Philippa Kaur

Gao, A. L. & Zhou, K. Y. Growth and reproduction of three populations of finless porpoise, Neophocaena phocaenoides, in Chinese waters. Aquat Mamm 19, 3–12 (1993).
Google Scholar 
Jefferson, T. A. Preliminary analysis of geographic variation in cranial morphometrics of the finless porpoise (Neophocaena phocaenoides). Raffles Bull Zool 10, 3–14 (2002).
Google Scholar 
Pilleri, G. & Gihr, M. Contribution to the knowledge of the cetaceans of Pakistan with particular reference to the genera Neomeris, Sousa, Delphinus and Tursiops and description of a new Chinese porpoise (Neomeris asiaeorientalis). Investig Cetacea 4, 107–162 (1972).
Google Scholar 
Pilleri, G. & Gihr, M. On the taxonomy and ecology of the finless black porpoise, Neophocaena (Cetacea, Delphinidae). Mammalia 39, 657–673 (1975).Article 

Google Scholar 
Wang, P. L. The morphological characters and the problem of subspecies identifications of the finless porpoise. Fish Sci 11, 4–8 (1992).
Google Scholar 
Wang, P. L. On the taxonomy of the finless porpoise in China. Fish Sci 6, 10–14 (1992).
Google Scholar 
Gao, A. L. & Zhou, K. Y. Geographical variation of external measurements and three subspecies of Neophocaena phocaenoides in Chinese waters. Acta Theriol Sin 15, 81–92 (1995).
Google Scholar 
Wang, J. Y., Frasier, T. R., Yang, S. C. & White, B. N. Detecting recent speciation events: the case of the finless porpoise (genus Neophocaena). Heredity 101, 145–155 (2008).Article 

Google Scholar 
Jefferson, T. A. & Wang, J. Y. Revision of the taxonomy of finless porpoises (genus Neophocaena): the existence of two species. J Mar Anim Ecol 4, 3–16 (2011).
Google Scholar 
Zhou, X. M. et al. Population genomics of finless porpoises reveal an incipient cetacean species adapted to freshwater. Nat Commun 9, 1276 (2018).Article 
ADS 

Google Scholar 
Wang, D., Turvey, S.T., Zhao, X. & Mei, Z. Neophocaena asiaeorientalis ssp. asiaeorientalis. The IUCN Red List of Threatened Species https://www.iucnredlist.org/species/43205774/45893487 (2013).Wang, J. Y. & Reeves, R. Neophocaena Asiaeorientalis. The IUCN Red List of Threatened Species https://www.iucnredlist.org/species/41754/50381766 (2017).Kasuya, T. Japanese whaling and other cetacean fisheries. Environ Sci Pollut Res Int 14, 39–48 (2007).Article 

Google Scholar 
Yoshida, H., Shirakihara, K., Kishino, H. & Shirakihara, M. A population size estimate of the finless porpoise, Neophocaena phocaenoides, from aerial sighting surveys in Ariake Sound and Tachibana Bay, Japan. Popul Ecol 39, 239–247 (1997).Article 

Google Scholar 
Amano, M., Nakahara, F., Hayano, A. & Shirakihara, K. Abundance estimate of finless porpoises off the Pacific coast of eastern Japan based on aerial surveys. Mamm Study 28, 103–110 (2003).Article 

Google Scholar 
Shirakihara, K., Shirakihara, M. & Yamamoto, Y. Distribution and abundance of finless porpoise in the Inland Sea of Japan. Mar Biol 150, 1025–1032 (2007).Article 

Google Scholar 
Zuo, T., Sun, J. Q., Shi, Y. Q. & Wang, J. Primary survey of finless porpoise population in the Bohai Sea. Acta Theriol Sin 38, 551–561 (2018).
Google Scholar 
Ruan, R., Guo, A. H., Hao, Y. J., Zheng, J. S. & Wang, D. De novo assembly and characterization of narrow-ridged finless porpoise renal transcriptome and identification of candidate genes involved in osmoregulation. Int J Mol Sci 16, 2220–2238 (2015).Article 

Google Scholar 
Li, S. H. et al. Echolocation click sounds from wild inshore finless porpoise (Neophocaena phocaenoides sunameri) with comparisons to the sonar of riverine N. p. asiaeorientalis. J Acoust Soc Am 121, 3938–3946 (2007).Article 
ADS 

Google Scholar 
Dong, J. H., Wang, G. J. & Xiao, Z. Z. Migration and population difference of the finless porpoise in China. Mar Sci 5, 42–45 (1993).
Google Scholar 
Lu, Z. C. et al. Analysis of the diet of finless porpoise (Neophocaena asiaeorientalis sunameri) based on prey morphological characters and DNA barcoding. Conserv Genet Resour 8, 523–531 (2016).Article 

Google Scholar 
Chen, B. et al. Finless porpoises (Neophocaena asiaeorientalis) in the East China Sea: insights into feeding habits using morphological, molecular, and stable isotopic techniques. Can J Fish Aquat Sci 74, 1628–1645 (2017).Article 

Google Scholar 
Nurk, S. et al. The complete sequence of a human genome. Science 376, 44–53 (2022).Article 
ADS 

Google Scholar 
Chen, Y. X. et al. SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data. Gigascience 7, 1–6 (2018).Article 
ADS 

Google Scholar 
Chikhi, R. & Medvedev, P. Informed and automated k-mer size selection for genome assembly. Bioinformatics 30, 31–37 (2014).Article 

Google Scholar 
Chin, C. S. et al. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat Methods 10, 563–569 (2013).Article 

Google Scholar 
Cheng, H. Y., Concepcion, G. T., Feng, X. W., Zhang, H. W. & Li, H. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat Methods 18, 170–175 (2021).Article 

Google Scholar 
Roach, M. J., Schmidt, S. A. & Borneman, A. R. Purge Haplotigs: allelic contig reassignment for third-gen diploid genome assemblies. BMC Bioinformatics 19, 1–10 (2018).Article 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).Article 

Google Scholar 
Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst 3, 95–98 (2016).Article 

Google Scholar 
Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).Article 
ADS 

Google Scholar 
Xiong, Y., Brandley, M. C., Xu, S. X., Zhou, K. Y. & Yang, G. Seven new dolphin mitochondrial genomes and a time-calibrated phylogeny of whales. BMC Evol Biol 9, 1–13 (2009).Article 

Google Scholar 
Alonge, M. et al. RaGOO: fast and accurate reference-guided scaffolding of draft genomes. Genome Biol 20, 1–17 (2019).Article 

Google Scholar 
Mayer, A., Lahr, G., Swaab, D. F., Pilgrim, C. & Reisert, I. The Y-chromosomal genes SRY and ZFY are transcribed in adult human brain. Neurogenetics 1, 281–288 (1998).Article 

Google Scholar 
Sinclair, A. H. et al. A gene from the human sex-determining region encodes a protein with homology to a conserved DNA-binding motif. Nature 346, 240–244 (1990).Article 
ADS 

Google Scholar 
Koopman, P., Gubbay, J., Vivian, N., Goodfellow, P. & Lovell-Badge, R. Male development of chromosomally female mice transgenic for Sry. Nature 351, 117–121 (1991).Article 
ADS 

Google Scholar 
Salo, P. et al. Molecular mapping of the putative gonadoblastoma locus on the Y chromosome. Genes Chromosomes Cancer 14, 210–214 (1995).Article 

Google Scholar 
Tsuchiya, K., Reijo, R., Page, D. C. & Disteche, C. M. Gonadoblastoma: molecular definition of the susceptibility region on the Y chromosome. Am J Hum Genet 57, 1400–1407 (1995).
Google Scholar 
Gegenschatz-Schmid, K., Verkauskas, G., Stadler, M. B. & Hadziselimovic, F. Genes located in Y-chromosomal regions important for male fertility show altered transcript levels in cryptorchidism and respond to curative hormone treatment. Basic Clin Androl 29, 1–8 (2019).Article 

Google Scholar 
Chen, N. Using Repeat Masker to identify repetitive elements in genomic sequences. Curr protoc Bioinf 5, 4–10 (2004).Article 

Google Scholar 
Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res 35, W265–W268 (2007).Article 

Google Scholar 
Price, A. L., Jones, N. C. & Pevzner, P. A. De novo identification of repeat families in large genomes. Bioinformatics 21, i351–i358 (2005).Article 

Google Scholar 
Bao, W. D., Kojima, K. K. & Kohany, O. Repbase Update, a database of repetitive elements in eukaryotic genomes. Mob DNA 6, 1–6 (2015).Article 

Google Scholar 
Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res 27, 573–580 (1999).Article 

Google Scholar 
Liu, W. et al. Blood Transcriptome Analysis Reveals Gene Expression Differences between Yangtze Finless Porpoises from Two Habitats: Natural and Ex Situ Protected Waters. Fishes 7, 96 (2022).Article 

Google Scholar 
Yin, D. H. et al. Integrated analysis of blood mRNAs and microRNAs reveals immune changes with age in the Yangtze finless porpoise (Neophocaena asiaeorientalis). Comp Biochem Physiol B Biochem Mol Biol 256, 110635 (2021).Article 

Google Scholar 
Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol 37, 907–915 (2019).Article 

Google Scholar 
Kovaka, S. et al. Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome Biol 20, 1–13 (2019).Article 

Google Scholar 
Stanke, M., Diekhans, M., Baertsch, R. & Haussler, D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics 24, 637–644 (2008).Article 

Google Scholar 
Keane, M. et al. Insights into the evolution of longevity from the bowhead whale genome. Cell Rep 10, 112–122 (2015).Article 

Google Scholar 
Yim, H. S. et al. Minke whale genome and aquatic adaptation in cetaceans. Nat Genet 46, 88–92 (2014).Article 

Google Scholar 
Jones, S. J. et al. The genome of the beluga whale (Delphinapterus leucas). Genes 8, 378 (2017).Article 
ADS 

Google Scholar 
Zhou, X. M. et al. Baiji genomes reveal low genetic variability and new insights into secondary aquatic adaptations. Nat Commun 4, 1–6 (2013).Article 
ADS 

Google Scholar 
Foote, A. D. et al. Convergent evolution of the genomes of marine mammals. Nat Genet 47, 272–275 (2015).Article 

Google Scholar 
Keilwagen, J., Hartung, F. & Grau, J. GeMoMa: homology-based gene prediction utilizing intron position conservation and RNA-seq data. Methods Mol Biol 1962, 161–177 (2019).Article 

Google Scholar 
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 44, D457–D462 (2016).Article 

Google Scholar 
Bairoch, A. & Apweiler, R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res 28, 45–48 (2000).Article 

Google Scholar 
Korf, I. Gene finding in novel genomes. BMC bioinformatics 5, 1–9 (2004).Article 

Google Scholar 
Finn, R. D. et al. InterPro in 2017-beyond protein family and domain annotations. Nucleic Acids Res 45, D190–D199 (2017).Article 

Google Scholar 
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J Mol Biol 215, 403–410 (1990).Article 

Google Scholar 
Mulder, N. J. & Apweiler, R. InterPro and InterProScan: tools for protein sequence classification and comparison. Methods Mol Biol 396, 59–70 (2007).Article 

Google Scholar 
Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat Genet 25, 25–29 (2000).Article 

Google Scholar 
NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR21047154 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20760935 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20760936 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20997931 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20997932 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20997933 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20997934 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20997935 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRP389529 (2022).Yin, D. H. et al. Neophocaena asiaeorientalis sunameri isolate NAS202207, whole genome shotgun sequencing project. GenBank https://identifiers.org/insdc.gca:GCA_026225855.1 (2022).Yin, D. H. et al. Gapless genome assembly of East Asian finless porpoise, Neophocaena asiaeorientalis sunameri. figshare https://doi.org/10.6084/m9.figshare.20381274.v2 (2022).Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).Article 

Google Scholar 
Marçais, G. et al. MUMmer4: A fast and versatile genome alignment system. PLoS Comput Biol 14, e1005944 (2018).Article 

Google Scholar  More

Jackson, R. B. et al. The Ecology of soil carbon: Pools, vulnerabilities, and biotic and abiotic controls. Annu. Rev. Ecol. Evol. Syst. 48, 419–445. https://doi.org/10.1146/annurev-ecolsys-112414-054234 (2017).Article 

Google Scholar 
Sanderman, J., Hengl, T. & Fiske, G. J. Soil carbon debt of 12,000 years of human land use. PNAS 114, 9575–9580 (2017).Article 
ADS 
CAS 

Google Scholar 
Amelung, W. et al. Towards a global-scale soil climate mitigation strategy. Nat. Commun. 11, 5427. https://doi.org/10.1038/s41467-020-18887-7 (2020).Article 
ADS 
CAS 

Google Scholar 
Hopkins, A. & Holz, B. Grassland for agriculture and nature conservation: Production, quality and multi-functionality. Agron 4, 3–20 (2006).
Google Scholar 
van Veen, J. A., Liljeroth, E., Lekkerkerk, L. J. A. & van de Geijn, S. C. Carbon fluxes in plant-soil systems at elevated atmospheric CO2 levels. Ecol. Appl. 1, 175–181. https://doi.org/10.2307/1941810 (1991).Article 

Google Scholar 
Jones, M. B. & Donnelly, A. Carbon sequestration in temperate grassland ecosystems and the influence of management, climate and elevated CO2. New Phytol. 164, 423–439. https://doi.org/10.1111/j.1469-8137.2004.01201.x (2004).Article 

Google Scholar 
Ward, S. E. et al. Legacy effects of grassland management on soil carbon to depth. Glob. Change Biol. 22, 2929–2938. https://doi.org/10.1111/gcb.13246 (2016).Article 
ADS 

Google Scholar 
Hungate, B. A. et al. The fate of carbon in grasslands under carbon dioxide enrichment. Nature 388, 576–579. https://doi.org/10.1038/41550 (1997).Article 
ADS 
CAS 

Google Scholar 
Six, J., Conant, R. T., Paul, E. A. & Paustian, K. Stabilization mechanisms of soil organic matter: Implications for C-saturation of soils. Plant Soil 241, 155–176. https://doi.org/10.1023/A:1016125726789 (2002).Article 
CAS 

Google Scholar 
Chang, J. et al. Climate warming from managed grasslands cancels the cooling effect of carbon sinks in sparsely grazed and natural grasslands. Nat. Commun. 12, 118. https://doi.org/10.1038/s41467-020-20406-7 (2021).Article 
ADS 
CAS 

