Philippa Kaur

Martins, L. C. et al. Poluição atmosférica e atendimentos por pneumonia e gripe em São Paulo, Brasil. Revista de Saúde Pública 36, 88–94 (2002).Article 
PubMed 

Google Scholar 
Goudarzi, G. et al. Health risk assessment on human exposed to heavy metals in the ambient air PM10 in Ahvaz, Southwest Iran. Int. J. Biometeorol. 62, 1075–1083 (2018).Article 
ADS 
PubMed 

Google Scholar 
Makri, A. & Stilianakis, N. I. Vulnerability to air pollution health effects. Int. J. Hygiene Environ. Health 211, 326–336 (2008).Article 

Google Scholar 
Idani, E. et al. Characteristics, sources, and health risks of atmospheric PM10-bound heavy metals in a populated Middle Eastern City. Toxin Rev. 39, 266–274 (2020).Article 

Google Scholar 
Wang, J., Hu, Z., Chen, Y., Chen, Z. & Xu, S. Contamination characteristics and possible sources of PM10 and PM2.5 in different functional areas of Shanghai, China. Atmos. Environ. 68, 221–229 (2013).Article 
ADS 
CAS 

Google Scholar 
Guarnieri, M. & Balmes, J. R. Outdoor air pollution and asthma. Lancet 383, 1581–1592 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Anderson, J. O., Thundiyil, J. G. & Stolbach, A. Clearing the air: A review of the effects of particulate matter air pollution on human health. J. Med. Toxicol. 8, 166–175 (2012).Article 
CAS 
PubMed 

Google Scholar 
Roy, D., Seo, Y.-C., Kim, S. & Oh, J. Human health risks assessment for airborne PM10-bound metals in Seoul, Korea. Environ. Sci. Pollut. Res. 26, 24247–24261 (2019).Article 
CAS 

Google Scholar 
Maesano, C. et al. Impacts on human mortality due to reductions in PM10 concentrations through different traffic scenarios in Paris, France. Sci. The Total. Environ. 698, 134257 (2020).Article 
ADS 
CAS 

Google Scholar 
Maleki, H., Sorooshian, A., Goudarzi, G., Nikfal, A. & Baneshi, M. M. Temporal profile of PM10 and associated health effects in one of the most polluted cities of the world (Ahvaz, Iran) between 2009 and 2014. Aeolian Res. 22, 135–140 (2016).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Medina, S., Le Tertre, A. & Saklad, M. The Apheis project: Air pollution and health—A European information system. Air Qual. Atmos. Heal. 2, 185–198 (2009).Article 

Google Scholar 
Medina, S., Plasencia, A., Ballester, F., Mücke, H. & Schwartz, J. Apheis: Public health impact of PM10 in 19 European cities. J. Epidemiol. Community Heal. 58, 831–836 (2004).Article 
CAS 

Google Scholar 
Pérez-Martínez, P. J., de Fátima Andrade, M. & de Miranda, R. M. Traffic-related air quality trends in São Paulo, Brazil. J. Geophys. Res. Atmos. 120, 6290–6304 (2015).Article 
ADS 

Google Scholar 
Sánchez-Ccoyllo, O. R. et al. Vehicular particulate matter emissions in road tunnels in Sao Paulo, Brazil. Environ. Monitoring Assess. 149, 241–249 (2009).Article 

Google Scholar 
Ribeiro, H. & de Assunção, J. V. Historical overview of air pollution in São Paulo Metropolitan Area, Brazil: Influence of mobile sources and related health effects. WIT Trans. Built Environ. 52,10 (2001).Bravo, M. A. & Bell, M. L. Spatial heterogeneity of PM10 and O3 in São Paulo, Brazil, and implications for human health studies. J. Air Waste Manag. Assoc. 61, 69–77 (2011).Article 
CAS 
PubMed 

Google Scholar 
De Freitas, E. D., Martins, L. D., da Silva Dias, P. L. & de Fátima Andrade, M. A simple photochemical module implemented in rams for tropospheric ozone concentration forecast in the metropolitan area of Sao Paulo, Brazil: Coupling and validation. Atmos. Environ. 39, 6352–6361 (2005).Article 
ADS 

Google Scholar 
Encalada-Malca, A. A., Cochachi-Bustamante, J. D., Rodrigues, P. C., Salas, R. & López-Gonzales, J. L. A spatio-temporal visualization approach of PM10 concentration data in Metropolitan Lima. Atmosphere 12, 609 (2021).Article 
ADS 

Google Scholar 
do Meio Ambiente, C. N. Institutes the national air quality control programee. Tech. Rep., Official Journal of the Federative Republic of Brazil (1989).do Meio Ambiente, C. N. Sets standards of primary and secondary air quality and even the criteria for acute episodes of air pollution. Tech. Rep., Official Journal of the Federative Republic of Brazil (1990).Artaxo, P. O estado da qualidade do ar no brasil. Work. Pap. WRI Brasil 32 (2021).Costa, A. F., Hoek, G., Brunekreef, B. & Ponce de Leon, A. C. Air pollution and deaths among elderly residents of Sao Paulo, Brazil: An analysis of mortality displacement. Environ. Health Perspectives 125, 349–354 (2017).Article 
CAS 

Google Scholar 
Bravo, M. A., Son, J., De Freitas, C. U., Gouveia, N. & Bell, M. L. Air pollution and mortality in São Paulo, Brazil: Effects of multiple pollutants and analysis of susceptible populations. J. Exposure Sci. Environ. Epidemiol. 26, 150–161 (2016).Article 
CAS 

Google Scholar 
Chiarelli, P. S. et al. The association between air pollution and blood pressure in traffic controllers in Santo André, São Paulo, Brazil. Environ. Res. 111, 650–655 (2011).Article 
CAS 

Google Scholar 
Ventura, L. M. B., de Oliveira Pinto, F., Soares, L. M., Luna, A. S. & Gioda, A. Forecast of daily PM2.5 concentrations applying artificial neural networks and holt-winters models. Air Qual. Atmos. Heal. 12, 317–325 (2019).Article 
CAS 

Google Scholar 
Leão, M. L. P., Zhang, L. & da Silva Júnior, F. M. R. Effect of particulate matter (PM2.5 and PM10) on health indicators: Climate change scenarios in a Brazilian Metropolis. Environ. Geochem. Heal. 44, 1–12 (2022).Habermann, M. & Gouveia, N. Application of land use regression to predict the concentration of inhalable particular matter in São Paulo City, Brazil. Engenharia Sanit. e Ambiental 17, 155–162 (2012).Article 

Google Scholar 
Braga, A. L. F., Pereira, L. A. A., Procópio, M., André, P. A. D. & Saldiva, P. H. D. N. Association between air pollution and respiratory and cardiovascular diseases in Itabira, Minas Gerais State. Brazil. Cadernos de Saúde Pública 23, S570–S578 (2007).Article 
PubMed 

Google Scholar 
Pinto, W. D. P., Reisen, V. A. & Monte, E. Z. Previsão da concentração de material particulado inalável, na região da grande vitória, ES, Brasil, utilizando o modelo sarimax. Engenharia Sanitária e Ambiental 23, 307–318 (2018).Article 

Google Scholar 
Schornobay-Lui, E. et al. Prediction of short and medium term PM10 concentration using artificial neural networks. Manag. Environ. Qual. An Int. J. 30, 414–436 (2018).Neto, P. S. D. M. et al. Neural-based ensembles for particulate matter forecasting. IEEE Access 9, 14470–14490 (2021).Article 

Google Scholar 
Albuquerque Filho, F. S. D., Madeiro, F., Fernandes, S. M., de Mattos Neto, P. S. & Ferreira, T. A. Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks. Química Nova 36, 783–789 (2013).Lei, T. M., Siu, S. W., Monjardino, J., Mendes, L. & Ferreira, F. Using machine learning methods to forecast air quality: A case study in Macao. Atmosphere 13, 1412 (2022).Article 
ADS 
CAS 

Google Scholar 
Yu, T. et al. Study on the regional prediction model of PM2.5 concentrations based on multi-source observations. Atmos. Pollut. Res. 13, 101363 (2022).Article 
CAS 

Google Scholar 
Li, J., Xu, G. & Cheng, X. Combining spatial pyramid pooling and long short-term memory network to predict PM2.5 concentration. Atmos. Pollut. Res. 13, 101309 (2022).Article 
CAS 

Google Scholar 
Cordova, C. H. et al. Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru. Sci. Rep. 11, 1–19 (2021).Article 

Google Scholar 
Plocoste, T., Calif, R. & Jacoby-Koaly, S. Temporal multiscaling characteristics of particulate matter PM10 and ground-level ozone O3 concentrations in caribbean region. Atmos. Environ. 169, 22–35 (2017).Article 
ADS 
CAS 

Google Scholar 
Calif, R. & Schmitt, F. G. Multiscaling and joint multiscaling description of the atmospheric wind speed and the aggregate power output from a wind farm. Nonlinear Process. Geophys. 21, 379–392 (2014).Article 
ADS 

Google Scholar 
Hyndman, R. J. & Khandakar, Y. Automatic time series forecasting: The forecast package for r. J. Stat. Softw. 27, 1–22 (2008).Article 

Google Scholar 
Harvey, A. C. Forecasting, structural time series models and the Kalman filter (Cambridge University Press, 1990).Book 
MATH 

Google Scholar 
Zhang, G. P. Time series forecasting using a hybrid arima and neural network model. Neurocomputing 50, 159–175 (2003).Article 
MATH 

Google Scholar 
Liao, T. W. Clustering of time series data—A survey. Pattern Recognit. 38, 1857–1874 (2005).Article 
ADS 
MATH 

Google Scholar 
Bell, M. L., Samet, J. M. & Dominici, F. Time-series studies of particulate matter. Annu. Rev. Public Heal. 25, 247–280 (2004).Article 

Google Scholar 
Hyndman, R. J. & Athanasopoulos, G. Forecasting: Principles and Practice (OTexts, 2018).
Google Scholar 
Box, G. E., Hillmer, S. C. & Tiao, G. C. Analysis and modeling of seasonal time series. in Seasonal analysis of economic time series, 309–344 (NBER, 1978).Sulandari, W., Suhartono, Subanar & Rodrigues, P. C. Exponential smoothing on modeling and forecasting multiple seasonal time series: An overview. Fluctuation Noise Lett. 20, 2130003 (2021).Article 
ADS 

Google Scholar 
Rodrigues, P. C., Awe, O. O., Pimentel, J. S. & Mahmoudvand, R. Modelling the behaviour of currency exchange rates with singular spectrum analysis and artificial neural networks. Stats 3, 137–157 (2020).Article 

Google Scholar 
Sako, K., Mpinda, B. N. & Rodrigues, P. C. Neural networks for financial time series forecasting. Entropy 24, 657 (2022).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Coelho, Leite et al. Statistical and artificial neural networks models for electricity consumption forecasting in the Brazilian industrial sector. Energies 15, 588 (2022).Article 

Google Scholar 
Sulandari, W., Subanar, S., Lee, M. H. & Rodrigues, P. C. Time series forecasting using singular spectrum analysis, fuzzy systems and neural networks. MethodsX 7, 101015 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Sulandari, W. et al. Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks. Energy 190, 116408 (2020).Article 

Google Scholar 
Rodrigues, P. C. & Mahmoudvand, R. The benefits of multivariate singular spectrum analysis over the univariate version. J. Frankl. Inst. 355, 544–564 (2018).Article 
MathSciNet 
MATH 

Google Scholar  More

Lal R, Bruce JP. The potential of world cropland soils to sequester C and mitigate the greenhouse effect. Environ Sci Policy. 1999;2:177–85.Article 
CAS 

Google Scholar 
Wang J, Feng L, Palmer PI, Liu Y, Fang S, Bosch H, et al. Large Chinese land carbon sink estimated from atmospheric carbon dioxide data. Nature. 2020;586:720–3.Article 
CAS 
PubMed 

Google Scholar 
Lal R. Managing soils and ecosystems for mitigating anthropogenic carbon emissions and advancing global food security. Bioscience. 2010;60:708–21.Article 

Google Scholar 
Rumpel C, Lehmann J, Chabbi A. ‘4 per 1,000’ initiative will boost soil carbon for climate and food security. Nature. 2018;553:27–27.Article 
CAS 
PubMed 

Google Scholar 
Zhao Y, Wang M, Hu S, Zhang X, Ouyang Z, Zhang G, et al. Economics- and policy-driven organic carbon input enhancement dominates soil organic carbon accumulation in Chinese croplands. Proc Natl Acad Sci USA. 2018;115:4045–50.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yang F, Xu Y, Cui Y, Meng Y, Dong Y, Li R, et al. Variation of soil organic matter content in croplands of china over the last three decades (in Chinese). Acta Pedol Sin. 2017;5:1047–56.
Google Scholar 
Lehmann J, Hansel CM, Kaiser C, Kleber M, Maher K, Manzoni S, et al. Persistence of soil organic carbon caused by functional complexity. Nat Geosci. 2020;13:529–34.Article 
CAS 

Google Scholar 
Schmidt M, Torn MS, Abiven S, Dittmar T, Guggenberger G, Janssens IA. et al. Persistence of soil organic matter as an ecosystem property. Nature. 2011;478:49–56.Article 
CAS 
PubMed 

Google Scholar 
Lehmann J, Kleber M. The contentious nature of soil organic matter. Nature. 2015;528:60–68.Article 
CAS 
PubMed 

Google Scholar 
Cotrufo MF, Soong JL, Horton AJ, Campbell EE, Haddix ML, Wall DH, et al. Formation of soil organic matter via biochemical and physical pathways of litter mass loss. Nat Geosci. 2015;8:776–9.Article 
CAS 

Google Scholar 
Schnitzer M, Monreal CM. Quo vadis soil organic matter research? A biological link to the chemistry of humification. Adv Agron. 2011;113:139–213.
Google Scholar 
Wang X, Sun B, Mao J, Sui Y, Cao X. Structural convergence of maize and wheat straw during two-year decomposition under different climate conditions. Environ Sci Technol. 2012;46:7159–65.Article 
CAS 
PubMed 

Google Scholar 
Liang C, Schimel JP, Jastrow JD. The importance of anabolism in microbial control over soil carbon storage. Nat Microbiol. 2017;2:17105.Article 
CAS 
PubMed 

Google Scholar 
Wickings K, Grandy AS, Reed SC, Cleveland CC. The origin of litter chemical complexity during decomposition. Ecol Lett. 2012;15:1180–8.Article 
PubMed 

Google Scholar 
Grandy AS, Neff JC. Molecular C dynamics downstream: the biochemical decomposition sequence and its impact on soil organic matter structure and function. Sci Total Environ. 2008;404:297–307.Article 
CAS 
PubMed 

Google Scholar 
Jenkinson DS, Ayanaba A. Decomposition of 14C labeled plant material under tropical conditions. Soil Sci Soc Am J. 1977;41:912–5.Article 
CAS 

Google Scholar 
Li Y, Chen N, Harmon ME, Li Y, Cao X, Chappell MA, et al. Plant species rather than climate greatly alters the temporal pattern of litter chemical composition during long-term decomposition. Sci Rep. 2015;5:15783.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Preston CM, Nault JR, Trofymow JA, Smyth C, Grp CW. Chemical changes during 6 years of decomposition of 11 litters in some Canadian forest sites. Part 1. Elemental composition, tannins, phenolics, and proximate fractions. Ecosystems. 2009;12:1053–77.Article 
CAS 

Google Scholar 
Kallenbach CM, Frey SD, Grandy AS. Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls. Nat Commun. 2016;7:13630.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wickings K, Stuart Grandy A, Reed S, Cleveland C. Management intensity alters decomposition via biological pathways. Biogeochemistry. 2011;104:365–79.Article 

Google Scholar 
Schimel JP, Schaeffer SM. Microbial control over carbon cycling in soil. Front Microbiol. 2012;3:348.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sun B, Wang X, Wang F, Jiang Y, Zhang X-X. Assessing the relative effects of geographic location and soil type on microbial communities associated with straw decomposition. Appl Environ Microbiol. 2013;79:3327.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Balser TC, Firestone MK. Linking microbial community composition and soil processes in a California annual grassland and mixed-conifer forest. Biogeochemistry. 2005;73:395–415.Article 
CAS 

Google Scholar 
Grandy AS, Neff JC, Weintrau MN. Carbon structure and enzyme activities in alpine and forest ecosystems. Soil Biol Biochem. 2007;39:2701–11.Article 
CAS 

Google Scholar 
Maynard DS, Crowther TW, Bradford MA. Competitive network determines the direction of the diversity-function relationship. Proc Natl Acad Sci USA. 2017;114:11464–9.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wagg C, Bender SF, Widmer F, van der Heijden MGA. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc Natl Acad Sci USA. 2014;111:5266–70.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Snajdr J, Cajthaml T, Valaskova V, Merhautova V, Petrankova M, Spetz P, et al. Transformation of Quercus petraea litter: successive changes in litter chemistry are reflected in differential enzyme activity and changes in the microbial community composition. FEMS Microbiol Ecol. 2011;75:291–303.Article 
CAS 
PubMed 

Google Scholar 
Banerjee S, Kirkby CA, Schmutter D, Bissett A, Kirkegaard JA, Richardson AE. Network analysis reveals functional redundancy and keystone taxa amongst bacterial and fungal communities during organic matter decomposition in an arable soil. Soil Biol Biochem. 2016;97:188–98.Article 
CAS 

Google Scholar 
Banerjee S, Schlaeppi K, van der Heijden MGA. Keystone taxa as drivers of microbiome structure and functioning. Nat Rev Microbiol. 2018;16:567–76.Article 
CAS 
PubMed 

