Philippa Kaur

In addition to the raw data, we provide two means of exploring the Avian Diet Database and extracting species- or prey-specific summaries. The first is through the website https://aviandiet.unc.edu where users can enter a bird species name to explore a summary of diet information known for that species, or a prey name to explore which bird species are known to eat that prey taxon. We also provide an R package (‘aviandietdb’) for exploring the database, which should be loaded in R by typing:
install.packages(“devtools”)

library(devtools)

devtools::install_github(“ahhurlbert/aviandietdb”)

library(aviandietdb)
Three useful R functions for summarizing records in the database are detailed below.dbSummary().Example usage:
dbSummary()
This function returns the total number of database records, the unique number of bird species, and the unique number of publications summarized in the Diet Database. In addition, it provides a tally of the number of records by bird species listed in alphabetical order, as well as a summary for each bird family in the American Birding Association (ABA) Checklist (version 8.0.6a) of 1) the number of species in the family in the database, 2) the total number of species in the family based on the ABA checklist, and 3) the percent of the family represented based on the species expected in North America. This information on taxonomic coverage is also provided in Online-only Table 2.speciesSummary().Example usage:
speciesSummary(“Bald Eagle”, by = ”Order”)
This function provides a summary of the total number of records and total number of studies available in the database for this species, along with a summary of how those records are distributed across seasons, years, and geographic regions. The number of records are also summarized by taxonomic level to which prey were identified and by analysis type (by number of items, weight or volume, occurrence, or unspecified). Finally, for each analysis type, the mean fraction of diet is given for each prey category at the hierarchical taxonomic level specified with the “by” argument. This is an overall mean, averaged across year, region, and season. If the original data source indicated that specific parts of the prey taxon were consumed (e.g. fruit, seed, vegetation, etc.) then they are listed in the Prey_Part field.dietSummary().Example usage:
dietSummary(“Bald Eagle”, season = ”summer”, region = ”California”, yearRange = c(1940, 1970), by = ”Order”, dietType = ”Items”)
This function allows one to specify season, region, a year range, analysis type, and taxonomic level for prey summarization, and then provides the mean fraction of diet information based on all studies meeting the stated criteria.dietSummaryByPrey().Example usage:
dietSummaryByPrey(“Lepidoptera”, preyLevel = ”Order”, dietType = ”Items”, yearRange = c(1985, 2000), season = ”summer”, preyStage = ”larva”, speciesMean = TRUE)
This function provides a list of all bird species that consume a particular prey taxon in decreasing order of importance. In addition to providing the prey taxon name, you must also specify the taxonomic level (preyLevel) of that name. Like dietSummary(), this function allows one to specify season, region, a year range, and analysis type. There are two additional arguments not present in dietSummary(). One is preyStage, which specifies the life stage of the prey item (if applicable) for which a summary should be conducted. By default (‘any’), diet records will be included regardless of prey stage. Alternatively, one can specify that the summary should only be conducted for records including the terms ‘larva’, ‘adult’, or ‘pupa’ in the Diet Database’s ‘Prey_Stage’ field. This is most relevant for Lepidoptera and a few other insect groups, where one might want to single out the importance of caterpillars or other larvae, for example.By specifying speciesMean = TRUE, only a single value is returned for each bird species that is known to consume a specified prey taxon which represents the average across all analyses meeting the season, region, and year criteria. If speciesMean is FALSE, then each analysis of a bird species which meets the specified criteria will be listed separately.Example code and output is available in the Github README.md document (https://github.com/ahhurlbert/aviandietdb). More

1.Gallo RL, Hooper LV. Epithelial antimicrobial defence of the skin and intestine. Nat Rev Immunol. 2012;12:503–16.PubMed 
PubMed Central 
CAS 

Google Scholar 
2.Erin Chen Y, Fischbach MA, Belkaid Y. Skin microbiota-host interactions. Nature. 2018;553:427–36.PubMed 
PubMed Central 

Google Scholar 
3.Toledo RC, Jared C. Cutaneous granular glands and amphibian venoms. Comp Biochem Physiol A Physiol. 1995;111:1–29.4.Walke JB, Becker MH, Loftus SC, House LL, Cormier G, Jensen RV, et al. Amphibian skin may select for rare environmental microbes. ISME J. 2014;8:2207–17.PubMed 
PubMed Central 
CAS 

Google Scholar 
5.Jani AJ, Briggs CJ. Host and aquatic environment shape the amphibian skin microbiome but effects on downstream resistance to the pathogen Batrachochytrium dendrobatidis are variable. Front Microbiol. 2018;9:487.PubMed 
PubMed Central 

Google Scholar 
6.Bletz MC, Archer H, Harris RN, McKenzie VJ, Rabemananjara FCE, Rakotoarison A, et al. Host ecology rather than host phylogeny drives amphibian skin microbial community structure in the biodiversity hotspot of Madagascar. Front Microbiol. 2017;8:1–14.
Google Scholar 
7.Becker MH, Walke JB, Murrill L, Woodhams DC, Reinert LK, Rollins‐Smith LA, et al. Phylogenetic distribution of symbiotic bacteria from Panamanian amphibians that inhibit growth of the lethal fungal pathogen Batrachochytrium dendrobatidis. Mol Ecol. 2015;24:1628–41.PubMed 

