut microbes. Science (80–) 320, 1647–1651 (2008).ADS 
CAS 
PubMed Central 

Google Scholar 
22.Sommer, F. et al. The gut microbiota modulates energy metabolism in the hibernating brown bear Ursus arctos. Cell Rep. 14, 1655–1661 (2016).CAS 
PubMed 

Google Scholar 
23.Borbón-García, A., Reyes, A., Vives-Flórez, M. & Caballero, S. Captivity shapes the gut microbiota of Andean bears: Insights into health surveillance. Front. Microbiol. 8, 1316 (2017).PubMed 
PubMed Central 

Google Scholar 
24.Ferguson, S. H., Stirling, I. & McLoughlin, P. Climate change and ringed seal (Phoca hispida) recruitment in western Hudson Bay. Mar. Mammal Sci. 21, 121–135 (2005).
Google Scholar 
25.Thiemann, G., Iverson, S. & Stirling, I. Polar bear diets and arctic marine food webs: Insights from fatty acid analysis. Ecol. Monogr. 78, 591–613 (2008).
Google Scholar 
26.Muir, D. C., Norstrom, R. J. & Simon, M. Organochlorine contaminants in Arctic marine food chains: Accumulation of specific polychlorinated biphenyls and chlordane-related compounds. Environ. Sci. Technol. 22, 1071–1079 (1988).ADS 
CAS 
PubMed 

Google Scholar 
27.Young, B. G., Loseto, L. L. & Ferguson, S. H. Diet differences among age classes of Arctic seals: Evidence from stable isotope and mercury biomarkers. Polar Biol. 33, 153–162 (2010).
Google Scholar 
28.Correa, L., Castellini, J. M., Quakenbush, L. T. & O’Hara, T. M. Mercury and selenium concentrations in skeletal muscle, liver, and regions of the heart and kidney in bearded seals from Alaska, USA. Environ. Toxicol. Chem. 34, 2403–2408 (2015).CAS 
PubMed 

Google Scholar 
29.Brown, T. M. et al. Mercury and cadmium in ringed seals in the Canadian Arctic: Influence of location and diet. Sci. Total Environ. 545–546, 503–511 (2016).ADS 
PubMed 

Google Scholar 
30.McKinney, M. A., Atwood, T. C., Pedro, S. & Peacock, E. Ecological change drives a decline in mercury concentrations in southern Beaufort Sea polar bears. Environ. Sci. Technol. 51, 7814–7822 (2017).ADS 
CAS 
PubMed 

Google Scholar 
31.Watson, S. E. et al. Global change-driven use of onshore habitat impacts polar bear faecal microbiota. ISME J. 20, 1–1 (2019).
Google Scholar 
32.Calvert, W. & Ramsay, M. A. Evaluation of age determination of polar bears by counts of cementum growth layer groups. Ursus 10, 449–453 (1998).
Google Scholar 
33.Cattet, M. R., Caulkett, N. A., Obbard, M. E. & Stenhouse, G. B. A body-condition index for ursids. Can. J. Zool. 80, 1156–1161 (2002).
Google Scholar 
34.Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).CAS 
PubMed 

Google Scholar 
35.Albanese, D., Fontana, P., De Filippo, C., Cavalieri, D. & Donati, C. MICCA: A complete and accurate software for taxonomic profiling of metagenomic data. Sci. Rep. 5, 9743 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
36.Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).PubMed 
PubMed Central 

Google Scholar 
37.Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
38.Price, M. N., Dehal, P. S. & Arkin, A. P. Fasttree: Computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641–1650 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Golzadeh, N. et al. Evaluating the concentrations of total mercury, methylmercury, selenium, and selenium:mercury molar ratios in traditional foods of the Bigstone Cree in Alberta Canada. Chemosphere 250, 20 (2020).
Google Scholar 
40.Iverson, S. J., Field, C., DonBowen, W. & Blanchard, W. Quantitative fatty acid signature analysis: A new method of estimating predator diets. Ecol. Monogr. 74, 211–235 (2004).
Google Scholar 
41.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).
Google Scholar 
42.Bolker, B. M. et al. Generalized linear mixed models: A practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).
Google Scholar 
43.Grandjean, P. & Budtz-Jørgensen, E. Total imprecision of exposure biomarkers: Implications for calculating exposure limits. Am. J. Ind. Med. 50, 712–719 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Dietz, R. et al. Temporal trends and future predictions of mercury concentrations in Northwest Greenland polar bear (Ursus maritimus) hair. Environ. Sci. Technol. 45, 1458–1465 (2011).ADS 
CAS 
PubMed 

Google Scholar 
45.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 

Google Scholar 
46.Foster, Z. S. L., Sharpton, T. J. & Grünwald, N. J. Metacoder: An R package for visualization and manipulation of community taxonomic diversity data. PLoS Comput. Biol. 13, e1005404 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
47.Xia, J. et al. Effects of short term lead exposure on gut microbiota and hepatic metabolism in adult zebrafish. Comp. Biochem. Physiol. Part C Toxicol. Pharmacol. 209, 1–8 (2018).CAS 

Google Scholar 
48.Rothenberg, S. E. et al. The role of gut microbiota in fetal methylmercury exposure: Insights from a pilot study. Toxicol. Lett. 242, 60–67 (2016).CAS 
PubMed 

Google Scholar 
49.Wu, J. et al. Perinatal lead exposure alters gut microbiota composition and results in sex-specific bodyweight increases in adult mice. Toxicol. Sci. 151, 324–333 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Pascoe, E. L., Hauffe, H. C., Marchesi, J. R. & Perkins, S. E. Network analysis of gut microbiota literature: An overview of the research landscape in non-human animal studies. ISME J. 11, 2644–2651 (2017).PubMed 
PubMed Central 

Google Scholar 
51.Gilmour, C. C. et al. Mercury methylation by novel microorganisms from new environments. Environ. Sci. Technol. 47, 11810–11820 (2013).ADS 
CAS 
PubMed 

Google Scholar 
52.Li, H. et al. Intestinal methylation and demethylation of mercury. Bull. Environ. Contam. Toxicol. 1025(102), 597–604 (2018).
Google Scholar 
53.Guo, X. et al. Metagenomic profiles and antibiotic resistance genes in gut microbiota of mice exposed to arsenic and iron. Chemosphere 112, 1–8 (2014).ADS 
CAS 
PubMed 

Google Scholar 
54.Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006).ADS 
PubMed 
PubMed Central 

Google Scholar 
55.Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Microbial ecology: Human gut microbes associated with obesity. Nature 444, 1022–1023 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Hollister, E. B. et al. Structure and function of the healthy pre-adolescent pediatric gut microbiome. Microbiome 3, 36 (2015).PubMed 
PubMed Central 

Google Scholar 
57.Rowland, I., Davies, M. & Grasso, P. Biosynthesis of methylmercury compounds by the intestinal flora of the rat. Arch. Environ. Health Int. J. 32, 24–28 (1977).CAS 

Google Scholar 
58.Paredes-Sabja, D., Setlow, P. & Sarker, M. R. Germination of spores of Bacillales and Clostridiales species: Mechanisms and proteins involved. Trends Microbiol. 19, 85–94 (2011).CAS 
PubMed 

Google Scholar 
59.Setlow, P., Wang, S. & Li, Y. Q. Germination of spores of the orders Bacillales and Clostridiales. Annu. Rev. Microbiol. 71, 459–477 (2017).CAS 
PubMed 

Google Scholar 
60.Ilinskaya, O. N., Ulyanova, V. V., Yarullina, D. R. & Gataullin, I. G. Secretome of intestinal bacilli: A natural guard against pathologies. Front. Microbiol. 8, 25 (2017).
Google Scholar 
61.Hiller-Bittrolff, K., Foreman, K., Bulseco-McKim, A. N., Benoit, J. & Bowen, J. L. Effects of mercury addition on microbial community composition and nitrate removal inside permeable reactive barriers. Environ. Pollut. 242, 797–806 (2018).CAS 
PubMed 