Google Scholar 
Carrias J-F, Gerphagnon M, Rodríguez-Pérez H, Borrel G, Loiseau C, Corbara B, et al. Resource availability drives bacterial succession during leaf-litter decomposition in a bromeliad ecosystem. FEMS Microbiol Ecol. 2020;96:fiaa045.Article 
CAS 
PubMed 

Google Scholar 
Zhan P, Liu Y, Wang H, Wang C, Xia M, Wang N, et al. Plant litter decomposition in wetlands is closely associated with phyllospheric fungi as revealed by microbial community dynamics and co-occurrence network. Sci Total Environ. 2021;753:142194.Article 
CAS 
PubMed 

Google Scholar 
Panettieri M, Knicker H, Murillo JM, Madejon E, Hatcher PG. Soil organic matter degradation in an agricultural chronosequence under different tillage regimes evaluated by organic matter pools, enzymatic activities and CPMAS C-13 NMR. Soil Biol Biochem. 2014;78:170–81.Article 
CAS 

Google Scholar 
Skjemstad JO, Clarke P, Taylor JA, Oades JM, Newman RH. The removal of magnetic-materials from surface soils – a solid state 13C CP/MAS NMR study. Aust J Soil Res. 1994;32:1215–29.Article 
CAS 

Google Scholar 
Sokolenko S, Jézéquel T, Hajjar G, Farjon J, Akoka S, Giraudeau P. Robust 1D NMR lineshape fitting using real and imaginary data in the frequency domain. J Magn Reson. 2019;298:91–100.Article 
CAS 
PubMed 

Google Scholar 
Grandy AS, Strickland MS, Lauber CL, Bradford MA, Fierer N. The influence of microbial communities, management, and soil texture on soil organic matter chemistry. Geoderma.2009;150:278–86.Article 
CAS 

Google Scholar 
Saiya-Cork KR, Sinsabaugh RL, Zak DR. The effects of long term nitrogen deposition on extracellular enzyme activity in an Acer saccharum forest soil. Soil Biol Biochem. 2002;34:1309–15.Article 
CAS 

Google Scholar 
Allison SD, Jastrow JD. Activities of extracellular enzymes in physically isolated fractions of restored grassland soils. Soil Biol Biochem. 2006;38:3245–56.Article 
CAS 

Google Scholar 
Zhang XD, Amelung W. Gas chromatographic determination of muramic acid, glucosamine, mannosamine, and galactosamine in soils. Soil Biol Biochem. 1996;28:1201–6.Article 
CAS 

Google Scholar 
Lee CK, Barbier BA, Bottos EM, McDonald IR, Cary SC. The inter-valley soil comparative survey: the ecology of dry valley edaphic microbial communities. ISME J. 2012;6:1046–57.Article 
CAS 
PubMed 

Google Scholar 
Degnan PH, Ochman H. Illumina-based analysis of microbial community diversity. ISME J. 2012;6:183–94.Article 
CAS 
PubMed 

Google Scholar 
Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007;35:7188–96.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kõljalg U, Nilsson RH, Abarenkov K, Tedersoo L, Taylor AFS, Bahram M, et al. Towards a unified paradigm for sequence-based identification of fungi. Mol Ecol. 2013;22:5271–7.Article 
PubMed 

Google Scholar 
Faust K, Sathirapongsasuti JF, Izard J, Segata N, Gevers D, Raes J, et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comp Biol. 2012;8:e1002606.Article 
CAS 

Google Scholar 
Chong IG, Jun CH. Performance of some variable selection methods when multicollinearity is present. Chemometrics Intell Lab Syst. 2005;78:103–12.Article 
CAS 

Google Scholar 
Strukelj M, Brais S, Mazerolle MJ, Pare D, Drapeau P. Decomposition patterns of foliar litter and deadwood in managed and unmanaged stands: A 13-year experiment in boreal mixedwoods. Ecosystems. 2018;21:68–84.Article 
CAS 

Google Scholar 
Manzoni S, Piñeiro G, Jackson RB, Jobbágy EG, Kim JH, Porporato A. Analytical models of soil and litter decomposition: Solutions for mass loss and time-dependent decay rates. Soil Biol Biochem. 2012;50:66–76.Article 
CAS 

Google Scholar 
Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.Article 

Google Scholar 
Grace JB (ed). Structural Equation Modeling and Natural Systems. Cambridge University Press, Cambridge, 2006.Shen Y, Cheng R, Xiao W, Yang S, Guo Y, Wang N, et al. Labile organic carbon pools and enzyme activities of Pinus massoniana plantation soil as affected by understory vegetation removal and thinning. Sci Rep. 2018;8:573.Article 
PubMed 
PubMed Central 

Google Scholar 
Gallo ME, Lauber CL, Cabaniss SE, Waldrop MP, Sinsabaugh RL, Zak DR. Soil organic matter and litter chemistry response to experimental N deposition in northern temperate deciduous forest ecosystems. Glob Change Biol. 2005;11:1514–21.Article 

Google Scholar 
Wilhelm RC, Singh R, Eltis LD, Mohn WW. Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME J. 2019;13:413–29.Article 
CAS 
PubMed 

Google Scholar 
Sahay H, Yadav AN, Singh AK, Singh S, Kaushik R, Saxena AK. Hot springs of Indian Himalayas: potential sources of microbial diversity and thermostable hydrolytic enzymes. 3 Biotech. 2017;7:118.Article 
PubMed 
PubMed Central 

Google Scholar 
Robledo M, Rivera L, Jimenez-Zurdo JI, Rivas R, Dazzo F, Velazquez E, et al. Role of Rhizobium endoglucanase CelC2 in cellulose biosynthesis and biofilm formation on plant roots and abiotic surfaces. Micro Cell Factories. 2012;11:125.Article 
CAS 

Google Scholar 
Wang X, Bian Q, Jiang Y, Zhu L, Chen Y, Liang Y, et al. Organic amendments drive shifts in microbial community structure and keystone taxa which increase C mineralization across aggregate size classes. Soil Biol Biochem. 2021;153:108062.Article 
CAS 

Google Scholar 
Joergensen RG. Amino sugars as specific indices for fungal and bacterial residues in soil. Biol Fert Soils. 2018;54:559–68.Article 
CAS 

Google Scholar 
Chen Y, Sun R, Sun T, Chen P, Yu Z, Ding L, et al. Evidence for involvement of keystone fungal taxa in organic phosphorus mineralization in subtropical soil and the impact of labile carbon. Soil Biol Biochem. 2020;148:107900.Article 
CAS 

Google Scholar 
Puentes-Tellez PE, Salles JF. Construction of effective minimal active microbial consortia for lignocellulose degradation. Micro Ecol. 2018;76:419–29.Article 
CAS 

Google Scholar 
Zark M, Dittmar T. Universal molecular structures in natural dissolved organic matter. Nat Commun. 2018;9:3178.Article 
PubMed 
PubMed Central 

Google Scholar 
Lynch LM, Sutfin NA, Fegel TS, Boot CM, Covino TP, Wallenstein MD. River channel connectivity shifts metabolite composition and dissolved organic matter chemistry. Nat Commun. 2019;10:459.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Filley TR, Boutton TW, Liao JD, Jastrow JD, Gamblin DE. Chemical changes to nonaggregated particulate soil organic matter following grassland-to-woodland transition in a subtropical savanna. J Geophys Res Biogeosci. 2008;113:G03009.Article 

Google Scholar 
Stewart CE, Neff JC, Amatangelo KL, Vitousek PM. Vegetation effects on soil organic matter chemistry of aggregate fractions in a Hawaiian forest. Ecosystems. 2011;14:382–97.Article 
CAS 