Google Scholar 
8.Flechas SV, Acosta-González A, Escobar LA, Kueneman JG, Sánchez-Quitian ZA, Parra-Giraldo CM, et al. Microbiota and skin defense peptides may facilitate coexistence of two sympatric Andean frog species with a lethal pathogen. ISME J. 2019;13:361–73.PubMed 
CAS 

Google Scholar 
9.Brunetti AE, Lyra ML, Melo WGP, Andrade LE, Palacios-Rodríguez P, Prado BM, et al. Symbiotic skin bacteria as a source for sex-specific scents in frogs. Proc Natl Acad Sci USA. 2019;116:2124–9.PubMed 
PubMed Central 
CAS 

Google Scholar 
10.Vaelli PM, Theis KR, Williams JE, O’Connell LA, Foster JA, Eisthen HL. The skin microbiome facilitates adaptive tetrodotoxin production in poisonous newts. Elife. 2020;9:e53898.PubMed 
PubMed Central 

Google Scholar 
11.Pukala TL, Bowie JH, Maselli VM, Musgrave IF, Tyler MJ. Host-defence peptides from the glandular secretions of amphibians: Structure and activity. Nat Prod Rep. 2006;23:368–93.PubMed 
CAS 

Google Scholar 
12.Bevins CL, Zasloff M. Peptides from frog skin. Annu Rev Biochem. 1990;59:395–414.PubMed 
CAS 

Google Scholar 
13.Woodhams DC, Rollins-Smith LA, Reinert LK, Lam BA, Harris RN, Briggs CJ, et al. Probiotics modulate a novel amphibian skin defense peptide that is antifungal and facilitates growth of antifungal bacteria. Micro Ecol. 2020;79:192–202.CAS 

Google Scholar 
14.Mergaert P. Role of antimicrobial peptides in controlling symbiotic bacterial populations. Nat Prod Rep. 2018;35:336–56.PubMed 
CAS 

Google Scholar 
15.Pontes MH, Smith KL, de Vooght L, van den Abbeele J, Dale C. Attenuation of the sensing capabilities of PhoQ in transition to obligate insect-bacterial association. PLoS Genet. 2011;7:e1002349.16.Bosch TCG. Cnidarian-microbe interactions and the origin of innate immunity in metazoans. Annu Rev Microbiol. 2013;67:499–518.PubMed 
CAS 

Google Scholar 
17.Foster KR, Schluter J, Coyte KZ, Rakoff-Nahoum S. The evolution of the host microbiome as an ecosystem on a leash. Nature. 2017;548:43–51.PubMed 
PubMed Central 
CAS 

Google Scholar 
18.Joo H-S, Fu C-I, Otto M. Bacterial strategies of resistance to antimicrobial peptides. Philos Trans R Soc B Biol Sci. 2016;371:20150292.
Google Scholar 
19.Stock AM, Robinson VL, Goudreau PN. Two-component signal transduction. Annu Rev Biochem. 2000;69:183–215.PubMed 
CAS 

Google Scholar 
20.Jacob-Dubuisson F, Mechaly A, Betton J-M, Antoine R. Structural insights into the signalling mechanisms of two-component systems. Nat Rev Microbiol. 2018;16:585–93.PubMed 
CAS 

Google Scholar 
21.Gao R, Stock AM. Biological insights from structures of two-component proteins. Annu Rev Microbiol. 2009;63:133–54.22.Piddock LJV. Multidrug-resistance efflux pumps? not just for resistance. Nat Rev Microbiol. 2006;4:629–36.PubMed 
CAS 

Google Scholar 
23.Jeannot K, Bolard A, Plésiat P. Resistance to polymyxins in Gram-negative organisms. Int J Antimicrob Agents. 2017;49:526–35.PubMed 
CAS 

Google Scholar 
24.Fernández L, Jenssen H, Bains M, Wiegand I, Gooderham WJ, Hancock REW. The two-component system CprRS senses cationic peptides and triggers adaptive resistance in Pseudomonas aeruginosa independently of ParRS. Antimicrob Agents Chemother. 2012;56:6212–22.PubMed 
PubMed Central 

Google Scholar 
25.Hong J, Jiang H, Hu J, Wang L, Liu R. Transcriptome analysis reveals the resistance mechanism of Pseudomonas aeruginosa to tachyplesin I. Infect Drug Resist. 2020;13:155.PubMed 
PubMed Central 
CAS 

Google Scholar 
26.Brunetti AE, Neto FC, Vera MC, Taboada C, Pavarini DP, Bauermeister A, et al. An integrative omics perspective for the analysis of chemical signals in ecological interactions. Chem Soc Rev. 2018;47:1574–91.PubMed 
CAS 

Google Scholar 
27.Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 

Google Scholar 
28.Tatusova T, DiCuccio M, Badretdin A, Chetvernin V, Nawrocki EP, Zaslavsky L, et al. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 2016;44:6614–24.PubMed 
PubMed Central 
CAS 

Google Scholar 
29.Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, Von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.PubMed 
PubMed Central 
CAS 

Google Scholar 
30.Blin K, Shaw S, Steinke K, Villebro R, Ziemert N, Lee SY, et al. antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res. 2019;47:W81–W87.PubMed 
PubMed Central 
CAS 

Google Scholar 
31.Hesse C, Schulz F, Bull CT, Shaffer BT, Yan Q, Shapiro N, et al. Genome-based evolutionary history of Pseudomonas spp. Environ Microbiol. 2018;20:2142–59.PubMed 
CAS 