Google Scholar 
62.Kuhn, K. A. et al. Bacteroidales recruit IL-6-producing intraepithelial lymphocytes in the colon to promote barrier integrity. Mucosal Immunol. 11, 357–368 (2018).CAS 
PubMed 

Google Scholar 
63.Wei, Z. S. et al. Effect of gaseous mercury on nitric oxide removal performance and microbial community of a hybrid catalytic membrane biofilm reactor. Chem. Eng. J. 316, 584–591 (2017).CAS 

Google Scholar 
64.Pagano, A. M. et al. High-energy, high-fat lifestyle challenges an Arctic apex predator, the polar bear. Science (80–) 359, 568–572 (2018).ADS 
CAS 

Google Scholar 
65.Van Waaij, D., Berghuis-de Vries, J. M. & Lekkerkerk-Van Der Wees, J. E. C. Colonization resistance of the digestive tract in conventional and antibiotic-treated mice. J. Hyg. (Lond.) 69, 405–411 (1971).
Google Scholar 
66.Girvan, M. S., Campbell, C. D., Killham, K., Prosser, J. I. & Glover, L. A. Bacterial diversity promotes community stability and functional resilience after perturbation. Environ. Microbiol. 7, 301–313 (2005).CAS 
PubMed 

Google Scholar 
67.Cowan, T. E. et al. Chronic coffee consumption in the diet-induced obese rat: Impact on gut microbiota and serum metabolomics. J. Nutr. Biochem. 25, 489–495 (2014).CAS 
PubMed 

Google Scholar 
68.Bishara, J. et al. Obesity as a risk factor for Clostridium difficile infection. Clin. Infect. Dis. 57, 489–493 (2013).PubMed 

Google Scholar 
69.Pohlner, M. et al. The majority of active Rhodobacteraceae in marine sediments belong to uncultured genera: A molecular approach to link their distribution to environmental conditions. Front. Microbiol. 10, 659 (2019).PubMed 
PubMed Central 

Google Scholar 
70.Simon, M. et al. Phylogenomics of Rhodobacteraceae reveals evolutionary adaptation to marine and non-marine habitats. ISME J. 11, 1483–1499 (2017).PubMed 
PubMed Central 

Google Scholar 
71.Castonguay-Paradis, S. et al. Dietary fatty acid intake and gut microbiota determine circulating endocannabinoidome signaling beyond the effect of body fat. Sci. Rep. 10, 1–11 (2020).
Google Scholar  More

1.Galbraith, S. M., Griswold, T., Price, W. J. & Bosque-Pérez, N. A. Biodiversity and community composition of native bee populations vary among human-dominated land uses within the seasonally dry tropics. J. Insect Conserv. https://doi.org/10.1007/s10841-020-00274-8 (2020).Article 

Google Scholar 
2.Imbach, P. et al. Climate change, ecosystems and smallholder agriculture in Central America: An introduction to the special issue. Clim. Change 141, 1–12 (2017).
Google Scholar 
3.HilleRisLambers, J., Harsch, M. A., Ettinger, A. K., Ford, K. R. & Theobald, E. J. How will biotic interactions influence climate change-induced range shifts?. Ann. N. Y. Acad. Sci. 1297, 112–125 (2013).PubMed 

Google Scholar 
4.Butt, N. et al. Cascading effects of climate extremes on vertebrate fauna through changes to low-latitude tree flowering and fruiting phenology. Glob. Chang. Biol. 21, 3267–3277 (2015).ADS 
PubMed 

Google Scholar 
5.Garibaldi, L. A. et al. Wild pollinators enhance fruit set of crops regardless of honey bee abundance. Science 339, 1608–1611 (2013).ADS 
CAS 
PubMed 

Google Scholar 
6.Orr, M. C. et al. Global patterns and drivers of bee distribution. Curr. Biol. 31, 451-458.e4 (2021).CAS 
PubMed 

Google Scholar 
7.Bezerra, E. S., Lopes, A. V. & Machado, I. C. Biologia reprodutiva de Byrsonima gardnerana A. Juss. (Malpighiaceae) e interações com abelhas Centris (Centridini) no Nordeste do Brasil. Rev. Bras. Bot. 32, 95–108 (2009).
Google Scholar 
8.Schleuning, M. et al. Trait-based assessments of climate-change impacts on interacting species. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2019.12.010 (2020).Article 
PubMed 

Google Scholar 
9.Hoiss, B., Krauss, J. & Steffan-Dewenter, I. Interactive effects of elevation, species richness and extreme climatic events on plant-pollinator networks. Glob. Chang. Biol. 21, 4086–4097 (2015).ADS 
PubMed 

Google Scholar 
10.Freitas, B. M. et al. Diversity, threats and conservation of native bees in the Neotropics. Apidologie 40, 332–346 (2009).MathSciNet 

Google Scholar 
11.Classen, A. et al. Temperature versus resource constraints: Which factors determine bee diversity on Mount Kilimanjaro, Tanzania?. Glob. Ecol. Biogeogr. 24, 642–652 (2015).
Google Scholar 
12.Ramos-Jiliberto, R. et al. Topological change of Andean plant–pollinator networks along an altitudinal gradient. Ecol. Complex. 7, 86–90 (2010).
Google Scholar 
13.Dellinger, A. S. et al. Low bee visitation rates explain pollinator shifts to vertebrates in tropical mountains. New Phytol. https://doi.org/10.1111/nph.17390 (2021).Article 
PubMed 

Google Scholar 
14.González-Vanegas, P. A., Rös, M., García-Franco, J. G. & Aguirre-Jaimes, A. Buzz-pollination in a tropical montane cloud forest: Compositional similarity and plant-pollinator interactions. Neotrop. Entomol. https://doi.org/10.1007/s13744-021-00867-1 (2021).Article 
PubMed 

Google Scholar 
15.Aslan, C. E., Zavaleta, E. S., Tershy, B. & Croll, D. Mutualism disruption threatens global plant biodiversity: A systematic review. PLoS ONE 8, 1–11 (2013).
Google Scholar 
16.García-Robledo, C., Kuprewicz, E. K., Staines, C. L., Erwin, T. L. & Kress, W. J. Limited tolerance by insects to high temperatures across tropical elevational gradients and the implications of global warming for extinction. Proc. Natl. Acad. Sci. U. S. A. 113, 680–685 (2016).ADS 
PubMed 
PubMed Central 

Google Scholar 
17.Sheldon, K. S. Climate change in the tropics: Ecological and evolutionary responses at low latitudes. Annu. Rev. Ecol. Evol. Syst. 50, 303–333 (2019).
Google Scholar 
18.McCain, C. M. & Colwell, R. K. Assessing the threat to montane biodiversity from discordant shifts in temperature and precipitation in a changing climate. Ecol. Lett. 14, 1236–1245 (2011).PubMed 

Google Scholar 
19.Aguilar, I., Herrera, E. & Zamora, G. Stingless bees of Costa Rica. Pot-Honey https://doi.org/10.1007/978-1-4614-4960-7 (2012).Article 

Google Scholar 
20.Köppler, K., Vorwohl, G. & Koeniger, N. Comparison of pollen spectra collected by four different subspecies of the honey bee Apis mellifera. Apidologie 38, 341–353 (2007).
Google Scholar 
21.Brehm, G., Colwell, R. K. & Kluge, J. The role of environment and mid-domain effect on moth species richness along a tropical elevational gradient. Glob. Ecol. Biogeogr. 16, 205–219 (2007).
Google Scholar 
22.Ortiz-Mora, R. A., Van Veen, J. W., Corrales, G. & Sommeijer, M. J. Influence of altitude on the distribution of stingless bees (Hymenoptera Apidae: Meliponinae). Apiacta 30, 101–105 (1995).
Google Scholar 
23.Michener, C. D. The Bees of the World (The Johns Hopkins University Press, 2007).
Google Scholar 
24.Rehan, S. M., Tierney, S. M. & Wcislo, W. T. Evidence for social nesting in Neotropical ceratinine bees. Insectes Soc. 62, 465–469 (2015).
Google Scholar 
25.Gonzalez, V. H. et al. Thermal tolerance varies with dim-light foraging and elevation in large carpenter bees (Hymenoptera: Apidae: Xylocopini). Ecol. Entomol. 45, 688–696 (2020).
Google Scholar 
26.Bolker, B. M. et al. Generalized linear mixed models: A practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).PubMed 