Google Scholar  More

Genome reduction is associated with a termite comb-associated lifestyleFor our studies, we collected fungus comb samples originating from mounds of Macrotermes natalensis, Odontotermes spp., and Microtermes spp. termites and were able to obtain seven viable Pseudoxylaria cultures (X802 [Microtermes sp.], Mn132, Mn153, X187, X3-2 [Macrotermes natalensis], and X167, X170LB [Odontotermes spp.], Table S1-S3).To test if a fungus comb-associated lifestyle of Pseudoxylaria was reflected in differences at the genome level, we sequenced the genomes of all seven isolates using a combination of paired-end shotgun sequencing (BGISEQ-500, BGI) and long-read sequencing (PacBio sequel, BGI or Oxford Nanopore Technologies, Oxford, UK). In addition, we sequenced the transcriptomes (BGISEQ, BGI) of two isolates (X802, X170LB). Eleven publicly available genomes of free-living Xylaria (Fig. 2A, B) were used as reference genomes (Table S4). Hybrid draft genomes were comprised on average of 33–742 scaffolds with total haploid assembly lengths of 33.2–40.4 Mb, and a high BUSCO completeness of genomes ( > 95 %) with a total number of predicted proteins ranging from 8.8 to 12.1 × 103. The GC content was comparable to reference genomes with 49.7–51.6%. To verify the phylogenetic placement of the isolates, different genetic loci encoding conserved protein sequences (α-actin (ACT), second largest subunit of RNA polymerase (RPB2), β-tubulin (TUB) and the internal transcribed spacer (ITS) were used as genetic markers [7, 13].Fig. 2: Geographic and comparative phylogenomic analysis of termite-associated Pseudoxylaria isolates (strains 1-7) and free-living Xylaria (strains 8–18).A Geographic origins of genome-sequenced free-living Xylaria and termite-associated Pseudoxylaria isolates, B phylogenomic placement based on single-copy ortholog protein sequences, and C comparison of genome assembly length, and numbers of predicted proteins per genome.Full size imagePhylogenies were reconstructed from ITS sequences and three aligned sequence datasets (RPB2, TUB, ACT) using reference sequences of twelve different taxa (Table S4–S7). Consistent with previous findings, all isolates grouped within the monophyletic termite-associated Pseudoxylaria group [9,10,11,12,13], which diverged from the free-living members of the genus Xylaria (Fig. 2B, Figure S1–S4).As our seven isolates covered a larger portion of the previously reported phylogenetic diversity of the termite-associated subgenus, we elaborated on genomic characteristics of our isolates to uncover features of the termite-associated ecology of Pseudoxylaria. Indeed, comparative genome analysis of the South African Pseudoxylaria isolates with publicly available genomes of free-living Xylaria species of similar genome quality revealed significantly reduced genome assembly lengths in Pseudoxylaria with reduced numbers of predicted genes per genome (Table S4). Comparison of the annotated mitochondrial (mt) genomes (Figure S5, Table S8) also indicated that all seven mt genomes were shorter in length (assembly lengths: 18.5–63.8 kbp) compared to the, albeit few, publicly available mitochondrial genomes of free-living species (48.9–258.9 kbp). The reduction in mitochondrial genome size also corresponded to a significantly reduced mean number of annotated genes (7.6) and tRNAs (14.3) in Pseudoxylaria spp. compared to on average 30.0 (annotated genes) and 25.8 (tRNAs) found in free-living species.Analysis of the abundance and composition of transposable elements (TEs), which account for up to 30–35% of the genomes of (endo)parasitic fungi due to the expansion of certain gene families [20, 21], showed that the mean total numbers of TEs across Pseudoxylaria spp. genomes were comparable (1530), but the numbers were reduced compared to free-living Xylaria species (3690) (Table S9). We also identified high variation in the TE composition across genomes (1.5–9.9 %), comparable to what was observed in free-living Xylaria spp. (1.3–8.1 %), with reductions in long terminal repeat retrotransposons (LTRs: Copia and unknown LTRs) in two inverted tandem repeat DNA transposons (TIRs; CACTA, Mutator and hAT). As Pseudoxylaria spp. contained increased numbers of non-ITR transposons of the helitron class and LTRs of the Gypsy class compared to Xylaria strains, we concluded that Pseudoxylaria exhibits no typical features of an (endo)parasitic lifestyle, but that the overall composition and the reduced numbers of TEs could serve as a fingerprint to distinguish the genetically divergent Pseudoxylaria taxa.Repertoire of carbohydrate-active enzymes indicates specialized substrate useAs the fungus comb is mostly composed of partially-digested plant material interspersed with fungal mycelium of the termite mutualist [3], we anticipated that Pseudoxylaria should exhibit features of a substrate specialist similar to the fungal mutualist Termitomyces, which should be reflected in a Carbohydrate-Active enzyme (CAZyme) repertoire distinguishable from  free-living saprophytic Xylaria species [22,23,24]. In particular, numbers and composition of redox-active enzymes (e.g., benzoquinone reductase (EC 1.6.5.6/EC 1.6.5.7), catalase (EC 1.11.1.6), glutathione reductase (EC 1.11.1.9), hydroxy acid oxidase (EC 1.1.3.15), laccase (EC 1.10.3.2), manganese peroxidase (EC 1.11.1.13), peroxiredoxin (EC 1.11.1.15), superoxide dismutase (EC 1.15.1.1), dye-decolorization or unspecific peroxygenase (EC 1.11.2.1), Table S10), which catalyze the degradation of lignin-rich biomass, were expected to differ between free-living strains and substrate specialists [22].Identification of CAZymes using Peptide Pattern Recognition (PPR) revealed that Pseudoxylaria genomes encoded on average a reduced number of CAZymes (mean 264) compared to the free-living taxa in the family Xylaria (mean 367 CAZymes, pANOVA; F = 41.4, p = 3.5 × 10–8, pairwise p = 1.69 × 10–7) (Fig. 3A, B, Figure S6), but similar numbers to those identified in Termitomyces (mean 265, pairwise p = 0.949).Fig. 3: Comparison of carbohydrate-active enzymes (CAZymes) in Xylaria, Pseudoxylaria and the fungal mutualist Termitomyces.A Predicted CAZymes, B Principal Coordinates Analysis (PCoA) of predicted CAZyme families, and C heatmap of representatives CAZyme families in the predicted proteomes of free-living Xylaria, Termitomyces and Pseudoxylaria species.Full size imageOverall, significant differences in the composition of CAZymes were observed [8], most notably in the reduction of auxiliary activity enzymes (AA), carbohydrate esterases (CE), glycosyl hydrolases (GH), and polysaccharide lyases (PL). The most significant reduction was observed in the AA3 family (Fig. 3C), which typically displays a high multigenicity in wood-degrading fungi as many  enzymes of this family catalyze the oxidation of alcohols or carbohydrates with the concomitant formation of hydrogen peroxide or hydroquinones thereby supporting lignocellulose degradation by other AA-enzymes, such as peroxidases (AA2). Similarly, although to a lesser extent, reduced numbers within the related AA1 family were detected, which included oxidizing enzymes like laccases, ferroxidases, and laccase-like multicopper oxidases. Along these lines, glycosyl hydrolases of the GH3 and GH5 family, including enzymes responsible for degradation of cellulose-containing biomass and xylose, were less abundant. We also noted that all Pseudoxylaria lacked homologs of the unspecific peroxygenases (UPO; EC 1.11.2.1), while almost all free-living Xylaria spp. and the fungal symbiont Termitomyces harbored at least one or two copies of similar gene sequences.
Pseudoxylaria shows reduced biosynthetic capacity for secondary metabolite productionA healthy termite colony is engulfed in several layers of social immunity [5, 6], which pose a constant selection pressure on associated and potentially antagonistic microbes. As Pseudoxylaria evolved measures to remain inconspicuously present within the comb environment, we hypothesized that one of the possible adaptations to evade hygiene measures of termites could be reflected in a reduced biosynthetic capability to produce antibiotic or volatile natural products, which often serve as infochemicals triggering defense mechanisms [25,26,27], or as alarm pheromones [4, 28].The biosynthesis of secondary metabolites is encoded in so called Biosynthetic Gene Cluster (BGC) regions. We explored the abundance and diversity of encoded BGCs using FungiSMASH 6.0.0 and manually cross-checked the obtained data set by BLAST to account for possible biases due to varying genome qualities across strains of both groups [29]. Overall, the herein investigated Xylaria genomes harbored on average 90 BGCs per genome, while Pseudoxylaria encoded on average 45 BGCs (Fig. 4, Figure S7). Fig. 4: Similarity network analysis of biosynthetic gene clusters.Comparative analysis of termite associated-associated Pseudoxylaria isolates (strains 1–7, red circles) and free-living Xylaria (strains 8–18, green circles) with BiG-SCAPE 1.0 annotations (blue hexagon) ACR ACR toxin, Alt alternariol, Bio biotin, Chr chromene, Cyt cytochalasins, Cur curvupalide, Dep depiudecin, Fus fusarin, Gri griseofulvin, Mon monascorubin, MSA 6-methylsalicylic acid, Pho phomasetin, Sol solanapyrone, Swa swasionine, Xen xenolozoyenone, Xsp xylasporins, Xyl xylacremolide. Singletons are not shown.Full size imageThe nature and relatedness of the BGCs were analyzed by creating a curated similarity network analysis using BiG-SCAPE 1.0 [30]. Overall, 28 orthologous BGCs were shared across all genomes, including the biosynthesis of polyketides like 6-methylsalicylic acid (MSA), chromenes (Chr) and polyketide-non-ribosomal peptide (PKS-NRPS) hybrids like the cytochalasins (Cyt) [31]. Furthermore, five BGC networks, which were shared by Pseudoxylaria and Xylaria, contained genes encoding natural product modifying dimethylallyltryptophan synthases (DMATS). In contrast, and despite the significant reduction in the biosynthetic capacity within Pseudoxylaria genomes [29], about 29 BGC networks were unique to Pseudoxylaria and thus could possibly relate to the comb-associated lifestyle (Figure S8 and S9). Notably, Pseudoxylaria genomes lacked genes encoding ribosomally synthesized and posttranslationally modified peptides (RiPPs) or halogenases. In comparision, free-living Xylaria spp. harbored at least one sequence encoding a RiPP, and up to two orthologous sequences encoding putative halogenases. In contrast, a reduced average number of terpene synthases (TPS) in Pseudoxylaria (9 TPS) compared to free-living Xylaria (18 TPS) was detected, which included three BGCs encoding TPSs that were unique to Pseudoxylaria.  In comparison, genomes of the fungal mutualist Termitomyces were reported to encode for about 20-25 terpene cyclases, but haboured only about two loci containing genes for a PKS and NRPS each [24].Manual BLAST searches were conducted to identify BGCs that could be putatively assigned to previously isolated metabolites from Pseudoxylaria (vide infra Fig. 7, Figure S8) [32, 33]. Using e.g., the known NRPS-PKS-hybrid cluster sequence ccs (Aspergillus clavatus) of cytochalasins as query, an orthologous BGC, here named cytA, was identified in the cytochalasin-producing strain X802 [34]. Although the putative PKS-NRPS hybrid and CcsA shared 60 % identical amino acids (aa), the sequences of the accessory enzymes were less related to CcsC-G (45–47% identical aa) and the BGC in X802 lacked a gene of a homologue to ccsB. Similarly, five free-living Xylaria species carried orthologous gene loci (Xylaria sp. BCC 1067, Xylaria sp. MSU_SB201401, X. flabelliformis G536, X. grammica EL000614, and X. multiplex DSM 110363) supporting previous isolation reports of cytochalasins with varying structural features. Furthermore, three Pseudoxylaria strains (X187, and closely related Mn153, and Mn132) were found to share a highly similar PKS-NRPS hybrid BGC (99–100 % identical aa, named xya), which likely encodes for the enzymatic production of previously identified xylacremolides [32]. Four Pseudoxylaria strains (X802, Mn132, Mn153, and X187) also shared a BGC (50–98 % amino acid identity) resembling the fog BGC (Aspergillus ruber) [35, 36], which putatively encodes the biosynthetic machinery to produce xylasporin/cytosporin-like metabolites. In this homology search, we also uncovered that fog-like BGC arrangements are likely more common than previously anticipated, as clusters with similar arrangements and identity were also found in genomes of Rosellinia necatrix, Pseudomasariella vexata, Stachybotrys chartarum, and Hyaloscypha bicolor (Fig. 4, Figure S8).A detailed analysis of the fog-like cluster arrangements within Pseudoxylaria genomes revealed – similar to homologs of the ccs cluster – variation in the abundance and arrangement of several accessory genes coding for a cupin protein (pxF), a short chain oxidoreductase (pxB; SDR), and an additional SnoaL-like polyketide cyclase (pxP), which could account for the production of strain-specific structural congeners (vide infra, Fig. 7).Change of nutrient sources causes dedicated transcriptomic changes in Pseudoxylaria
To further solidify our in silico indications of substrate specialization with comb material as preferred substrate and fungus garden as environment, we analyzed Pseudoxylaria growth on different media (PDA, and reduced medium 1/3-PDA) including comb-like agar matrices (wood-rice medium (WRM), agar-agar or 1/3-PDA medium containing lyophilized (dead) Termitomyces sp. T112 biomass (T112, respectively T112-PDA), PDB covering glass-based surface-structuring elements (GB), Table S11–S14).Cultivation of Pseudoxylaria on agar-agar containing lyophilized biomass of Termitomyces (T112) as the sole nutrient source allowed Pseudoxylaria to sustain growth, although to a reduced extent compared to growth on nutrient-rich PDA medium (Table S3). Wood-rice medium (WRM) induced comparable growth rates as observed on PDA and also the appearance of phenotypic stromata.To investigate the influence of these growth conditions on the transcriptomic level, we harvested RNA from vegetative mycelium after growth on comb-like media (WRM, T112, T112-PDA, and GB), PDA, and reduced medium 1/3-PDA (Fig. 5A). The most significant transcript changes (normalized to data obtained from growth on PDA) were observed for genes coding for specific CAZymes including several redox active enzymes (Fig. 5B). The 30 most variable transcripts coded for specific glycoside hydrolases (GH), lytic polysaccharide monooxygenases (AA), ligninolytic enzymes, and a glycoside transferase (GT). Similarly, chitinases (CHT2; CHT4; CHI2; CHI4) were upregulated (up to 243-fold on T112) under almost all conditions compared to PDA, but some of these specific transcript changes were exclusive to growth on Termitomyces biomass or artificial comb material (WRM) suggesting the ability to regulate and increase chitin metabolism if necessary [37].Fig. 5: Transcriptomic analysis of Pseudoxylaria sp. X802 in dependence of growth conditions.A Representative pictures of Pseudoxylaria sp. X802 growing on PDA, PDB on glass beads (GB), wood-rice medium (WRM), and agar-agar medium containing lyophilized Termitomyces sp. T112 biomass (T112). B Heatmap of the most variable transcripts coding for CAZymes (red), redox enzymes (orange), secondary metabolite-related core genes (green), and more specifically on key genes within the boundaries of cytochalasin (turquoise) and xylasporin/cytosporin BGCs (blue). RNA was obtained from vegetative mycelium after growth on PDA, reduced medium (1/3-PDA), PDB on glass beads (GB), wood-rice medium (WRM), 1/3-PDA-medium enriched with Termitomyces sp. T112 biomass (T112-PDA) and agar-agar medium containing lyophilized Termitomyces biomass (T112). Transcript counts are shown as log10 transformed transcripts per million (top; TPM). Significance of the changes in transcript counts are compared to control (X802 grown on PDA) and depicted in log-10 transformed p values.Full size imageWhen X802 was grown on T112 (agar matrix containing lyophilized Termitomyces sp. T112 biomass), we observed a >400-fold increase in the expression of transcripts encoding glycoside hydrolases in the GH43 family, GH7 (~140-fold), GH3, and GH64 (5–12-fold). Similarly, transcripts for a putative mannosyl-oligosaccharide-α-1,2-mannosidase (MNS1B; 8.2-fold), chitinase CHT4 (2.9-fold), β-glucosidase BGL4 (5.7-fold), and copper-dependent lytic polysaccharide monooxygenase AA11 (1.6-fold) were significantly upregulated. Growth on WRM (wood-rice medium) or T112 (Termitomyces sp. T112 biomass) also caused a significant upregulation of genes coding for glycoside transferase GT2, glycoside hydrolases GH15, GH3, and aldehyde oxidase AOX1, which indicated the ability to expand the degradation portfolio if necessary. Along these lines, specific transcript levels were reduced when X802 was grown on T112, in particular class II lignin-modifying peroxidases (AA2), carbohydrate-binding module family 21 (CBM21), multicopper oxidases (AA1), secreted β-glucosidases (SUN4), and glycoside hydrolases GH16, and GH128.When the fungus was challenged with lignocellulose-rich WRM medium, higher transcript levels putatively assigned to glutathione peroxidase (GXP2), superoxide dismutase (SOD2), and laccases (LCC5) were observed, which indicated that despite the reduced wood-degrading capacity, Pseudoxylaria activates available enzymatic mechanisms to degrade the provided material and respond to the resulting oxidative stress. Cultivation on GB (glass-based surfaces covered in liquid PD broth) influenced the expression of certain genes coding for glycoside hydrolases (GH64, GH76, GH72, GH128, BGL4) and lytic polysaccharide monooxygenases (AA1, AA2, AA11), presumably enabling the fungus to utilize soluble carbohydrates.To test the hypothesis that the presence of Termitomyces biomass stimulates secondary metabolite production in Pseudoxylaria to eventually displace the mutualist, we also analyzed changes in the transcript levels of core BGC genes that encode the production of bioactive secondary metabolites. Overall, only slight transcript variations were detectable within the  most variable expressed genes. (Fig. 5B). Cultivation on GB, WRM, and T112 media caused lower transcript levels of genes coding for terpene synthase TC1, polyketide synthases (PKS7, PKS8), and the NRPS-like1, while an upregulation of NRPS-like2 on WRM (2.5-fold), and of PKS7 (1.7-fold) on reduced 1/3-PDA medium was observed.Transcript levels of core genes within BGCs assigned to cytochalasines (cyt) or xylasporins/cytosporins (px), e.g., remained nearly constant, while minor transcript level variations of neighboring genes and reduced transcript levels for pxI (flavin-dependent monooxygenase), pxH (ABBA-type prenyltransferase), pxF (cupin fold oxidoreductase), and pxJ (short-chain dehydrogenase) were detectable. Hence, it was concluded that the presence of Termitomyces biomass only weakly triggers secondary metabolite production in general, but varying transcript levels coding for decorating enzymes could cause substantial structural alterations within the produced natural product composition. It was also notable that transcript levels of the terpene synthase TC1 were downregulated, which could cause a reduced production level of specific volatiles.
Pseudoxylaria antagonizes Termitomyces growth and metabolizes fungal biomassThe growth behavior of Pseudoxylaria isolates was also analyzed in co-culture assays with Termitomyces. As expected from prior studies, both fungi showed reduced growth when co-cultured on agar plates, often causing the formation of zones of inhibition (ZOI) between the fungal colonies (Fig. 6A–D, Table S11–S14) [7]. When fungus-fungus co-cultures were maintained for longer than two weeks on agar plates, Pseudoxylaria started to overcome the ZOI and overgrew Termitomyces via the extension of aerial mycelium. The observation was even more pronounced when co-cultures were performed on wood-rice medium (WRM), where Pseudoxylaria remained the only visible fungus after two weeks.Fig. 6: Co-cultivation of Pseudoxylaria sp. X170LB and Termitomyces sp. T112 and results of isotope fractionation experiments.Representative pictures of fungal growth and co-cultivation of A Termitomyces sp. T112, B Pseudoxylaria sp. X170LB, C co-culture of Pseudoxylaria sp. X802 and Termitomyces sp. T153 exhibiting a ZOI, in which X802 overgrowths T153 in proximity to the interaction zone (red arrow), and D Pseudoxylaria sp. X802 growing on the surface of a living Termitomyces sp. T153 culture. E, F Shown is the relative change in the carbon isotope pattern (δ13C values, ± standard deviation, with n = 3) of lipid and carbohydrate fractions isolated from fungal biomass of Termitomyces sp. T112, Pseudoxylaria sp. X170LB, and Pseudoxylaria sp. X170LB cultivated on vegetative Termitomyces sp. T112 biomass (T112ǂ), or on lyophilized Termitomyces sp. T112 biomass (T112). Fungal strains were grown on E medium with natural 13C abundance and F medium artificially enriched in 13C content.Full size imageTo verify whether Pseudoxylaria consumes Termitomyces or even partially degrades specific metabolites present within the fungal biomass, we pursued stable isotope fingerprinting commonly used to analyse trophic relations [38, 39]. This diagnostic method relies on measurable changes in the bulk stable isotope composition, because biosynthetic enzymes preferentially convert lighter metabolites enriched in 12C compared to their heavier 13C-enriched congeners. This intrinsic kinetic isotope effect results in an overall change in the 13C/12C ratio of the respective educts and products, in particular in biomarkers such as phospholipid fatty acids, carbohydrates, and amino acids. Using this isotope enrichment effect, we determined the natural trophic isotope fractionation of 13C in lipids and carbohydrates produced by Termitomyces sp. T112 and Pseudoxylaria sp. X170LB. For clearer differentiation, both fungi were cultivated on PDA medium containing naturally abundant 13C/12C, Fig. 6E) and on PDA medium enriched with 13C-glucose (Fig. 6F). Lipids and carbohydrates were isolated from mycelium harvested after 21 days (Fig. 6E, Table S15).Analysis of fungal carbohydrate and lipid-rich metabolite fractions by Elemental Analysis-Isotope Ratio Mass Spectrometry (EA-IRMS) [40, 41] uncovered that under normal growth conditions (full medium), Termitomyces sp. T112 and Pseudoxylaria sp. X170LB showed only a slight negative trophic fractionation of stable carbon isotopes (δ13C/12C ratio (expressed as δ13C values [‰]), Fig. 6F) within the carbohydrate fractions (T112: −1.2 ‰; for X170LB: −1.3 ‰), and expectedly a stronger depletion in the lipid fraction (T112: −6.7 ‰, and less pronounced for X170LB: −3.1 ‰). To determine if Pseudoxylaria metabolizes Termitomyces biomass, the isotope pattern of metabolites derived from Pseudoxylaria thriving on living biomass of Termitomyces (T112ǂ) was analysed next. Here, an overall positive carbon isotope (13C/12C) fractionation by approximately +0.6 ‰ relative to the control medium was detectable, while the δ13C values of lipids remained largely unchanged (Fig. 6F, Table S15). These results suggested that Pseudoxylaria might pursue a preferential uptake of Termitomyces-derived carbohydrates.In a last experiment, Pseudoxylaria was grown on lyophilized (dead) Termitomyces biomass (T112) as sole food source. In this experiment, the isotope fingerprint showed converging δ13C values of −1.9 ‰ (relative to the media) for both carbohydrate and lipid fractions, which indicated that Pseudoxylaria is able to simultaneously metabolize and cycle carbohydrates as well as lipids resulting in the equilibration of isotopic levels between carbohydrates and lipids. Thus, it was concluded that in nature, Pseudoxylaria likely harvests nutrients firstly from vegetative Termitomyces, and then—if possible—subsequently degrades dying or dead mycelium.
Pseudoxylaria produces antimicrobial secondary metabolitesBased on the observation that Pseudoxylaria antagonizes growth of Termitomyces, we questioned if the formation of a ZOI might be caused by the secretion of Pseudoxylaria-derived antimicrobial metabolites [26, 42]. Thus, we performed an ESI(+)-HRMS/MS based metabolic survey using the web-based platform “Global Natural Product Social Molecular Networking” (GNPS) [43] to correlate the encoded biosynthetic repertoire of Pseudoxylaria with secreted metabolites.A partial similar metabolic repertoire across the six analyzed strains was detectable and allowed us to match some of the detectable chemical features and previously isolated metabolites to the predicted shared BGCs, such as antifungal and histone deacetylase inhibitory xylacremolides (Xyl; X187/Mn132) [32, 33], pseudoxylaramides (Psa; X187/Mn132) [32], antibacterial pseudoxylallemycins (Psm; X802/OD126) [18], xylasporin/cytosporins (Xsp; X802/OD126/X187/Mn132) [36], and cytotoxic cytochalasins (X802/OD126) (Fig. 7A and B) [18].Fig. 7: Comparative metabolomic analysis of six Pseudoxylaria strains (OD126 (red), Mn132 (orange), X170 (black), X187 (green), X3.2 (yellow) and X802 (blue)).A Overview of the GNPS network. Identified metabolite clusters xylacremolides (Xyl; X187/Mn132) [32, 33], pseudoxylaramides [32] (Psa; X187/Mn132), pseudoxylallemycins (Psm; X802/OD126) [18], xylasporin/cytosporins (Xsp; X802/OD126/X187/Mn132) and cytochalasins (X802/OD126) [18]. B xylasporin/cytosporin-related cluster formed by nodes from X802 (blue), OD126 (red), X187 (green) and Mn132 (orange). C Chemical structures of natural products isolated from Pseudoxylaria species and related compounds. Red box highlights proposed structures of isolated xylasporin G and I in this study.Full size imageA cluster that contained MS2 signals of molecular ions assigned to the cytosporin/xylasporin family, which was shared by at least four strains, caught our attention as a certain degree of structural diversity of xylasporin/cytosporin family was predicted from the comparison of their respective BGCs. The assigned nodes of this GNPS cluster split into two subclusters with only very little overlap between both regions. Analysis of the mass fragment shifts suggested that both subclusters belong to two different families of xylasporin/cytosporin congeners (Figure S9). To verify these deductions, we pursued an MS-guided purification of xylasporin/cytosporins from chemical extracts of Pseudoxylaria sp. X187, which yielded xylasporin G (3.23 mg, pale-yellow solid) and xylasporin I (1.75 mg, pale-yellow solid). The sum formulas of xylasporin G and xylasporin I were determined to be C17H22O5 (calcd. for [M + H]+ C17H23O5+ = 307.1540, found 307.15347, −1.726 ppm) and C17H24O5 (calcd. for [M + H]+ C17H25O5+ = 309.1697, found 309.1691, −1.68 ppm) by ESI-(+)-HRMS and were predicted to have six degrees of unsaturation (Fig. 7B, Figure S10, Table S16-S17). Planar structures were deduced by comparative 1D and 2D NMR analyses, which revealed the presence of an unsaturated polyketide chain that matched the unsaturation degree and the anticipated structural variation from cytosporins (Fig. 7C, Figure S11-S25).To evaluate if Pseudoxylaria-derived culture extracts and produced natural products (e.g., cytochalasins) are responsible for the observed antimicrobial activity, standardized antimicrobial activity assays were performed (Table S17, S18 and Figure S26). As neither culture extracts nor single compounds exhibited significant antimicrobial activity, they could not be held fully accountable for the antagonistic behavior in co-cultures. Thus, we hypothesized that the observed ZOI might be caused by yet unknown effects like nutrient depletion or bioactive enzymes.
Pseudoxylaria has a negative impact on the fitness of insect larvaeDue to the production of structurally diverse and weakly antimicrobial secondary metabolites, we questioned if mycelium of Pseudoxylaria exhibits intrinsic insecticidal or other insect-detrimental activities, which could discourage or ward off grooming behavior of termite workers. Due to the technical challenges associated with behavioral studies of termites, we evaluated instead the effect of Pseudoxylaria biomass on Spodoptera littoralis, a well-established insect model system and a destructive agricultural lepidopterous pest [44, 45]. When S. littoralis larvae were fed with mycelium-covered agar plugs of Pseudoxylaria sp. X802, a clear decrease of the relative growth rate (RGR) and decline in survival was observed (Fig. 8: treatment D (green), Table S19, S20) compared to feeding with untreated agar plugs (treatment A (black)). In comparison, when larvae were fed with agar plugs covered with the fungal mutualist Termitomyces sp. T153 (treatment B (blue)) an increased growth rate of larvae was observed.Fig. 8: Effect of Termitomyces sp. T153 and Pseudoxylaria sp. X802 mycelia on the relative growth rate and survival of S. littoralis larvae.Insects were fed with either A PDA, B PDA agar plug covered with vegetative Termitomyces sp. T153, C PDA agar plug from which vegetative Termitomyces sp. T153 was removed prior to feeding, D PDA agar plug covered with vegetative Pseudoxylaria sp. X802 mycelium, and E PDA agar plug from which vegetative Pseudoxylaria sp. X802 mycelium was removed prior to feeding. All experiments were performed with 25 replicates per treatment, a duration of 10 days, and larval weights and survival rates were recorded every day. Statistical significances were determined using ANOVA on ranks (p  More