Google Scholar 
32.Lechner M, Findeiß S, Steiner L, Marz M, Stadler PF, Prohaska SJ. Proteinortho: detection of (co-)orthologs in large-scale analysis. BMC Bioinforma. 2011;12:124.
Google Scholar 
33.Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.PubMed 
PubMed Central 
CAS 

Google Scholar 
34.Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–W245.PubMed 
PubMed Central 
CAS 

Google Scholar 
35.Meier-Kolthoff JP, Göker M. TYGS is an automated high-throughput platform for state-of-the-art genome-based taxonomy. Nat Commun. 2019;10:1–10.CAS 

Google Scholar 
36.Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High-throughput ANI analysis of 90k prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.37.Goloboff PA, Catalano SA. TNT version 1.5, including a full implementation of phylogenetic morphometrics. Cladistics. 2016;32:221–38.
Google Scholar 
38.Garber M, Grabherr MG, Guttman M, Trapnell C. Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods. 2011;8:469–77.PubMed 
CAS 

Google Scholar 
39.Min XJ, Butler G, Storms R, Tsang A. OrfPredictor: predicting protein-coding regions in EST-derived sequences. Nucleic Acids Res. 2005;33:W677–W680.PubMed 
PubMed Central 
CAS 

Google Scholar 
40.Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016;34:525–7.PubMed 
CAS 

Google Scholar 
41.Nacif-Marçal L, Pereira GR, Abranches MV, Costa NCS, Cardoso SA, Honda ER, et al. Identification and characterization of an antimicrobial peptide of Hypsiboas semilineatus (Spix, 1824) (Amphibia, Hylidae). Toxicon. 2015;99:16–22.PubMed 

Google Scholar 
42.Magalhães BS, Melo JAT, Leite JRSA, Silva LP, Prates MV, Vinecky F, et al. Post-secretory events alter the peptide content of the skin secretion of Hypsiboas raniceps. Biochem Biophys Res Commun. 2008;377:1057–61.PubMed 

Google Scholar 
43.Brunetti AE, Marani MM, Soldi RA, Mendonça JN, Faivovich J, Cabrera GM, et al. Cleavage of peptides from amphibian skin revealed by combining analysis of gland secretion and in situ MALDI imaging mass spectrometry. ACS Omega. 2018;3:5426–34.PubMed 
PubMed Central 
CAS 

Google Scholar 
44.Van Zundert GCP, Rodrigues J, Trellet M, Schmitz C, Kastritis PL, Karaca E, et al. The HADDOCK2. 2 web server: user-friendly integrative modeling of biomolecular complexes. J Mol Biol. 2016;428:720–5.PubMed 

Google Scholar 
45.Cheung J, Bingman CA, Reyngold M, Hendrickson WA, Waldburger CD. Crystal structure of a functional dimer of the PhoQ sensor domain. J Biol Chem. 2008;283:13762–70.PubMed 
PubMed Central 
CAS 

Google Scholar 
46.Rebollar EA, Hughey MC, Medina D, Harris RN, Ibáñez R, Belden LK. Skin bacterial diversity of Panamanian frogs is associated with host susceptibility and presence of Batrachochytrium dendrobatidis. ISME J. 2016;10:1682–95.47.Bletz MC, Bunk B, Spröer C, Biwer P, Reiter S, Rabemananjara FCE, et al. Amphibian skin-associated Pigmentiphaga: genome sequence and occurrence across geography and hosts. PLoS One. 2019;14:1–14.
Google Scholar 
48.Reimer LC, Vetcininova A, Carbasse JS, Söhngen C, Gleim D, Ebeling C, et al. Bac Dive in 2019: bacterial phenotypic data for high-throughput biodiversity analysis. Nucleic Acids Res. 2019;47:D631–D636.PubMed 
CAS 

Google Scholar 
49.Ramette A, Frapolli M, Saux MF-L, Gruffaz C, Meyer JM, Défago G, et al. Pseudomonas protegens sp. nov., widespread plant-protecting bacteria producing the biocontrol compounds 2,4-diacetylphloroglucinol and pyoluteorin. Syst Appl Microbiol. 2011;34:180–8.PubMed 
CAS 

Google Scholar 
50.Flury P, Aellen N, Ruffner B, Péchy-Tarr M, Fataar S, Metla Z, et al. Insect pathogenicity in plant-beneficial pseudomonads: phylogenetic distribution and comparative genomics. ISME J. 2016;10:2527–42.PubMed 
PubMed Central 

Google Scholar 
51.Flury P, Vesga P, Dominguez-Ferreras A, Tinguely C, Ullrich CI, Kleespies RG, et al. Persistence of root-colonizing Pseudomonas protegens in herbivorous insects throughout different developmental stages and dispersal to new host plants. ISME J. 2019;13:860–72.PubMed 
CAS 

Google Scholar 
52.Pupin M, Esmaeel Q, Flissi A, Dufresne Y, Jacques P, Leclère V. Norine: a powerful resource for novel nonribosomal peptide discovery. Synth Syst Biotechnol. 2016;1:89–94.PubMed 
CAS 