Google Scholar 
27.Theobald, E. J., Gabrielyan, H. & HilleRisLambers, J. Lilies at the limit: Variation in plant-pollinator interactions across an elevational range. Am. J. Bot. 103, 189–197 (2016).PubMed 

Google Scholar 
28.Colwell, R. K., Brehm, G., Cardelús, C. L., Gilman, A. C. & Longino, J. T. Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. Science 322, 258–261 (2008).ADS 
CAS 
PubMed 

Google Scholar 
29.Bode, R. F., Linhart, R. D. & Dufresne, C. Variation in the pollinator community visiting invasive Cytisus scoparius L. Link (Fabaceae) along an elevation gradient. Arthropod. Plant. Interact. https://doi.org/10.1007/s11829-020-09755-8 (2020).Article 

Google Scholar 
30.Sheldon, K. S., Yang, S. & Tewksbury, J. J. Climate change and community disassembly: Impacts of warming on tropical and temperate montane community structure. Ecol. Lett. 14, 1191–1200 (2011).PubMed 

Google Scholar 
31.Dymond, K. et al. The role of insect pollinators in avocado production: A global review. J. Appl. Entomol. https://doi.org/10.1111/jen.12869 (2021).Article 

Google Scholar 
32.Giannini, T. C. et al. Identifying the areas to preserve passion fruit pollination service in Brazilian Tropical Savannas under climate change. Agric. Ecosyst. Environ. 171, 39–46 (2013).
Google Scholar 
33.Ashworth, L., Quesada, M., Casas, A., Aguilar, R. & Oyama, K. Pollinator-dependent food production in Mexico. Biol. Conserv. 142, 1050–1057 (2009).
Google Scholar 
34.Tepedino, V. J. The Pollination efficiency of the squash bee (Peponapis pruinosa) and the honey bee (Apis mellifera) on summer squash (Cucurbita pepo). J. Kansas Entomol. Soc. 54, 359–377 (1981).
Google Scholar 
35.Didham, R. K. et al. Interpreting insect declines: Seven challenges and a way forward. Insect Conserv. Divers. 13, 103–114 (2020).
Google Scholar 
36.Barrantes, G. The role of historical and local factors in determining species composition of the highland avifauna of Costa Rica and western Panamá. Rev. Biol. Trop. 57, 333–346 (2009).
Google Scholar 
37.Macedo, M. V. et al. Insect elevational specialization in a tropical biodiversity hotspot. Insect Conserv. Divers. 11, 240–254 (2018).
Google Scholar 
38.Frankie, G. W. et al. Diversity and abundance of bees visiting a mass flowering tree species in disturbed seasonal dry forest, Costa Rica. Kansas Entomol. Soc. 70, 281–296 (1997).
Google Scholar 
39.Heard, T. A. The role of stingless bees in crop pollination. Annu. Rev. Entomol. 44, 183–206 (1999).CAS 
PubMed 

Google Scholar 
40.Abrol, D. P. Wild bees and crop pollination. In Pollination Biology: Biodiversity Conservation and Agricultural Production 111–184 (Springer, 2012).
Google Scholar 
41.Tucker, E. M. & Rehan, S. M. Farming for bees: Annual variation in pollinator populations across agricultural landscapes. Agric. For. Entomol. 20, 541–548 (2018).
Google Scholar 
42.Peters, V. E., Mordecai, R., Carroll, C. R., Cooper, R. J. & Greenberg, R. Bird community response to fruit energy. J. Anim. Ecol. 79, 824–835 (2010).PubMed 

Google Scholar 
43.Baker, C. P. Moon Costa Rica (Moon Travel, 2007).
Google Scholar 
44.Hinton, C. R. & Peters, V. E. Plant species with the trait of continuous flowering do not hold core roles in a Neotropical lowland plant-pollinating insect network. Ecol. Evol. 11, 2346–2359 (2021).PubMed 
PubMed Central 

Google Scholar 
45.Dew, R. M., Rehan, S. M. & Schwarz, M. P. Biogeography and demography of an Australian native bee Ceratina australensis (Hymenoptera, Apidae) since the last glacial maximum. J. Hymenopt. Res. 49, 25–41 (2016).
Google Scholar 
46.Engel, M. S. A new interpretation of the oldest fossil bee (Hymenoptera: Apidae). Am. Museum Nat. Hist. 3296, 1–11 (2000).
Google Scholar 
47.Calfee, E., Agra, M. N., Palacio, M. A., Ramírez, S. R. & Coop, G. Selection and hybridization shaped the Africanized honey bee invasion of the Americas. bioRxiv https://doi.org/10.1101/2020.03.17.994632 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
48.Davila, Y. C. & Wardle, G. M. Variation in native pollinators in the absence of honeybees: Implications for reproductive success of an Australian generalist-pollinated herb Trachymene incisa (Apiaceae). Bot. J. Linn. Soc. 156, 479–490 (2008).
Google Scholar 
49.Chen, H., Morrell, P. L., Ashworth, V. E. T. M., De La Cruz, M. & Clegg, M. T. Tracing the geographic origins of major avocado cultivars. J. Hered. 100, 56–65 (2009).PubMed 

Google Scholar 
50.Bender, G. S. Avocado flowering and pollination. Avocado Prod. Calif. 1, 39–49 (2002).ADS 

Google Scholar 
51.Bergh, B. O. The remarkable avocado flower. Calif. Avocado Soc. Yearb. 57, 40–41 (1973).
Google Scholar 
52.Wilson, H. D. Gene flow in squash species. Bioscience 40, 449–455 (1990).
Google Scholar 
53.Hurd, P. D., Linsley, E. G. & Whitaker, T. W. Squash and gourd bees (Peponapis, Xenoglossa) and the origin of the cultivated Cucurbita. Evolution 25, 218–234 (1971).PubMed 

Google Scholar 
54.Willis, S. D. & Kevan, P. G. Foraging dynamics of Peponapis pruinosa (Hymenoptera: Anthophoridae) on pumpkin (Cucurbita pepo) in Southern Ontario. Can. Entomol. 127, 167–175 (1995).
Google Scholar 
55.Gómez-Escobar, E., Liedo, P., Montoya, P., Vandame, R. & Sánchez, D. Behavioral response of two species of stingless bees and the honey bee (Hymenoptera: Apidae) to GF-120. J. Econ. Entomol. 107, 1447–1449 (2014).PubMed 

Google Scholar 
56.Jarau, S. & Barth, F. G. Stingless bees of the Golfo Dulce region, Costa Rica (Hymenoptera, Apidae, Apinae, Meliponini). Stapfia 88, 267–276 (2008).
Google Scholar 
57.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
58.Becker, R. A., Wilks, A. R., Brownrigg, R., Minka, T. P. & Deckmyn, A. Maps: Draw Geographical Maps. (2018).59.Hijmans, R. J. Raster: Geographic Data Analysis and Modeling. (2020).60.Wickham, H. Ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).MATH 

Google Scholar 
61.Salim, H. M. W. et al. Stingless bee (Hymenoptera: Apidae: Meliponini) diversity in dipterocarp forest reserves in Peninsular Malaysia. Raffles Bull. Zool. 60, 213–219 (2012).MathSciNet 