Different picocyanobacterial communities exhibit distinct gene repertoiresTo analyze the distribution of Prochlorococcus and Synechococcus reads along the Tara Oceans transect, metagenomic reads corresponding to the bacterial size fraction were recruited against 256 picocyanobacterial reference genomes, including SAGs and MAGs representative of uncultured lineages (e.g., Prochlorococcus HLIII-IV, Synechococcus EnvA or EnvB). This yielded a total of 1.07 billion recruited reads, of which 87.7% mapped onto Prochlorococcus genomes and 12.3% onto Synechococcus genomes, which were then functionally assigned by mapping them onto the manually curated Cyanorak v2.1 CLOG database [19]. In order to identify picocyanobacterial genes potentially involved in niche adaptation, we analyzed the distribution across the oceans of flexible (i.e. non-core) genes. Clustering of Tara Oceans stations according to the relative abundance of flexible genes resulted in three well-defined clusters for Prochlorococcus (Fig. 1A), which matched those obtained when stations were clustered according to the relative abundance of Prochlorococcus ESTUs, as assessed using the high-resolution marker gene petB, encoding cytochrome b6 (Fig. 1A; [24]). Only a few discrepancies can be observed between the two trees, including stations TARA-070 that displayed one of the most disparate ESTU compositions and TARA-094, dominated by the rare HLID ESTU (Fig. 1A). Similarly, for Synechococcus, most of the eight assemblages of stations discriminated based on the relative abundance of ESTUs (Fig. 1B) were also retrieved in the clustering based on flexible gene abundance, except for a few intra-assemblage switches between stations, notably those dominated by ESTU IIA (Fig. 1B). Despite these few variations, four major clusters can be clearly delineated in both Synechococcus trees, corresponding to four broadly defined ecological niches, namely (i) cold, nutrient-rich, pelagic or coastal environments (blue and light red in Fig. 1B), (ii) Fe-limited environments (purple and grey), (iii) temperate, P-depleted, Fe-replete areas (yellow) and (iv) warm, N-depleted, Fe-replete regions (dark red). This correspondence between taxonomic and functional information was also confirmed by the high congruence between distance matrices based on ESTU relative abundance and on CLOG relative abundance (p-value  0.01) are marked by a cross. Φsat: index of iron limitation derived from satellite data. PAR30: satellite-derived photosynthetically available radiation at the surface, averaged on 30 days. DCM: depth of the deep chlorophyll maximum.Full size imageIdentification of individual genes potentially involved in niche partitioningTo identify genes relevant for adaptation to a specific set of environmental conditions and enriched in specific ESTU assemblages, we selected the most representative genes from each module (Dataset 5; Figs. 3, S2). Most genes retrieved this way encode proteins of unknown or hypothetical function (85.7% of 7,485 genes). However, among the genes with a functional annotation (Dataset 6), a large fraction seems to have a function related to their realized environmental niche (Figs. 3, S2). For instance, many genes involved in the transport and assimilation of nitrite and nitrate (nirA, nirX, moaA-C, moaE, mobA, moeA, narB, M, nrtP; [6]) as well as cyanate, an organic form of nitrogen (cynA, B, D, S), are enriched in the Prochlorococcus blue module, which is correlated with the HLIIA-D ESTU and to low inorganic N, P, and silica levels and anti-correlated with Fe availability (Fig. 2A–C). This is consistent with previous studies showing that while only a few Prochlorococcus strains in culture possess the nirA gene and even less the narB gene, natural Prochlorococcus populations inhabiting N-poor areas do possess one or both of these genes [40,41,42]. Similarly, numerous genes amongst the most representative of Prochlorococcus brown, red and turquoise modules are related to adaptation of HLIIIA/IVA, HLIA and LLIA ESTUs to Fe-limited, cold P-limited, and cold, mixed waters, respectively (Fig. 3). Comparable results were obtained for Synechococcus, although the niche delineation was less clear than for Prochlorococcus since genes within each module exhibited lower correlations with the module eigenvalue (Fig. S2). These results therefore constitute a proof of concept that this network analysis was able to retrieve niche-related genes from metagenomics data.Fig. 3: Violin plots highlighting the most representative genes of each Prochlorococcus module.For each module, each gene is represented as a dot positioned according to its correlation with the eigengene for each module, the most representative genes being localized on top of each violin plot. Genes mentioned in the text and/or in Dataset 6 have been colored according to the color of the corresponding module, indicated by a colored bar above each module. The text above violin plots indicates the most significant environmental parameter(s) and/or ESTU(s) for each module, as derived from Fig. 2.Full size imageIdentification of eCAGs potentially involved in niche partitioningIn order to better understand the function of niche-related genes, notably of the numerous unknowns, we then integrated global distribution data with gene synteny in reference genomes using a network approach (Datasets 7, 8). This led us to identify clusters of adjacent genes in reference genomes, and thus potentially involved in the same metabolic pathway (Figs. 4, S3, S4; Dataset 6). These clusters were defined within each module and thus encompass genes with similar distribution and abundance in situ. Hereafter, these environmental clusters of adjacent genes will be called “eCAGs”.Fig. 4: Delineation of Prochlorococcus eCAGs, defined as a set of genes that are both adjacent in reference genomes and share a similar in situ distribution.Nodes correspond to individual genes with their gene name (or significant numbers of the CK number, e.g. 1234 for CK_00001234) and are colored according to their WGCNA module. A link between two nodes indicates that these two genes are less than five genes apart in at least one genome. The bottom insert shows the most significant environmental parameter(s) and/or ESTU(s) for each module, as derived from Fig. 2.Full size imageeCAGs related to nitrogen metabolismThe well-known nitrate/nitrite gene cluster involved in uptake and assimilation of inorganic forms of N (see above), which is present in most Synechococcus genomes (Dataset 6), was expectedly not restricted to a particular niche in natural Synechococcus populations, as shown by its quasi-absence from WGCNA modules. In Prochlorococcus, this cluster is separated into two eCAGs enriched in low-N areas (Fig. S5A, B), most genes being included in Pro-eCAG_002, present in only 13 out of 118 Prochlorococcus genomes, while nirA and nirX form an independent eCAG (Pro-eCAG_001) due to their presence in many more genomes. The quasi-core ureA-G/urtB-E genomic region was also found to form a Prochlorococcus eCAG (Pro-eCAG_003) that was impoverished in low-Fe compared to other regions (Fig. S5C, D), in agreement with its presence in only two out of six HLIII/IV genomes. We also uncovered several other Prochlorococcus and Synechococcus eCAGs that seem to be involved in the transport and/or assimilation of more unusual and/or complex forms of nitrogen, which might either be degraded into elementary N molecules or possibly directly used by cells for e.g. the biosynthesis of proteins or DNA. Indeed, we detected in both genera an eCAG (Pro-eCAG_004 and Syn-eCAG_001; Fig. S6A, B; Dataset 6) that encompasses speB2, an ortholog of Synechocystis PCC 6803 sll1077, previously annotated as encoding an agmatinase [29, 43] and which was recently characterized as a guanidinase that degrades guanidine rather than agmatine to urea and ammonium [44]. E. coli produces guanidine under nutrient-poor conditions, suggesting that guanidine metabolism is biologically significant and potentially prevalent in natural environments [44, 45]. Furthermore, the ykkC riboswitch candidate, which was shown to specifically sense guanidine and to control the expression of a variety of genes involved in either guanidine metabolism or nitrate, sulfate, or bicarbonate transport, is located immediately upstream of this eCAG in Synechococcus reference genomes, all genes of this cluster being predicted by RegPrecise 3.0 to be regulated by this riboswitch (Fig. S6C; [45, 46]). The presence of hypA and B homologs within this eCAG furthermore suggests that, in the presence of guanidine, these homologs could be involved in the insertion of Ni2+, or another metal cofactor, in the active site of guanidinase. The next three genes of this eCAG, which encode an ABC transporter similar to the TauABC taurine transporter in E. coli (Fig. S6C), could be involved in guanidine transport in low-N areas. Of note, the presence in most Synechococcus/Cyanobium genomes possessing this eCAG of a gene encoding a putative Rieske Fe-sulfur protein (CK_00002251) downstream of this gene cluster, seems to constitute a specificity compared to the homologous gene cluster in Synechocystis sp. PCC 6803. The presence of this Fe-S protein suggests that Fe is used as a cofactor in this system and might explain why this gene cluster is absent from picocyanobacteria thriving in low-Fe areas, while it is present in a large proportion of the population in most other oceanic areas (Fig. S6A, B).Another example of the use of organic N forms concerns compounds containing a cyano radical (C ≡ N). The cyanate transporter genes (cynABD) were indeed found in a Prochlorococcus eCAG (Pro-eCAG_005, also including the conserved hypothetical gene CK_00055128; Fig. S7A, B). While only a small proportion of the Prochlorococcus community possesses this eCAG in warm, Fe-replete waters, it is absent from other oceanic areas in accordance with its low frequency in Prochlorococcus genomes (present in only two HLI and five HLII genomes). In Synechococcus these genes were not included in a module, and thus are not in an eCAG (Dataset 6; Fig. S7C), but seem widely distributed despite their presence in only a few Synechococcus genomes (mostly in clade III strains; [6, 47, 48]). Interestingly, we also uncovered a 7-gene eCAG (Pro-eCAG_006 and Syn-eCAG_002), encompassing a putative nitrilase gene (nitC), which also suggests that most Synechococcus cells and a more variable fraction of the Prochlorococcus population could use nitriles or cyanides in warm, Fe-replete waters and more particularly in low-N areas such as the Indian Ocean (Fig. 5A, B). The whole operon (nitHBCDEFG; Fig. 5C), called Nit1C, was shown to be upregulated in the presence of cyanide and to trigger an increase in the rate of ammonia accumulation in the heterotrophic bacterium Pseudomonas fluorescens [49], suggesting that like cyanate, cyanide could constitute an alternative nitrogen source in marine picocyanobacteria as well. However, given the potential toxicity of these C ≡ N-containing compounds [50], we cannot exclude that these eCAGs could also be devoted to cell detoxification [45, 47]. Such an example of detoxification has been described for arsenate and chromate that, as analogs of phosphate and sulfate respectively, are toxic to marine phytoplankton and must be actively exported out of the cells [51, 52].Fig. 5: Global distribution map of the eCAG involved in nitrile or cyanide transport and assimilation.A Prochlorococcus Pro-eCAG_006. B Synechococcus Syn-eCAG_002. C The genomic region in Prochlorococcus marinus MIT9301. The size of the circle is proportional to relative abundance of each genus as estimated based on the single-copy core gene petB and this gene was also used to estimate the relative abundance of other genes in the population. Black dots represent Tara Oceans stations for which Prochlorococcus or Synechococcus read abundance was too low to reach the threshold limit.Full size imageWe detected the presence of an eCAG encompassing asnB, pyrB2, and pydC (Pro-eCAG_007, Syn-eCAG_003, Fig. S8), which could contribute to an alternative pyrimidine biosynthesis pathway and thus provide another way for cells to recycle complex nitrogen forms. While this eCAG is found in only one fifth of HLII genomes and in quite specific locations for Prochlorococcus, notably in the Red Sea, it is found in most Synechococcus cells in warm, Fe-replete, N and P-depleted niches, consistent with its phyletic pattern showing its absence only from most clade I, IV, CRD1, and EnvB genomes (Fig. S8; Dataset 6). More generally, most N-uptake and assimilation genes in both genera were specifically absent from Fe-depleted areas, including the nirA/narB eCAG for Prochlorococcus, as mentioned by Kent et al. [36] as well as guanidinase and nitrilase eCAGs. In contrast, picocyanobacterial populations present in low-Fe areas possess, in addition to the core ammonium transporter amt1, a second transporter amt2, also present in cold areas for Synechococcus (Fig. S9). Additionally, Prochlorococcus populations thriving in HNLC areas also possess two amino acid-related eCAGs that are present in most Synechococcus genomes, the first one involved in polar amino acid N-II transport (Pro-eCAG_008; natF-G-H-bgtA; [53]; Fig. S10A, B) and the second one (leuDH-soxA-CK_00001744, Pro-eCAG_009, Fig. S10C, D) that notably encompasses a leucine dehydrogenase, able to produce ammonium from branched-chain amino acids. This highlights the profound difference in N acquisition mechanisms between HNLC regions and Fe-replete, N-deprived areas: the primary nitrogen sources for picocyanobacterial populations dwelling in HNLC areas seem to be ammonium and amino acids, while N acquisition mechanisms are more diverse in N-limited, Fe-replete regions.eCAGs related to phosphorus metabolismAdaptation to P depletion has been well documented in marine picocyanobacteria showing that while in P-replete waters Prochlorococcus and Synechococcus essentially rely on inorganic phosphate acquired by core transporters (PstSABC), strains isolated from low-P regions and natural populations thriving in these areas additionally contain a number of accessory genes related to P metabolism, located in specific genomic islands [6, 14, 30,31,32, 54]. Here, we indeed found in Prochlorococcus an eCAG containing the phoBR operon (Pro-eCAG_010) that encodes a two-component system response regulator, as well as an eCAG including the alkaline phosphatase phoA (Pro-eCAG_011), both present in virtually the whole Prochlorococcus population from the Mediterranean Sea, the Gulf of Mexico and the Western North Atlantic Ocean, which are known to be P-limited [30, 55] (Fig. S11A, B). By comparison, in Synechococcus, we only identified the phoBR eCAG (Syn-eCAG_005, Fig. S11C) that is systematically present in warm waters whatever the limiting nutrient, in agreement with its phyletic pattern in reference genomes showing its specific absence from cold thermotypes (clades I and IV, Dataset 6). Furthermore, although our analysis did not retrieve them within eCAGs due to the variability of gene content and synteny in this genomic region, even within each genus, several other P-related genes were enriched in low-P areas but partially differed between Prochlorococcus and Synechococcus (Figs. 3, S2, S11; Dataset 6). While the genes putatively encoding a chromate transporter (ChrA) and an arsenate efflux pump ArsB were present in both genera in different proportions, a putative transcriptional phosphate regulator related to PtrA (CK_00056804; [56]) was specific to Prochlorococcus. Synechococcus in contrast harbors a large variety of alkaline phosphatases (PhoX, CK_00005263 and CK_00040198) as well as the phosphate transporter SphX (Fig. S11).Phosphonates, i.e. reduced organophosphorus compounds containing C–P bonds that represent up to 25% of the high-molecular-weight dissolved organic P pool in the open ocean, constitute an alternative P form for marine picocyanobacteria [57]. We indeed identified, in addition to the core phosphonate ABC transporter (phnD1-C1-E1), a second previously unreported putative phosphonate transporter phnC2-D2-E2-E3 (Pro-eCAG_012; Fig. 6A). Most of the Prochlorococcus population in strongly P-limited areas of the ocean harbored these genes, while they were absent from other areas, consistent with their presence in only a few Prochlorococcus and no Synechococcus genomes. Furthermore, as previously described [58,59,60], we found a Prochlorococcus eCAG encompassing the phnYZ operon involved in C-P bond cleavage, the putative phosphite dehydrogenase ptxD, and the phosphite and methylphosphonate transporter ptxABC (Pro-eCAG_0013, Dataset 6; Fig. 6B, [60,61,62]). Compared to these previous studies that mainly reported the presence of these genes in Prochlorococcus cells from the North Atlantic Ocean, here we show that they actually occur in a much larger geographic area, including the Mediterranean Sea, the Gulf of Mexico, and the ALOHA station (TARA_132) in the North Pacific, even though they were present in a fairly low fraction of Prochlorococcus cells. These genes occurred in an even larger proportion of the Synechococcus population, although not found in an eCAG for this genus (Fig. S12; Dataset 6). Synechococcus cells from the Mediterranean Sea, a P-limited area dominated by clade III [24], seem to lack phnYZ, in agreement with the phyletic pattern of these genes in reference genomes, showing the absence of this two-gene operon in the sole clade III strain that possesses the ptxABDC gene cluster. In contrast, the presence of the complete gene set (ptxABDC-phnYZ) in the North Atlantic, at the entrance of the Mediterranean Sea, and in several clade II reference genomes rather suggests that it is primarily attributable to this clade. Altogether, our data indicate that part of the natural populations of both Prochlorococcus and Synechococcus would be able to assimilate phosphonate and phosphite as alternative P-sources in low-P areas using the ptxABDC-phnYZ operon. Yet, the fact that no picocyanobacterial genome except P. marinus RS01 (Fig. 6C) possesses both phnC2-D2-E2-E3 and phnYZ, suggests that the phosphonate taken up by the phnC2-D2-E2-E3 transporter could be incorporated into cell surface phosphonoglycoproteins that may act to mitigate cell mortality by grazing and viral lysis, as recently suggested [63].Fig. 6: Global distribution map of eCAGs putatively involved in phosphonate and phosphite transport and assimilation.A Prochlorococcus Pro-eCAG_012 putatively involved in phosphonate transport. B Prochlorococcus Pro-eCAG_013, involved in phosphonate/phosphite uptake and assimilation and phosphonate C-P bond cleavage. C The genomic region encompassing both phnC2-D2-E2-E3 and ptxABDC-phnYZ specific to P. marinus RS01. The size of the circle is proportional to relative abundance of Prochlorococcus as estimated based on the single-copy core gene petB and this gene was also used to estimate the relative abundance of other genes in the population. Black dots represent Tara Oceans stations for which Prochlorococcus read abundance was too low to reach the threshold limit.Full size imageeCAGs related to iron metabolismAs for macronutrients, it has been hypothesized that the survival of marine picocyanobacteria in low-Fe regions was made possible through several strategies, including the loss of genes encoding proteins that contain Fe as a cofactor, the replacement of Fe by another metal cofactor, and the acquisition of genes involved in Fe uptake and storage [14, 15, 36, 39, 64]. Accordingly, several eCAGs encompassing genes encoding proteins interacting with Fe were found in modules anti-correlated to HNLC regions in both genera. These include three subunits of the (photo)respiratory complex succinate dehydrogenase (SdhABC, Pro-eCAG_014, Syn-eCAG_006, Fig. S13; [65]) and Fe-containing proteins encoded in most abovementioned eCAGs involved in N or P metabolism, such as the guanidinase (Fig. S6), the NitC1 (Fig. 5), the pyrB2 (Fig. S8), the phosphonate (Fig. 6, S12), and the urea and inorganic nitrogen eCAGs (Fig. S5). Most Synechococcus cells thriving in Fe-replete areas also possess the sodT/sodX eCAG (Syn-eCAG_007, Fig. S14A, B) involved in nickel transport and maturation of the Ni-superoxide dismutase (SodN), these three genes being in contrast core in Prochlorococcus. Additionally, Synechococcus from Fe-replete areas, notably from the Mediterranean Sea and the Indian Ocean, specifically possess two eCAGs (Syn-eCAG_008 and 009; Fig. S14C, D), involved in the biosynthesis of a polysaccharide capsule that appear to be most similar to the E. coli groups 2 and 3 kps loci [66]. These extracellular structures, known to provide protection against biotic or abiotic stress, were recently shown in Klebsiella to provide a clear fitness advantage in nutrient-poor conditions since they were associated with increased growth rates and population yields [67]. However, while these authors suggested that capsules may play a role in Fe uptake, the significant reduction in the relative abundance of kps genes in low-Fe compared to Fe-replete areas (t-test p-value  More