Google Scholar 
IPCC. 2001. Climate change 2001: The scientific basis contribution of working group 1 to the third assessment report of the intergovernmental panel on climate change In (eds Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., Van Der Linden, P. J., Dai, X., Maskell, K. & Johnson, C. A.) (Cambridge University Press).Gwin, L. Scaling-up sustainable livestock production: Innovation and challenges for grass-fed beef in the U.S. J. Sustain. Agric. 33, 189–209. https://doi.org/10.1080/10440040802660095 (2009).Article 

Google Scholar 
Iqbal, J., Siegrist, J. A., Nelson, J. A. & McCulley, R. L. Fungal endophyte infection increases carbon sequestration potential of southeastern USA tall fescue stands. Soil Biol. Biochem. 44, 81–92. https://doi.org/10.1016/j.soilbio.2011.09.010 (2012).Article 
CAS 

Google Scholar 
Robinson, R. A. & Sutherland, W. J. Post-war changes in arable farming and biodiversity in Great Britain. J. Appl. Ecol. 39, 157–176. https://doi.org/10.1046/j.1365-2664.2002.00695.x (2002).Article 

Google Scholar 
Law, Q. D., Bigelow, C. A. & Patton, A. J. Selecting turfgrasses and mowing practices that reduce mowing requirements. Crop Sci. 56, 3318–3327. https://doi.org/10.2135/cropsci2015.09.0595 (2016).Article 

Google Scholar 
White, L. M. Carbohydrate reserves of grasses: A review. Rangel Ecol. Manag. 26(1), 13–18 (1973).Article 
CAS 

Google Scholar 
Virkajarvi, P. Effects of defoliation height on regrowth of timothy and meadow fescue in the generative and vegetative phases of growth. Agric. Food Sci. 12, 177–193 (2003).Article 

Google Scholar 
Reicher, Z., Patton, A. J., Bigelow, C. A. & Voigt, T. Mowing, Thatching, Aerifying, and Rolling Turf (Turf Grass Sci. Purdue Univ, 2006).
Google Scholar 
Kaatz, P. Cutting management for cool-season forage grasses. Michigan State University Extension, https://www.canr.msu.edu/news/cutting_management_for_cool_season_forage_grasses (2011).Briske, D. D. Strategies of plant survival in grazed systems: A functional interpretation. Ecol. Manag. Graz. Syst. 37–67 (1996).Crider, F. J. Root-growth stoppage resulting from defoliation of grass (No. 156759). United States Department of Agriculture, Economic Research Service (1995).Lal, R., Negassa, W. & Lorenz, K. Carbon sequestration in soil. Curr. Opin. Environ. Sustain. 15, 79–86. https://doi.org/10.1016/j.cosust.2015.09.002 (2015).Article 

Google Scholar 
Coughenour, M. B., McNaughton, S. J. & Wallace, L. L. Modelling primary production of perennial graminoids – uniting physiological processes and morphometric traits. Ecol. Modell. 23, 101–134. https://doi.org/10.1016/0304-3800(84)90121-2 (1984).Article 
CAS 

Google Scholar 
Whipps, J. M. & Lynch, J. M. Energy losses by the plant in rhizodeposition. Plant products and the new technology / edited by K.W. Fuller and J.R. Gallon (1985).Johansson, G. Release of organic C from growing roots of meadow fescue (Festuca pratensis L.). Soil Biol. Biochem. 24, 427–433. https://doi.org/10.1016/0038-0717(92)90205-C (1992).Article 

Google Scholar 
Woodburn, A. T. Glyphosate: Production, pricing and use worldwide. Pest Manag. Sci. 56, 309–312. https://doi.org/10.1002/(SICI)1526-4998(200004)56:4%3c309::AID-PS143%3e3.0.CO;2-C (2000).Article 
CAS 

Google Scholar 
Duke, S. O. & Powles, S. B. Glyphosate: A once-in-a-century herbicide. Pest Manag. Sci. 64, 319–325. https://doi.org/10.1002/ps.1518 (2008).Article 
CAS 

Google Scholar 
Helander, M., Saloniemi, I. & Saikkonen, K. Glyphosate in northern ecosystems. Trends Plant Sci. 17, 569–574. https://doi.org/10.1016/j.tplants.2012.05.008 (2012).Article 
CAS 

Google Scholar 
Benbrook, C. M. Trends in glyphosate herbicide use in the United States and globally. Environ. Sci. Eur. 28, 3. https://doi.org/10.1186/s12302-016-0070-0 (2016).Article 
CAS 

Google Scholar 
Helander, M. et al. Glyphosate decreases mycorrhizal colonization and affects plant-soil feedback. Sci. Total Environ. 642, 285–291. https://doi.org/10.1016/j.scitotenv.2018.05.377 (2018).Article 
ADS 
CAS 

Google Scholar 
Helander, M., Pauna, A., Saikkonen, K. & Saloniemi, I. Glyphosate residues in soil affect crop plant germination and growth. Sci. Rep. 9, 19653. https://doi.org/10.1038/s41598-019-56195-3 (2019).Article 
ADS 
CAS 

Google Scholar 
Zaller, J. G. & Brühl, C. A. Editorial: Non-target effects of pesticides on organisms inhabiting agroecosystems. Front Environ. Sci. 7, 75. https://doi.org/10.3389/fenvs.2019.00075 (2019).Article 

Google Scholar 
Muola, A. et al. Risk in the circular food economy: Glyphosate-based herbicide residues in manure fertilizers decrease crop yield. Sci. Total Environ. 750, 141422. https://doi.org/10.1016/j.scitotenv.2020.141422 (2021).Article 
ADS 
CAS 

Google Scholar 
Fuchs, B., Saikkonen, K. & Helander, M. Glyphosate-modulated biosynthesis driving plant defense and species interactions. Trends Plant Sci. 26, 312–323. https://doi.org/10.1016/j.tplants.2020.11.004 (2021).Article 
CAS 

Google Scholar 
Fuchs, B. et al. A Glyphosate-based herbicide in soil differentially affects hormonal homeostasis and performance of non-target crop plants. Front Plant Sci. 12, 787958 (2022).Article 

Google Scholar 
Borggaard, O. K. & Gimsing, A. L. Fate of glyphosate in soil and the possibility of leaching to ground and surface waters: A review. Pest Manag. Sci. 64, 441–456. https://doi.org/10.1002/ps.1512 (2008).Article 
CAS 

Google Scholar 
Rueppel, M. L., Brightwell, B. B., Schaefer, J. & Marvel, J. T. Metabolism and degradation of glyphosate in soil and water. J. Agric. Food Chem. 25, 517–528. https://doi.org/10.1021/jf60211a018 (1977).Article 
CAS 

Google Scholar 
Carlisle, S. M. & Trevors, J. T. Glyphosate in the environment. Wat Air Soil Poll 39, 409–420 (1988).Article 
ADS 
CAS 

Google Scholar 
Torstensson, N. T. L., Lundgren, L. N. & Stenström, J. Influence of climatic and edaphic factors on persistence of glyphosate and 2,4-D in forest soils. Ecotoxicol. Environ. Saf. 18, 230–239. https://doi.org/10.1016/0147-6513(89)90084-5 (1989).Article 
CAS 

Google Scholar 
Stenrød, M., Eklo, O. M., Charnay, M.-P. & Benoit, P. Effect of freezing and thawing on microbial activity and glyphosate degradation in two Norwegian soils. Pest Manag. Sci. 61, 887–898. https://doi.org/10.1002/ps.1107 (2005).Article 
CAS 

Google Scholar 
Antier, C. et al. Glyphosate use in the European agricultural sector and a framework for its further monitoring. Sustainability 12, 5682. https://doi.org/10.3390/su12145682 (2020).Article 
CAS 

Google Scholar 
Jones, R. J. Effect of an associate grass, cutting interval, and cutting height on yield and botanical composition of Siratro pastures in a sub-tropical environment. Aust. J. Exp. Agric. 14, 334–342. https://doi.org/10.1071/ea9740334 (1974).Article 

Google Scholar 
Volenec, J. J. & Nelson, C. J. Responses of Tall Fescue leaf meristems to N fertilization and harvest frequency. Crop Sci. 23(4), 720–724. https://doi.org/10.2135/cropsci1983.0011183X002300040028x (1983).Article 

Google Scholar 
Saikkonen, K. et al. Fungal endophytes help prevent weed invasions. Agric. Ecosyst. Environ. 165, 1–5. https://doi.org/10.1016/j.agee.2012.12.002 (2013).Article 

Google Scholar 
Scavo, A. & Mauromicale, G. Integrated weed management in herbaceous field crops. Agronomy 10, 466. https://doi.org/10.3390/agronomy10040466 (2020).Article 

Google Scholar 
Clay, K. & Holah, J. Fungal endophyte symbiosis and plant diversity in successional fields. Science 285, 1742–1744. https://doi.org/10.1126/science.285.5434.1742 (1999).Article 
CAS 

Google Scholar 
Gundel, P. E., Pérez, L. I., Helander, M. & Saikkonen, K. Symbiotically modified organisms: Nontoxic fungal endophytes in grasses. Trends Plant Sci. 18, 420–427. https://doi.org/10.1016/j.tplants.2013.03.003 (2013).Article 
CAS 

Google Scholar 
Kauppinen, M., Saikkonen, K., Helander, M., Pirttilä, A. M. & Wäli, P. R. Epichloë grass endophytes in sustainable agriculture. Nat. Plants 2, 15224 (2016).Article 

Google Scholar 
Clay, K. Fungal endophytes of grasses. Annu. Rev. Ecol. Syst. 21, 275–297 (1990).Article 

Google Scholar 
Saikkonen, K., Young, C. A., Helander, M. & Schardl, C. L. Endophytic Epichloë species and their grass hosts: From evolution to applications. Plant Mol. Biol. 90, 665–675. https://doi.org/10.1007/s11103-015-0399-6 (2016).Article 
CAS 

Google Scholar 
Ahlholm, J. U., Helander, M., Lehtimäki, S., Wäli, P. & Saikkonen, K. Vertically transmitted fungal endophytes: Different responses of host-parasite systems to environmental conditions. Oikos 99, 173–183. https://doi.org/10.1034/j.1600-0706.2002.990118.x (2002).Article 

Google Scholar 
Easton, H. S. & Fletcher, L. R. in Proc. 6th International Symposium Fungal Endophytes of Grasses (eds Popay, A. J. & Thom, E. R.) 11–18 (New Zealand Grassland Association, 2007).Saari, S., Lehtonen, P., Helander, M. & Saikkonen, K. High variation in frequency of infection by endophytes in cultivars of meadow fescue in Finland. Grass Forage Sci. 64, 169–176. https://doi.org/10.1111/j.1365-2494.2009.00680.x (2009).Article 

Google Scholar 
König, J., Fuchs, B., Krischke, M., Mueller, M. J. & Krauss, J. Hide and seek: Infection rates and alkaloid concentrations of Epichloë festucae var. lolii in Lolium perenne along a land-use gradient in Germany. Grass Forage Sci. 73, 510–516. https://doi.org/10.1111/gfs.12330 (2018).Article 
CAS 

Google Scholar 
Krauss, J. et al. Epichloë endophyte infection rates and alkaloid content in commercially available grass seed mixtures in Europe. Microorganisms 8, 498. https://doi.org/10.3390/microorganisms8040498 (2020).Article 
CAS 

Google Scholar 
Brink, G. E., Casler, M. D. & Martin, N. P. Meadow Fescue, Tall Fescue, and Orchardgrass response to defoliation management. Agronomy J 102, 667–674. https://doi.org/10.2134/agronj2009.0376 (2010).Article 

Google Scholar 
Conant, R. T., Cerri, C. E. P., Osborne, B. B. & Paustian, K. Grassland management impacts on soil carbon stocks: A new synthesis. Ecol. Appl. 27, 662–668. https://doi.org/10.1002/eap.1473 (2017).Article 

Google Scholar 
Trlica, M. J. Distribution and utilization of carbohydrate reserves in range plants. In (ed Sosebee, R. E.) 73–96 (Rangeland Plant Physiology, 1977).Faeth, S. H. & Sullivan, T. J. Mutualistic asexual endophytes in a native grass are usually parasitic. Am. Nat. 161, 310–325. https://doi.org/10.1086/345937 (2003).Article 

Google Scholar 
Saikkonen, K., Saari, S. & Helander, M. Defensive mutualism between plants and endophytic fungi?. Fungal Divers. 41, 101–113. https://doi.org/10.1007/s13225-010-0023-7 (2010).Article 

Google Scholar 
Clay, K. & Schardl, C. Evolutionary origins and ecological consequences of endophyte symbiosis with grasses. Am. Nat. 160, 99–127. https://doi.org/10.1086/342161 (2002).Article 

Google Scholar 
Rozpądek, P. et al. The fungal endophyte Epichloë typhina improves photosynthesis efficiency of its host orchard grass (Dactylis glomerata). Planta 242, 1025–1035. https://doi.org/10.1007/s00425-015-2337-x (2015).Article 
CAS 

Google Scholar 
Xia, C. et al. An Epichloë endophyte improves photosynthetic ability and dry matter production of its host Achnatherum inebrians infected by Blumeria graminis under various soil water conditions. Fungal Ecol. 22, 26–34. https://doi.org/10.1016/j.funeco.2016.04.002 (2016).Article 

Google Scholar 
Malinowski, D., Leuchtmann, A., Schmidt, D. & Nosberger, J. Symbiosis with Neotyphodium uncinatum endophyte may increase the competitive ability of meadow fescue. Agron. J. 89, 833–839 (1997).Article 

Google Scholar 
Schardl, C. L., Leuchtmann, A. & Spiering, M. J. Symbioses of grasses with seedborne fungal endophytes. Ann. Rev. Plant Biol. 55, 315–340. https://doi.org/10.1146/annurev.arplant.55.031903.141735 (2004).Article 
CAS 

Google Scholar 
Chen, Z. et al. Fungal endophyte improves survival of Lolium perenne in low fertility soils by increasing root growth, metabolic activity and absorption of nutrients. Plant Soil 452, 185–206. https://doi.org/10.1007/s11104-020-04556-7 (2020).Article 
CAS 