Google Scholar 
53.Wilson DJ, Shi C, Teitelbaum AM, Gulick AM, Aldrich CC. Characterization of AusA: a dimodular nonribosomal peptide synthetase responsible for the production of aureusimine pyrazinones. Biochemistry. 2013;52:926–37.PubMed 
CAS 

Google Scholar 
54.Ryona I, Leclerc S, Sacks GL. Correlation of 3-isobutyl-2-methoxypyrazine to 3-Isobutyl-2-hydroxypyrazine during maturation of bell pepper (Capsicum annuum) and wine grapes (Vitis vinifera). J Agric Food Chem. 2010;58:9723–30.PubMed 
CAS 

Google Scholar 
55.Brucker RM, Baylor CM, Walters RL, Lauer A, Harris RN, Minbiole KPC. The identification of 2, 4-diacetylphloroglucinol as an antifungal metabolite produced by cutaneous bacteria of the salamander Plethodon cinereus. J Chem Ecol. 2008;34:39–43.PubMed 
CAS 

Google Scholar 
56.König E, Bininda-Emonds ORP, Shaw C. The diversity and evolution of anuran skin peptides. Peptides. 2015;63:96–117.PubMed 

Google Scholar 
57.Yeaman MR, Yount NY. Mechanisms of antimicrobial peptide action and resistance. Pharm Rev. 2003;55:27–55.PubMed 
CAS 

Google Scholar 
58.Kumar P, Kizhakkedathu JN, Straus SK. Antimicrobial peptides: diversity, mechanism of action and strategies to improve the activity and biocompatibility in vivo. Biomolecules. 2018;8:4.PubMed Central 

Google Scholar 
59.Castro MS, Ferreira TCG, Cilli EM, Crusca E, Mendes-Giannini MJS, Sebben A, et al. Hylin a1, the first cytolytic peptide isolated from the arboreal South American frog Hypsiboas albopunctatus (‘spotted treefrog’). Peptides. 2009;30:291–6.PubMed 
CAS 

Google Scholar 
60.Resnick NM, Maloy WL, Guy HR, Zasloff M. A novel endopeptidase from Xenopus that recognizes α-helical secondary structure. Cell. 1991;66:541–54.PubMed 
CAS 

Google Scholar 
61.Gutu AD, Sgambati N, Strasbourger P, Brannon MK, Jacobs MA, Haugen E, et al. Polymyxin resistance of Pseudomonas aeruginosa phoQ mutants is dependent on additional two-component regulatory systems. Antimicrob Agents Chemother. 2013;57:2204–15.PubMed 
PubMed Central 
CAS 

Google Scholar 
62.Möglich A, Ayers RA, Moffat K. Structure and signaling mechanism of Per-ARNT-Sim domains. Structure. 2009;17:1282–94.PubMed 
PubMed Central 

Google Scholar 
63.Zschiedrich CP, Keidel V, Szurmant H. Molecular mechanisms of two-component signal transduction. J Mol Biol. 2016;428:3752–75.PubMed 
PubMed Central 
CAS 

Google Scholar 
64.Shah N, Gaupp R, Moriyama H, Eskridge KM, Moriyama EN, Somerville GA. Reductive evolution and the loss of PDC/PAS domains from the genus Staphylococcus. BMC Genomics. 2013;14:524.65.Bader MW, Sanowar S, Daley ME, Schneider AR, Cho U, Xu W, et al. Recognition of antimicrobial peptides by a bacterial sensor kinase. Cell. 2005;122:461–72.PubMed 
CAS 

Google Scholar 
66.Chang C, Tesar C, Gu M, Babnigg G, Joachimiak A, Raj Pokkuluri P, et al. Extracytoplasmic PAS-like domains are common in signal transduction proteins. J Bacteriol. 2010;192:1156–9.PubMed 
CAS 

Google Scholar  More

1.Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 355, 6332 (2017).Article 
CAS 

Google Scholar 
3.Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Wiens, J. J., Litvinenko, Y., Harris, L. & Jezkova, T. Rapid niche shifts in introduced species can be a million times faster than changes among native species and ten times faster than climate change. J. Biogeogr. 46, 2115–2125 (2019).Article 

Google Scholar 
5.Estrada, A., Morales-Castilla, I., Caplat, P. & Early, R. Usefulness of species traits in predicting range shifts. Trends Ecol. Evol. 31, 190–203 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
6.MacLean, S. A. & Beissinger, S. R. Species’ traits as predictors of range shifts under contemporary climate change: A review and meta-analysis. Global Chang. Biol. 23, 4094–4105 (2017).ADS 
Article 

Google Scholar 
7.Winkler, E. & Fischer, M. The role of vegetative spread and seed dispersal for optimal life histories of clonal plants: A simulation study. In Ecology and Evolutionary Biology of Clonal Plants 59–79 (Springer, 2002).8.Neiman, M., Meirmans, S. & Meirmans, P. What can asexual lineage age tell us about the maintenance of sex?. Ann. N. Y. Acad. Sci. 1168, 185–200 (2009).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Steffen, W. et al. Trajectories of the earth system in the anthropocene. PNAS 115, 8252–8259 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Dawson, T. P., Jackson, S. T., House, J. I., Prentice, I. C. & Mace, G. M. Beyond predictions: Biodiversity conservation in a changing climate. Science 332, 53–58 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Hoffmann, A. A. & Sgro, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Bell, G. Evolutionary rescue. Annu. Rev. Ecol. Evol. Syst. 48, 605–627 (2017).Article 