Screening for viral AMGsViral protein annotation using VIBRANT and DRAM-v software combined with manual proofreading identified the viral AMGs in Napahai plateau wetland, including the viral AMGs phoH, phoU and pstS, which were associated with phosphorus metabolism.Phylogenetic analysis of AMGs associated with phosphorus metabolism in Napahai plateau wetlandThere were 24 amino acid sequences of phoH gene in Napahai plateau wetland (Fig. 1A). They were divided into 5 clusters, the largest of which had 10 sequences, while the smallest cluster had only 1 sequence. The remaining 3 clusters contained 6, 5 and 2 sequences, respectively. The phoH gene was genetically diverse in Napahai plateau wetland, which might be related to the different host origins. A total of 74 sequences of phoU gene could be found in seven clusters (Fig. 1B), with the largest cluster containing 27 sequences and the smallest cluster having two sequences. Similar to phoH, phoU was also genetically diverse, but richer than that of phoH. There were 71 pstS sequences forming 9 clusters, with the largest cluster of 23 sequences and the smallest cluster only 1 sequence (Fig. 1C). It could be seen that the genetic diversity of pstS was better than that of phoH and phoU, which might be related to the unique geographical location. Napahai plateau wetland is located in the Three Parallel Rivers of Yunnan protected areas, which forms a complex landscape, and then controls the evolution and characteristics of organisms, thus showing abundant biodiversity. Li et al. obtained 58 phoH gene sequences from Northeastern wetland sediments of China, which were 22%–99% consistent at the amino acid level, and found that the phoH gene could regulate phosphate uptake and metabolism under the low phosphate or phosphate limitation conditions16. However, the exact function remained unclear. The phoH gene clustered into five clusters in Napahai plateau wetland, indicating high genetic diversity. Additionally, water and soil samples were collected from eight separate sampling sites, and there were differences between samples environments, which might also have an impact on the genetic diversity of the three genes.Figure 1Phylogenetic analysis of phosphorus metabolism AMGs in Napahai plateau wetland, different colors represent different branches. (A) Phylogenetic analysis of phoH genes. (B) Phylogenetic analysis of phoU genes. (C) Phylogenetic analysis of pstS genes.Full size imagePhylogenetic analysis and PCoA analysis of AMGs associated with phosphorus metabolism from different habitats and host originsIn order to understand the genetic diversity of viral AMGs (phoH, phoU, pstS) associated with phosphorus metabolism in Napahai plateau wetland, a phylogenetic tree of phosphorus metabolism AMGs from different habitats was constructed, and PCoA analysis was performed (Fig. 2). The results showed that most sequences of phoH, phoU and pstS genes in Napahai plateau wetland clustered individually, especially phoU and pstS genes, and only a few sequences were closely related to those of other habitats. In Fig. 2A, 14 sequences clustered individually and were relatively far from sequences of other habitats, whereas 7 sequences were close to those from freshwater lakes, and other 3 sequences were close to those from rice fields, oceans and other wetlands, respectively. Therefore, the genetic diversity of phoH in Napahai plateau wetland was independent of the habitat. Moreover, some of the phoH sequences were clustered with those of other habitats and distributed in the fourth quadrants (Fig. 2D). From Fig. 2B, apart from 3 sequences which clustered with those from the marine habitats and freshwater lakes, the rest were clustered separately. Whereas in Fig. 2E, apart from only a few sequences, most sequences of phoU were far away from those of different habitats, which was consistent with Fig. 2B. Thus, the genetic diversity of phoU gene in Napahai wetland was also independent of habitat, where the separately clustered sequences may be unique. From Fig. 2C, we can seen that apart from 8 sequences which more closely related to those from the freshwater lake, ocean, rice field, and other wetlands, all the rest were individually clustered. The result was consistent with that of Fig. 2F. Therefore, the genetic diversity of the pstS gene was also habitat-independent.Figure 2Phylogenetic analysis and PCoA of phosphorus metabolism AMGs in different habitats, different colors represent different habitats. (A) Phylogenetic analysis of phoH genes in different habitats. (B) Phylogenetic analysis of phoU genes in different habitats. (C) Phylogenetic analysis of pstS genes in different habitats. (D) PCoA analysis of phoH genes in different habitats. (E) PCoA analysis of phoU genes in different habitats. (F) PCoA analysis of pstS genes in different habitats.Full size imageTo study whether the genetic diversity was related to host origins, three AMGs associated with phosphorus metabolism were selected for phylogenetic and PCoA analyses with AMGs sequences from different host origins (Fig. 3). It showed that some sequences of all three genes were similar to those from different host origins, while the remaining were separately clustered. In Fig. 3A, apart from 14 sequences which clustered with those from fungi, bacteria, non-culturable phages, phages and viruses, all the rest were clustered separately. Whereas, most sequences were clustered with those from different host origins together, and only six sequences were far from other sequences of different host origins based on PCoA analysis (Fig. 3D). Only three sequences were clustered with those of archaea and uncultured archaea, and the rest were clustered together to form independent clusters (Fig. 3B). A small amount of sequences were gathered with bacteria, uncultured bacteria, archaea and uncultured archaea, and the rest were clustered individually (Fig. 3E). As can be seen in Fig. 3C, six sequences were clustered with those of archaea, fungi, bacteria, while the rest were clustered separately. Some sequences were gathered with bacteria, uncultured bacteria, archaea and uncultured archaea, and others were clustered separately (Fig. 3F). PCoA analysis was largely consistent with phylogenetic analysis. So the genetic diversity of phoH, phoU and pstS genes in Napahai plateau wetland was independent of the host origins.Figure 3Phylogenetic analysis and PCoA of phosphorus metabolism AMGs from different host origins, different colors represent different host origins. (A) Phylogenetic analysis of phoH gene from different host origins. (B) Phylogenetic analysis of phoU gene from different host origins. (C) Phylogenetic analysis of pstS gene from different host origins. (D) PCoA analysis of phoH genes from different host origins. (E) PCoA analysis of phoU genes from different host origins. (F) PCoA analysis of pstS genes from different host origins.Full size imageOverall, the genetic diversity of phoH, phoU and pstS genes associated with phosphorus metabolism in Napahai plateau wetland was independent of both the habitats and host origins based on phylogenetic and PCoA analyses. It suggested that three genes showed relatively rich genetic diversity and were not genetically limited by differences in habitats or host origins. Han et al. showed that phoH sequences were widely distributed in soil, freshwater, and seawater environments in different locations around the world, indicating the genetic diversity independent of the environment17, which corroborated the conclusions in our study. Phylogenetic analysis of the 58 viral phoH gene sequences in Northeastern wetland of China revealed that some sequences were clustered with bacterial sequences and others clustered with phages sequences16. In Napahai plateau wetland, some phoH gene sequences were clustered with fungal, bacterial, phage, uncultured phage, and viruses. Hence, the genetic diversity of phoH gene was independent of the host origins in either Northeastern wetland or Napahai plateau wetland. Compared with Northeastern wetland, the phoH genes in Napahai plateau wetland showed more abundant genetic diversity, which may be related to geographical location and climate. Additionally, compared with sequences from different habitats and host sources, partial sequences from Napahai plateau wetland were clustered individually, thus they were unique, which might be related to the unique geography. Napahai plateau wetland is located in the Three Parallel Rivers with low latitude and high altitude, and shows specific characteristics which not found in other habitats, and then the species very different, thus providing the possibility for the emergence of unique genetic sequences. Of course, it would require further verification by subsequent study.As far as the current studies are concerned, most reports on phosphorus AMGs focused on the function. Wang et al. mentioned that the phoH gene regulated phosphate uptake or metabolism under the low phosphorus or phosphate limitation conditions18. Kelly et al. isolated several phages from oligotrophic water bodies with low phosphorus condition, found that they contained the phosphate binding transporter gene pstS by sequencing, which enhanced the host cell with increasing the infection cycle of phages by increasing phosphate utilization19. Gardner et al. studied the PhoR-PhoB two-component regulatory system in E. coli, which regulated the expression of relevant genes according to environmental phosphate concentration and enabled cells to adapt the phosphate starvation20. The phoU existed in many bacteria and was identified as an auxiliary protein of the phosphate-specific transporter system, regulating phosphate metabolism in the host cell acting as phosphate regulators21. Few studies had been conducted on its genetic diversity, therefore, the information on the genetic diversity was relatively scarce.α diversity analysis of phosphorus metabolism AMGs in different habitats and different host originsChao, Shannon and Simpson diversity indices are common mathematical measure of species alpha diversity in the community. Chao focuses on species richness. Shannon index and Simpson index measure species richness and evenness. Simpson reinforces evenness and Shannon reinforces richness22.Sequences from different habitats, such as Napahai plateau wetland, Pacific Ocean, Lake Baikal, Northeast rice fields, glaciers, and wetlands, were selected for α-diversity analysis (Fig. 4). The genetic diversity indices, such as Chao, Shannon and Simpson, calculated based on the OUT dataset, were used to characterize the alpha diversity. Among them, larger Chao values, smaller Simpson values or larger Shannon values indicate higher genetic diversity. Only at the level of Chao values (Fig. 4A,D,G) and Shannon values (Fig. 4B,E,H), the values of phoH, phoU, and pstS in Napahai plateau wetland were greater than those from other habitats, indicating better heritable, which might be related to the unique geographical location and abundant water resources. The geographical location made it unique and less influenced by external factors, and abundant water resources created a rich biodiversity, thus providing a good genetic environment. From the Simpson values (Fig. 4C,F,I), the values of phoU and pstS genes were smaller than those of other habitats, indicating better inherited. For the phoH gene, the Simpson value was closer in magnitude and lower than those in Antarctic Lake and wetlands, indicating better heritable.Figure 4Plots of genetic diversity indices analysis of phosphorus metabolism AMGs in different habitats, different colors represent different genetic diversity indices. (A, D, G) Represent respectively the Chao values of phoH, phoU, and pstS genes in different habitats. (B, E, H) Represent respectively the Shannon values of phoH, phoU, and pstS genes in different habitats. (C, F, I) Represent respectively the Simpson values of phoH, phoU, and pstS genes in different habitats.Full size imageThree AMGs associated with phosphorus metabolism in Napahai plateau wetland were selected for α-diversity analysis with AMGs sequences from different host origins (Fig. 5). In Fig. 5A, the Chao values of phoH gene from bacteria, phages, uncultured phages and uncultured viruses in Napahai plateau wetland were smaller than those of bacteria, phages, uncultured phages and uncultured viruses, indicating the poor genetic diversity. In addition, compared to the genetic diversity of sequences from other host sources, the genetic diversity of phoH gene from bacteria in Napahai plateau wetland was better. As can be seen in Fig. 5D, G, the Chao values of phoU and pstS genes from bacteria in Napahai plateau wetland were greater than those of other host origins, indicating better genetic diversity, while the Chao values of pstS genes from archaea in Napahai plateau wetland were smaller than those of other host origins, indicating poor genetic diversity.Figure 5Plots of genetic diversity indices analysis of phosphorus metabolism AMGs from different host origins, different colors represent different genetic diversity indices. (A, D, G) Represent respectively the Chao values of phoH, phoU, and pstS genes from different host origins. (B, E, H) Represent respectively the Shannon values of phoH, phoU, and pstS genes from different host origins. (C, F, I) Represent respectively the Simpson values of phoH, phoU, and pstS genes from different host origins.Full size imageThe Shannon value of phoH gene from bacteria in Napahai plateau wetland was smaller than that of bacteria and uncultured viruses, indicating poor diversity, but larger than other host sources, indicating better genetic diversity (Fig. 5B). The Shannon values of phoH gene from phages and uncultured phages in Napahai plateau wetland were lower than those of other host origins, indicating poor diversity. The Shannon value of phoH genes from uncultured viruses in Napahai plateau wetland was 0, probably due to sample size too small to calculate the Shannon value. In Fig. 5E, H, the Chao values of phoU and pstS genes from bacteria in Napahai plateaus wetland were greater than those from other host sources, indicating better diversity, while the Shannon value of pstS gene from archaea in the Napahai plateau wetland was 0, probably small sample size.The Simpson values of phoH genes from phage, uncultured phage and uncultured virus in Napahai plateau wetland were smaller than those of other host origins (except uncultured virus), indicating better diversity. The smaller Simpson values of phoH genes related to fungi, phages, uncultured phages, and viruses indicated better diversity, while the larger Simpson values compared to bacteria, phages, and uncultured viruses indicated poor diversity (Fig. 5C). As can be seen in Figs. 5F,I, the Simpson values of phoU genes from bacteria and pstS genes from bacteria and archaea in Napahai plateau wetland were smaller than those of other host origins, indicating better genetic diversity.Currently, most studies on phosphorus AMGs employed phylogenetic analysis16,23. In contrast, relatively few AMGs associated with phosphorus had been reported based on α-diversity analysis, so it was difficult to obtain specific values of α-diversity indices in other studies.Biogeochemical cycling of AMGs associated with phosphorus metabolism in Napahai plateau wetlandViruses are the gene carriers in susceptible hosts, and AMGs introduced by viruses into new hosts can enhance viral replication and/or influence key microbial metabolic pathways of the biogeochemical cycles24. It is well known that phosphorus is an essential nutrient and plays essential roles in cells25. Phosphorus deficiency leads to restricted cell division, down-regulation of photosynthesis, reduced protein and nitrogen content and chlorophyll synthesis26. To study the effect of AMGs associated with phosphorus metabolism, a phosphorus metabolic pathway containing phoH, phoU and pstS genes was constructed based on metagenomic data (Fig. 6). When phosphorus deficiency occurs in the host, it leads to the expression of phoH, phoU and pstS genes. phoH is a phosphate starvation inducible gene, while pstS acts as a phosphate transport gene and phoU belongs to a phosphate regulatory gene that produces dissolved inorganic phosphorus (DIPs), which then undergoes a series of reactions to produce ATP. The generated ATP becomes PolyP under the action of ppK which encoding polyphosphate kinase, or is used in Calvin cycle to provide energy for Ru5P to produce RuBP, or is used for DNA biosynthesis to provide energy. PolyP is regenerate into DIP with ppX which encoding exopolyphosphatase, and also involves in the biosynthesis process of DNA as Pi to provide phosphate for the nucleic acids synthesis. Thus, phosphorus metabolism of AMGs invoved plays a significant role in the life process of the virus and host. In addition, phoE and ugpQ genes also are identified in Napahai plateau wetland, but their roles in the phosphorus cycling are currently unknown and need further study.Figure 6Biogeochemical cycling of AMGs associated with phosphorus metabolism in Napahai plateau wetland. Red line indicates the process of phosphorus metabolism.Full size imageBased on the phylogenetic and PCoA analyses, we found that the phoH, phoU, and pstS genes all showed unique sequences, which might be drive the microorganisms to produce the phosphorus metabolic pathway in Napahai plateau wetland. Of course, in order to prove this pathway, further validation might be done by metabolomics or metabolic flow method. Furthermore, the phosphorus metabolic pathway was poorly reported, so we could not compare with the phosphorus pathway from other environment to find commonalities and differences. More