Google Scholar 
Franz, J. E., Mao, M.K. and Sikorski, J.A. (1997). Uptake, transport and metabolism of glyphosate in plants, in Glyphosate: A unique global herbicide, ed by Franz JE, ACS Monograph No 189, American Chemical Society, Washington, DC, pp 143–181.Pline, W. A., Wilcut, J. W., Edmisten, K. L. & Wells, R. Physiological and morphological response of glyphosate-resistant and non-glyphosate-resistant cotton seedlings to root-absorbed glyphosate. Pestic. Biochem. Phys. 73, 48–58. https://doi.org/10.1016/S0048-3575(02)00014-7 (2002).Article 
CAS 

Google Scholar 
Johansson, G. Carbon distribution in grass (Festuca pratensis L.) during regrowth after cutting—utilization of stored and newly assimilated carbon. Plant Soil 151, 11–20. https://doi.org/10.1007/BF00010781 (1993).Article 
ADS 
CAS 

Google Scholar 
Ergon, Å. et al. How can forage production in Nordic and Mediterranean Europe adapt to the challenges and opportunities arising from climate change?. Euro J. Agron. 92, 97–106. https://doi.org/10.1016/j.eja.2017.09.016 (2018).Article 

Google Scholar 
Niemelainen, O. et al. Increase in perennial forage yields driven by climate change, at Apukka Research Station, Rovaniemi, 1980–2017. Agric. Food Sci. 29, 139–153 (2020).Article 

Google Scholar 
Anwar, M. R., Liu, D. L., Macadam, I. & Kelly, G. Adapting agriculture to climate change: A review. Theor. Appl. Climatol. 113, 225–245. https://doi.org/10.1007/s00704-012-0780-1 (2013).Article 
ADS 

Google Scholar 
Farmit. Nurmea yli kymppitonni hehtaarilta. Farmit.net. (accessed 28 June 2022); https://www.farmit.net/nurmikasvit-lypsylehma/2016/05/24/nurmea-yli-kymppitonni-hehtaarilta (2016).Peltonen, S., Aalto, K., Hennola, I. & Anttila, S. (Eds.). Peltojen kunnostus. (Tieto Tuottamaan; No. 145), (ProAgria Keskusten Liiton julkaisuja; No. 1163). ProAgria maaseutukeskusten liitto (2019).Laihonen, M., Saikkonen, K., Helander, M. & Tammaru, T. Insect oviposition preference between Epichloë-symbiotic and Epichloë-free grasses does not necessarily reflect larval performance. Ecol. Evol. 10, 7242–7249. https://doi.org/10.1002/ece3.6450 (2020).Article 

Google Scholar  More

Semple, K. T., Morriss, A. W. J. & Paton, G. I. Bioavailability of hydrophobic organic contaminants in soils: Fundamental concepts and techniques for analysis. Eur. J. Soil Sci. 54, 809–818 (2003).Article 
CAS 

Google Scholar 
Ivshina, I. et al. Removal of polycyclic aromatic hydrocarbons in soil spiked with model mixtures of petroleum hydrocarbons and heterocycles using biosurfactants from Rhodococcus ruber IEGM 231. J. Hazard. Mater. 312, 8–17 (2016).Article 
CAS 

Google Scholar 
Varjani, S. J. Microbial degradation of petroleum hydrocarbons. Bioresour. Technol. 223, 277–286 (2017).Article 
CAS 

Google Scholar 
Chen, J. et al. Long-chain n-alkane biodegradation coupling to methane production in an enriched culture from production water of a high-temperature oil reservoir. AMB Express 10, 63 (2020).Article 
CAS 

Google Scholar 
Li, Y. & Xiong, Y. Identification and quantification of mixed sources of oil spills based on distributions and isotope profiles of long-chain n-alkanes. Mar. Pollut. Bull. 58, 1868–1873 (2009).Article 
CAS 

Google Scholar 
Stout, S. A., Payne, J. R., Emsbo-Mattingly, S. D. & Baker, G. Weathering of field-collected floating and stranded Macondo oils during and shortly after the Deepwater Horizon oil spill. Mar. Pollut. Bull. 105, 7–22 (2016).Article 
CAS 

Google Scholar 
Wang, X. et al. Polycyclic aromatic hydrocarbons, polychlorinated biphenyls and legacy and current pesticides in indoor environment in Australia—occurrence, sources and exposure risks. Sci. Total Environ. 693, 133588 (2019).Article 
ADS 
CAS 

Google Scholar 
Qiao, M., Qi, W., Liu, H. & Qu, J. Oxygenated polycyclic aromatic hydrocarbons in the surface water environment: Occurrence, ecotoxicity, and sources. Environ. Int. 163, 107232 (2022).Article 

Google Scholar 
Abbasnezhad, H., Foght, J. M. & Gray, M. R. Adhesion to the hydrocarbon phase increases phenanthrene degradation by Pseudomonas fluorescens LP6a. Biodegradation 22, 485–496 (2011).Article 
CAS 

Google Scholar 
Abbasnezhad, H., Gray, M. & Foght, J. M. Influence of adhesion on aerobic biodegradation and bioremediation of liquid hydrocarbons. Appl. Microbiol. Biotechnol. 92, 653–675 (2011).Article 
CAS 

Google Scholar 
Dewangan, N. K. & Conrad, J. C. Bacterial motility enhances adhesion to oil droplets. Soft Matter 16, 8237–8244 (2020).Article 
ADS 
CAS 

Google Scholar 
Rodrigues, E. M., Cesar, D. E., Santos de Oliveira, R., de Paula Siqueira, T. & Tótola, M. R. Hydrocarbonoclastic bacterial species growing on hexadecane: Implications for bioaugmentation in marine ecosystems. Environ. Pollut. 267, (2020).Wang, J. D., Qu, C. T. & Song, S. F. Temperature-induced changes in the proteome of Pseudomonas aeruginosa during petroleum hydrocarbon degradation. Arch. Microbiol. 203, 2463–2473 (2021).Article 
CAS 

Google Scholar 
Bastiaens, L. et al. Isolation of adherent polycyclic aromatic hydrocarbon (PAH)-degrading bacteria using PAH-sorbing carriers. Appl. Environ. Microbiol. 66, 1834–1843 (2000).Article 
ADS 
CAS 

Google Scholar 
Tao, K., Zhao, S., Gao, P., Wang, L. & Jia, H. Impacts of Pantoea agglomerans strain and cation-modified clay minerals on the adsorption and biodegradation of phenanthrene. Ecotoxicol. Environ. Saf. 161, 237–244 (2018).Article 
CAS 

Google Scholar 
Xu, X. et al. Biodegradation potential of polycyclic aromatic hydrocarbons by immobilized Klebsiella sp. in soil washing effluent. Chemosphere 223, 140–147 (2019).Wang, H. et al. Transmembrane transport of polycyclic aromatic hydrocarbons by bacteria and functional regulation of membrane proteins. Front. Environ. Sci. Eng. 14, 1–21 (2020).Article 

Google Scholar 
Tarasova, E. V., Grishko, V. V. & Ivshina, I. B. Cell adaptations of Rhodococcus rhodochrous IEGM 66 to betulin biotransformation. Process Biochem. 52, 1–9 (2017).Article 
CAS 

Google Scholar 
Bohinc, K. et al. Available surface dictates microbial adhesion capacity. Int. J. Adhes. Adhes. 50, 265–272 (2014).Article 
CAS 

Google Scholar 
Carniello, V., Peterson, B. W., van der Mei, H. C. & Busscher, H. J. Physico-chemistry from initial bacterial adhesion to surface-programmed biofilm growth. Adv. Colloid Interface Sci. 261, 1–14 (2018).Article 
CAS 

Google Scholar 
Dorobantu, L. S., Bhattacharjee, S., Foght, J. M. & Gray, M. R. Analysis of force interactions between AFM tips and hydrophobic bacteria using DLVO theory. Langmuir 25, 6968–6976 (2009).Article 
CAS 

Google Scholar 
Lehocký, M. et al. Adhesion of Rhodococcus sp. S3E2 and Rhodococcus sp. S3E3 to plasma prepared Teflon-like and organosilicon surfaces. J. Mater. Process. Technol. 209, 2871–2875 (2009).Hori, K. & Matsumoto, S. Bacterial adhesion: From mechanism to control. Biochem. Eng. J. 48, 424–434 (2010).Article 
CAS 

Google Scholar 
Ivshina, I. B. et al. Biosurfactant-enhanced immobilization of hydrocarbon-oxidizing Rhodococcus ruber on sawdust. Appl. Microbiol. Biotechnol. 97, 5315–5327 (2013).Article 
CAS 

Google Scholar 
Pen, Y. et al. Effect of extracellular polymeric substances on the mechanical properties of Rhodococcus. Biochim. Biophys. Acta – Biomembr. 1848, 518–526 (2015).Article 
CAS 

Google Scholar 
De Cesare, F., Di Mattia, E., Zussman, E. & Macagnano, A. A study on the dependence of bacteria adhesion on the polymer nanofibre diameter. Environ. Sci. Nano 6, 778–797 (2019).Article 

Google Scholar 
Bergeau, D. et al. Unusual extracellular appendages deployed by the model strain Pseudomonas fluorescens C7R12. PLoS ONE 14, 1–20 (2019).Article 

Google Scholar 
Jin, X. & Marshall, J. S. Mechanics of biofilms formed of bacteria with fimbriae appendages. PLoS ONE 15, 1–22 (2020).Article 

Google Scholar 
Tarafdar, A., Sarkar, T. K., Chakraborty, S., Sinha, A. & Masto, R. E. Biofilm development of Bacillus thuringiensis on MWCNT buckypaper: Adsorption-synergic biodegradation of phenanthrene. Ecotoxicol. Environ. Saf. 157, 327–334 (2018).Article 
CAS 

Google Scholar 
Rodrigues, A. C., Wuertz, S., Brito, A. G. & Melo, L. F. Fluorene and phenanthrene uptake by Pseudomonas putida ATCC 17514: Kinetics and physiological aspects. Biotechnol. Bioeng. 90, 281–289 (2005).Article 
CAS 

Google Scholar 
Yang, H. Y., Jia, R. B., Chen, B. & Li, L. Degradation of recalcitrant aliphatic and aromatic hydrocarbons by a dioxin-degrader Rhodococcus sp. strain p52. Environ. Sci. Pollut. Res. 21, 11086–11093 (2014).Auffret, M. D., Yergeau, E., Labbé, D., Fayolle-Guichard, F. & Greer, C. W. Importance of Rhodococcus strains in a bacterial consortium degrading a mixture of hydrocarbons, gasoline, and diesel oil additives revealed by metatranscriptomic analysis. Appl. Microbiol. Biotechnol. 99, 2419–2430 (2015).Article 
CAS 

Google Scholar 
Ahmed, R. Z. & Ahmed, N. Isolation of Rhodococcus sp. CMGCZ capable to degrade high concentration of fluoranthene. Water. Air. Soil Pollut. 227, 162 (2016).Ivshina, I. B., Kuyukina, M. S. & Krivoruchko, A. V. Hydrocarbon-oxidizing bacteria and their potential in eco-biotechnology and bioremediation. in Microbial Resources (ed. Kurtboke, I.) 121–148 (Elsevier Inc., 2017). https://doi.org/10.1016/B978-0-12-804765-1.00006-0.Pi, Y. et al. Microbial degradation of four crude oil by biosurfactant producing strain Rhodococcus sp. Bioresour. Technol. 232, 263–269 (2017).Article 
CAS 

Google Scholar 
Cappelletti, M., Fedi, S. & Zannoni, D. Degradation of alkanes in Rhodococcus. in Biology of Rhodococcus, Microbiology Monographs 16 (ed. Alvarez, H. M.) 137–171 (Springer Nature Switzerland AG, 2019). https://doi.org/10.1007/978-3-030-11461-9_6.Kuyukina, M. S. & Ivshina, I. B. Application of Rhodococcus in bioremediation of contaminated environments. in Biology of Rhodococcus, Microbiology Monographs 16 (ed. Alvarez, H. M.) 231–262 (Springer Nature Switzerland, 2019). https://doi.org/10.1007/978-3-642-12937-7_9.Krivoruchko, A. V. et al. Adhesion of Rhodococcus ruber IEGM 342 to polystyrene studied using contact and non-contact temperature measurement techniques. Appl. Microbiol. Biotechnol. 102, 8525–8536 (2018).Article 
CAS 

Google Scholar 
Rubtsova, E. V., Kuyukina, M. S. & Ivshina, I. B. Effect of cultivation conditions on the adhesive activity of Rhodococcus cells towards n-hexadecane. Appl. Biochem. Microbiol. 48, 452–459 (2012).Article 
CAS 

Google Scholar 
Pearlman, R. S., Yalkowsky, S. H. & Banerjee, S. Water solubilities of polynuclear aromatic and heteroaromatic compounds. J. Phys. Chem. Ref. Data 13, 555–562 (1984).Article 
ADS 
CAS 

Google Scholar 
Wrenn, B. A. & Venosa, A. D. Selective enumeration of aromatic and aliphatic hydrocarbon degrading bacteria by a most-probable-number procedure. Can. J. Microbiol. 42, 252–258 (1996).Article 
CAS 

Google Scholar 
Christofi, N., Ivshina, I. B., Kuyukina, M. S. & Philp, J. C. Biological treatment of crude oil contaminated soil in Russia. Geol. Soc. Eng. Geol. Spec. Publ. 14, 45–51 (1998).
Google Scholar 
Sorongon, M. L., Bloodgood, R. A. & Burchard, R. P. Hydrophobicity, adhesion, and surface-exposed proteins of gliding bacteria. Appl. Environ. Microbiol. 57, 3193–3199 (1991).Article 
ADS 
CAS 

Google Scholar 
Bellon-Fontaine, M.-N., Rault, J. & van Ossb, C. J. Microbial adhesion to solvents : a novel method to determine the electron-donor/electron-acceptor or Lewis acid-base properties of microbial cells. Colloids Surf. B Biointerfaces 7, 47–53 (1996).Article 
CAS 

Google Scholar 
Mattos-Guaraldi, A. L., Formiga, L. C. D. & Andrade, A. F. B. Cell surface hydrophobicity of sucrose fermenting and nonfermenting Corynebacterium diphtheriae strains evaluated by different methods. Curr. Microbiol. 38, 37–42 (1999).Article 
CAS 

Google Scholar 
Nikiyan, H., Vasilchenko, A. & Deryabin, D. Humidity-dependent bacterial cells functional morphometry investigations using atomic forcemicroscope. Int. J. Microbiol. 2010, 704170 (2010).Article 