Google Scholar 
62.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Google Scholar 
63.Baselga, A. et al. Partitioning Beta Diversity into Turnover and Nestedness Components (Wiley, 2021).
Google Scholar 
64.Oksanen, J. et al. Package ‘vegan’. Community Ecol. Packag. 2, 1–295 (2013).
Google Scholar 
65.Wang, Y. et al. Statistical Methods for Analysing Multivariate Abundance Data. (2021).66.Kindt, R. & Coe, R. Tree Diversity Analysis. A Manual and Software for Common Statistical Methods for Ecological and Biodiversity Studies (World Agroforestry Centre (ICRAF), 2005).
Google Scholar  More

1.Hildebrand, J. A. Anthropogenic and natural sources of ambient noise in the ocean. Mar. Ecol. Prog. Ser. 395, 5–20 (2009).2.Wenz, G. M. Acoustic ambient noise in the ocean: Spectra and sources. J. Acoust. Soc. Am. 34, 1936–1956 (1962).ADS 

Google Scholar 
3.Ross, D. Ship sources of ambient noise. IEEE J. Ocean. Eng. 30, 257–261 (2005).ADS 

Google Scholar 
4.Duarte, C. M. et al. The soundscape of the Anthropocene ocean. Science (80-). 371, eaba4658 (2021).CAS 
PubMed 

Google Scholar 
5.Tyack, P., Frisk, G., Boyd, I., Urban, E. & Seeyave, S. (eds). An International Quiet Ocean Experiment Science Plan. Scientific Committee on Oceanic Research / Partnership for Observation of the Global Oceans (2015).6.Kaplan, M. B. & Solomon, S. A coming boom in commercial shipping? The potential for rapid growth of noise from commercial ships by 2030. Mar. Policy 73, 119–121 (2016).
Google Scholar 
7.McDonald, M. A., Hildebrand, J. A. & Wiggins, S. M. Increases in deep ocean ambient noise in the Northeast Pacific west of San Nicolas Island, California. J. Acoust. Soc. Am. 120, 711 (2006).ADS 
PubMed 

Google Scholar 
8.Kyhn, L. A. et al. Basin-wide contributions to the underwater soundscape by multiple seismic surveys with implications for marine mammals in Baffin Bay, Greenland. Mar. Pollut. Bull. 138, 474–490 (2019).CAS 
PubMed 

Google Scholar 
9.Bailey, H. et al. Assessing underwater noise levels during pile-driving at an offshore windfarm and its potential effects on marine mammals. Mar. Pollut. Bull. 60, 888–897 (2010).CAS 
PubMed 

Google Scholar 
10.Nieukirk, S. L., Stafford, K. M., Mellinger, D. K., Dziak, R. P. & Fox, C. G. Low-frequency whale and seismic airgun sounds recorded in the mid-Atlantic Ocean. J. Acoust. Soc. Am. 115, 1832–1843 (2004).ADS 
PubMed 

Google Scholar 
11.Guerra, M., Thode, A. M., Blackwell, S. B. & Michael Macrander, A. Quantifying seismic survey reverberation off the Alaskan North Slope. J. Acoust. Soc. Am. 130, 3046–3058 (2011).ADS 
PubMed 

Google Scholar 
12.OSPAR Commission. The North-East Atlantic Environment Strategy: Strategy of the OSPAR Commission for the Protection of the Marine Environment of the North-East Atlantic 2010–2020. OSPAR Secretariat, London (2010).13.UN. General Assembly (74th sess.: 2019–2020). Oceans and the law of the sea: Resolution/adopted by the General Assembly. A/RES/74/19 (2019).14.Arctic Council. Arctic Marine Shipping Assessment 2009 Report, second printing. Arctic Council, Tromsø, Norway (2009).15.International Maritime Organization. Guidelines from the International Maritime Organization for the reduction of underwater noise from commercial shipping, to address adverse impacts on marine life. MEPC. 1/Circ. 833. IMO, London (2014).16.European Commission. Directive 2008/56/EC of the European Parliament and of the Council of 17 June 2008 establishing a framework for community action in the field of marine environmental policy (Marine Strategy Framework Directive). European Commission, Brussels (2008).17.Halliday, W. D., Pine, M. K. & Insley, S. J. Underwater noise and Arctic marine mammals: Review and policy recommendations. Environ. Rev. https://doi.org/10.1139/er-2019-0033 (2020).Article 

Google Scholar 
18.PAME. Underwater Noise in the Arctic: A State of Knowledge Report, Roveniemi, May 2019. Protection of the Arctic Marine Environment (PAME) Secretariat, Akureyri (2019).19.Stroeve, J. & Notz, D. Changing state of Arctic sea ice across all seasons. Environ. Res. Lett. 13, 103001 (2018).ADS 

Google Scholar 
20.Melia, N., Haines, K. & Hawkins, E. Sea ice decline and 21st century trans-Arctic shipping routes. Geophys. Res. Lett. 43, 9720–9728 (2016).ADS 

Google Scholar 
21.Smith, L. C. & Stephenson, S. R. New Trans-Arctic shipping routes navigable by midcentury. PNAS 110, E1191–E1195 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Ebinger, C. K. & Zambetakis, E. The geopolitics of Arctic melt. Int. Aff. 85, 1215–1232 (2009).
Google Scholar 
23.Huntington, H. P. A preliminary assessment of threats to arctic marine mammals and their conservation in the coming decades. Mar. Policy 33, 77–82 (2009).
Google Scholar 
24.Merchant, N. D. et al. Measuring acoustic habitats. Methods Ecol. Evol. 6, 257–265 (2015).PubMed 
PubMed Central 

Google Scholar 
25.Baumgartner, M. F., Stafford, K. M. & Latha, G. Near real-time underwater passive acoustic monitoring of natural and anthropogenic sounds. In Observing the Oceans in Real Time (eds Venkatesan, R. et al.) 203–226 (Springer Oceanography, 2018). https://doi.org/10.1007/978-3-319-66493-4_10.Chapter 

Google Scholar 
26.Mellinger, D. K. & Clark, C. W. Blue whale (Balaenoptera musculus) sounds from the North Atlantic. J. Acoust. Soc. Am. 114, 1108 (2003).ADS 
PubMed 

Google Scholar 
27.Mustonen, M. et al. Spatial and temporal variability of ambient underwater sound in the Baltic Sea. Sci. Rep. 9, 1–13 (2019).CAS 

Google Scholar 
28.Pieretti, N. & Danovaro, R. Acoustic indexes for marine biodiversity trends and ecosystem health. Philos. Trans. R. Soc. B 375, 20190447 (2020).
Google Scholar 
29.Palmer, K. J., Brookes, K. L., Davies, I. M., Edwards, E. & Rendell, L. Habitat use of a coastal delphinid population investigated using passive acoustic monitoring. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 254–270 (2019).
Google Scholar 
30.Sigray, P. et al. BIAS: A regional management of underwater sound in the Baltic Sea. In The Effects of Noise on Aquatic Life II (eds. Popper A., Hawkins A.) 1015–1023. Advances in Experimental Medicine and Biology. 875. (Springer New York, 2016).31.Farcas, A., Powell, C. F., Brookes, K. L. & Merchant, N. D. Validated shipping noise maps of the Northeast Atlantic. Sci. Total Environ. 735, 139509 (2020).ADS 
CAS 
PubMed 

Google Scholar 
32.Davis, G. E. et al. Long-term passive acoustic recordings track the changing distribution of North Atlantic right whales (Eubalaena glacialis) from 2004 to 2014. Sci. Rep. 7, 13460 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
33.Caruso, F. et al. Long-term monitoring of dolphin biosonar activity in deep pelagic waters of the Mediterranean Sea. Sci. Rep. 7, 1–12 (2017).CAS 

Google Scholar 
34.Thomas, L. et al. Last call: Passive acoustic monitoring shows continued rapid decline of critically endangered vaquita. J. Acoust. Soc. Am. 142, EL512–EL517 (2017).PubMed 