Céréghino, R., Biggs, J., Oertli, B. & Declerck, S. The ecology of European ponds: Defining the characteristics of a neglected freshwater habitat. In Pond Conservation in Europe (eds Oertli, B. et al.) 1–6 (Springer Netherlands, 2007).
Google Scholar 
Olmo, C. et al. The environmental framework of temporary ponds: A tropical-Mediterranean comparison. CATENA 210, 105845 (2022).CAS 

Google Scholar 
Griffiths, R. A. Temporary ponds as amphibian habitats. Aquat. Conserv. Mar. Freshw. Ecosyst. 7, 119–126 (1997).
Google Scholar 
Boix, D. et al. Conservation of temporary wetlands. In Encyclopedia of the World’s Biomes 279–294 (Elsevier, 2020). https://doi.org/10.1016/B978-0-12-409548-9.12003-2.Chapter 

Google Scholar 
Fritz, K. A. & Whiles, M. R. Reciprocal subsidies between temporary ponds and riparian forests. Limnol. Oceanogr. 66, 3149–3161 (2021).ADS 

Google Scholar 
Jeffries, M. The spatial and temporal heterogeneity of macrophyte communities in thirty small, temporary ponds over a period of ten years. Ecography 31, 765–775 (2008).
Google Scholar 
Hassall, C. The ecology and biodiversity of urban ponds. WIREs Water 1, 187–206 (2014).
Google Scholar 
Lukács, B. A. et al. Macrophyte diversity of lakes in the Pannon Ecoregion (Hungary). Limnologica 53, 74–83 (2015).
Google Scholar 
Florencio, M., Díaz-Paniagua, C., Gómez-Rodríguez, C. & Serrano, L. Biodiversity patterns in a macroinvertebrate community of a temporary pond network. Insect Conserv. Divers. 7, 4–21 (2014).
Google Scholar 
Meland, S., Sun, Z., Sokolova, E., Rauch, S. & Brittain, J. E. A comparative study of macroinvertebrate biodiversity in highway stormwater ponds and natural ponds. Sci. Total Environ. 740, 140029 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Hahn, M. W. The microbial diversity of inland waters. Curr. Opin. Biotechnol. 17, 256–261 (2006).CAS 
PubMed 

Google Scholar 
Felföldi, T. Microbial communities of soda lakes and pans in the Carpathian Basin: A review. Biol. Futura 71, 393–404 (2020).
Google Scholar 
Grossart, H., Massana, R., McMahon, K. D. & Walsh, D. A. Linking metagenomics to aquatic microbial ecology and biogeochemical cycles. Limnol. Oceanogr. 65, S2–S20 (2020).CAS 

Google Scholar 
Marrone, F., Fontaneto, D. & Naselli-Flores, L. Cryptic diversity, niche displacement and our poor understanding of taxonomy and ecology of aquatic microorganisms. Hydrobiologia https://doi.org/10.1007/s10750-022-04904-x (2022).Article 

Google Scholar 
Ducklow, H. Microbial services: Challenges for microbial ecologists in a changing world. Aquat. Microb. Ecol. 53, 13–19 (2008).ADS 

Google Scholar 
Bodelier, P. L. E. Toward understanding, managing, and protecting microbial ecosystems. Front. Microbiol. 2, 80 (2011).PubMed 
PubMed Central 

Google Scholar 
Bell, T., Newman, J. A., Silverman, B. W., Turner, S. L. & Lilley, A. K. The contribution of species richness and composition to bacterial services. Nature 436, 1157–1160 (2005).ADS 
CAS 
PubMed 

Google Scholar 
Trivedi, C. et al. Losses in microbial functional diversity reduce the rate of key soil processes. Soil Biol. Biochem. 135, 267–274 (2019).CAS 

Google Scholar 
Wellborn, G. A., Skelly, D. K. & Werner, E. E. Mechanisms creating community structure across freshwater habitat gradient. Annu. Rev. Ecol. Syst. 27, 337–363 (1996).
Google Scholar 
Chase, J. M. Drought mediates the importance of stochastic community assembly. Proc. Natl. Acad. Sci. 104, 17430–17434 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tweed, S., Grace, M., Leblanc, M., Cartwright, I. & Smithyman, D. The individual response of saline lakes to a severe drought. Sci. Total Environ. 409, 3919–3933 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Aguilar, P., Acosta, E., Dorador, C. & Sommaruga, R. Large differences in bacterial community composition among three nearby extreme waterbodies of the High Andean Plateau. Front. Microbiol. 7, 976 (2016).PubMed 
PubMed Central 

Google Scholar 
Boros, E., Balogh, K., Vörös, L. & Horváth, Z. Multiple extreme environmental conditions of intermittent soda pans in the Carpathian Basin (Central Europe). Limnologica 62, 38–46 (2017).CAS 
PubMed 

Google Scholar 
Lengyel, E., Pálmai, T., Padisák, J. & Stenger-Kovács, C. Annual hydrological cycle of environmental variables in astatic soda pans (Hungary). J. Hydrol. 575, 1188–1199 (2019).ADS 
CAS 

Google Scholar 
Vieira-Silva, S. & Rocha, E. P. C. The Systemic imprint of growth and its uses in ecological (Meta)genomics. PLoS Genet. 6, e1000808 (2010).PubMed 
PubMed Central 

Google Scholar 
Cunillera-Montcusí, D. et al. Freshwater salinisation: A research agenda for a saltier world. Trends Ecol. Evol. 37, 440–453 (2022).PubMed 

Google Scholar 
Šolić, M. et al. Structure of microbial communities in phosphorus-limited estuaries along the eastern Adriatic coast. J. Mar. Biol. Assoc. U.K. 95, 1565–1578 (2015).
Google Scholar 
Traving, S. J. et al. The Effect of increased loads of dissolved organic matter on estuarine microbial community composition and function. Front. Microbiol. 8, 351 (2017).PubMed 
PubMed Central 

Google Scholar 
Zhang, G. et al. Salinity controls soil microbial community structure and function in coastal estuarine wetlands. Environ. Microbiol. 23, 1020–1037 (2021).CAS 
PubMed 

Google Scholar 
Tkavc, R. et al. Bacterial communities in the ‘petola’ microbial mat from the Sečovlje salterns (Slovenia): Bacterial communities in the ‘petola’. FEMS Microbiol. Ecol. 75, 48–62 (2011).CAS 
PubMed 

Google Scholar 
Ali, I. et al. Comparative study of physical factors and microbial diversity of four man-made extreme ecosystems. Proc. Natl. Acad. Sci. India Sect. B Biol. Sci. 86, 767–778 (2016).
Google Scholar 
Paul, V., Banerjee, Y., Ghosh, P. & Busi, S. B. Depthwise microbiome and isotopic profiling of a moderately saline microbial mat in a solar saltern. Sci. Rep. 10, 20686 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Stenger-Kovács, C. et al. Vanishing world: Alkaline, saline lakes in Central Europe and their diatom assemblages. Inland Waters 4, 383–396 (2014).
Google Scholar 
Stenger-Kovács, C., Hajnal, É., Lengyel, E., Buczkó, K. & Padisák, J. A test of traditional diversity measures and taxonomic distinctness indices on benthic diatoms of soda pans in the Carpathian basin. Ecol. Indic. 64, 1–8 (2016).
Google Scholar 
Szabó, B., Lengyel, E., Padisák, J., Vass, M. & Stenger-Kovács, C. Structuring forces and β-diversity of benthic diatom metacommunities in soda pans of the Carpathian Basin. Eur. J. Phycol. 53, 219–229 (2018).
Google Scholar 
Szabó, A. et al. Soda pans of the Pannonian steppe harbor unique bacterial communities adapted to multiple extreme conditions. Extremophiles 21, 639–649 (2017).PubMed 

Google Scholar 
Szabó, A. et al. Grazing pressure-induced shift in planktonic bacterial communities with the dominance of acIII-A1 actinobacterial lineage in soda pans. Sci. Rep. 10, 19871 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Benlloch, S. et al. Prokaryotic genetic diversity throughout the salinity gradient of a coastal solar saltern. Environ. Microbiol. 4, 349–360 (2002).PubMed 

Google Scholar 
Horváth, Z. et al. Opposing patterns of zooplankton diversity and functioning along a natural stress gradient: When the going gets tough, the tough get going. Oikos 123, 461–471 (2014).
Google Scholar 
Mo, Y. et al. Low shifts in salinity determined assembly processes and network stability of microeukaryotic plankton communities in a subtropical urban reservoir. Microbiome 9, 128 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Gómez-Rodríguez, C., Bustamante, J. & Díaz-Paniagua, C. Evidence of hydroperiod shortening in a preserved system of temporary ponds. Remote Sens. 2, 1439–1462 (2010).ADS 

Google Scholar 
Finger Higgens, R. A. et al. Changing lake dynamics indicate a drier arctic in western greenland. J. Geophys. Res. Biogeosciences 124, 870–883 (2019).ADS 

Google Scholar 
Zacharias, I. & Zamparas, M. Mediterranean temporary ponds. A disappearing ecosystem. Biodivers. Conserv. 19, 3827–3834 (2010).
Google Scholar 
Horváth, Z., Ptacnik, R., Vad, C. F. & Chase, J. M. Habitat loss over six decades accelerates regional and local biodiversity loss via changing landscape connectance. Ecol. Lett. 22, 1019–1027 (2019).PubMed 
PubMed Central 

Google Scholar 
Grillas, P., Rhazi, L., Lefebvre, G., El Madihi, M. & Poulin, B. Foreseen impact of climate change on temporary ponds located along a latitudinal gradient in Morocco. Inland Waters 11, 492–507 (2021).CAS 

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

Google Scholar 
Zhong, Y. et al. Shrinking habitats and native species loss under climate change: a multifactorial risk Assessment of China’s inland wetlands. 28 (2022).Atkinson, S. T. et al. Substantial long-term loss of alpha and gamma diversity of lake invertebrates in a landscape exposed to a drying climate. Glob. Change Biol. 27, 6263–6279 (2021).CAS 

Google Scholar 
Whiting, G. J. & Chanton, J. P. Greenhouse carbon balance of wetlands: Methane emission versus carbon sequestration: Greenhouse carbon balance of wetlands. Tellus B 53, 521–528 (2001).ADS 

Google Scholar 
Mitsch, W. J. et al. Wetlands, carbon, and climate change. Landsc. Ecol. 28, 583–597 (2013).
Google Scholar 
Ardón, M., Helton, A. M. & Bernhardt, E. S. Salinity effects on greenhouse gas emissions from wetland soils are contingent upon hydrologic setting: A microcosm experiment. Biogeochemistry 140, 217–232 (2018).
Google Scholar 
Jeppesen, E., Beklioğlu, M., Özkan, K. & Akyürek, Z. Salinization increase due to climate change will have substantial negative effects on inland waters: A call for multifaceted research at the local and global scale. Innovation 1, 100030 (2020).PubMed 
PubMed Central 

Google Scholar 
Boros, E., Horváth, Z., Wolfram, G. & Vörös, L. Salinity and ionic composition of the shallow astatic soda pans in the Carpathian Basin. Ann. Limnol. Int. J. Limnol. 50, 59–69 (2014).
Google Scholar 
Sorokin, D. Y. et al. Microbial diversity and biogeochemical cycling in soda lakes. Extremophiles 18, 791–809 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Horváth, Z., Vad, C. F., Vörös, L. & Boros, E. The keystone role of anostracans and copepods in European soda pans during the spring migration of waterbirds: The keystone trophic role of crustaceans in European soda pans. Freshw. Biol. 58, 430–440 (2013).
Google Scholar 
Stenger-Kovács, C. & Lengyel, E. Taxonomical and distribution guide of diatoms in soda pans of Central Europe. Stud. Bot. Hung. 46, 3–203 (2015).
Google Scholar 
Szabó, B. et al. Microbial stowaways: Waterbirds as dispersal vectors of aquatic pro- and microeukaryotic communities. J. Biogeogr. 49, 1286–1298 (2022).
Google Scholar 
Williams, D. D. The Ecology of Temporary Waters (Springer Netherlands, 1987).
Google Scholar 
Hammer, U. T. The effects of climate change on the salinity, water levels and biota of Canadian prairie saline lakes. SIL Proc. 1922–2010(24), 321–326 (1990).
Google Scholar 
Schallenberg, M., Hall, C. & Burns, C. Consequences of climate-induced salinity increases on zooplankton abundance and diversity in coastal lakes. Mar. Ecol. Prog. Ser. 251, 181–189 (2003).ADS 

Google Scholar 
Felföldi, T., Somogyi, B., Márialigeti, K. & Vörös, L. Characterization of photoautotrophic picoplankton assemblages in turbid, alkaline lakes of the Carpathian Basin (Central Europe). J. Limnol. 68, 385 (2009).
Google Scholar 
Somogyi, B. et al. Winter bloom of picoeukaryotes in Hungarian shallow turbid soda pans and the role of light and temperature. Aquat. Ecol. 43, 735–744 (2009).CAS 

Google Scholar 
Pálffy, K. et al. Unique picoeukaryotic algal community under multiple environmental stress conditions in a shallow, alkaline pan. Extremophiles 18, 111–119 (2014).PubMed 

Google Scholar 
Padisák, J. & Naselli-Flores, L. Phytoplankton in extreme environments: Importance and consequences of habitat permanency. Hydrobiologia 848, 157–176 (2021).
Google Scholar 
Olli, K., Ptacnik, R., Klais, R. & Tamminen, T. Phytoplankton species richness along coastal and estuarine salinity continua. Am. Nat. 194, E41–E51 (2019).PubMed 

Google Scholar 
Olli, K., Tamminen, T. & Ptacnik, R. Predictable shifts in diversity and ecosystem function in phytoplankton communities along coastal salinity continua. Limnol. Oceanogr. Lett. https://doi.org/10.1002/lol2.10242 (2022).Article 

Google Scholar 
Tikhonenkov, D. V., Burkovsky, I. V. & Mazei, Y. A. Is there a relation between the distribution of heterotrophic flagellates and the zonation of a marine intertidal flat?. Oceanology 55, 13 (2015).
Google Scholar 
Arndt, H. et al. Functional diversity of heterotrophic flagellates in aquatic ecosystems. In Flagellates 252–280 (CRC Press, 2000). https://doi.org/10.1201/9781482268225-18.Chapter 

Google Scholar 
JeLee, W. & Patterson, D. J. Diversity and geographic distribution of free-living heterotrophic flagellates—Analysis by PRIMER. Protist 149, 229–244 (1998).CAS 

Google Scholar 
Azovsky, A. I., Tikhonenkov, D. V. & Mazei, Y. A. An estimation of the global diversity and distribution of the smallest eukaryotes: Biogeography of marine benthic heterotrophic flagellates. Protist 167, 411–424 (2016).PubMed 

Google Scholar 
Tikhonenkov, D. V., Mazei, Y. A. & Mylnikov, A. P. Species diversity of heterotrophic flagellates in White Sea littoral sites. Eur. J. Protistol. 42, 191–200 (2006).PubMed 

Google Scholar 
Van der Gucht, K. et al. The power of species sorting: Local factors drive bacterial community composition over a wide range of spatial scales. Proc. Natl. Acad. Sci. 104, 20404–20409 (2007).PubMed 
PubMed Central 

Google Scholar 
Vanschoenwinkel, B. et al. Species sorting in space and time—The impact of disturbance regime on community assembly in a temporary pool metacommunity. J. North Am. Benthol. Soc. 29, 1267–1278 (2010).
Google Scholar 
Datry, T. et al. Metacommunity patterns across three neotropical catchments with varying environmental harshness. Freshw. Biol. 61, 277–292 (2016).
Google Scholar 
Hansen, H. P. & Koroleff, F. Determination of nutrients. In Methods of Seawater Analysis (eds Grasshoff, K. et al.) 159–228 (Wiley-VCH Verlag GmbH, 1999).
Google Scholar 
Clesceri, L. S., Greenberg, A. E. & Eaton, A. D. Standard methods for examination of water and wastewater. 20th ed. http://ipkosar.ir/jspui/handle/961944/280820 (1999).Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples: Primers for marine microbiome studies. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
PubMed 

Google Scholar 
Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).
Google Scholar 
Ray, J. L. et al. Metabarcoding and metabolome analyses of copepod grazing reveal feeding preference and linkage to metabolite classes in dynamic microbial plankton communities. Mol. Ecol. 25, 5585–5602 (2016).CAS 
PubMed 

Google Scholar 
Hadziavdic, K. et al. Characterization of the 18S rRNA gene for designing universal eukaryote specific primers. PLoS One 9, e87624 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence sata on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kunin, V., Engelbrektson, A., Ochman, H. & Hugenholtz, P. Wrinkles in the rare biosphere: Pyrosequencing errors can lead to artificial inflation of diversity estimates. Environ. Microbiol. 12, 118–123 (2010).CAS 
PubMed 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).PubMed 
PubMed Central 

Google Scholar 
Rohwer, R. R., Hamilton, J. J., Newton, R. J. & McMahon, K. D. TaxAss: Leveraging a custom freshwater database achieves fine-scale taxonomic resolution. mSphere 3, e00327-18 (2018).PubMed 
PubMed Central 

Google Scholar 
Guillou, L. et al. The Protist Ribosomal Reference database (PR2): A catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41, D597–D604 (2012).PubMed 
PubMed Central 

Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7. https://CRAN.R-project.org/package=vegan (2020).Martinez Arbizu, P. pairwiseAdonis: Pairwise multilevel comparison using Adonis. Pairwise Adonis R package version 0.4. R package. https://cran.r-project.org/web/packages/pairwise/index.html (2017).Kassambara, A. ggpubr: ‘ggplot2’ based publication ready plots. ggpubr R package version 0.4.0. https://CRAN.R-project.org/package=ggpubr (2020).Burian, A. et al. Predation increases multiple components of microbial diversity in activated sludge communities. ISME J. 16, 1086–1094 (2022).CAS 
PubMed 