Google Scholar 
Xu, J. L. et al. Rhodococcus qingshengii sp. nov., a carbendazim-degrading bacterium. Int. J. Syst. Evol. Microbiol. 57, 2754–2757 (2007).Lee, S. D. & Kim, I. S. Rhodococcus spelaei sp. nov., isolated from a cave, and proposals that Rhodococcus biphenylivorans is a later synonym of Rhodococcus pyridinivorans, Rhodococcus qingshengii and Rhodococcus baikonurensis are later synonym. Int. J. Syst. Evol. Microbiol. 71, (2021).Korshunova, I. O., Pistsova, O. N., Kuyukina, M. S. & Ivshina, I. B. The effect of organic solvents on the viability and morphofunctional properties of Rhodococcus. Appl. Biochem. Microbiol. 52, 53–61 (2016).Article 

Google Scholar 
de Carvalho, C. C. C. R., Wick, L. Y. & Heipieper, H. J. Cell wall adaptations of planktonic and biofilm Rhodococcus erythropolis cells to growth on C5 to C16 n-alkane hydrocarbons. Appl. Microbiol. Biotechnol. 82, 311–320 (2009).Article 
CAS 

Google Scholar 
Kuyukina, M. S. et al. Oilfield wastewater biotreatment in a fluidized-bed bioreactor using co-immobilized Rhodococcus cultures. J. Environ. Chem. Eng. 5, 1252–1260 (2017).Article 
CAS 

Google Scholar 
Abdel-Shafy, H. I. & Mansour, M. S. M. A review on polycyclic aromatic hydrocarbons: Source, environmental impact, effect on human health and remediation. Egypt. J. Pet. 25, 107–123 (2016).Article 

Google Scholar 
He, J. et al. Subchronic exposure of benzo(a)pyrene interferes with the expression of Bcl-2, Ki-67, C-myc and p53, Bax, Caspase-3 in sub-regions of cerebral cortex and hippocampus. Exp. Toxicol. Pathol. 68, 149–156 (2016).Article 
CAS 

Google Scholar 
Boente, C., Baragaño, D. & Gallego, J. R. Benzo[a]pyrene sourcing and abundance in a coal region in transition reveals historical pollution, rendering soil screening levels impractical. Environ. Pollut. 266, (2020).Cao, Y. et al. Interfacial interaction between benzo[a]pyrene and pulmonary surfactant: Adverse effects on lung health. Environ. Pollut. 287, 117669 (2021).Article 
CAS 

Google Scholar 
Gallardo-Moreno, A. M. et al. Thermodynamic analysis of growth temperature dependence in the adhesion of Candida parapsilosis to polystyrene. Appl. Environ. Microbiol. 68, 2610–2613 (2002).Article 
ADS 
CAS 

Google Scholar 
Kuyukina, M. S., Ivshina, I. B., Korshunova, I. O., Stukova, G. I. & Krivoruchko, A. V. Diverse effects of a biosurfactant from Rhodococcus ruber IEGM 231 on the adhesion of resting and growing bacteria to polystyrene. AMB Express 6, 1–12 (2016).Article 
CAS 

Google Scholar 
Letek, M. et al. The genome of a pathogenic Rhodococcus: Cooptive virulence underpinned by key gene acquisitions. PLoS Genet. 6, 1–17 (2010).Article 

Google Scholar 
Dayan, A. et al. The involvement of coordinative interactions in the binding of dihydrolipoamide dehydrogenase to titanium dioxide – Localization of a putative binding site. J. Mol. Recognit. 30, 1–11 (2017).Article 
ADS 

Google Scholar 
Choi, E. J. & Dimitriadis, E. K. Cytochrome c adsorption to supported, anionic lipid bilayers studied via atomic force microscopy. Biophys. J. 87, 3234–3241 (2004).Article 
ADS 
CAS 

Google Scholar 
Wright, C. J. & Armstrong, I. The application of atomic force microscopy force measurements to the characterisation of microbial surfaces. Surf. Interface Anal. 38, 1419–1428 (2006).Article 
CAS 

Google Scholar 
Salerno, M., Dante, S., Patra, N. & Diaspro, A. AFM measurement of the stiffness of layers of agarose gel patterned with polylysine. Microsc. Res. Tech. 73, 982–990 (2010).CAS 

Google Scholar 
Campbell, J. E., Yang, J. & Day, G. M. Predicted energy-structure-function maps for the evaluation of small molecule organic semiconductors. J. Mater. Chem. C 5, 7574–7584 (2017).Article 
CAS 

Google Scholar 
Wang, N. et al. Molecular elucidating of an unusual growth mechanism for polycyclic aromatic hydrocarbons in confined space. Nat. Commun. 11, 1079 (2020).Article 
ADS 
CAS 

Google Scholar  More

Keenan, R. J. et al. Dynamics of global forest area: results from the FAO Global Forest Resources Assessment 2015. For. Ecol. Manage. 352, 9–20 (2015).Article 

Google Scholar 
Arneth, A. et al. in Special Report on Climate Change and Land (eds Shukla, P. R. et al.) Ch. 1 (IPCC, 2019).Piao, S. et al. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Glob. Biogeochem. Cycles 21, GB3018 (2007).Article 

Google Scholar 
Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).Article 

Google Scholar 
Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).Article 

Google Scholar 
Liu, Y. Y. et al. Recent reversal in loss of global terrestrial biomass. Nat. Clim. Change 5, 470–474 (2015).Article 

Google Scholar 
Chen, J. M. et al. Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink. Nat. Commun. 10, 4259 (2019).Article 

Google Scholar 
Myneni, R. B. et al. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).Article 

Google Scholar 
Filipchuk, A. et al. Russian forests: a new approach to the assessment of carbon stocks and sequestration capacity. Environ. Dev. 26, 68–75 (2018).Article 

Google Scholar 
Goodale, C. L. et al. Forest carbon sinks in the Northern Hemisphere. Ecol. Appl. 12, 891–899 (2002).Article 

Google Scholar 
Tchebakova, N. M. et al. Energy and mass exchange and the productivity of main Siberian ecosystems (from eddy covariance measurements). 2. Carbon exchange and productivity. Biol. Bull. 42, 579–588 (2015).Article 

Google Scholar 
Vaganov, E. A. et al. Forests and swamps of Siberia in the global carbon cycle. Contemp. Probl. Ecol. 1, 168–182 (2008).Article 

Google Scholar 
Schepaschenko, D. et al. Russian forest sequesters substantially more carbon than previously reported. Sci. Rep. 11, 12825 (2021).Article 

Google Scholar 
Shvidenko, A. & Schepaschenko, D. Climate change and wildfires in Russia. Contemp. Probl. Ecol. 6, 683–692 (2013).Article 

Google Scholar 
Bradshaw, C. J. A. & Warkentin, I. G. Global estimates of boreal forest carbon stocks and flux. Glob. Planet. Change 128, 24–30 (2015).Article 

Google Scholar 
Curtis, P. G. et al. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).Article 

Google Scholar 
Sukhinin, A. I. et al. AVHRR-based mapping of fires in Russia: new products for fire management and carbon cycle studies. Remote Sens. Environ. 93, 546–564 (2004).Article 

Google Scholar 
Soja, A. J. et al. Climate-induced boreal forest change: predictions versus current observations. Glob. Planet. Change 56, 274–296 (2007).Article 

Google Scholar 
Dolman, A. J. et al. An estimate of the terrestrial carbon budget of Russia using inventory-based, eddy covariance and inversion methods. Biogeosciences 9, 5323–5340 (2012).Article 

Google Scholar 
Schaphoff, S. et al. Tamm review: Observed and projected climate change impacts on Russia’s forests and its carbon balance. For. Ecol. Manage. 361, 432–444 (2016).Article 

Google Scholar 
de Jong, R. et al. Trend changes in global greening and browning: contribution of short-term trends to longer-term change. Glob. Change Biol. 18, 642–655 (2012).Article 

Google Scholar 
Buermann, W. et al. Recent shift in Eurasian boreal forest greening response may be associated with warmer and drier summers. Geophys. Res. Lett. 41, 1995–2002 (2014).Article 

Google Scholar 
Rödig, E. et al. Spatial heterogeneity of biomass and forest structure of the Amazon rain forest: Linking remote sensing, forest modelling and field inventory. Glob. Ecol. Biogeogr. 26, 1292–1302 (2017).Article 

Google Scholar 
Quegan, S. et al. Estimating the carbon balance of central Siberia using a landscape-ecosystem approach, atmospheric inversion and dynamic global vegetation models. Glob. Change Biol. 17, 351–365 (2011).Article 

Google Scholar 
Gurney, K. R. et al. Interannual variations in continental-scale net carbon exchange and sensitivity to observing networks estimated from atmospheric CO2 inversions for the period 1980 to 2005. Glob. Biogeochem. Cycles 22, GB3025 (2008).Article 

Google Scholar 
Stephens, B. B. et al. Weak northern and strong tropical land carbon uptake from vertical profiles of atmospheric CO2. Science 316, 1732–1735 (2007).Article 

Google Scholar 
Leskinen, P. et al. Russian Forests and Climate Change: What Science Can Tell Us 11 (EFI, 2020); https://doi.org/10.36333/wsctu11Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020).Article 

Google Scholar 
Stow, D. A. et al. Remote sensing of vegetation and land-cover change in Arctic tundra ecosystems. Remote Sens. Environ. 89, 281–308 (2004).Article 

Google Scholar 
Karlsen, S. R. et al. A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high Arctic plant productivity. Environ. Res. Lett. 13, 025011 (2018).Article 

Google Scholar 
Ding, Z. et al. Nearly half of global vegetated area experienced inconsistent vegetation growth in terms of greenness, cover, and productivity. Earths Future 8, e2020EF001618 (2020).Article 

Google Scholar 
Fan, L. et al. Satellite-observed pantropical carbon dynamics. Nat. Plants 5, 944–951 (2019).Article 

Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).Article 

Google Scholar 
Giglio, L. et al. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178, 31–41 (2016).Article 

Google Scholar 
Blunden, J. & Arndt, D. S. State of the climate in 2015. Bull. Am. Meteorol. Soc. 97, Si–S275 (2016).Article 

Google Scholar 
Bastos, A. et al. Was the extreme Northern Hemisphere greening in 2015 predictable? Environ. Res. Lett. 12, 044016 (2017).Article 

Google Scholar 
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).Article 

Google Scholar 
Kukavskaya, E. A. et al. Biomass dynamics of central Siberian Scots pine forests following surface fires of varying severity. Int. J. Wildland Fire 23, 872–886 (2014).Article 

Google Scholar 
Gauthier, S. et al. Boreal forest health and global change. Science 349, 819 (2015).Article 

Google Scholar 
Harris, N. L. et al. Baseline map of carbon emissions from deforestation in tropical regions. Science 336, 1573 (2012).Article 

Google Scholar 
Qin, Y. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Change 11, 442–448 (2021).Article 

Google Scholar 
Rogers, B. M. et al. Influence of tree species on continental differences in boreal fires and climate feedbacks. Nat. Geosci. 8, 228–234 (2015).Article 

Google Scholar 
Shvetsov, E. G. et al. Assessment of post-fire vegetation recovery in southern Siberia using remote sensing observations. Environ. Res. Lett. 14, 055001 (2019).Article 

Google Scholar 
Wang, J. A. et al. Disturbance suppresses the aboveground carbon sink in North American boreal forests. Nat. Clim. Change 11, 435–441 (2021).Article 

Google Scholar 
Xu, L. et al. Changes in global terrestrial live biomass over the 21st century. Sci. Adv. 7, eabe9829 (2021).Article 

Google Scholar 
Shuman, J. K. et al. Forest forecasting with vegetation models across Russia. Can. J. For. Res. 45, 175–184 (2014).Article 

Google Scholar 
Flannigan, M. et al. Impacts of climate change on fire activity and fire management in the circumboreal forest. Glob. Change Biol. 15, 549–560 (2009).Article 

Google Scholar 
Yuan, W. et al. Differentiating moss from higher plants is critical in studying the carbon cycle of the boreal biome. Nat. Commun. 5, 4270 (2014).Article 

Google Scholar 
Harris, N. L. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Change 11, 234–240 (2021).Article 

Google Scholar 
Larjavaara, M. et al. Post-fire carbon and nitrogen accumulation and succession in Central Siberia. Sci. Rep. 7, 12776 (2017).Article 

Google Scholar 
Berner, L. T. et al. Cajander larch (Larix cajanderi) biomass distribution, fire regime and post-fire recovery in northeastern Siberia. Biogeosciences 9, 3943–3959 (2012).Article 

Google Scholar 
Myneni, R. et al. MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500 m SIN Grid v.006 (LAADS DAAC, 2015).Houghton, R. A. et al. Mapping Russian forest biomass with data from satellites and forest inventories. Environ. Res. Lett. 2, 045032 (2007).Article 

Google Scholar 
DiMiceli, C. et al. Annual Global Automated MODIS Vegetation Continuous Fields (MOD44B) at 250 m Spatial Resolution for Data Years Beginning Day 65, 2000–2014, Collection 5 Percent Tree Cover v.6 (University of Maryland, 2017).Simard, M. et al. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. 116, G04021 (2011).
Google Scholar 
Broxton, P. et al. A global land cover climatology using MODIS data. J. Appl. Meteorol. Climatol. 53, 1593–1605 (2014).Article 

Google Scholar 
Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).Article 

Google Scholar 
Santoro, M. et al. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth Syst. Sci. Data. 13, 3927–3950 (2021).Article 

Google Scholar 
Carreiras, J. M. B. et al. Coverage of high biomass forests by the ESA BIOMASS mission under defense restrictions. Remote Sens. Environ. 196, 154–162 (2017).Article 

Google Scholar 
Penman, J. et al. Good Practice Guidance for Land Use, Land-Use Change and Forestry (IGES, 2013).Avitabile, V. et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Change Biol. 22, 1406–1420 (2016).Article 

Google Scholar 
Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182–185 (2012).Article 

Google Scholar 
Fernandez-Moran, R. et al. SMOS-IC: an alternative SMOS soil moisture and vegetation optical depth product. Remote Sens. 9, 457 (2017).Article 

Google Scholar 
Wigneron, J.-P. et al. SMOS-IC data record of soil moisture and L-VOD: historical development, applications and perspectives. Remote Sens. Environ. 254, 112238 (2021).Article 