Google Scholar 
14.Capblancq, T., Fitzpatrick, M. C., Bay, R. A., Exposito-Alonso, M. & Keller, S. R. Genomic prediction of (Mal) adaptation across current and future climatic landscapes. Annu. Rev. Ecol. Evol. Syst. 51, 245–269 (2020).Article 

Google Scholar 
15.Barrett, R. D. & Schluter, D. Adaptation from standing genetic variation. Trends Ecol. Evol. 23, 38–44 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Lai, Y. T. et al. Standing genetic variation as the predominant source for adaptation of a songbird. PNAS 116, 2152–2157 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Bonin, A. et al. How to track and assess genotyping errors in population genetics studies. Mol. Ecol. 13, 3261–3273 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Honnay, O. & Jacquemyn, H. A meta-analysis of the relation between mating system, growth form and genotypic diversity in clonal plant species. Evol. Ecol. 22, 299–312 (2008).Article 

Google Scholar 
19.Arnaud-Haond, S. et al. Assessing genetic diversity in clonal organisms: Low diversity or low resolution? Combining power and cost efficiency in selecting markers. J. Hered. 96, 434–440 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Wolfe, A. D. & Liston, A. Contributions of PCR-based methods to plant systematics and evolutionary biology. In Molecular systematics of plants II 43–86 (Springer, 1998).21.Nicolè, S. et al. Biodiversity studies in Phaseolus species by DNA barcoding. Genome 54, 529–545 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Baldwin, B. G. et al. The ITS region of nuclear ribosomal DNA: A valuable source of evidence on angiosperm phylogeny. Ann. Mo. Bot. Gard. 1, 247–277 (1995).Article 

Google Scholar 
23.Álvarez, I. J. F. W. & Wendel, J. F. Ribosomal ITS sequences and plant phylogenetic inference. Mol. Phylogenet. Evol. 29, 417–434 (2003).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
24.Choudhary, N. et al. Insight into the origin of common bean (Phaseolus vulgaris L.) grown in the state of Jammu and Kashmir of North-Western Himalayas. Genet. Resour. Crop Evol. 65, 963–977 (2018).Article 

Google Scholar 
25.Doh, E. J., Kim, J. H., Oh, S. E. & Lee, G. Identification and monitoring of Korean medicines derived from Cinnamomum spp. by using ITS and DNA marker. Genes Genom. 39, 101–109 (2017).CAS 
Article 

Google Scholar 
26.Singh, S. K., Meghwal, P. R., Pathak, R., Bhatt, R. K. & Gautam, R. Assessment of genetic diversity among Indian jujube varieties based on nuclear ribosomal DNA and RAPD polymorphism. Agric. Res. 3, 218–228 (2014).CAS 
Article 

Google Scholar 
27.Urbatsch, L. E., Baldwin, B. G. & Donoghue, M. J. Phylogeny of the coneflowers and relatives (Heliantheae: Asteraceae) based on nuclear rDNA internal transcribed spacer (ITS) sequences and chlorplast DNA restriction site data. Syst. Bot. 1, 539–565 (2000).Article 

Google Scholar 
28.Eriksson, T. & Donoghue, M. J. Phylogenetic relationships of Sambucus and Adoxa (Adoxoideae, Adoxaceae) based on nuclear ribosomal ITS sequences and preliminary morphological data. Syst. Bot. 1, 555–573 (1997).Article 

Google Scholar 
29.Ferrero, V. et al. Global patterns of reproductive and cytotype diversity in an invasive clonal plant. Biol. Invasions 3, 1–13 (2020).
Google Scholar 
30.Hamrick, J. L. & Godt, M. J. Allozyme diversity in plant species. In Plant Population Genetics, Breeding and Genetic Resources 44–64 (Sinauer Associates Inc, 1989).31.Lee, C. E. Evolutionary genetics of invasive species. Trends Ecol. Evol. 17, 386–391 (2002).Article 

Google Scholar 
32.Crooks, J. A. Lag times and exotic species: The ecology and management of biological invasions in slow-motion1. Ecoscience 12, 316–329 (2005).Article 

Google Scholar 
33.Peakall, R. & Beattie, A. J. The genetic consequences of worker ant pollination in a self-compatible, clonal orchid. Evolution 45, 1837–1848 (1991).PubMed 

Google Scholar 
34.Sydes, M. A. & Peakall, R. O. D. Extensive clonality in the endangered shrub Haloragodendron lucasii (Haloragaceae) revealed by allozymes and RAPDs. Mol. Ecol. 7, 87–93 (1998).Article 

Google Scholar 
35.Brzosko, E., Wróblewska, A., Tałałaj, I. & Wasilewska, E. Genetic diversity of Cypripedium calceolus in Poland. Plant Syst. Evol. 295, 83–96 (2011).Article 

Google Scholar 
36.Guerra-García, A., Golubov, J. & Mandujano, M. C. Invasion of Kalanchoe by clonal spread. Biol. Invasions 17, 1615–1622 (2015).Article 

Google Scholar 
37.Ellstrand, N. C. & Roose, M. L. Patterns of genotypic diversity in clonal plant species. Am. J. Bot. 74, 123–131 (1987).Article 