Google Scholar 
35.Hildebrand, J. A. et al. Passive acoustic monitoring of beaked whale densities in the Gulf of Mexico. Sci. Rep. 5, 16343 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
36.ANSI S1.11-2004. Specification for Octave, Half-Octave, and Third Octave Band Filters. American National Standards Institute Inc., New York (2004).37.Jakobsson, M. et al. The International Bathymetric Chart of the Arctic Ocean Version 4.0. Sci. Data 7, 1–14 (2020).
Google Scholar 
38.Gillespie, D. et al. PAMGUARD: Semiautomated, open source software for real-time acoustic detection and localisation of cetaceans. J. Acoust. Soc. Am. 30, 54–62 (2008).
Google Scholar 
39.Gillespie, D., Caillat, M., Gordon, J. & White, P. Automatic detection and classification of odontocete whistles. J. Acoust. Soc. Am. 134, 2427–2437 (2013).ADS 
PubMed 

Google Scholar 
40.Mellinger, D. K. et al. Ishmael 3.0 User Manual ISHMAEL 3.O User Guide. (2018).41.Jensen, F. H., Johnson, M., Ladegaard, M., Wisniewska, D. M. & Madsen, P. T. Narrow acoustic field of view drives frequency scaling in toothed whale biosonar. Curr. Biol. 28, 3878-3885.e3 (2018).CAS 
PubMed 

Google Scholar 
42.Madsen, P. T., Wahlberg, M. & Møhl, B. Male sperm whale (Physeter macrocephalus) acoustics in a high-latitude habitat: Implications for echolocation and communication. Behav. Ecol. Sociobiol. 53, 31–41 (2002).
Google Scholar 
43.Zahn, M. J., Laidre, K. L., Stilz, P., Rasmussen, M. H. & Koblitz, J. C. Vertical sonar beam width and scanning behavior of wild belugas (Delphinapterus leucas) in West Greenland. PLoS ONE 16, e0257054 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Frouin-Mouy, H., Kowarski, K., Martin, B. & Bröker, K. Seasonal trends in acoustic detection of marine mammals in Baffin Bay and Melville Bay, Northwest Greenland. Source Arct. 70, 59–76 (2017).
Google Scholar 
45.Commission, E. Commission Decision (EU) 2017/848 of 17 May 2017 laying down criteria and methodological standards on good environmental status of marine waters and specifications and standardised methods for monitoring and assessment, and repealing Decision 2010/477/EU. Off. J. Eur. Union 125, 43–74 (2017).
Google Scholar 
46.Diachok, O. I. Effects of sea-ice ridges on sound propagation in the Arctic Ocean. J. Acoust. Soc. Am. 59, 1110 (1998).ADS 

Google Scholar 
47.McGrath, J. R. Depth and Seasonal Dependence of Ambient Sea Noise Near the Marginal Ice Zone of the Greenland Sea. Naval Research Laboratory. Washington DC (1976).48.Ahonen, H. et al. The underwater soundscape in western Fram Strait: Breeding ground of Spitsbergen’s endangered bowhead whales. Mar. Pollut. Bull. 123, 97–112 (2017).CAS 
PubMed 

Google Scholar 
49.Merchant, N. D. et al. Underwater noise levels in UK waters. Sci. Rep. 6, 36942, (2016).50.Urick, R. J. Ambient Noise in the Sea (Undersea Warfare Technology Office, Naval Sea Systems Command, Department of the Navy, 1984).
Google Scholar 
51.Kinda, G. B., Simard, Y., Gervaise, C., Mars, J. I. & Fortier, L. Arctic underwater noise transients from sea ice deformation: Characteristics, annual time series, and forcing in Beaufort Sea. J. Acoust. Soc. Am. 138, 2034 (2015).ADS 
PubMed 

Google Scholar 
52.Urick, R. J. The noise of melting icebergs. J. Acoust. Soc. Am. 50, 337–341 (1971).ADS 

Google Scholar 
53.Roth, E. H., Hildebrand, J. A., Wiggins, S. M. & Ross, D. Underwater ambient noise on the Chukchi Sea continental slope from 2006–2009. J. Acoust. Soc. Am. 131, 104–110 (2012).ADS 
PubMed 

Google Scholar 
54.Tervo, O. M., Parks, S. E. & Miller, L. A. Seasonal changes in the vocal behavior of bowhead whales (Balaena mysticetus) in Disko Bay, Western-Greenland. J. Acoust. Soc. Am. 126, 1570–1580 (2009).ADS 
PubMed 

Google Scholar 
55.Boye, T. K., Simon, M. J., Laidre, K. L., Rigét, F. & Stafford, K. M. Seasonal detections of bearded seal (Erignathus barbatus) vocalizations in Baffin Bay and Davis Strait in relation to sea ice concentration. Polar Biol. 43, 1493–1502 (2020).
Google Scholar 
56.De Vreese, S. et al. Marine mammal acoustic detections in the Greenland and Barents Sea, 2013–2014 seasons. Sci. Rep. 8, 1–14 (2018).
Google Scholar 
57.Simon, M., Stafford, K. M., Beedholm, K., Lee, C. M. & Madsen, P. T. Singing behavior of fin whales in the Davis Strait with implications for mating, migration and foraging. J. Acoust. Soc. Am. 128, 3200–3210 (2010).ADS 
PubMed 

Google Scholar 
58.Meire, L. et al. Marine-terminating glaciers sustain high productivity in Greenland fjords. Glob. Chang. Biol. 23, 5344–5357 (2017).ADS 
PubMed 

Google Scholar 
59.Møhl, B. Masking effects of noise: their distribution in time and space. In The question of sound from icebreaker operations: The proceedings of a workshop (ed. Peterson, N. M.) 259–266. Arctic Pilot Project. Calgary, AB (1981).60.Erbe, C. & Farmer, D. M. Masked hearing thresholds of a beluga whale (Delphinapterus leucas) in icebreaker noise. Deep Sea Res. Part II Top. Stud. Oceanogr. 45, 1373–1388 (1998).ADS 

Google Scholar 
61.Gordon, J. C. D. et al. A review of the effects of seismic survey on marine mammals. Mar. Technol. Soc. J. 37, 14–32 (2004).
Google Scholar 
62.Nowacek, D. P., Thorne, L. H., Johnston, D. W. & Tyack, P. L. Responses of cetaceans to anthropogenic noise. Mamm. Rev. 37, 81–115 (2007).
Google Scholar 
63.Southall, B. L. et al. Marine mammal noise exposure criteria: Updated scientific recommendations for residual hearing effects. Aquat. Mamm. 45, 125–232 (2019).
Google Scholar 
64.Frid, A. & Dill, L. Human-caused Disturbance Stimuli as a Form of Predation Risk. Conserv. Ecol. 6, 11 (2002). More