Google Scholar 
Mitchard, E. T. A. et al. Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites. Glob. Ecol. Biogeogr. 23, 935–946 (2014).Article 

Google Scholar 
Mitchard, E. T. A. et al. Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps. Carbon Balance Manage. 8, 10 (2013).Article 

Google Scholar 
Harmon, M. E. et al. Release of coarse woody detritus-related carbon: a synthesis across forest biomes. Carbon Balance Manage. 15, 1 (2020).Article 

Google Scholar 
Bartalev, S. A. & Stytsenko, F. V. Assessment of forest-stand destruction by fires based on remote-sensing data on the seasonal distribution of burned areas. Contemp. Probl. Ecol. 14, 711–716 (2021).Article 

Google Scholar 
van Wees, D. et al. The role of fire in global forest loss dynamics. Glob. Change Biol. 27, 2377–2391 (2021).Article 

Google Scholar 
Vicente‐Serrano, S. M. et al. A multiscalar drought index sensitive to global warming: the Standardized Precipitation Evapotranspiration Index. J. Clim. 23, 1696–1718 (2010).Article 

Google Scholar 
Schepaschenko, D. et al. A new hybrid land cover dataset for Russia: a methodology for integrating statistics, remote sensing and in situ information. J. Land Use Sci. 6, 245–259 (2011).Article 

Google Scholar 
Du, J. et al. A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations. Earth Syst. Sci. Data. 9, 791–808 (2017).Article 

Google Scholar 
Brandt, M. et al. Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands. Nat. Ecol. Evol. 2, 827–835 (2018).Article 

Google Scholar 
De Grandpré, L. et al. Long-term post-fire changes in the northeastern boreal forest of Quebec. J. Veg. Sci. 11, 791–800 (2000).Article 

Google Scholar  More

Bhatt, S. et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature 526, 207–211 (2015).Article 
ADS 

Google Scholar 
WHO. Test procedures for insecticide resistance monitoring in malaria vector mosquitoes (2016).WHO. Guidelines for malaria vector control (2019).Gnankiné, O. et al. Insecticide resistance in Bemisia tabaci Gennadius (Homoptera: Aleyrodidae) and Anopheles gambiae Giles (Diptera: Culicidae) could compromise the sustainability of malaria vector control strategies in West Africa. Acta Trop. 128, 7–17 (2013).Article 

Google Scholar 
Ranson, H. & Lissenden, N. Insecticide resistance in African Anopheles mosquitoes: A worsening situation that needs urgent action to maintain malaria control. Trends Parasitol. 32, 187–196 (2016).Article 

Google Scholar 
Reid, M. C. & McKenzie, F. E. The contribution of agricultural insecticide use to increasing insecticide resistance in African malaria vectors. Malar. J. 15, 1–8 (2016).Article 

Google Scholar 
Huijben, S. & Paaijmans, K. P. Putting evolution in elimination: Winning our ongoing battle with evolving malaria mosquitoes and parasites. Spec. Issue Rev. Synth. https://doi.org/10.1111/eva.12530 (2017).Article 

Google Scholar 
WHO. Global technical strategy for malaria 2016–2030, 2021 update (2021).Ranson, H. et al. Identification of a point mutation in the voltage-gated sodium channel gene of Kenyan Anopheles gambiae associated with resistance to DDT and pyrethroids. Insect Mol. Biol. 9, 491–497 (2000).Article 

Google Scholar 
Weill, M. et al. The unique mutation in ace-1 giving high insecticide resistance is easily detectable in mosquito vectors. Insect Mol. Biol. 13, 1–7 (2004).Article 
ADS 

Google Scholar 
Ranson, H. et al. Pyrethroid resistance in African anopheline mosquitoes: What are the implications for malaria control?. Trends Parasitol. 27, 91–98 (2011).Article 

Google Scholar 
Hemingway, J., Hawkes, N. J., McCarroll, L. & Ranson, H. The molecular basis of insecticide resistance in mosquitoes. Insect Biochem. Mol. Biol. 34, 653–665 (2004).Article 

Google Scholar 
Martinez-Torres, D. et al. Molecular characterization of pyrethroid knockdown resistance (kdr) in the major malaria vector Anopheles gambiae s.s.. Insect Mol. Biol. 7, 179–184 (1998).Article 

Google Scholar 
Jones, C. et al. Footprints of positive selection associated with a mutation (N1575Y) in the voltage-gated sodium channel of Anopheles gambiae. Proc. Natl. Acad. Sci. U. S. A. 109, 6614–6619 (2012).Article 
ADS 

Google Scholar 
Hunt, R. H., Brooke, B. D., Pillay, C., Koekemoer, L. L. & Coetzee, M. Laboratory selection for and characteristics of pyrethroid resistance in the malaria vector Anopheles funestus. Med. Vet. Entomol. 19, 271–275 (2005).Article 

Google Scholar 
Glunt, K. D., Thomas, M. B. & Read, A. F. The effects of age, exposure history and malaria infection on the susceptibility of Anopheles mosquitoes to low concentrations of pyrethroid. PLoS One 6, e24968 (2011).
Article 
ADS 

Google Scholar 
Rajatileka, S., Burhani, J. & Ranson, H. Mosquito age and susceptibility to insecticides. Trans. R. Soc. Trop. Med. Hyg. 105, 247–253 (2011).Article 

Google Scholar 
Chouaibou, M. S. et al. Increase in susceptibility to insecticides with aging of wild Anopheles gambiae mosquitoes from Côte d’Ivoire. BMC Infect. Dis. 12, 1–7 (2012).Article 

Google Scholar 
Jones, C. M. et al. Aging partially restores the efficacy of malaria vector control in insecticide-resistant populations of Anopheles gambiae s.l. from Burkina Faso. Malar. J. 11, 1–11 (2012).Article 

Google Scholar 
Kulma, K., Saddler, A. & Koella, J. C. Effects of age and larval nutrition on phenotypic expression of insecticide-resistance in Anopheles Mosquitoes. PLoS ONE 8, 8–11 (2013).Article 

Google Scholar 
Aïzoun, N., Aïkpon, R., Azondekon, R., Asidi, A. & Akogbéto, M. Comparative susceptibility to permethrin of two Anopheles gambiae s.l. populations from Southern Benin, regarding mosquito sex, physiological status and mosquito age. Asian Pac. J. Trop. Biomed. 4, 312–317 (2014).Article 

Google Scholar 
Collins, E. et al. The relationship between insecticide resistance, mosquito age and malaria prevalence in Anopheles gambiae s.l. from Guinea. Sci. Rep. 9, 1–12 (2019).Article 

Google Scholar 
Oliver, S. & Brooke, B. The effect of larval nutritional deprivation on the life history and DDT resistance phenotype in laboratory strains of the malaria vector Anopheles arabiensis. Malar. J. 12, 1–9 (2013).Article 

Google Scholar 
Owusu, H. F., Chitnis, N. & Müller, P. Insecticide susceptibility of Anopheles mosquitoes changes in response to variations in the larval environment. Sci. Rep. 7, 1–9 (2017).Article 

Google Scholar 
Sovegnon, P. M., Fanou, M. J., Akoton, R. & Djihinto, O. Y. Effects of larval diet on the life-history traits and phenotypic expression of pyrethroid resistance in the major malaria vector Anopheles gambiae s.s. Preprint at bioRxiv http://doi.org/https://doi.org/10.1101/2022.01.11.475801 (2022).Halliday, W. R. & Feyereisen, R. Why does DDT toxicity change after a blood meal in adult female Culex pipiens?. Pestic. Biochem. Physiol. 28, 172–181 (1987).Article 

Google Scholar 
Oliver, S. V., Lyons, C. L. & Brooke, B. D. The effect of blood feeding on insecticide resistance intensity and adult longevity in the major malaria vector Anopheles funestus (Diptera: Culicidae). Sci. Rep. 12, 1–9 (2022).Article 

Google Scholar 
Farenhorst, M. et al. Fungal infection counters insecticide resistance in African malaria mosquitoes. Proc. Natl. Acad. Sci. U. S. A. 106, 17443–17447 (2009).Article 
ADS 

Google Scholar 
Koella, J. C., Saddler, A. & Karacs, T. P. S. Blocking the evolution of insecticide-resistant malaria vectors with a microsporidian. Evol. Appl. 5, 283–292 (2012).Article 

Google Scholar 
Alout, H. et al. Interplay between Plasmodium infection and resistance to insecticides in vector mosquitoes. J. Infect. Dis. 210, 1464–1470 (2014).Article 

Google Scholar 
Glunt, K. D., Oliver, S. V., Hunt, R. H. & Paaijmans, K. P. The impact of temperature on insecticide toxicity against the malaria vectors Anopheles arabiensis and Anopheles funestus. Malar. J. 17, 1–8 (2018).Article 

Google Scholar 
Oliver, S. & Brooke, B. The effect of commercial herbicide exposure on the life history and insecticide resistance phenotypes of the major malaria vector Anopheles arabiensis (Diptera: culicidae). Acta Trop. 188, 152–160 (2018).Article 

Google Scholar 
Oliver, S. & Brooke, B. The effect of metal pollution on the life history and insecticide resistance phenotype of the major malaria vector Anopheles arabiensis (Diptera: Culicidae). PLoS ONE 13, 1–17 (2018).Article 

Google Scholar 
Foster, W. A. Mosquito sugar feeding and reproductive energetics. Annu. Rev. Entomol. 40, 443–474 (1995).Article 

Google Scholar 
Nyasembe, V. O., Tchouassi, D. P., Pirk, C. W. W., Sole, C. L. & Torto, B. Host plant forensics and olfactory-based detection in Afro-tropical mosquito disease vectors. PLoS Negl. Trop. Dis. 12, 1–21 (2018).Article 

Google Scholar 
Barredo, E. & DeGennaro, M. Not just from blood: Mosquito nutrient acquisition from nectar sources. Trends Parasitol. 36, 473–484 (2020).Article 

Google Scholar 
Stone, C. M. & Foster, W. A. Plant-sugar feeding and vectorial capacity. In Ecology of Parasite-Vector Interactions (eds Takken, W. & Koenraadt, C.) 35–79 (Wageningen Academic, 2013). https://doi.org/10.3920/978-90-8686-744-8_3.Chapter 

Google Scholar 
Hien, D. F. D. S. et al. Plant-mediated effects on mosquito capacity to transmit human malaria. PLoS Pathog. 12, e1005773 (2016).Article 

Google Scholar 
Stone, C., Witt, A., Walsh, G., Foster, W. & Murphy, S. Would the control of invasive alien plants reduce malaria transmission? A review. Parasites Vectors 11, 1–18 (2018).Article 

Google Scholar 
Ebrahimi, B. et al. Alteration of plant species assemblages can decrease the transmission potential of malaria mosquitoes. J. Appl. Ecol. 55, 841–851 (2018).Article 

Google Scholar 
Manda, H. et al. Discriminative feeding behaviour of Anopheles gambiae s.s. on endemic plants in western Kenya. Med. Vet. Entomol. 21, 103–111 (2007).Article 

Google Scholar 
Nyasembe, V. O. et al. Plasmodium falciparum infection increases Anopheles gambiae attraction to nectar sources and sugar uptake. Curr. Biol. 24, 217–221 (2014).Article 

Google Scholar 
Després, L., David, J. P. & Gallet, C. The evolutionary ecology of insect resistance to plant chemicals. Trends Ecol. Evol. 22, 298–307 (2007).Article 

Google Scholar 
Nkya, T. E., Akhouayri, I., Kisinza, W. & David, J. P. Impact of environment on mosquito response to pyrethroid insecticides: Facts, evidences and prospects. Insect Biochem. Mol. Biol. 43, 407–416 (2013).Article 

Google Scholar 
Li, X., Schuler, M. A. & Berenbaum, M. R. Molecular mechanisms of metabolic resistance to synthetic and natural xenobiotics. Annu. Rev. Entomol. 52, 231–253 (2007).Article 

Google Scholar 
Bationo, C. S. et al. Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Diébougou health district, Burkina Faso, 2016–2017. Sci. Rep. 11, 1–12 (2021).Article 

Google Scholar 
Namountougou, M. et al. First report of the L1014S kdr mutation in wild populations of Anopheles gambiae M and S molecular forms in Burkina Faso (West Africa). Acta Trop. 125, 123–127 (2013).Article 

Google Scholar 
Service, M. W. A critical review of procedures for sampling populations of adult mosquitoes. Bull. Entomol. Res. 67, 343–382 (1977).Article 

Google Scholar 
Thiombiano, A. et al. Catalogue des plantes vasculaires du Burkina Faso. In Boissiera Vol. 65 (ed Cyrille Chatelain) (Conservatoire et Jardin botaniques, 2012).Morlais, I., Ponçon, N., Simard, F., Cohuet, A. & Fontenille, D. Intraspecific nucleotide variation in Anopheles gambiae: New insights into the biology of malaria vectors. Am. J. Trop. Med. Hyg. 71, 795–802 (2004).Article 

Google Scholar 
Santolamazza, F. et al. Insertion polymorphisms of SINE200 retrotransposons within speciation islands of Anopheles gambiae molecular forms. Malar. J. 7, 163 (2008).Article 

Google Scholar 
R Core Team. A language and environment for statistical computing (2021).Crawley, M. J. The R Book (Wiley, 2007).Book 
MATH 

Google Scholar 
Lenth, R. V. emmeans: Estimated marginal means, aka least-squares means (2021).Hien, A. et al. Evidence supporting deployment of next generation insecticide treated nets in Burkina Faso: Bioassays with either chlorfenapyr or piperonyl butoxide increase mortality of pyrethroid-resistant Anopheles gambiae. Malar. J. 20, 1–13 (2021).Article 

Google Scholar 
Nicolson, S. W., Nepi, M. & Pacini, E. Nectaries and Nectar (Springer, Dordrecht, 2007).Book 

Google Scholar 
Abdu-Allah, G. et al. Dietary antioxidants impact DDT resistance in Drosophila melanogaster. PLoS ONE 15, 1–12 (2020).Article 

Google Scholar 
Gnankiné, O. & Bassolé, I. L. H. N. Essential oils as an alternative to pyrethroids’ resistance against Anopheles species complex giles (Diptera: Culicidae). Molecules 22, 1321 (2017).Article 