Google Scholar 
Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology, picante R package version 1.8.2. Bioinformatics 26, 1463–1464. https://cran.r-project.org/web/packages/picante/index.html (2010).Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S. 4th ed. MASS R package version 7.3-54 (Springer, 2002). https://cran.r-project.org/web/packages/MASS/index.html. ISBN 0-387-95457-0.Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. mgcv R package version 1.8-38. J. R. Stat. Soc. B 73(1), 3–36. https://cran.r-project.org/web/packages/mgcv/index.html (2011).Gu, Z. Complex heatmap visualization. iMeta 1 (2022).R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2022). https://www.R-project.org/. More

Bindoff, N. L. et al. Changing ocean, marine ecosystems, and dependent communities. IPCC Special Report on the Ocean Cryosphere in a Changing Climate 477–587 (2019).Atkinson, A., Siegel, V., Pakhomov, E. & Rothery, P. Long-term decline in krill stock and increase in salps within the Southern Ocean. Nature 432, 100–103 (2004).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Chang. 9, 142–147 (2019).Article 
ADS 

Google Scholar 
Lowther, A. D., Staniland, I., Lydersen, C. & Kovacs, K. M. Male Antarctic fur seals: Neglected food competitors of bioindicator species in the context of an increasing Antarctic krill fishery. Sci. Rep. 10, 1–12 (2020).Article 

Google Scholar 
Barbosa, A., Benzal, J., de León, A. & Moreno, J. Population decline of chinstrap penguins (Pygoscelis antarctica) on Deception Island, South Shetlands, Antarctica. Polar Biol. 35, 1453–1457 (2012).Article 

Google Scholar 
Forcada, J., Trathan, P. N., Reid, K. & Murphy, E. J. The effects of global climate variability in pop production of Antarctic fur seals. Ecology 86, 2408–2417 (2005).Article 

Google Scholar 
Forcada, J. & Trathan, P. N. Penguin responses to climate change in the Southern Ocean. Glob. Change Biol. https://doi.org/10.1111/j.1365-2486.2009.01909.x (2009).Article 

Google Scholar 
Fraser, W. & Hofmann, E. A predator’s perspective on causal links between climate change, physical forcing and ecosystem response. Mar. Ecol. Prog. Ser. 265, 1–15 (2003).Article 
ADS 

Google Scholar 
Tulloch, V. J. D., Plagányi, É. E., Brown, C., Richardson, A. J. & Matear, R. Future recovery of baleen whales is imperiled by climate change. Glob. Change Biol. 25, 1263–1281 (2019).Article 
ADS 

Google Scholar 
Gosler, A. G. Environmental and social determinants of winter fat storage in the great tit Parus major. J. Anim. Ecol. 65, 1–17 (1996).Article 

Google Scholar 
Green, A. J. Mass/Length residuals: Measures of body condition or generators of spurious results?. Ecology 13, 1473–1483 (2001).Article 

Google Scholar 
Schulte-Hostedde, A. I., Zinner, B., Millar, J. S. & Hickling, G. J. Restitution of mass-size residuals: Validating body condition indices. Ecology 86, 155–163 (2005).Article 

Google Scholar 
Arnbom, T., Fedak, M. A. & Boyd, I. L. Factors affecting maternal expenditure in southern elephant seals during lactation. Ecology 78, 471–483 (1997).Article 

Google Scholar 
Boltnev, A. I. & York, A. E. Maternal investment in northern fur seals (Callorhinus ursinus): Interrelationships among mothers’ age, size, parturition date, offspring size and sex ratios. J. Zool. 254, 219–228 (2001).Article 

Google Scholar 
Tollefson, T. N., Shipley, L. A., Myers, W. L., Keisler, D. H. & Dasgupta, N. Influence of summer and autumn nutrition on body condition and reproduction in lactating mule deer. J. Wildl. Manag. 74, 974–986 (2010).Article 

Google Scholar 
Wheatley, K. E., Bradshaw, C. J. A., Davis, L. S., Harcourt, R. G. & Hindell, M. A. Influence of maternal mass and condition on energy transfer in Weddell seals. J. Anim. Ecol. 75, 724–733 (2006).Article 
PubMed 

Google Scholar 
Miller, C. A. et al. Blubber thickness in right whales Eubalaena glacialis and Eubalaena australis related with reproduction, life history status and prey abundance. Mar. Ecol. Prog. Ser. 438, 267–283 (2011).Article 
ADS 

Google Scholar 
Christiansen, F., Víkingsson, G. A., Rasmussen, M. H. & Lusseau, D. Female body condition affects foetal growth in a capital breeding mysticete. Funct. Ecol. 28, 579–588 (2014).Article 

Google Scholar 
Christiansen, F. et al. Maternal body size and condition determine calf growth rates in southern right whales. Mar. Ecol. Prog. Ser. 592, 267–282 (2018).Article 
ADS 

Google Scholar 
Norris, K. S. Some observations on the migration and orientation of marine mammals. Anim. Orientat. Migr. 101, 125 (1967).
Google Scholar 
Corkeron, P. J. & Connor, R. C. Why do baleen whales migrate?. Mar. Mammal Sci. 15, 1228–1245 (1999).Article 

Google Scholar 
Frazer, J. F. D. & Huggett, A. S. G. Specific foetal growth rates of cetaceans. J. Zool. 169, 111–126 (1973).Article 

Google Scholar 
Stearns, S. C. Trade-offs in life-history evolution. Funct. Ecol. 3, 259–268 (1989).Article 

Google Scholar 
Castrillon, J. & Bengtson Nash, S. Evaluating cetacean body condition; A review of traditional approaches and new developments. Ecol. Evol. 10, 6144–6162 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Leaper, R. et al. Global climate drives southern right whale (Eubalaena australis) population dynamics. Biol. Lett. 2, 289–292 (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
Lockyer, C. All creatures great and smaller: A study in cetacean life history energetics. J. Mar. Biol. Assoc. UK 87, 1035–1045 (2007).Article 

Google Scholar 
Seyboth, E. et al. Southern right whale (Eubalaena australis) reproductive success is influenced by Krill (Euphausia superba) density and climate. Sci. Rep. 6, 28205 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Williams, R. et al. Evidence for density-dependent changes in body condition and pregnancy rate of North Atlantic fin whales over four decades of varying environmental conditions. ICES J. Mar. Sci. 70, 1273–1280 (2013).Article 

Google Scholar 
Best, P. B. Trends in the inshore right whale population off South Africa, 1969–1987. Mar. Mammal Sci. 6, 93–108 (1990).Article 

Google Scholar 
Best, P. B. Seasonality of reproduction and the length of gestation in southern right whales Eubalaena australis. J. Zool. 232, 175–189 (1994).Article 

Google Scholar 
Best, P. B., Brandão, A. & Butterworth, D. S. Demographic parameters of southern right whales off South Africa. J. Cetacean Res. Manag. https://doi.org/10.47536/jcrm.vi.296 (2001).Article 

Google Scholar 
Vermeulen, E., Wilkinson, C. & Thornton, M. Report of the 2018 South African Southern Right. Paper SC/68A/SH/01 Presented to IWC Scientific Committee, 2019 (unpublished). 25 pp. (Available from Off. this Journal) (2019).Knowlton, A. R., Kraus, S. D. & Kenney, R. D. Reproduction in North Atlantic right whales (Eubalaena glacialis). Can. J. Zool. 72, 1297–1305 (1994).Article 

Google Scholar 
van den Berg, G. L. et al. Decadal shift in foraging strategy of a migratory Southern Ocean predator. Glob. Change Biol. https://doi.org/10.1111/gcb.15465 (2021).Article 

Google Scholar 
Carroll, E. L. et al. First Direct evidence for natal wintering ground fidelity and estimate of Juvenile Survival in the New Zealand southern right whale Eubalaena australis. PLoS ONE 11, e0146590 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Valenzuela, L. O., Sironi, M., Rowntree, V. J. & Seger, J. Isotopic and genetic evidence for culturally inherited site fidelity to feeding grounds in southern right whales (Eubalaena australis). Mol. Ecol. 18, 782–791 (2009).Article 
CAS 
PubMed 

Google Scholar 
Best, P. B. & Ruther, H. Aerial photogrammetry of southern right whales, Eubalaena australis. J. Zool. 228, 595–614 (1992).Article 

Google Scholar 
Mate, B., Best, P., Lagerquist, B. A. & Winsor, M. H. Coastal, offshore, and migratory movements of South African right whales revealed by satellite telemetry. Mar. Mammal Sci. 27, 455–476 (2011).Article 

Google Scholar 
Christiansen, F., Dujon, A. M., Sprogis, K. R., Arnould, J. P. Y. & Bejder, L. Non-invasive unmanned aerial vehicle provides estimates of the energetic cost of reproduction in humpback whales. Ecosphere 7, 1–7 (2016).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (2020).Lockyer, C. Growth and energy budgets of large baleen whales from the Southern Hemisphere. Mamm. Seas, vol. 3, (FAO Fisheries Series No. 5) 379–487 (1981).Christiansen, F. et al. Estimating body mass of free-living whales using aerial photogrammetry and 3D volumetrics. Methods Ecol. Evol. 10, 2034–2044 (2019).Article 

Google Scholar 
Christiansen, F. et al. Population comparison of right whale body condition reveals poor state of the North Atlantic right whale. Mar. Ecol. Prog. Ser. 640, 1–16 (2020).Article 
ADS 

Google Scholar 
Braithwaite, J. E., Meeuwig, J. J., Letessier, T. B., Jenner, K. C. S. & Brierley, A. S. From sea ice to blubber: Linking whale condition to krill abundance using historical whaling records. Polar Biol. 38, 1195–1202 (2015).Article 

Google Scholar 
Loeb, V. J., Hofmann, E. E., Klinck, J. M., Holm-Hansen, O. & White, W. B. ENSO and variability of the antarctic peninsula pelagic marine ecosystem. Antarct. Sci. 21, 135–148 (2009).Article 
ADS 

Google Scholar 
Reid, K. & Croxall, J. P. Environmental response of upper trophic-level predators reveals a system change in an Antarctic marine ecosystem. Proc. R. Soc. B Biol. Sci. 268, 377–384 (2001).Article 
CAS 

Google Scholar 
Trathan, P. N., Forcada, J. & Murphy, E. J. Environmental forcing and Southern Ocean marine predator populations: Effects of climate change and variability. Philos. Trans. R. Soc. Biol. Sci. https://doi.org/10.1098/rstb.2006.1953 (2007).Article 

Google Scholar 
Bost, C. A. et al. The importance of oceanographic fronts to marine birds and mammals of the Southern Oceans. J. Mar. Syst. 78, 363–376 (2009).Article 

Google Scholar 
Crocker, D. E., Costa, D. P., Le Boeuf, B. J., Webb, P. M. & Houser, D. S. Impact of El Niño on the foraging behavior of female northern elephant seals. Mar. Ecol. Prog. Ser. 309, 1–10 (2006).Article 
ADS 

Google Scholar 
Flores, H. et al. Impact of climate change on Antarctic krill. Mar. Ecol. Prog. Ser. 458, 1–19 (2012).Article 
ADS 

Google Scholar 
Forcada, J. et al. Responses of Antarctic pack-ice seals to environmental change and increasing krill fishing. Biol. Conserv. 149, 40–50 (2012).Article 

Google Scholar 
Garcia-Rojas, M. I. et al. Environmental evidence for a pygmy blue whale aggregation area in the Subtropical Convergence Zone south of Australia. Mar. Mammal Sci. 34, 901–923 (2018).Article 

Google Scholar 
Tormosov, D. et al. Soviet catches of southern right whales Eubalaena australis, 1951–1971: Biological data and conservation implications. Biol. Conserv. 86, 185–197 (1998).Article 

Google Scholar 
Trathan, P. N. et al. Foraging dynamics of macaroni penguins Eudyptes chrysolophus at South Georgia during brood-guard. Mar. Ecol. Prog. Ser. 323, 239–251 (2006).Article 
ADS 

Google Scholar 
Murphy, E. J. et al. Climatically driven fluctuations in Southern Ocean ecosystems. Proc. R. Soc. B Biol. Sci. 274, 3057–3067 (2007).Article 

Google Scholar 
Nicol, S. Krill, currents, and sea ice: Euphausia superba and its changing environment. Bioscience 56, 111–120 (2006).Article 

Google Scholar 
Atkinson, A. et al. Oceanic circumpolar habitats of Antarctic krill. Mar. Ecol. Prog. Ser. 362, 1–23 (2008).Article 
ADS 
CAS 

Google Scholar 
Atkinson, A. et al. South Georgia, Antarctica: A productive, cold water, pelagic ecosystem. Mar. Ecol. Prog. Ser. 216, 279–308 (2001).Article 
ADS 
CAS 

Google Scholar 
Atkinson, A., Ward, P., Hill, A., Brierley, A. S. & Cripps, G. C. Krill-copepod interactions at South Georgia, Antarctica, II. Euphausia superba as a major control on copepod abundance. Mar. Ecol. Prog. Ser. 176, 63–79 (1999).Article 
ADS 

Google Scholar 
DeLorenzo Costa, A., Durbin, E. G. & Mayo, C. A. Variability in the nutritional value of the major copepods in Cape Cod Bay (Massachusetts, USA) with implications for right whales. Mar. Ecol. 27, 109–123 (2006).Article 
ADS 

Google Scholar 
Linder, M., Belhaj, N., Sautot, P. & Tehrany, E. A. From krill to whale: An overview of marine fatty acids and lipid compositions. Oleagineux Corps Gras Lipides: OCL 17, 194–204 (2010).Article 

Google Scholar 
McKinstry, C. A. E., Westgate, A. J. & Koopman, H. N. Annual variation in the nutritional value of stage V Calanus finmarchicus: Implications for right whales and other copepod predators. Endanger. Species Res. 20, 195–204 (2013).Article 

Google Scholar 
Maron, C. F. et al. Fatty acids and stable isotopes (13C, 15N) in southern right whale Eubalaena australis calves in relation toage and mortality at Peninsula Valdes, Argentina. Mar. Ecol. Prog. Ser. 646, 189–200 (2020).Article 
ADS 
CAS 

Google Scholar 
Thomas, V. G. Control of reproduction in animal species with high and low body fat reserves. Prog. Reprod. Biol. Med. 14, 27–41 (1990).
Google Scholar 
Ford, J. K. B., Ellis, G. M., Olesiuk, P. F. & Balcomb, K. C. Linking killer whale survival and prey abundance: Food limitation in the oceans’ apex predator?. Biol. Lett. 6, 139–142 (2010).Article 
PubMed 

Google Scholar 
Greene, C. H., Pershing, A. J., Kenney, R. D. & Jossi, J. W. Impact of climate variability on the recovery of endangered North Atlantic right whales. Oceanography 16, 98–103 (2003).Article 

Google Scholar 
Vermeulen, E., Wilkinson, C. & Van Den Berg, G. Report of the Southern Right Whale Aerial Surveys—2019. Paper SC/68B/SH/02 Presented to IWC Scientific Committee, 2020 (unpublished). 25 pp. (Available from Off. this Journal) (2020).Douhard, F., Gaillard, J. M., Pellerin, M., Jacob, L. & Lemaître, J. F. The cost of growing large: Costs of post-weaning growth on body mass senescence in a wild mammal. Oikos 126, 1329–1338 (2017).Article 

Google Scholar 
Sigurjónsson, J., Halldórsson, S. D. & Konráðsson, A. New Information on Age and Reproduction in Minke Whales (Balaenoptera acutorostrata) in Icelandic Waters. Page Doc. SC/42/NHMi27 Scientific Communication International Whaling Commission. Noordwijkerhout, Netherlands (1990).Charlton, C. et al. Demographic Parameters of Southern Right Whales (Eubalaena australis) off Australia. Paper SC/67B/INFO/22 Presented to IWC Scientific Committee, 2018 (Unpublished). 28 pp. (Available from Off. This Journal) (2018).Marón, C. F. et al. Increased wounding of southern right Whale (Eubalaena australis) calves by Kelp Gulls (Larus dominicanus) at Península Valdés, Argentina. PLoS ONE 10, 1–20 (2015).
Google Scholar 
Rowntree, V. J. et al. Unexplained recurring high mortality of southern right whale Eubalaena australis calves at Península Valdés, Argentina. Mar. Ecol. Prog. Ser. 493, 275–289 (2013).Article 
ADS 

Google Scholar 
Brandão, A., Vermeulen, E., Ross-gillespie, A., Findlay, K. & Butterworth, D. S. Updated Application of a Photo-Identification Based Assessment Model to Southern Right Whales in South African Waters , Focussing on Inferences to be Drawn from a Series of Appreciably Lower Counts of Calving Females Over 2015 to 2017. Paper SC/67B/SH2 Presented to IWC Scientific Committee, 2018 (unpublished). 18 pp. (Available from Off. this Journal) (2018).Crespo, E. A. et al. The southwestern Atlantic southern right whale, Eubalaena australis, population is growing but at a decelerated rate. Mar. Mammal Sci. 35, 93–107 (2019).Article 

Google Scholar 
Agrelo, M. et al. Ocean warming threatens southern right whale population recovery. Sci. Adv. 7, eabh2823 (2021).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Stenseth, N. C. et al. Ecological effects of climate fluctuations. Science (80-) 297, 1292–1296 (2002).Article 
ADS 
CAS 

Google Scholar 
Nicol, S., Worby, A. & Leaper, R. Changes in the Antarctic sea ice ecosystem: Potential effects on krill and baleen whales. Mar. Freshw. Res. 59, 361–382 (2008).Article 