Porewater concentrations of dissolved oxygen and nutrientsThe sampling location and appearance of the microbial mats used in this study in cross section are shown in Fig. 1. Profound changes in dissolved oxygen concentration were observed over the diel cycle because of high rates of oxygenic photosynthesis in the daytime and oxygen-requiring respiration at night (Table 1). Briefly, Layer 1 was characterized by oxygen concentration fluctuations in the range of 200–800 µM. Layers 2 and 3 ranged from 0–1200 µM and 0–200 µM, respectively. Mat Layer 4 (3–4 mm below the surface) may contain some dissolved oxygen near noon on days when there is high solar irradiance but stays anoxic for most hours of most days. Layers 5–7 (4–7 mm from the surface) remain anoxic.Table 1 Oxygen concentrations throughout the first 4 mm of the mat measured at 100 µm resolution using microsensors, measured on 22 August, 2019.Full size tableConcentrations of ammonium (Table 1) reveal a pattern of increasing concentration with depth (34–124 µM) through the layers examined here. Nitrate concentrations ranged between 26–33 µM, with low variation across depths. The concentration of phosphate ranged between 3–6 µM, with the highest concentration detected in Layer 1 (0–1 mm from surface) at 5.5 µM.Analysis of genes and transcripts in mat layers by qPCR and RT-qPCRGene-copy number ranges for both DNA and cDNA across all layers for all genes examined are summarized as follows: Bacteria, 104−1010 per g mat and 101−105, per ng nucleic acid; Archaea, 106−108 and 102−104; nifH, 108−1011 and 104−107; archaeal-amoA, 104−105 and 2–3; bacterial-amoA, 104−107 and 3–335; Nitrospira-nxrB, 105−107 and 27–372; nosZ, 103−105 and 2–10; nirS, 105−107 and 33–1941; Planctomycetes-16S rRNA gene and cDNA of transcripts, 104−106 and 6–66 (Fig. 2, S1).Fig. 2: Vertical patterns in the abundance (DNA) and expression (cDNA) of Bacterial and Archaeal ribosomal and nitrogen cycling genes.Number of copies of DNA and cDNA genes recovered for Bacteria (A), Archaea (B), nifH (C), Archaeal-amoA (D), Bacterial-amoA (E), Nitrospira-nxrB (F), nosZ (G), nirS (H) and Planctomycetes-16S rRNA gene marker (anammox proxy) (I), per g of microbial mat, quantified by qPCR and RT-qPCR in hypersaline microbial mat profiles from different depths. P-values from Kruskal–Wallis test are overlain on each, and different letters indicate significantly different values for the given gene based on a Conover-Iman test p-value of  0.8, Table 2).Fig. 4: Non-metric multidimensional scaling (NMDS) plots of quantification of all nitrogen genes across all layers examined in this study.Genes associated with the following nitrogen transformations were examined: nitrogen fixation (nifH), nitrification (Bacterial-amoA, Archaeal-amoA, Nitrospira-nxrB), denitrification (nosZ, nirS) and Planctomycetes-16S rRNA gene marker (anammox proxy). The biotic data was standardized, and a sample resemblance matrix was generated using Bray-Curtis coefficient of similarity. In order to analyze the influence of abiotic variables (porewater nutrient and oxygen concentration) on the patterns of the biotic data, monotonic correlations of the abiotic variables were performed. In the plots, the distance between the samples’ points reflects their relative similarity, according to Bray-Curtis similarity matrices based on cDNA/DNA ratios of nitrogen genes examined. The vectors in panel A represent the cDNA/DNA ratios of nitrogen gene examined. In panel B, the vectors represent the environmental variables.Full size imageTable 2 (A) Spearman correlations coefficient (r) between the ratios of cDNA/DNA of nitrogen fixation (nifH), nitrification (Bacterial-amoA, Archaeal-amoA, Nitrospira-nxrB), denitrification (nosZ, nirS) and Planctomycetes-16S rRNA gene marker (anammox proxy) and oxygen, ammonium, nitrate and phosphate concentrations. (B) Spearman correlation p-value.Full size tablenifH, Bacterial-amoA and Archaeal-amoA were positively correlated with oxygen concentration (r ≥ 0.22, Table 2), while Nitrospira-nxrB was negatively correlated with oxygen (r = −0.68, Table 2). Denitrification genes (nosZ, nirS) and Planctomycetes-16S rRNA genes were all positively correlated with ammonium (r ≥ 0.5) and orthophosphate (r ≥ 0.13) and negatively correlated with oxygen (r  > −0.70).Metagenome analysis of nitrogen cyclingA total number of 922 324 genes were identified; 1305 of these genes were annotated with KOs that are part of KEGG’s Nitrogen Metabolism pathway (Table S2, S3). A dendrogram based on Bray-Curtis similarities of normalized coverages of all recovered nitrogen metabolism genes is shown in Fig. 5A. Overall, the similarity between the layers was >75%. According to SIMPROF analysis, there was a significant difference in the N-related gene coverages (based on an alpha value of 0.05) between Layers 1-Layer 2, Layer 3, and Layer 4 (p = 0.001) and Layer 2-Layer 3, and Layer 4 (p = 0.001), but not between Layers 3 and Layer 4 (p = 1), where the similarity was >90%.Fig. 5: Functional nitrogen gene distribution based on metagenome analysis.A Cluster analysis illustrating the similarity of normalized coverages of all recovered nitrogen metabolism genes across the uppers 4 layers examined [(Layer 1 (0–1 mm from surface), Layer 2 (1–2 mm from surface), Layer 3 (2–3 mm from surface), Layer 4 (3–4 mm from surface)]. Red lines show non-significant differences, according to SIMPROF analysis (p  > 0.05). B The bar plots show the genes of the metabolic pathways in the nitrogen cycle identified in the mat, according metagenome analysis, with relative coverage of each nitrogen cycling gene across depths examined (Fraction of Depth Integrated Coverage, FDIC). 355 unique genes were recovered from KEGG’s Nitrogen Metabolism pathway: 60 annotated as involved in nitrogen fixation, 15 in assimilatory nitrate reduction, 38 in dissimilatory nitrate reduction to ammonia (DNRA), 52 in hydroxylamine dehydrogenase EC 1.7.2.6, 121 in hydroxylamine reductase, 69 in denitrification pathway. C Values of Nitrogen-focused Coverage per Million (N-CPM). The following enzymes perform nitrogen transformation in the mat: nitrogenase molybdenum-iron protein alpha chain (nifD), nitrogenase iron protein NifH, nitrogenase molybdenum-iron protein beta chain (nifK), hydroxylamine dehydrogenase EC 1.7.2.6 (hao), hydroxylamine reductase (hcp), nitrate reductase/nitrite oxidoreductase, alpha subunit (narG, narZ, nxrA), nitrate reductase/nitrite oxidoreductase, beta subunit (narH, narY, nxrB), nitrate reductase (cytochrome) (napA), nitrate reductase (cytochrome), electron transfer subunit (napB), nitrite reductase (NO-forming) / hydroxylamine reductase (nirS), nitrogenase molybdenum-iron protein beta chain (nirK), nitric oxide reductase subunit B (norB), nitric oxide reductase subunit C (norC), nitrous-oxide reductase (nosZ), nitrate reductase gamma subunit (narI, narV), cytochrome c nitrite reductase small subunit (nrfH), nitrite reductase (cytochrome c-552) (nrfA), ferredoxin-nitrite reductase (nirA), ferredoxin-nitrate reductase (narB), MFS transporter, NNP family, nitrate/nitrite transporter (NRT, nark, nrtP, nasA). D Nitrogen cycling genes recovered in this study and the transformation that they catalyze.Full size imageThe nitrogen fixation pathway was identified with nifD, nifH, and nifK genes (Fig. 5B, C, Table S4). Of the 60 genes detected in this metabolic pathway 17 genes were annotated as nifD, 22 genes as nifH, and 21 genes as nifK. The normalized coverage of these genes showed a decreasing trend with depth. Layer 1 was characterized by the highest values of Nitrogen-focused coverage per million (N-CPM, see Supplementary Text 1) of nifD, nifH, and nifK genes: 56264.7, 54934.2 and 60059.2, respectively. On average, the three genes involved in nitrogen fixation, nifD, nifH, and nifK, decreased with depth, (2.7-fold from Layer 1 to Layer 4, with a nearly 2-fold difference solely between Layer 1 and Layer 2).Genes involved in nitrate assimilation, annotated as nirA and narB which code for ferredoxin nitrate reductase, were 3 times as abundant in Layer 1 than Layer 2, but decreased less markedly from Layer 2 to Layers 3 and 4.Genes for dissimilatory nitrite reduction (nrfA, and nrfH) were 4 and 16 times more abundant in Layer 4 than Layer 1. Similarly, the nitrate/nitrite regulator protein genes narl and narV displayed a nearly inverse pattern, with Layer 1 having the least proportion of genes, a large increase from Layer 1 to Layer 2, and additional increases from Layer 2 to Layers 3 and Layer 4 (Fig. 5B, C, Table S4).Genes associated with nitrification were very poorly represented in the metagenome. No genes associated with ammonia oxidation (amoA) were detected. Genes associated with nitrite oxidation (nrxA, nrxB) that were detected are so closely related to denitrifier genes (narG, narZ, narH, narY) as to be annotated with the same KEGG KO models (K00370 representing narG, narZ, nxrA; and K00371 representing narH, narY, nxrB).The following genes involved in denitrification were detected: napA, napB, narG, narZ, narH, narY, narI, narV, nirK, nirS, norB, norC, and nosZ (Fig. 5B, C). The nitrate reduction metabolic pathway was represented by 4 genes encoding the nitrate reductase-nitrite oxidoreductase-alpha subunit (narG, narZ, nxrA genes), 6 genes encoding the nitrate reductase-nitrite oxidoreductase-beta subunit (narH, narY, nxrB genes), 31 genes encoding the nitrate reductase gamma subunit (narI, narV), 5 genes encoding the nitrate reductase -cytochrome electron transfer subunit (napB) and 7 genes encoding the nitrate reductase -cytochrome (napA) (Table S4). The N-CPM of nitrate reductase increased with depth, but with a similar proportion of those genes in Layers 3 and 4. With respect to nitrite reductase (nirk and nirS genes, 2 and 1 genes, respectively), no nirK genes were detected in Layer 1, where the highest N-CPM of nirS was recovered (Fig. 5B). In contrast, Layer 3 had no detected nirS and the highest N-CPM of nirK. Regarding nitric oxide reductase (norB and norC genes, 6 and 1 genes, respectively), the highest normalized coverage of norB was detected in Layer 3, while highest for norC was in Layer 1. Finally, nosZ (6 genes) was detected in all the layers, steadily decreasing in normalized coverage from the top layer to the deepest (Fig. 5B, C; Table S4).DNRA metabolism was represented by nrfA (26 genes) and nrfH (12 genes), and by narI, narV (31). Layer 1 was characterized by the lowest normalized coverage of narI, narV, nrfA, and nrfH genes (6880.2, 3724.6, and 284.6 N-CPM, respectively), while Layer 3 was characterized by the greatest coverage of narI, narV, nrfA, and nrfH genes (32760.5, 14417.9 and 4504.1, respectively; Fig. 5B, C; Table S4).Genes for hydroxylamine dehydrogenase EC 1.7.2.6 and hydroxylamine reductase (hao and hcp, respectively) were the most abundant nitrogen metabolism genes in the mat: hao having a cumulative N-CPM of ~150000 and hcp having a cumulative N-CPM of nearly 350,000 across the 4 depths (Fig. 5C). Both genes increased in abundance with depth; hcp increased two-fold between Layer 1 and Layer 2, and more gradually in Layer 3 and Layer 4. Hao exhibited a three-fold increase in relative abundance from Layer 1 to Layer 2 and remained relatively constant through Layer 3 and Layer 4 (Fig. 5B, C; Table S4). More