Google Scholar 
Gendrin, M. & Christophides, G. K. The Anopheles mosquito microbiota and their impact on pathogen transmission. In Anopheles Mosquitoes—New Insights into Malar. Vectors (ed. Manguin, S.) (IntechOpen, 2013).Saab, S. A. et al. The environment and species affect gut bacteria composition in laboratory co-cultured Anopheles gambiae and Aedes albopictus mosquitoes. Sci. Rep. 10, 1–13 (2020).Article 

Google Scholar 
Dada, N., Sheth, M., Liebman, K., Pinto, J. & Lenhart, A. Whole metagenome sequencing reveals links between mosquito microbiota and insecticide resistance in malaria vectors. Sci. Rep. 8, 1–13 (2018).Article 

Google Scholar 
Barnard, K., Jeanrenaud, A. C. S. N., Brooke, B. D. & Oliver, S. V. The contribution of gut bacteria to insecticide resistance and the life histories of the major malaria vector Anopheles arabiensis (Diptera: Culicidae). Sci. Rep. 9, 1–11 (2019).Article 

Google Scholar 
Omoke, D. et al. Western Kenyan Anopheles gambiae showing intense permethrin resistance harbour distinct microbiota. Malar. J. 20, 1–14 (2021).Article 

Google Scholar 
Pelloquin, B. et al. Overabundance of Asaia and Serratia Bacteria is associated with deltamethrin insecticide susceptibility in Anopheles coluzzii from Agboville, Côte d’Ivoire. Microbiol. Spectr. 9, e00157-21 (2021).Article 

Google Scholar 
WHO. Test procedures for insecticide resistance monitoring in malaria vector mosquitoes (2013).Owusu, H. F., Jančáryová, D., Malone, D. & Müller, P. Comparability between insecticide resistance bioassays for mosquito vectors: Time to review current methodology?. Parasites Vectors 8, 1–11 (2015).Article 

Google Scholar  More

All statistical analyses were performed through R version 4.1.050. Besides the explicitly mentioned packages, the R packages cowplot51, data.table52, dplyr53, ggplot254, itsadug55, purrr56, raster57, sf58, sfheaders59, tibble60 and tidyr61 were key for data handling, data analysis, and plotting. Posterior distributions were summarised through means and credible intervals (CIs). CIs are the highest density intervals, calculated through the package bayestestR62. To summarise multiple posterior distributions, 5000 Monte Carlo simulations were used.Study regionThe study included data from the whole of Switzerland. As an observation unit for records, we chose 1 × 1 km squares (henceforth squares). Switzerland covers 41,285 km2, spanning a large gradient in elevation, climate and land use. It can be divided into five coarse biogeographic regions based on floristic and faunistic distributions and on institutional borders of municipalities63 (Fig. 1b). The Jura is a mountainous but agricultural landscape in the northwest (~4200 km2, 300–1600 m asl; annual mean temperature: ~9.4 °C, annual precipitation: ~1100 mm (gridded climate data here and in the following from MeteoSwiss (https://www.meteoswiss.admin.ch), average 1980–2020, at sites ~500 m asl.)). The Jura is separated from the Alps by the Plateau, which is the lowland region spanning from the southwest to the northeast (~11,300 km2, 250–1400 m asl, mostly below 1000 m asl; ~9.5 °C, ~1100 mm). It is the most densely populated region with most intensive agricultural use. For the Alps, three regions can be distinguished. The Northern Alps (~10,700 km2, 350–4000 m asl; ~9.2 °C, ~1400 mm), which entail the area from the lower Prealps, which are rather densely populated and largely used agriculturally, up to the northern alpine mountain range. The Central Alps (~11,300 km2, 450–4600 m asl; ~9.5 °C, ~800 mm) comprise of the highest mountain ranges in Switzerland and the inner alpine valleys characterised by more continental climate (i.e., lower precipitation). Intensive agricultural land use is concentrated in the lower elevations and agriculture in higher elevations is mostly restricted to grassland areas used for summer grazing. The Southern Alps (~3800 km2, 200–3800 m asl; ~10.4 °C, 1700 mm) range from the southern alpine mountain range down to the lowest elevations of Switzerland and are clearly distinguished from the other regions climatically, as they are influenced by Mediterranean climate, resulting in, e.g., milder winters. Besides differences between biogeographic regions, climate, land use and changes therein vary greatly between different elevations (Supplementary Fig. S9). To account for these differences, we split the regions in two elevation classes at the level of squares. All squares with a mean elevation of less than 1000 m asl were assigned to the low elevation, whereas squares above 1000 m asl were assigned to the high elevation (no squares in the Plateau fell in the high elevation). This resulted in nine bioclimatic zones (Fig. 1b), for which separate species trends were estimated in the subsequent analyses. The threshold of 1000 m asl enabled a meaningful distinction based on the studied drivers (climate and land-use change) and was also determined by the availability of records data (high coverage in all nine bioclimatic zones).Species detection dataWe extracted records of butterflies (refers here to Papilionoidea as well as Zygaenidae moths), grasshoppers (refers here to all Orthoptera) and dragonflies (refers here to all Odonata) from the database curated by info fauna (The Swiss Faunistic Records Centre; metadata available from the GBIF database at https://doi.org/10.15468/atyl1j, https://doi.org/10.15468/bcthst, https://doi.org/10.15468/fcxtjg). This database unites faunistic records made in Switzerland from various sources including both records by private persons and from projects such as research projects, Red-List inventories or checks of revitalisation measures. Only records with a sufficient precision, both temporally (day of recording) and spatially (place of recording known to the precision of 1 km2 or less), were used for analyses. Besides temporal and spatial information, information on the observer and the project (if any) was obtained for each record. All records made by a person/project on a day in a square were attributed to one visit, which was later used as replication unit to model the observation process (see below).We included records from the focal time range 1980–2020. Additionally, we included records from 1970–1979 for butterflies in occupancy-detection models to increase the robustness of mean occupancy estimates. We excluded the mean occupancy estimates for these additional years from further analyses to cover the same period for all groups. Prior to analyses, following the approach in ref. 26, we excluded observations of non-adult stages and observations from squares that only were visited in 1 year of the studied period, because these would not contain any information on change between years64. This resulted in 18,018 squares (15,248 for butterflies, 9870 for grasshoppers, 5188 for dragonflies) and 1,448,134 records (879,207 butterflies, 272,863 grasshoppers, 296,064 dragonflies) that we included in the analyses (Supplementary Fig. S2). The three datasets for the different groups were treated separately for occupancy-detection modelling, following the same procedures for all three groups. To determine detections and non-detections for each species and visit, which could then be used for occupancy-detection modelling, we only included visits that (a) did not originate from a project, which had a restricted taxonomic focus not including the focal species, (b) were not below the 5% quantile or above the 95% quantile of the day of the year at which the focal species has been recorded26 and (c) were from a bioclimatic zone, from which the focal species was recorded at least once.Occupancy-detection modelsWe used occupancy-detection models65,66 to estimate annual mean occupancy of squares for the whole of Switzerland and for the nine bioclimatic zones for each species (i.e., mean number of squares occupied by a species), mostly following the approach in ref. 26. We fitted a separate model for each species, based on different datasets for the three groups. We included only species that were recorded in any square in at least 25% of all analysed years. Occupancy-detection models are hierarchical models in which two interconnected processes are modelled jointly, one of which describes occurrence probability (ecological process; used to infer mean occupancy), whereas the other describes detection probability (observation process)65. The two processes are modelled through logistic regression models. The occupancy model estimates occurrence probability for all square and year combinations, whereas the observation model estimates the probability that a species has been detected by an observer during a visit. More formally, each square i in the year t has the latent occupancy status zi,t, which may be either 1 (present) or 0 (absent). zi,t depends on the occurrence probability ψi,t as follows$${z}_{i,t}sim {{{mbox{Bern}}}}left({psi }_{i,t}right)$$
(1)
The occupancy status is linked to the detection/non-detection data yi,t,j at square i in year t at visit j as$${y}_{i,t,, j}{{|}}{z}_{i,t}sim {mathrm {Bern}}({z}_{i,t}{p}_{i,t,j})$$
(2)
where pi,t,j is the detection probability.The regression model for occurrence probability (occupancy model) looked as follows$${{mbox{logit}}}({psi }_{i,t})={mu }_{o}+{beta }_{o1}{{{{{rm{elevatio}}}}}}{{{{{{rm{n}}}}}}}_{i}+{beta }_{o2}{{{{{rm{elevatio}}}}}}{{{{{{rm{n}}}}}}}_{i}^{2}+{alpha }_{o1,i}+{alpha }_{o2,i}+{gamma }_{r(i),t}$$
(3)
with μo being the global intercept, elevationi being the scaled elevation above sea level and αo1,i, αo2,i and γr(i),t being the random effects for fine biogeographic region (12 levels, Supplementary Fig. S10; these were again defined based on floristic and faunistic distributions and followed institutional borders63), square and year. The random effects for fine biogeographic region and square were modelled as follows:$${alpha }_{o1}sim {{{{{rm{Normal}}}}}}left(0,{sigma }_{o1}right)$$
(4)
and$${alpha }_{o2}sim {{{{{rm{Normal}}}}}}left(0,{sigma }_{o2}right)$$
(5)
The random effect of the year was implemented with separate random walks per zone following ref. 67, which allowed the effect to vary between the nine bioclimatic zones, while accounting for dependencies among consecutive years. Conceptually, in random walks, the effect of 1 year is dependent on the previous year’s effect, resulting in trajectories with less sudden changes between consecutive years. This was implemented as follows:$${gamma }_{r,t}sim left{begin{array}{c}{{{{{rm{Normal}}}}}}left(0,{1.5}^{2}right){{{{rm{for}}}}},t=1\ {{{{{rm{Normal}}}}}}left({gamma }_{r,t-1},{sigma }_{gamma r}^{2}right){{{{rm{for}}}}},t , > ,1end{array}right.$$
(6)
with$${sigma }_{gamma r}sim {{mbox{Cauchy}}}left(0,1right)$$
(7)
The regression model for detection probability (observation model) looked as follows$${{{{rm{logit}}}}}({p}_{i,t,j}) =, {mu }_{d}+{beta }_{d1}{{{{{rm{yda}}}}}}{{{{{{rm{y}}}}}}}_{j}+{beta }_{d2}{{{{{rm{yda}}}}}}{{{{{{rm{y}}}}}}}_{j}^{2}+{beta }_{d3}{{{{{rm{shortlis}}}}}}{{{{{{rm{t}}}}}}}_{j}+{beta }_{d4}{{{{{rm{longlis}}}}}}{{{{{{rm{t}}}}}}}_{j} \ quad+ {beta }_{d5}{{{{{rm{exper}}}}}}{{{{{{rm{t}}}}}}}_{j}+{beta }_{d6}{{{{{rm{projec}}}}}}{{{{{{rm{t}}}}}}}_{j}+{beta }_{d7}{{{{{rm{targeted}}}}}}_{{{{{rm{projec}}}}}}{{{{{{rm{t}}}}}}}_{j} \ quad+ {beta }_{d8}{{{{{rm{redlis}}}}}}{{{{{{rm{t}}}}}}}_{j}+{alpha }_{d1,t}$$
(8)
where μd is the global intercept, ydayj is the scaled day of the year of visit j, shortlistj and longlistj are dummies of a three-level factor denoting the number of species recorded during the visit (1; 2–3; >3), and expertj, projectj, targeted_projectj and redlistj are dummies of a five-level factor denoting the source of the data. The source might either be a common naturalist observation (reference level), an observation by an expert naturalist, an observation made during a not further specified project, an observation made in a project targeted at the focal species or an observation made during a Red-List inventory. An expert naturalist was defined as an observer that contributed a significant number of records, which was defined as the upper 2.5% quantile of all observers arranged by their total number of records, and that made at least one visit with an exceptionally long species list, which was defined as a visit in the upper 2.5% quantile of all visits arranged by the number of records. The proportions of records originating from these different sources changed across years, but change was not unidirectional and differed among the investigated groups (Supplementary Fig. S11), such that accounting for data source in the model should suffice to yield reliable estimates of occupancy trends. αd1,t is a random effect for year, which was modelled as$${alpha }_{d1}sim {{{{{rm{Normal}}}}}}left(0,{sigma }_{d1}right)$$
(9)
The occupancy and observation models were fitted jointly in Stan through the interface CmdStanR68. Four Markov chain Monte Carlo chains with 2000 iterations each, including 1000 warm-up iterations, were used. Priors of the model parameters are specified in Supplementary Table S5. For the prior distribution of global intercepts, a standard deviation of 1.5 was chosen to not overweight the extreme values on the probability scale. To ensure that chains mixed well, Rhat statistics for annual mean occupancy estimates were calculated through the package rstan69. For Switzerland-wide annual estimates (n = 18,140), 98.0% of values met the standard threshold of 1.1 (99.9% of values More

Clemente, J. C., Ursell, L. K., Parfrey, L. W. & Knight, R. The impact of the gut microbiota on human health: An integrative view. Cell 148, 1258–1270 (2012).Article 

Google Scholar 
Cryan, J. F. et al. The microbiota-gut-brain axis. Physiol. Rev. 99, 1877–2013 (2019).Article 

Google Scholar 
Parfrey, L. W., Walters, W. A. & Knight, R. Microbial eukaryotes in the human microbiome: Ecology, evolution, and future directions. Front. Microbiol. 2, 1–6 (2011).Article 

Google Scholar 
Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011).Article 

Google Scholar 
Björk, J. R., Dasari, M., Grieneisen, L. & Archie, E. A. Primate microbiomes over time: Longitudinal answers to standing questions in microbiome research. Am. J. Primatol. 81, 1–23 (2019).Article 

Google Scholar 
Costello, E. K., Stagaman, K., Dethlefsen, L., Bohannan, B. J. M. & Relman, D. A. The application of ecological theory toward an understanding of the human microbiome. Science 336, 1255–1262 (2012).Article 
ADS 

Google Scholar 
Miller, E. T., Svanbäck, R. & Bohannan, B. J. M. Microbiomes as metacommunities: Understanding host-associated microbes through metacommunity ecology. Trends Ecol. Evol. 33, 926–935 (2018).Article 

Google Scholar 
McKenney, E. A., Koelle, K., Dunn, R. R. & Yoder, A. D. The ecosystem services of animal microbiomes. Mol. Ecol. 27, 2164–2172 (2018).Article 