Google Scholar 
Meredith, M. P. & King, J. C. Rapid climate change in the ocean west of the Antarctic Peninsula during the second half of the 20th century. Geophys. Res. Lett. 32, 1–5 (2005).Article 

Google Scholar 
Croxall, J., Reid, K. & Prince, P. Diet, provisioning and productivity responses of marine predators to differences in availability of Antarctic krill. Mar. Ecol. Prog. Ser. 177, 115–131 (1999).Article 
ADS 

Google Scholar 
Tulloch, V. J. D., Plagányi, É. E., Brown, C., Richardson, A. J. & Matear, R. Future recovery of baleen whales is imperiled by climate change. Glob. Change Biol. 25, 1263–1281 (2019).Article 
ADS 

Google Scholar 
Hoegh-Guldberg, O. & Bruno, J. F. The impact of climate change on the world’s marine ecosystems. Science (80-) 328, 1523–1528 (2010).Article 
ADS 
CAS 

Google Scholar 
González Carman, V., Piola, A., O’Brien, T. D., Tormosov, D. D. & Acha, E. M. Circumpolar frontal systems as potential feeding grounds of Southern Right whales. Prog. Oceanogr. 176, 102123 (2019).Article 

Google Scholar  More

Raubenheimer, D., Simpson, S. J. & Mayntz, D. Nutrition, ecology and nutritional ecology: Toward an integrated framework. Funct. Ecol. 23, 4–16 (2009).Article 

Google Scholar 
Behmer, S. T. & Joern, A. Coexisting generalist herbivores occupy unique nutritional feeding niches. Proc. Natl. Acad. Sci. U. S. A. 105, 1977–1982. https://doi.org/10.1073/pnas.0711870105 (2008).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Lihoreau, M. et al. Nutritional ecology beyond the individual: A conceptual framework for integrating nutrition and social interactions. Ecol. Lett. 18, 273–286 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Raubenheimer, D., Simpson, S. J. & Tait, A. H. Match and mismatch: conservation physiology, nutritional ecology and the timescales of biological adaptation. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 367, 1628–1646. https://doi.org/10.1098/rstb.2012.0007 (2012).Article 
CAS 

Google Scholar 
von Liebig, J. Die organische Chemie in ihrer Anwendung auf Agricultur und Physiologie. (Vieweg, 1841).Simpson, C., Simpson, S. & Abisgold, J. In Symposium Biologica Hungarica. 39–46.Boersma, M. & Elser, J. Too much of a good thing: On stoichiometrically balanced diets and maximal growth. Ecology 87, 1325–1330 (2006).Article 
PubMed 

Google Scholar 
Simpson, S. J. & Raubenheimer, D. A multi-level analysis of feeding behaviour: The geometry of nutritional decisions. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 342, 381–402. https://doi.org/10.1098/rstb.1993.0166 (1993).Article 
ADS 

Google Scholar 
Zanotto, F. P., Raubenheimer, D. & Simpson, S. J. Haemolymph amino acid and sugar levels in locust fed nutritionally unbalanced diets. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 166, 223–229 (1996).Article 
CAS 

Google Scholar 
Kohl, K. D., Coogan, S. C. & Raubenheimer, D. Do wild carnivores forage for prey or for nutrients? Evidence for nutrient-specific foraging in vertebrate predators. BioEssays 37, 701–709. https://doi.org/10.1002/bies.201400171 (2015).Article 
PubMed 

Google Scholar 
Remonti, L., Balestrieri, A., Raubenheimer, D. & Saino, N. Functional implications of omnivory for dietary nutrient balance. Oikos 125, 1233–1240 (2016).Article 
CAS 

Google Scholar 
McIntyre, P. B. & Flecker, A. S. In Community Ecology of Stream Fishes: Concepts, Approaches, and Techniques. American Fisheries Society, Symposium. 539–558 (Citeseer).DeGabriel, J. L. et al. Translating nutritional ecology from the laboratory to the field: Milestones in linking plant chemistry to population regulation in mammalian browsers. Oikos 123, 298–308 (2014).Article 

Google Scholar 
Nielsen, S. E., Larsen, T. A., Stenhouse, G. B. & Coogan, S. C. P. Complementary food resources of carnivory and frugivory affect local abundance of an omnivorous carnivore. Oikos 126, 369–380. https://doi.org/10.1111/oik.03144 (2017).Article 
CAS 

Google Scholar 
Mayntz, D., Raubenheimer, D., Salomon, M., Toft, S. & Simpson, S. J. Nutrient-specific foraging in invertebrate predators. Science 307, 111–113 (2005).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Anderson, T. R., Boersma, M. & Raubenheimer, D. Stoichiometry: Linking elements to biochemicals. Ecology 85, 1193–1202 (2004).Article 

Google Scholar 
McManamay, R. A., Webster, J. R., Valett, H. M. & Dolloff, C. A. Does diet influence consumer nutrient cycling? Macroinvertebrate and fish excretion in streams. J. N. Am. Benthol. Soc. 30, 84–102. https://doi.org/10.1899/09-152.1 (2011).Article 

Google Scholar 
Vivas, M., Sánchez-Vázquez, F., García García, B. & Madrid, J. Macronutrient self-selection in European sea bass in response to dietary protein or fat restriction. Aquac. Res. 34, 271–280 (2003).Article 

Google Scholar 
Rubio, V., Navarro, D. B., Madrid, J. & Sánchez-Vázquez, F. Macronutrient self-selection in Solea senegalensis fed macronutrient diets and challenged with dietary protein dilutions. Aquaculture 291, 95–100 (2009).Article 
CAS 

Google Scholar 
Mayntz, D. et al. Balancing of protein and lipid intake by a mammalian carnivore, the mink, Mustela vison. Anim. Behav. 77, 349–355 (2009).Article 

Google Scholar 
Al Shareefi, E. & Cotter, S. C. The nutritional ecology of maturation in a carnivorous insect. Behav. Ecol. 30, 256–266 (2019).Article 

Google Scholar 
Jensen, K. et al. Nutrient-specific compensatory feeding in a mammalian carnivore, the mink, Neovison vison. Br. J. Nutr. 112, 1226–1233. https://doi.org/10.1017/S0007114514001664 (2014).Article 
CAS 
PubMed 

Google Scholar 
Hayward, M., Jędrzejewski, W. & Jedrzejewska, B. Prey preferences of the tiger Panthera tigris. J. Zool. 286, 221–231 (2012).Article 

Google Scholar 
Whitney, T. D., Sitvarin, M. I., Roualdes, E. A., Bonner, S. J. & Harwood, J. D. Selectivity underlies the dissociation between seasonal prey availability and prey consumption in a generalist predator. Mol. Ecol. 27, 1739–1748 (2018).Article 
PubMed 

Google Scholar 
Potter, T. I., Stannard, H. J., Greenville, A. C. & Dickman, C. R. Understanding selective predation: Are energy and nutrients important?. PLoS One 13, e0201300 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Machovsky-Capuska, G. E. et al. Sex-specific macronutrient foraging strategies in a highly successful marine predator: The Australasian gannet. Mar. Biol. 163, 75 (2016).Article 

Google Scholar 
Remonti, L., Balestrieri, A. & Prigioni, C. Percentage of protein, lipids, and carbohydrates in the diet of badger (Meles meles) populations across Europe. Ecol. Res. 26, 487–495 (2011).Article 
CAS 

Google Scholar 
Wilder, S. M. et al. Three-dimensional diet regulation and the consequences of choice for weight and activity level of a marsupial carnivore. J. Mammal. 97, 1645–1651 (2016).Article 

Google Scholar 
Yu, D.-H. et al. Effect of partial replacement of fish meal with soybean meal and feeding frequency on growth, feed utilization and body composition of juvenile Chinese sucker, Myxocyprinus asiaticus (Bleeker). Aquac. Res. 44, 388–394. https://doi.org/10.1111/j.1365-2109.2011.03043.x (2013).Article 
CAS 

Google Scholar 
Kaushik, S. J. & Seiliez, I. Protein and amino acid nutrition and metabolism in fish: current knowledge and future needs. Aquac. Res. 41, 322–332. https://doi.org/10.1111/j.1365-2109.2009.02174.x (2010).Article 
CAS 

Google Scholar 
Gaye-Siessegger, J., McCullagh, J. S. & Focken, U. The effect of dietary amino acid abundance and isotopic composition on the growth rate, metabolism and tissue delta13C of rainbow trout. Br. J. Nutr. 105, 1764–1771. https://doi.org/10.1017/S0007114510005696 (2011).Article 
CAS 
PubMed 

Google Scholar 
McCullagh, J., Gaye-Siessegger, J. & Focken, U. Determination of underivatized amino acid delta(13)C by liquid chromatography/isotope ratio mass spectrometry for nutritional studies: The effect of dietary non-essential amino acid profile on the isotopic signature of individual amino acids in fish. Rapid Commun. Mass Spectrom. RCM 22, 1817–1822. https://doi.org/10.1002/rcm.3554 (2008).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Gao, K. et al. Dietary L-arginine supplementation enhances placental growth and reproductive performance in sows. Amino Acids 42, 2207–2214 (2012).Article 
CAS 
PubMed 

Google Scholar 
Wu, G. et al. Amino acid nutrition in animals: Protein synthesis and beyond. Annu. Rev. Anim. Biosci. 2, 387–417. https://doi.org/10.1146/annurev-animal-022513-114113 (2014).Article 
CAS 
PubMed 

Google Scholar 
Dwyer, G. K., Stoffels, R. J., Silvester, E. & Rees, G. N. Prey amino acid composition affects rates of protein synthesis and N wastage of a freshwater carnivore. Mar. Freshw. Res. 71, 229–237. https://doi.org/10.1071/MF18410 (2020).Article 
CAS 

Google Scholar 
Kremen, N. et al. Body composition and amino acid concentrations of select birds and mammals consumed by cats in northern and central California. J. Anim. Sci. 91, 1270–1276 (2013).Article 
CAS 
PubMed 

Google Scholar 
Goodman-Lowe, G., Carpenter, J., Atkinson, S. & Ako, H. Nutrient, fatty acid, amino acid and mineral analysis of natural prey of the Hawaiian monk seal, Monachus schauinslandi. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 123, 137–146 (1999).Article 
CAS 

Google Scholar 
Dwyer, G. K., Stoffels, R. J., Rees, G. N., Shackleton, M. & Silvester, E. A predicted change in the amino acid landscapes available to freshwater carnivores. Freshw. Sci. 37, 000–000 (2018).Article 

Google Scholar 
Kolmakova, A. A. et al. Amino acid composition of epilithic biofilm and benthic animals in a large Siberian river. Freshw. Biol. 58, 2180–2195. https://doi.org/10.1111/fwb.12200 (2013).Article 
CAS 

Google Scholar 
Thera, J. C., Kidd, K. A. & Bertolo, R. F. Amino acids in freshwater food webs: Assessing their variability among taxa, trophic levels, and systems. Freshw. Biol. 65, 1101–1113 (2020).Article 
CAS 

Google Scholar 
Fargallo, J. A., Navarro-López, J., Palma-Granados, P. & Nieto, R. M. Foraging strategy of a carnivorous-insectivorous raptor species based on prey size, capturability and nutritional components. Sci. Rep. 10, 1–12 (2020).Article 

Google Scholar 
Shakya, M., Silvester, E., Holland, A. & Rees, G. Taxonomic, seasonal and spatial variation in the amino acid profile of freshwater macroinvertebrates. Aquat. Sci. 83, 1–15 (2021).Article 

Google Scholar 
Martinez, J. B., Chatzifotis, S., Divanach, P. & Takeuchi, T. Effect of dietary taurine supplementation on growth performance and feed selection of sea bass Dicentrarchus labrax fry fed with demand-feeders. Fish. Sci. 70, 74–79 (2004).Article 

Google Scholar 
Yamamoto, T. et al. Self-selection and feed consumption of diets with a complete amino acid composition and a composition deficient in either methionine or lysine by rainbow trout, Oncorhynchus mykiss (Walbaum). Aquac. Res. 32, 83–91 (2001).Article 
CAS 

Google Scholar 
Dabrowski, K., Arslan, M., Terjesen, B. F. & Zhang, Y. The effect of dietary indispensable amino acid imbalances on feed intake: Is there a sensing of deficiency and neural signaling present in fish?. Aquaculture 268, 136–142. https://doi.org/10.1016/j.aquaculture.2007.04.065 (2007).Article 
CAS 

Google Scholar 
Caprio, J. Olfaction and taste in the channel catfish: An electrophysiological study of the responses to amino acids and derivatives. J. Comp. Physiol. 123, 357–371 (1978).Article 

Google Scholar 
Hazlett, B. A. Crayfish feeding responses to zebra mussels depend on microorganisms and learning. J. Chem. Ecol. 20, 2623–2630. https://doi.org/10.1007/bf02036196 (1994).Article 
CAS 
PubMed 

Google Scholar 
Gietzen, D. W. & Aja, S. M. The brain’s response to an essential amino acid-deficient diet and the circuitous route to a better meal. Mol. Neurobiol. 46, 332–348. https://doi.org/10.1007/s12035-012-8283-8 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rees, G. N., Shackleton, M. E., Watson, G. O., Dwyer, G. K. & Stoffels, R. J. Metabarcoding demonstrates dietary niche partitioning in two coexisting blackfish species. Mar. Freshw. Res. 71, 512–517 (2020).Article 
CAS 

Google Scholar 
Antoine, F., Wei, C., Littell, R. & Marshall, M. HPLC method for analysis of free amino acids in fish using o-phthaldialdehyde precolumn derivatization. J. Agric. Food Chem. 47, 5100–5107 (1999).Article 
CAS 
PubMed 

Google Scholar 
Anderson, M. J. & Santana-Garcon, J. Measures of precision for dissimilarity-based multivariate analysis of ecological communities. Ecol. Lett. 18, 66–73 (2015).Article 
PubMed 

Google Scholar 
Fountoulakis, M. & Lahm, H.-W. Hydrolysis and amino acid composition analysis of proteins. J. Chromatogr. A 826, 109–134 (1998).Article 
CAS 
PubMed 

Google Scholar 
McArdle, B. H. When are rare species not there?. Oikos 57, 276–277 (1990).Article 

Google Scholar 
Machovsky-Capuska, G. E., Coogan, S. C., Simpson, S. J. & Raubenheimer, D. Motive for killing: What drives prey choice in wild predators?. Ethology 122, 703–711 (2016).Article 

Google Scholar 
Tait, A. H., Raubenheimer, D., Stockin, K. A., Merriman, M. & Machovsky-Capuska, G. E. Nutritional geometry and macronutrient variation in the diets of gannets: The challenges in marine field studies. Mar. Biol. 161, 2791–2801 (2014).Article 
CAS 

Google Scholar 
Bosch, G., Hagen-Plantinga, E. A. & Hendriks, W. H. Dietary nutrient profiles of wild wolves: Insights for optimal dog nutrition?. Br. J. Nutr. 113, S40–S54 (2015).Article 
CAS 
PubMed 

Google Scholar 
Machovsky-Capuska, G. E., Senior, A. M., Simpson, S. J. & Raubenheimer, D. The multidimensional nutritional niche. Trends Ecol. Evol. 31, 355–365 (2016).Article 
PubMed 

Google Scholar 
Jensen, K. et al. Optimal foraging for specific nutrients in predatory beetles. Proc. R. Soc. B 279, 2212–2218. https://doi.org/10.1098/rspb.2011.2410 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Machovsky-Capuska, G. E. & Raubenheimer, D. The nutritional ecology of marine apex predators. Ann. Rev. Mar. Sci. 12, 361–387 (2020).Article 
PubMed 

Google Scholar 
Schindler, D. E. & Eby, L. A. Stoichiometry of fishes and their prey: Implications for nutrient recycling. Ecology 78, 1816–1831 (1997).Article 

Google Scholar 
Morosinotto, C., Villers, A., Varjonen, R. & Korpimäki, E. Food supplementation and predation risk in harsh climate: Interactive effects on abundance and body condition of tit species. Oikos 126, 863–873. https://doi.org/10.1111/oik.03476 (2017).Article 

Google Scholar 
Österblom, H., Olsson, O., Blenckner, T. & Furness, R. W. Junk-food in marine ecosystems. Oikos 117, 967–977 (2008).Article 

Google Scholar 
Dwyer, G. K., Stoffels, R. J. & Pridmore, P. A. Morphology, metabolism and behaviour: responses of three fishes with different lifestyles to acute hypoxia. Freshw. Biol. 59, 819–831. https://doi.org/10.1111/fwb.12306 (2014).Article 
CAS 

Google Scholar 
Hubel, T. Y. et al. Energy cost and return for hunting in African wild dogs and cheetahs. Nat. Commun. 7, 11034 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ip, Y. K., Lim, C. K., Lee, S. L., Wong, W. P. & Chew, S. F. Postprandial increases in nitrogenous excretion and urea synthesis in the giant mudskipper Periophthalmodon schlosseri. J. Exp. Biol. 207, 3015–3023 (2004).Article 
CAS 
PubMed 

Google Scholar 
Wilkie, M. P. Mechanisms of ammonia excretion across fish gills. Comp. Biochem. Physiol. A Physiol. 118, 39–50 (1997).Article 

Google Scholar 
Yamamoto, T. et al. Self-selection of diets with different amino acid profiles by rainbow trout (Oncorhynchus mykiss). Aquaculture 187, 375–386 (2000).Article 
CAS 

Google Scholar 
Kilkenny, C., Browne, W. J., Cuthill, I. C., Emerson, M. & Altman, D. G. J. P. B. Improving bioscience research reporting: The ARRIVE guidelines for reporting animal research. J. Pharmacol. Pharmacother. 8, e1000412 (2010).
Google Scholar  More