Alcala N, Goldberg A, Ramakrishnan U, Rosenberg NA (2019) Coalescent theory of migration network motifs. Mol Biol Evol 36:2358–2374CAS 
PubMed 
PubMed Central 

Google Scholar 
Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664CAS 
PubMed 
PubMed Central 

Google Scholar 
Anjana S, Sammeta SP, Das R (2020) Developing ancestry informative marker panel for Nigeria-Cameroonian chimpanzees. J Genet 99:1–7
Google Scholar 
Armstrong E, Khan A, Taylor RW, Gouy A, Greenbaum G, Thiéry A et al. (2021) Recent evolutionary history of tigers highlights contrasting roles of genetic drift and selection. Mol Biol Evol 38:2366–2379CAS 
PubMed 
PubMed Central 

Google Scholar 
Ballou JD (1992) Genetic and demographic considerations in endangered species captive breeding and reintroduction programs. In: Wildlife 2001: populations, Springer: Dordrecht, pp. 262–275Balloux F, Lugon‐Moulin N (2002) The estimation of population differentiation with microsatellite markers. Mol Ecol 11:155–165PubMed 

Google Scholar 
Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120CAS 
PubMed 
PubMed Central 

Google Scholar 
Brandies P, Peel E, Hogg CJ, Belov K (2019) The value of reference genomes in the conservation of threatened species. Genes (Basel) 10:846CAS 

Google Scholar 
Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA (2013) Stacks: an analysis tool set for population genomics. Mol Ecol 22:3124–3140PubMed 
PubMed Central 

Google Scholar 
Chapman JR, Nakagawa S, Coltman DW, Slate J, Sheldon BC (2009) A quantitative review of heterozygosity–fitness correlations in animal populations. Mol Ecol 18:2746–2765CAS 
PubMed 

Google Scholar 
Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA et al. (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158CAS 
PubMed 
PubMed Central 

Google Scholar 
Das R, Roy R, Venkatesh N (2019) Using ancestry informative markers (AIMs) to detect fine structure within gorilla populations. Front Genet 10:43CAS 
PubMed 
PubMed Central 

Google Scholar 
Das R, Upadhyai P (2018) An ancestry informative marker set which recapitulates the known fine structure of populations in South Asia. Genome Biol Evol 10:2408–2416PubMed 
PubMed Central 

Google Scholar 
Das R, Upadhyai P (2019) Application of the geographic population genetic structure (GPS) algorithm for biogeographical analyses of wild and captive gorillas. BMC Bioinforma 20:35
Google Scholar 
Dudash MR, Fenster CB (2000) Inbreeding and outbreeding depression in fragmented populations. Conserv Biol Ser 35–54Esposito U, Das R, Syed S, Pirooznia M, Elhaik E (2018) Ancient ancestry informative markers for identifying fine-scale ancient population genetic structure in Eurasians. Genes (Basel) 9:625
Google Scholar 
Feng S, Stiller J, Deng Y, Armstrong J, Fang Q, Reeve AH et al. (2020) Dense sampling of bird diversity increases power of comparative genomics. Nature 587:252–257CAS 
PubMed 
PubMed Central 

Google Scholar 
Frantz AC, Pourtois JT, Heuertz M, Schley L, Flamand MC, Krier A et al. (2006) Genetic structure and assignment tests demonstrate illegal translocation of red deer (Cervus elaphus) into a continuous population. Mol Ecol 15:3191–3203CAS 
PubMed 

Google Scholar 
Friar EA, Boose DL, LaDoux T, Roalson EH, Robichaux RH (2001) Population genetic structure in the endangered Mauna Loa silversword, Argyroxiphium kauense (Asteraceae), and its bearing on reintroduction. Mol Ecol 10:1657–1663CAS 
PubMed 

Google Scholar 
Fuentes‐Pardo AP, Ruzzante DE (2017) Whole‐genome sequencing approaches for conservation biology: Advantages, limitations and practical recommendations. Mol Ecol 26:5369–5406PubMed 

Google Scholar 
Goodrich J, Lynam A, Miquelle D, Wibisono H, Kawanishi K, Pattanavibool A et al. (2015) Panthera tigris. In: The IUCN Red List of Threatened Species 2015, p. 15955 50659951Ivy JA, Lacy RC (2010) Using molecular methods to improve the genetic management of captive breeding programs for threatened species. In: Molecular approaches in natural resource conservation and management, pp. 267–295Jangtarwan K, Koomgun T, Prasongmaneerut T, Thongchum R, Singchat W, Tawichasri P et al. (2019) Take one step backward to move forward: Assessment of genetic diversity and population structure of captive Asian woolly-necked storks (Ciconia episcopus). PLoS One 14:e0223726CAS 
PubMed 
PubMed Central 

Google Scholar 
Jiménez‐Mena B, Schad K, Hanna N, Lacy RC (2016) Pedigree analysis for the genetic management of group‐living species. Ecol Evol 6:3067–3078PubMed 
PubMed Central 