Google Scholar 
38.Chung, M. G. & Epperson, B. K. Spatial genetic structure of clonal and sexual reproduction in populations of Adenophora grandiflora (Campanulaceae). Evolution 53, 1068–1078 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Stehlik, I. & Holderegger, R. Spatial genetic structure and clonal diversity of Anemone nemorosa in late successional deciduous woodlands of Central Europe. J. Ecol. 88, 424–435 (2000).Article 

Google Scholar 
40.Kudoh, H., Shibaike, H., Takasu, H., Whigham, D. F. & Kawano, S. Genet structure and determinants of clonal structure in a temperate deciduous woodland herb, Uvularia perfoliata. J. Ecol. 87, 244–257 (1999).Article 

Google Scholar 
41.Pornon, A., Escaravage, N., Thomas, P. & Taberlet, P. Dynamics of genotypic structure in clonal Rhododendron ferrugineum (Ericaceae) populations. Mol. Ecol. 9, 1099–1111 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Brzosko, E., Wróblewska, A. & Ratkiewicz, M. Spatial genetic structure and clonal diversity of island populations of lady’s slipper (Cypripedium calceolus) from the Biebrza National Park (northeast Poland). Mol. Ecol. 11, 2499–2509 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Smith, A. L. et al. Global gene flow releases invasive plants from environmental constraints on genetic diversity. PNAS 117, 4218–4227 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Dong, M. E. I., Lu, B. R., Zhang, H. B., Chen, J. K. & Li, B. O. Role of sexual reproduction in the spread of an invasive clonal plant Solidago canadensis revealed using intersimple sequence repeat markers. Plant Species Biol. 21, 13–18 (2006).Article 

Google Scholar 
45.You, W., Fan, S., Yu, D., Xie, D. & Liu, C. An invasive clonal plant benefits from clonal integration more than a co-occurring native plant in nutrient-patchy and competitive environments. PLoS ONE 9, e97246 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
46.Silvertown, J. The evolutionary maintenance of sexual reproduction: Evidence from the ecological distribution of asexual reproduction in clonal plants. Int. J. Plant Sci. 169, 157–168 (2008).Article 

Google Scholar 
47.Vallejo-Marín, M., Dorken, M. E. & Barrett, S. C. The ecological and evolutionary consequences of clonality for plant mating. Annu. Rev. Ecol. Evol. Syst. 41, 193–213 (2010).Article 

Google Scholar 
48.Uesugi, A., Baker, D. J., de Silva, N., Nurkowski, K. & Hodgins, K. A. A lack of genetically compatible mates constrains the spread of an invasive weed. New Phytol. 226, 1864–1872 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Allendorf, F. W. & Lundquist, L. L. Introduction: Population biology, evolution, and control of invasive species. Conserv. Biol. 1, 24–30 (2003).Article 

Google Scholar 
50.Pluess, A. R. & Stöcklin, J. Population genetic diversity of the clonal plant Geum reptans (Rosaceae) in the Swiss Alps. Am. J. Bot. 91, 2013–2021 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
51.Bialozyt, R., Ziegenhagen, B. & Petit, R. J. Contrasting effects of long distance seed dispersal on genetic diversity during range expansion. J. Evol. Biol. 19, 12–20 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Colautti, R. I., Grigorovich, I. A. & MacIsaac, H. J. Propagule pressure: A null model for biological invasions. Biol. Invasions 8, 1023–1037 (2006).Article 

Google Scholar 
53.Roman, J. & Darling, J. A. Paradox lost: Genetic diversity and the success of aquatic invasions. Trends Ecol. Evol. 22, 454–464 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
54.Dlugosch, K. M. & Parker, I. M. Founding events in species invasions: Genetic variation, adaptive evolution, and the role of multiple introductions. Mol. Ecol. 17, 431–449 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Shirk, R. Y., Hamrick, J. L., Zhang, C. & Qiang, S. Patterns of genetic diversity reveal multiple introductions and recurrent founder effects during range expansion in invasive populations of Geranium carolinianum (Geraniaceae). Heredity 112, 497–507 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Nobarinezhad, M. H., Challagundla, L. & Wallace, L. E. Small-scale population connectivity and genetic structure in Canada thistle (Cirsium arvense). Int. J. Plant Sci. 181, 473–484 (2020).Article 

Google Scholar 
57.Sakai, A. K. et al. The population biology of invasive species. Annu. Rev. Ecol. Evol. Syst. 32, 305–332 (2001).Article 

Google Scholar 
58.Maron, J. L., Vilà, M., Bommarco, R., Elmendorf, S. & Beardsley, P. Rapid evolution of an invasive plant. Ecol. Monogr. 74, 261–280 (2004).Article 

Google Scholar 
59.Bossdorf, O. et al. Phenotypic and genetic differentiation between native and introduced plant populations. Oecologia 144, 1–11 (2005).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Montague, J. L., Barrett, S. C. H. & Eckert, C. G. Re-establishment of clinal variation in flowering time among introduced populations of purple loosestrife (Lythrum salicaria, Lythraceae). J. Evol. Biol. 21, 234–245 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
61.Prentis, P. J., Wilson, J. R., Dormontt, E. E., Richardson, D. M. & Lowe, A. J. Adaptive evolution in invasive species. Trends Plant Sci. 13, 288–294 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Colautti, R. I., Maron, J. L. & Barrett, S. C. Common garden comparisons of native and introduced plant populations: Latitudinal clines can obscure evolutionary inferences. Evol. Appl. 2, 187–199 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
63.Colautti, R. I., Eckert, C. G. & Barrett, S. C. Evolutionary constraints on adaptive evolution during range expansion in an invasive plant. Proc. Roy. Soc. B 277, 1799–1806 (2010).Article 