Google Scholar 
Koskella, B., Hall, L. J. & Metcalf, C. J. E. The microbiome beyond the horizon of ecological and evolutionary theory. Nat. Ecol. Evol. 1, 1606–1615 (2017).Article 

Google Scholar 
Sarkar, A. et al. Microbial transmission in animal social networks and the social microbiome. Nat. Ecol. Evol. 4, 1020–1035 (2020).Article 

Google Scholar 
Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).Article 
ADS 

Google Scholar 
Degnan, P. H. et al. Factors associated with the diversification of the gut microbial communities within chimpanzees from Gombe National Park. Proc. Natl. Acad. Sci. 109, 13034–13039 (2012).Article 
ADS 

Google Scholar 
Bennett, G. et al. Host age, social group, and habitat type influence the gut microbiota of wild ring-tailed lemurs (Lemur catta). Am. J. Primatol. 78, 883–892 (2016).Article 

Google Scholar 
Amato, K. R. et al. Patterns in gut microbiota similarity associated with degree of sociality among sex classes of a neotropical primate. Microb. Ecol. 74, 250–258 (2017).Article 

Google Scholar 
Raulo, A. et al. Social behaviour and gut microbiota in red-bellied lemurs (Eulemur rubriventer): In search of the role of immunity in the evolution of sociality. J. Anim. Ecol. 87, 388–399 (2017).Article 

Google Scholar 
Springer, A. et al. Patterns of seasonality and group membership characterize the gut microbiota in a longitudinal study of wild Verreaux’s sifakas (Propithecus verreauxi). Ecol. Evol. 7, 5732–5745 (2017).Article 

Google Scholar 
Tung, J. et al. Social networks predict gut microbiome composition in wild baboons. Elife 2015, 1–18 (2015).
Google Scholar 
Moeller, A. H. et al. Social behavior shapes the chimpanzee pan-microbiome. Sci. Adv. 2, e1500997 (2016).Article 
ADS 

Google Scholar 
Perofsky, A. C., Lewis, R. J., Abondano, L. A., Di Fiore, A. & Meyers, L. A. Hierarchical social networks shape gut microbial composition in wild Verreaux’s sifaka. Proc. R. Soc. B Biol. Sci. 284, 20172274 (2017).Article 

Google Scholar 
Raulo, A. et al. Social networks strongly predict the gut microbiota of wild mice. ISME J. 15, 2601–2613 (2021).Article 

Google Scholar 
Arrieta, M. C., Stiemsma, L. T., Amenyogbe, N., Brown, E. & Finlay, B. The intestinal microbiome in early life: Health and disease. Front. Immunol. 5, 1–18 (2014).Article 

Google Scholar 
Ren, T., Grieneisen, L. E., Alberts, S. C., Archie, E. A. & Wu, M. Development, diet and dynamism: Longitudinal and cross-sectional predictors of gut microbial communities in wild baboons. Environ. Microbiol. 18, 1312–1325 (2016).Article 

Google Scholar 
Jagsi, R. et al. Seasonal cycling in the gut microbiome of the Hadza Hunter-Gatherers of Tanzania. Science 357, 802–806 (2017).Article 

Google Scholar 
Hicks, A. L. et al. Gut microbiomes of wild great apes fluctuate seasonally in response to diet. Nat. Commun. 9, 1786 (2018).Article 
ADS 

Google Scholar 
Murillo, T., Schneider, D., Fichtel, C. & Daniel, R. Dietary shifts and social interactions drive temporal fluctuations of the gut microbiome from wild redfronted lemurs. ISME Commun. 2, 3 (2022).Article 

Google Scholar 
Laforest-Lapointe, I. & Arrieta, M.-C. Microbial eukaryotes: A missing link in gut microbiome studies. mSystems 3, e00201-17 (2018).Article 

Google Scholar 
Mann, A. E. et al. Biodiversity of protists and nematodes in the wild nonhuman primate gut. ISME J. 14, 609–622 (2020).Article 

Google Scholar 
Vlčková, K. et al. Relationships between gastrointestinal parasite infections and the fecal microbiome in free-ranging western lowland gorillas. Front. Microbiol. 9, 1–12 (2018).Article 

Google Scholar 
Renelies-Hamilton, J. et al. Exploring interactions between Blastocystis sp., Strongyloides spp. and the gut microbiomes of wild chimpanzees in Senegal. Infect. Genet. Evol. 74, 104010 (2019).Article 

Google Scholar 
Martínez-Mota, R., Righini, N., Mallott, E. K., Gillespie, T. R. & Amato, K. R. The relationship between pinworm (Trypanoxyuris) infection and gut bacteria in wild black howler monkeys (Alouatta pigra). Am. J. Primatol. 83, e23330 (2021).Article 

Google Scholar 
Pereira, M. E., Kaufman, R., Kappeler, P. M. & Overdoff, D. J. Female dominance does not characterize all of the lemuridae. Folia Primatol. 55, 96–103 (1990).Article 

Google Scholar 
Ostner, J. & Kappeler, P. M. Central males instead of multiple pairs in redfronted lemurs, Eulemur fulvus rufus (Primates, Lemuridae)?. Anim. Behav. 58, 1069–1078 (1999).Article 

Google Scholar 
Kappeler, P. M. & Fichtel, C. A 15-year perspective on the social organization and life history of sifaka in Kirindy Forest. In Long-Term Field Studies of Primates 101–121 (Springer, 2012).Chapter 

Google Scholar 
Koch, F., Ganzhorn, J. U., Rothman, J. M., Chapman, C. A. & Fichtel, C. Sex and seasonal differences in diet and nutrient intake in Verreaux’s sifakas (Propithecus verreauxi). Am. J. Primatol. 79, 1–10 (2017).Article 

Google Scholar 
Scholz, F. & Kappeler, P. M. Effects of seasonal water scarcity on the ranging behavior of Eulemur fulvus rufus. Int. J. Primatol. 25, 599–613 (2004).Article 

Google Scholar 
Amoroso, C. R., Kappeler, P. M., Fichtel, C. & Nunn, C. L. Water availability impacts habitat use by red-fronted lemurs (Eulemur rufifrons): An experimental and observational study. Int. J. Primatol. 41, 61–80 (2020).Article 

Google Scholar 
Clough, D., Heistermann, M. & Kappeler, P. M. Host intrinsic determinants and potential consequences of parasite infection in free-ranging red-fronted lemurs (Eulemur fulvus rufus). Am. J. Phys. Anthropol. 142, 441–452 (2010).Article 

Google Scholar 
Ostner, J., Kappeler, P. & Heistermann, M. Androgen and glucocorticoid levels reflect seasonally occurring social challenges in male redfronted lemurs (Eulemur fulvus rufus). Behav. Ecol. Sociobiol. 62, 627–638 (2008).Article 

Google Scholar 
Heistermann, M., Palme, R. & Ganswindt, A. Comparison of different enzymeimmunoassays for assessment of adrenocortical activity in primates based on fecal analysis. Am. J. Primatol. 68, 257–273 (2006).Article 

Google Scholar 
Kappeler, P. M. & Fichtel, C. Female reproductive competition in Eulemur rufifrons: Eviction and reproductive restraint in a plurally breeding Malagasy primate. Mol. Ecol. 21, 685–698 (2012).Article 

Google Scholar 
Ostner, J., Kappeler, P. M. & Heistermann, M. Seasonal variation and social correlates of androgen excretion in male redfronted lemurs (Eulemur fulvus rufus). Behav. Ecol. Sociobiol. 52, 485–495 (2002).Article 

Google Scholar 
Clough, D. Gastro-intestinal parasites of red-fronted lemurs in Kirindy Forest, western Madagascar. J. Parasitol. 96, 245–251 (2010).Article 

Google Scholar 
Gogarten, J. F. et al. Metabarcoding of eukaryotic parasite communities describes diverse parasite assemblages spanning the primate phylogeny. Mol. Ecol. Resour. 20, 204–215 (2020).Article 

Google Scholar 
Barton, R. A. Allogrooming as mutualism in diurnal lemurs. Primates 28, 539–542 (1987).Article 

Google Scholar 
Noguera, J. C., Aira, M., Pérez-Losada, M., Domínguez, J. & Velando, A. Glucocorticoids modulate gastrointestinal microbiome in a wild bird. R. Soc. Open Sci. 5, 171743 (2018).Article 
ADS 

Google Scholar 
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, 1–11 (2013).Article 

Google Scholar 
Stoeck, T. et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol. Ecol. 19, 21–31 (2010).Article 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).Article 

Google Scholar 
Yarza, P. et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat. Rev. Microbiol. 12, 635–645 (2014).Article 

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, 597–604 (2013).Article 

Google Scholar 
Gao, X., Lin, H., Revanna, K. & Dong, Q. A Bayesian taxonomic classification method for 16S rRNA gene sequences with improved species-level accuracy. BMC Bioinform. 18, 1–10 (2017).Article 

Google Scholar 
Reitmeier, S. et al. Handling of spurious sequences affects the outcome of high-throughput 16S rRNA gene amplicon profiling. ISME Commun. 1, 1–12 (2021).Article 

Google Scholar 
Shutt, K., Setchell, J. M. & Heistermann, M. Non-invasive monitoring of physiological stress in the Western lowland gorilla (Gorilla gorilla gorilla): Validation of a fecal glucocorticoid assay and methods for practical application in the field. Gen. Comp. Endocrinol. 179, 167–177 (2012).Article 

Google Scholar 
Hämäläinen, A., Heistermann, M., Fenosoa, Z. S. E. & Kraus, C. Evaluating capture stress in wild gray mouse lemurs via repeated fecal sampling: Method validation and the influence of prior experience and handling protocols on stress responses. Gen. Comp. Endocrinol. 195, 68–79 (2014).Article 

Google Scholar 
Rudolph, K., Fichtel, C., Heistermann, M. & Kappeler, P. M. Dynamics and determinants of glucocorticoid metabolite concentrations in wild Verreaux’s sifakas. Horm. Behav. 124, 104760 (2020).Article 

Google Scholar 
Heitlinger, E., Ferreira, S. C. M., Thierer, D., Hofer, H. & East, M. L. The intestinal eukaryotic and bacterial biome of spotted hyenas: The impact of social status and age on diversity and composition. Front. Cell Infect. Microbiol. 7, 262 (2017).Article 

Google Scholar 
Barr, D. J., Levy, R., Scheepers, C. & Tily, H. J. Random effects structure for confirmatory hypothesis testing: Keep it maximal. J. Mem. Lang. 68, 255–278 (2013).Article 

Google Scholar 
Mallick, H. et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput. Biol. 17, 1–27 (2021).Article 

Google Scholar 
De Cáceres, M., Legendre, P. & Moretti, M. Improving indicator species analysis by combining groups of sites. Oikos 119, 1674–1684 (2010).Article 

Google Scholar 
Silk, J., Cheney, D. & Seyfarth, R. A practical guide to the study of social relationships. Evol. Anthropol. 22, 213–225 (2013).Article 

Google Scholar 
Ostner, J., Nunn, C. L. & Schülke, O. Female reproductive synchrony predicts skewed paternity across primates. Behav. Ecol. 19, 1150–1158 (2008).Article 

Google Scholar 
Bailey, M. T. et al. Exposure to a social stressor alters the structure of the intestinal microbiota: Implications for stressor-induced immunomodulation. Brain Behav. Immun. 25, 397–407 (2011).Article 

Google Scholar 
Bailey, M. T. et al. Stressor exposure disrupts commensal microbial populations in the intestines and leads to increased colonization by Citrobacter rodentium. Infect. Immun. 78, 1509–1519 (2010).Article 

Google Scholar 
Stothart, M. R. et al. Stress and the microbiome: Linking glucocorticoids to bacterial community dynamics in wild red squirrels. Biol. Lett. 12, 20150875 (2016).Article 

Google Scholar 
Vlčková, K. et al. Impact of stress on the gut microbiome of free-ranging western lowland gorillas. Microbiol 164, 40–44 (2018).Article 

Google Scholar 
Chu, H. & Mazmanian, S. K. Innate immune recognition of the microbiota promotes host-microbial symbiosis. Nat. Immunol. 14, 668–675 (2013).Article 

Google Scholar 
Zheng, D., Liwinski, T. & Elinav, E. Interaction between microbiota and immunity in health and disease. Cell Res. 30, 492–506 (2020).Article 

Google Scholar 
Ley, R. E. Prevotella in the gut: Choose carefully. Nat. Rev. Gastroenterol. Hepatol. 13, 69–70 (2016).Article 

Google Scholar 
Manara, S. et al. Microbial genomes from non-human primate gut metagenomes expand the primate-associated bacterial tree of life with over 1000 novel species. Genome Biol. 20, 299 (2019).Article 

Google Scholar 
Round, J. L. & Mazmanian, S. K. The gut microbiota shapes intestinal immune responses during health and disease. Nat. Rev. Immunol. 9, 313–323 (2009).Article 

Google Scholar 
Maltz, R. M. et al. Prolonged restraint stressor exposure in outbred CD-1 mice impacts microbiota, colonic inflammation, and short chain fatty acids. PLoS ONE 13, 1–19 (2018).Article 

Google Scholar 
Ostner, J. & Heistermann, M. Endocrine characterization of female reproductive status in wild redfronted lemurs (Eulemur fulvus rufus). Gen. Comp. Endocrinol. 131, 274–283 (2003).Article 

Google Scholar 
Peckre, L. R., Defolie, C., Kappeler, P. M. & Fichtel, C. Potential self-medication using millipede secretions in red-fronted lemurs: Combining anointment and ingestion for a joint action against gastrointestinal parasites?. Primates 59, 483–494 (2018).Article 

Google Scholar 
Jenkins, T. P. et al. Infections by human gastrointestinal helminths are associated with changes in faecal microbiota diversity and composition. PLoS ONE 12, 1–18 (2017).Article 

Google Scholar 
Rosa, B. A. et al. Differential human gut microbiome assemblages during soil-transmitted helminth infections in Indonesia and Liberia. Microbiome 6, 1–19 (2018).Article 

Google Scholar 
Reynolds, L. A., Finlay, B. B. & Maizels, R. M. Cohabitation in the intestine: Interactions among helminth parasites, bacterial microbiota, and host immunity. J. Immunol. 195, 4059–4066 (2015).Article 

Google Scholar 
Toro-Londono, M. A., Bedoya-Urrego, K., Garcia-Montoya, G. M., Galvan-Diaz, A. L. & Alzate, J. F. Intestinal parasitic infection alters bacterial gut microbiota in children. PeerJ 2019, 1–24 (2019).
Google Scholar 
Vacca, M. et al. The controversial role of human gut Lachnospiraceae. Microorganisms 8, 1–25 (2020).Article 