Google Scholar 
Kanthaswamy S, Johnson Z, Trask JS, Smith DG, Ramakrishnan R, Bahk J et al. (2014) Development and validation of a SNP‐based assay for inferring the genetic ancestry of rhesus macaques (Macaca mulatta). Am J Primatol 76:1105–1113CAS 
PubMed 
PubMed Central 

Google Scholar 
Khan A, Patel K, Bhattacharjee S, Sharma S, Chugani AN, Sivaraman K et al. (2020) Are shed hair genomes the most effective noninvasive resource for estimating relationships in the wild? Ecol Evol 10:4583–4594PubMed 
PubMed Central 

Google Scholar 
Khan A, Patel K, Shukla H, Viswanathan A, van der Valk T, Borthakur U, et al. (2021). Genomic evidence for inbreeding depression and purging of deleterious genetic variation in Indian tigers. bioRxivKhan A, Tyagi A (2021) Considerations for initiating a wildlife genomics research project in South and South-East Asia. J Indian Inst Sci 101:243–256. https://doi.org/10.1007/s41745-021-00243-3Article 

Google Scholar 
Kirk H, Freeland JR (2011) Applications and implications of neutral versus non-neutral markers in molecular ecology. Int J Mol Sci 12:3966–3988PubMed 
PubMed Central 

Google Scholar 
Kolipakam V, Singh S, Pant B, Qureshi Q, Jhala YV (2019) Genetic structure of tigers (Panthera tigris tigris) in India and its implications for conservation. Glob. Ecol Conserv 20:710
Google Scholar 
Kunde MN, Martins RF, Premier J, Fickel J, Förster DW (2020) Population and landscape genetic analysis of the Malayan sun bear Helarctos malayanus. Conserv Genet 21:123–135CAS 

Google Scholar 
Laikre L, Palm S, Ryman N (2005) Genetic population genetic structure of fishes: implications for coastal zone management. AMBIO A J Hum Environ 34:111–119
Google Scholar 
Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357CAS 
PubMed 
PubMed Central 

Google Scholar 
Liang Z, Bu L, Qin Y, Peng Y, Yang R, Zhao Y (2019) Selection of optimal ancestry informative markersfor classification and ancestry proportion estimation in pigs. Front genet 10:183PubMed 
PubMed Central 

Google Scholar 
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N et al. (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079PubMed 
PubMed Central 

Google Scholar 
Lott MJ, Wright BR, Kemp LF, Johnson RN, Hogg CJ (2020) Genetic Management of Captive and Reintroduced Bilby Populations. J Wildl Manag 84:20–32
Google Scholar 
Luo SJ, Johnson WE, Martenson J, Antunes A, Martelli P, Uphyrkina O et al. (2008) Subspecies genetic assignments of worldwide captive tigers increase conservation value of captive populations. Curr Biol 18:592–596CAS 
PubMed 

Google Scholar 
Miller W, Hayes VM, Ratan A, Petersen DC, Wittekindt NE, Miller J et al. (2011) Genetic diversity and population structure of the endangered marsupial Sarcophilus harrisii (Tasmanian devil) Proc Natl Acad Sci 108:12348–12353CAS 
PubMed 
PubMed Central 

Google Scholar 
Mondol S, Bruford MW, Ramakrishnan U (2013) Demographic loss, genetic structure and the conservation implications for Indian tigers. Proc R Soc B Biol Sci 280:20130496
Google Scholar 
Muñoz I, Henriques D, Johnston JS, Chávez-Galarza J, Kryger P, Pinto MA (2015) Reduced SNP Panels for Genetic Identification and Introgression Analysis in the Dark Honey Bee (Apis mellifera mellifera) PLOS One 10:e0124365PubMed 
PubMed Central 

Google Scholar 
Nassir R, Kosoy R, Tian C, White PA, Butler LM, Silva G et al. (2009) An ancestry informative marker set for determining continental origin: validation and extension using human genome diversity panels. BMC Genet 10:39PubMed 
PubMed Central 

Google Scholar 
Natesh M, Atla G, Nigam P, Jhala YV, Zachariah A, Borthakur U et al. (2017) Conservation priorities for endangered Indian tigers through a genomic lens. Sci Rep. 7:1–11
Google Scholar 
Natesh M, Taylor RW, Truelove NK, Hadly EA, Palumbi SR, Petrov DA et al. (2019) Empowering conservation practice with efficient and economical genotyping from poor quality samples. Methods Ecol evolution 10:853–859
Google Scholar 
Patterson N, Price AL, Reich D (2006) Population genetic structure and eigenanalysis. PLoS Genet 2:190
Google Scholar 
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909CAS 
PubMed 

Google Scholar 
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population genetic structure using multilocus genotype data. Genetics 155:945–959CAS 
PubMed 
PubMed Central 

Google Scholar 
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575CAS 
PubMed 
PubMed Central 

Google Scholar 
Putnam AS, Ivy JA (2014) Kinship-based management strategies for captive breeding programs when pedigrees are unknown or uncertain. J Hered 105:303–311PubMed 

Google Scholar 
Rosenberg NA, Li LM, Ward R, Pritchard JK (2003) Informativeness of genetic markers for inference of ancestry. Am J Hum Genet 73:1402–1422CAS 
PubMed 
PubMed Central 

Google Scholar 
Sagar V, Kaelin CB, Natesh M, Reddy PA, Mohapatra RK, Chhattani H et al. (2021) High frequency of an otherwise rare phenotype in a small and isolatedtiger population. Proc Natl Acad Sci p. 118Saunders CT, Wong WS, Swamy S, Becq J, Murray LJ, Cheetham RK (2012) Strelka: accurate somatic small-variant calling from sequenced tumor–normal sample pairs. Bioinformatics 28:1811–1817CAS 
PubMed 

Google Scholar 
Schlaepfer DR, Braschler B, Rusterholz HP, Baur B (2018) Genetic effects of anthropogenic habitat fragmentation on remnant animal and plant populations: a meta‐analysis. Ecosphere 9:2488
Google Scholar 
Shriver MD, Parra EJ, Dios S, Bonilla C, Norton H, Jovel C et al. (2003) Skin pigmentation, biogeographical ancestry and admixture mapping. Hum Genet 112:387–399PubMed 

Google Scholar 
Somenzi E, Ajmone-Marsan P, Barbato M (2020) Identification of ancestry informative marker (AIM) panels to assess hybridisation between feral and domestic sheep. Animals 10:582PubMed Central 

Google Scholar 
Supple MA, Shapiro B (2018) Conservation of biodiversity in the genomics era. Genome Biol 19:1–12
Google Scholar 
Svardal H, Jasinska AJ, Apetrei C, Coppola G, Huang Y, Schmitt CA et al. (2017) Ancient hybridization and strong adaptation to viruses across African vervet monkey populations. Nat genet 49:1705–1713CAS 
PubMed 
PubMed Central 

Google Scholar 
Vongpaisarnsin K, Listman JB, Malison RT, Gelernter J (2015) Ancestry informative markers for distinguishing between Thai populations based on genome-wide association datasets. Leg Med 17:245–250CAS 

Google Scholar 
Wasser SK, Brown L, Mailand C, Mondol S, Clark W, Laurie C et al. (2015) Genetic assignment of large seizures of elephant ivory reveals Africa’s major poaching hotspots. Science (80-) 349:84–87CAS 

Google Scholar 
Wilkinson S, Wiener P, Archibald AL, Law A, Schnabel RD, McKay SD et al. (2011) Evaluation of approaches for identifying population informative markers from high density SNP chips. BMC genetics 12:1–14Wright S (1969) Evolution and the genetics of populationsWultsch C, Caragiulo A, Dias-Freedman I, Quigley H, Rabinowitz S, Amato G (2016) Genetic diversity and population genetic structure of Mesoamerican jaguars (Panthera onca): implications for conservation and management. PLoS One 11:162377
Google Scholar  More