Google Scholar 
64.Barrett, S. C., Colautti, R. I. & Eckert, C. G. Plant reproductive systems and evolution during biological invasion. Mol. Ecol. 17, 373–383 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
65.Pappert, R. A., Hamrick, J. L. & Donovan, L. A. Genetic variation in Pueraria lobata (Fabaceae), an introduced, clonal, invasive plant of the southeastern United States. Am. J. Bot. 87, 1240–1245 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
66.Duchoslav, M. & Staňková, H. Population genetic structure and clonal diversity of Allium oleraceum (Amaryllidaceae), a polyploid geophyte with common asexual but variable sexual reproduction. Folia Geobot. 50, 123–136 (2015).Article 

Google Scholar 
67.Nevo, E. Genetic variation in natural populations: Patterns and theory. Theor. Popul. Biol. 13, 121–177 (1978).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Gargiulo, R., Ilves, A., Kaart, T., Fay, M. F. & Kull, T. High genetic diversity in a threatened clonal species, Cypripedium calceolus (Orchidaceae), enables long-term stability of the species in different biogeographical regions in Estonia. Bot. J. Linn. Soc. 186, 560–571 (2018).Article 

Google Scholar 
69.Xia, L., Geng, Q. & An, S. Rapid genetic divergence of an invasive species, Spartina alterniflora, in China. Front. Genet. 11, 284 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
70.Rosenthal, D. M., Ramakrishnan, A. P. & Cruzan, M. B. Evidence for multiple sources of invasion and intraspecific hybridization in Brachypodium sylvaticum (Hudson) Beauv, North America. Mol. Ecol. 17, 4657–4669 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
71.Lembicz, M. et al. Microsatellite identification of ramet genotypes in a clonal plant with phalanx growth: The case of Cirsium rivulare (Asteraceae). Flora 206, 792–798 (2011).Article 

Google Scholar 
72.Young, A., Boyle, T. & Brown, T. The population genetic consequences of habitat fragmentation for plants. Trends Ecol. Evol. 11, 413–418 (1996).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Lucardi, R. D., Wallace, L. E. & Ervin, G. N. Patterns of genetic diversity in highly invasive species: Cogongrass (Imperata cylindrica) expansion in the invaded range of the southern United States (US). Plants 9, 423 (2020).CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
74.Barbosa, C., Trevisan, R., Estevinho, T. F., Castellani, T. T. & Silva-Pereira, V. Multiple introductions and efficient propagule dispersion can lead to high genetic variability in an invasive clonal species. Biol. Invasions 21, 3427–3438 (2019).Article 

Google Scholar 
75.Hutchinson, J. Notes on the Indian species of Sambucus. Bull. Misc. Inf. 1909, 191–193 (1909).
Google Scholar 
76.Acharya, J. & Mukherjee, A. An account of Sambucus L. in the Himalayan regions of India. Indian J. Life Sci. 4, 77–84 (2014).
Google Scholar 
77.Rodgers, W. A. & Panwar, S. H. Biogeographical Classification of India (New Forest, 1988).
Google Scholar 
78.Shafiq, M. U., Rasool, R., Ahmed, P. & Dimri, A. P. Temperature and precipitation trends in Kashmir Valley, North Western Himalayas. Theor. Appl. Climatol. 135, 293–304 (2019).ADS 
Article 

Google Scholar 
79.Clarke, J. B. & Tobutt, K. R. Development of microsatellite primers and two multiplex polymerase chain reactions for the common elder (Sambucus nigra). Mol. Ecol. Notes 6, 453–455 (2006).CAS 
Article 

Google Scholar 
80.DARwin software v. 6.0. http://darwin.cirad.fr/darwin (2006).81.Gascuel, O. Concerning the NJ algorithm and its unweighted version, UNJ. Math. Hierarchies Biol. 37, 149–171 (1997).MathSciNet 
MATH 
Article 

Google Scholar 
82.Peakall, R. O. D. & Smouse, P. E. GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).Article 

Google Scholar 
83.Nei, M. Genetic distance between populations. Am. Nat. 106, 283–292 (1972).Article 

Google Scholar 
84.Nei, M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89, 583–590 (1978).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
85.Anderson, J. A., Churchill, G. A., Autrique, J. E., Tanksley, S. D. & Sorrells, M. E. Optimizing parental selection for genetic linkage maps. Genome 36, 181–186 (1993).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
86.Pritchard, J. K., Wen, X. & Falush, D. Documentation for STRUCTURE Software, Version 2.3 (University of Chicago, 2010).
Google Scholar 
87.Earl, D. A. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).Article 

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

Google Scholar 
89.Bradbury, P. J. et al. TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics 23, 2633–2635 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
90.Huerta-Cepas, J., Serra, F. & Bork, P. ETE 3: Reconstruction, analysis, and visualization of phylogenomic data. Mol. Biol. Evol. 33, 1635–1638 (2016).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
92.Tamura, K., Stecher, G., Peterson, D., Filipski, A. & Kumar, S. MEGA6: Molecular evolutionary genetics analysis version 6.0. Mol. Biol. Evol. 30, 2725–2729 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
93.Sneath, P. H. & Sokal, R. R. Numerical Taxonomy. The Principles and Practice of Numerical Classification (W.H. Freeman and Company, 1973).MATH 