Google Scholar 
Wei, Z. et al. The effects of non-fiber carbohydrate content and forage type on rumen microbiome of dairy cows. Animals 11, 1–17 (2021).Article 

Google Scholar 
Kaakoush, N. O. Insights into the role of Erysipelotrichaceae in the human host. Front. Cell Infect. Microbiol. 5, 1–4 (2015).Article 

Google Scholar 
Ricaboni, D. et al. ‘Colidextribacter massiliensis’ gen. nov., sp. nov., isolated from human right colon. New Microbes New Infect. 17, 27–29 (2017).Article 

Google Scholar 
Qin, P. et al. Characterization a novel butyric acid-producing bacterium Collinsella aerofaciens subsp. shenzhenensis subsp. nov. Microorganisms 7, 78 (2019).Article 

Google Scholar 
Wei, Y. et al. Commensal bacteria impact a protozoan’s integration into the murine gut microbiota in a dietary nutrient-dependent manner. Appl. Environ. Microbiol. 86, e00303-20 (2020).Article 
ADS 

Google Scholar 
Perofsky, A. C., Ancel Meyers, L., Abondano, L. A., Di Fiore, A. & Lewis, R. J. Social groups constrain the spatiotemporal dynamics of wild sifaka gut microbiomes. Mol. Ecol. 30, 6759–6775 (2021).Article 

Google Scholar 
Pyritz, L., Kappeler, P. M. & Fichtel, C. Coordination of group movements in wild red-fronted lemurs (Eulemur rufifrons): Processes and influence of ecological and reproductive seasonality. Int. J. Primatol. 32, 1325–1347 (2011).Article 

Google Scholar 
Amato, K. R. et al. Habitat degradation impacts black howler monkey (Alouatta pigra) gastrointestinal microbiomes. ISME J. 7, 1344–1353 (2013).Article 

Google Scholar 
Hippe, H., Hagelstein, A., Kramer, I., Swiderski, J. & Stackebrandt, E. Phylogenetic analysis of Formivibrio citricus, Propionivibrio dicarboxylicus, Anaerobiospirillum thomasii, Succinirnonas amylolytica and Succinivibrio dextrinosolvens and proposal of Succinivibrionaceae fam. nov. Int. J. Syst. Evol. Microbiol. 49, 779–782 (1999).Article 

Google Scholar 
Grieneisen, L. E., Livermore, J., Alberts, S., Tung, J. & Archie, E. A. Group living and male dispersal predict the core gut microbiome in wild baboons. Integr. Comp. Biol. 57, 770–785 (2017).Article 

Google Scholar 
Amoroso, C. R., Kappeler, P. M., Fichtel, C. & Nunn, C. L. Fecal contamination, parasite risk, and waterhole use by wild animals in a dry deciduous forest. Behav. Ecol. Sociobiol. 73, 1–11 (2019).Article 

Google Scholar 
Vandeputte, D. et al. Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut 65, 57–62 (2016).Article 

Google Scholar 
Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).Article 
ADS 

Google Scholar 
Sonnenburg, J. L. & Bäckhed, F. Diet-microbiota interactions as moderators of human metabolism. Nature 535, 56–64 (2016).Article 
ADS 

Google Scholar 
Zmora, N., Suez, J. & Elinav, E. You are what you eat: Diet, health and the gut microbiota. Nat. Rev. Gastroenterol. Hepatol. 16, 25–56 (2018).
Google Scholar 
Ortmann, S., Bradley, B. J., Stolter, C. & Ganzhorn, J. U. Estimating the quality and composition of wild animal diets—a critical survey of methods. In Feeding Ecology in Apes and Other Primates. Ecological, Physical, and Behavioral Aspects (eds Hohmann, G. et al.) 395–418 (Cambridge University Press, 2006).
Google Scholar  More

Wang, Z. & Yuan, J. Typhoon numerical simulation and visualization for disaster risk assessment. Bull. Surv. Mapp. 108–110, 132 (2015).ADS 

Google Scholar 
Tang, J., Xu, L., Li, Y., He, Y. & Cui, S. Assessing the damage caused by typhoon on urban green space ecosystme service based on UAV remote sensing. J. Nat. Disasters 27, 153–161 (2018).
Google Scholar 
Tian, Y., Zhou, W., Qian, Y., Zheng, Z. & Pan, X. The influence of Typhoon Mangkhut on urban green space and biomass in Shenzhen, China. Acta Ecol. Sin. 40, 2589–2598 (2020).
Google Scholar 
Lin, G. Major anti-wind and alkali-resisting landscape plants of south China’s seaside region. For. Invertory Plan. 29, 78–81 (2004).
Google Scholar 
Lin, T. C., Hogan, J. A. & Chang, C. T. Tropical cyclone ecology: a scale-link perspective. Trends Ecol. Evol. 35, 594–604 (2020).Article 

Google Scholar 
Lin, H. et al. Risk assessments of trees and urban spaces under attacks of typhoons: a survey and statistical study in Guangzhou, China. IOP Conf. Ser. Earth Environ. Sci. 588, 032055 (2020).Article 

Google Scholar 
Loehle, C. Biomechanical constraints on tree architecture. Trees Struct. Funct. 30, 2061–2070 (2016).Article 

Google Scholar 
Zhu, J., Matsuzaki, T. & Sakioka, K. Wind speeds within a single crown of Japanese black pine (Pinus thunbergii Parl.). For. Ecol. Manag. 135, 19–31 (2000).Article 

Google Scholar 
Ver Planck, N. R. & MacFarlane, D. W. Branch mass allocation increases wind throw risk for Fagus grandifolia. For. Int. J. For. Res. 92, 490–499 (2019).
Google Scholar 
Dunham, R. A. & Cameron, A. D. Crown, stem and wood properties of wind-damaged and undamaged Sitka spruce. For. Ecol. Manag. 135, 73–81 (2000).Article 

Google Scholar 
Burcham, D. C., Autio, W. R., Modarres-Sadeghi, Y. & Kane, B. After pruning, wind-induced bending moments and vibration decrease more on reduced than raised Senegal mahogany (Khaya senegalensis). Urban For. Urban Green. 61, 127100 (2021).Article 

Google Scholar 
Gilman, E. F., Masters, F. & Grabosky, J. C. Pruning affects tree movement in hurricane force wind. Arboric. Urban For. 34, 20–28 (2008).Article 

Google Scholar 
Päätalo, M. L., Peltola, H. & Kellomäki, S. Modelling the risk of snow damage to forests under short-term snow loading. For. Ecol. Manag. 116, 51–70 (1999).Article 

Google Scholar 
North, E., Johnson, G., Murphy, R., Giblin, C. & Rendahl, A. Boulevard tree failures during wind loading events. Arboric. Urban For. 45, 259–269 (2019).
Google Scholar 
Angelou, N., Dellwik, E. & Mann, J. Wind load estimation on an open-grown European oak tree. For. Int. J. For. Res. 92, 381–392 (2019).
Google Scholar 
Kontogianni, A., Tsitsoni, T. & Goudelis, G. An index based on silvicultural knowledge for tree stability assessment and improved ecological function in urban ecosystems. Ecol. Eng. 37, 914–919 (2011).Article 

Google Scholar 
He, D. & Li, Z. Wind tunnel test on wind- induced responses of roadside trees. J. Nat. Disasters 28, 44–53 (2019).
Google Scholar 
Cao, J., Tamura, Y. & Yoshida, A. Wind tunnel study on aerodynamic characteristics of shrubby specimens of three tree species. Urban For. Urban Green. 11, 465–476 (2012).Article 

Google Scholar 
Zhang, W., Kang, L., Zhang, Q., Li, C. & Zou, X. Speed upwind and downwind of a single plant. J. Beijing Norm. Univ. Nat. Sci. 56, 573–581 (2020).
Google Scholar 
Cheng, H. et al. Wind tunnel study of airflow recovery on the lee side of single plants. Agric. For. Meteorol. 263, 362–372 (2018).Article 
ADS 

Google Scholar 
Ma, S. et al. Experimental research of viscous flow around a Nitraria tangutorum boscage. Res. Soil Water Conserv. 13, 147–149 (2006).ADS 

Google Scholar 
Rahman, M. et al. Disentangling the role of competition, light interception, and functional traits in tree growth rate variation in South Asian tropical moist forests. For. Ecol. Manag. 483, 118908 (2021).Article 

Google Scholar 
Forrester, D. I., Benneter, A., Bouriaud, O. & Bauhus, J. Diversity and competition influence tree allometric relationships—Developing functions for mixed-species forests. J. Ecol. 105, 761–774 (2017).Article 

Google Scholar 
Coombes, A., Martin, J. & Slater, D. Defining the allometry of stem and crown diameter of urban trees. Urban For. Urban Green. 44, 1–15 (2019).Article 

Google Scholar 
Stoffel, M. Mechanical stability and growth performance of trees. (2009).Lento, M., Thijs, D., Jonas, A., Dominique, D. & Jan, C. Comparative study of flow field and drag coefficient of model and small natural trees in a wind tunnel. Urban For. Urban Green. 35, 230–239 (2018).Article 

Google Scholar 
West, G. B., Brown, J. H. & Enquist, B. J. A general model for the structure and allometry of plant vascular systems. Nature 400, 664–667 (1999).Article 
ADS 
CAS 

Google Scholar 
Enquist, B. J. Cope’s rule and the evolution of long-distance transport in vascular plants: Allometric scaling, biomass partitioning and optimization. Plant Cell Environ. 26, 151–161 (2003).Article 

Google Scholar 
Bentley, L. P. et al. An empirical assessment of tree branching networks and implications for plant allometric scaling models. Ecol. Lett. 16, 1069–1078 (2013).Article 

Google Scholar 
Eloy, C., Fournier, M., Lacointe, A. & Moulia, B. Wind loads and competition for light sculpt trees into self-similar structures. Nat. Commun. 8, 1–11 (2017).Article 
CAS 

Google Scholar 
Lin, M. Y. & Khlystov, A. Investigation of ultrafine particle deposition to vegetation branches in a wind tunnel. Aerosol Sci. Technol. 46, 465–472 (2012).Article 
ADS 
CAS 

Google Scholar 
Ji, W. & Zhao, B. A wind tunnel study on the effect of trees on PM2.5 distribution around buildings. J. Hazard. Mater. 346, 36–41 (2018).Article 
CAS 

Google Scholar 
Hui, K. K. W. et al. Unveiling falling urban trees before and during Typhoon Higos (2020): Empirical case study of potential structural failure using tilt sensor. Forests 13, 359 (2022).Article 

Google Scholar 
Liao, S., Huang, M., Lou, W., Lin, W. & Xiao, Z. Numerical simulation of a urban wind field under the influence of typhoon “Mangkhut”. Acta Aerodyn. Sin. 39, 107–116 (2021).
Google Scholar 
Gardiner, B., Berry, P. & Moulia, B. Review: Wind impacts on plant growth, mechanics and damage. Plant Sci. 245, 94–118 (2016).Article 
CAS 

Google Scholar 
Lüttge, U. & Buckeridge, M. Trees: structure and function and the challenges of urbanization. Trees Struct. Funct. https://doi.org/10.1007/s00468-020-01964-1 (2020).Article 

Google Scholar 
Hwang, H. M., Fiala, M. J., Park, D. & Wade, T. L. Review of pollutants in urban road dust and stormwater runoff: part 1. Heavy metals released from vehicles. Int. J. Urban Sci. 20, 334–360 (2016).Article 

Google Scholar 
Kuitert, W. The nature of urban Seoul: Potential vegetation derived from the soil map. Int. J. Urban Sci. 17, 95–108 (2013).Article 

Google Scholar 
Stubbs, C. J., Cook, D. D. & Niklas, K. J. A general review of the biomechanics of root anchorage. J. Exp. Bot. 70, 3439–3451 (2019).Article 
CAS 

Google Scholar 
Sterck, F. J. & Bongers, F. Ontogenetic changes in size, allometry, and mechanical design of tropical rain forest trees. Am. J. Bot. 85, 266–272 (1998).Article 
CAS 

Google Scholar 
Sim, V. & Jung, W. Wind fragility for urban street tree in Korea. J. Wetl. Res. 21, 298–304 (2019).
Google Scholar 
Ueda, M. & Shibata, E. Why do trees decline or dieback after a strong wind? Water status of Hinoki cypress standing after a typhoon. Tree Physiol. 24, 701–706 (2004).Article 

Google Scholar 
Jalkanen, A. & Mattila, U. Logistic regression models for wind and snow damage in northern Finland based on the National Forest Inventory data. For. Ecol. Manag. 135, 315–330 (2000).Article 

Google Scholar 
Hale, S. E., Gardiner, B. A., Wellpott, A., Nicoll, B. C. & Achim, A. Wind loading of trees: Influence of tree size and competition. Eur. J. For. Res. 131, 203–217 (2012).Article 

Google Scholar 
Olson, M. E., Aguirre-Hernández, R. & Rosell, J. A. Universal foliage-stem scaling across environments and species in dicot trees: Plasticity, biomechanics and Corner’s Rules. Ecol. Lett. 12, 210–219 (2009).Article 

Google Scholar 
Blanchard, E. et al. Contrasted allometries between stem diameter, crown area, and tree height in five tropical biogeographic areas. Trees Struct. Funct. 30, 1953–1968 (2016).Article 

Google Scholar 
King, D. A., Davies, S. J., Tan, S. & Noor, N. S. M. The role of wood density and stem support costs in the growth and mortality of tropical trees. J. Ecol. 94, 670–680 (2006).Article 

Google Scholar 
Petty, J. A. & Worrell, R. Stability of coniferous tree stems in relation to damage by snow. Forestry 54, 115–128 (1981).Article 

Google Scholar 
Gilman, E. F. & Lilly, S. Tree pruning (International Society of Arboriculture, 2002).
Google Scholar 
Zhang, J., Gou, Z., Zhang, F. & Shutter, L. A study of tree crown characteristics and their cooling effects in a subtropical city of Australia. Ecol. Eng. 158, 106027 (2020).Article 

Google Scholar 
Marchi, L. et al. State of the art on the use of trees as supports and anchors in forest operations. Forests 9, 467 (2018).Article 

Google Scholar  More