Google Scholar 
94.Saitou, N. & Nei, M. The neighbor-joining method: A new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425 (1987).CAS 

Google Scholar 
95.Tamura, K., Nei, M. & Kumar, S. Prospects for inferring very large phylogenies by using the neighbor-joining method. PNAS 101, 11030–11035 (2004).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
96.Felsenstein, J. Confidence limits on phylogenies: An approach using the bootstrap. Evolution 39, 783–791 (1985).PubMed 
Article 
PubMed Central 

Google Scholar 
97.Hall, T. A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. In Nucleic Acids Symposium Series 95–98 (Information Retrieval Ltd., c1979–c2000 1999).98.Hall, T., Biosciences, I. & Carlsbad, C. BioEdit: An important software for molecular biology. GERF Bull. Biosci. 2, 60–61 (2011).
Google Scholar  More

1.Gibbs, A. G. & Rajpurohit, S. Cuticular lipids and water balance. in Insect hydrocarbons: biology, biochemistry, and chemical ecology 100–120 (Cambridge University Press Cambridge, UK, 2010). https://doi.org/10.1017/CBO9780511711909.0072.Pedrini, N., Ortiz-Urquiza, A., Zhang, S. & Keyhani, N. O. Targeting of insect epicuticular lipids by the entomopathogenic fungus Beauveria bassiana: hydrocarbon oxidation within the context of a host-pathogen interaction. Front. Microbiol. 4, 24 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Howard, R. W. & Blomquist, G. J. Ecological, behavioral, and biochemical aspects of insect hydrocarbons. Annu. Rev. Entomol. 50, 371–393 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Lang, C. & Menzel, F. Lasius niger ants discriminate aphids based on their cuticular hydrocarbons. Anim. Behav. 82, 1245–1254 (2011).Article 

Google Scholar 
5.Sakata, I., Hayashi, M. & Nakamuta, K. Tetramorium tsushimae ants use methyl branched hydrocarbons of aphids for partner recognition. J. Chem. Ecol. 43, 966–970 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Salazar, A. et al. Aggressive mimicry coexists with mutualism in an aphid. Proc. Natl. Acad. Sci. 112, 1101–1106 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Endo, S. & Itino, T. The aphid-tending ant Lasius fuji exhibits reduced aggression toward aphids marked with ant cuticular hydrocarbons. Popul. Ecol. 54, 405–410 (2012).Article 

Google Scholar 
8.Endo, S. & Itino, T. Myrmecophilous aphids produce cuticular hydrocarbons that resemble those of their tending ants. Popul. Ecol. 55, 27–34 (2013).Article 

Google Scholar 
9.Stadler, B. & Dixon, A. F. G. Ecology and evolution of aphid-ant interactions. Annu. Rev. Ecol. Evol. Syst. 36, 345–372 (2005).Article 

Google Scholar 
10.Schillewaert, S. et al. The influence of facultative endosymbionts on honeydew carbohydrate and amino acid composition of the black bean aphid Aphis fabae. Physiol. Entomol. 42, 125–133 (2017).CAS 
Article 

Google Scholar 
11.Oliver, K. M., Degnan, P. H., Burke, G. R. & Moran, N. A. Facultative symbionts in aphids and the horizontal transfer of ecologically important traits. Annu. Rev. Entomol. 55, 247–266 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Douglas, A. E. Nutritional interactions in insect-microbial symbioses: aphids and their symbiotic bacteria Buchnera. Annu. Rev. Entomol. 43, 17–37 (1998).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Montllor, C. B., Maxmen, A. & Purcell, A. H. Facultative bacterial endosymbionts benefit pea aphids Acyrthosiphon pisum under heat stress. Ecol. Entomol. 27, 189–195 (2002).Article 

Google Scholar 
14.Russell, J. A. & Moran, N. A. Costs and benefits of symbiont infection in aphids: variation among symbionts and across temperatures. Proc. R. Soc. B Biol. Sci. 273, 603–610 (2005).Article 

Google Scholar 
15.Wagner, S. M. et al. Facultative endosymbionts mediate dietary breadth in a polyphagous herbivore. Funct. Ecol. 29, 1402–1410 (2015).Article 

Google Scholar 
16.Scarborough, C. L., Ferrari, J. & Godfray, H. C. J. Aphid protected from pathogen by endosymbiont. Science 310, 1781 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Łukasik, P., van Asch, M., Guo, H., Ferrari, J. & Godfray, H. C. J. Unrelated facultative endosymbionts protect aphids against a fungal pathogen. Ecol. Lett. 16, 214–218 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
18.Oliver, K. M., Russell, J. A., Moran, N. A. & Hunter, M. S. Facultative bacterial symbionts in aphids confer resistance to parasitic wasps. Proc. Natl. Acad. Sci. 100, 1803–1807 (2003).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Vorburger, C., Gehrer, L. & Rodriguez, P. A strain of the bacterial symbiont Regiella insecticola protects aphids against parasitoids. Biol. Lett. 6, 109–111 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Vorburger, C. & Gouskov, A. Only helpful when required: a longevity cost of harbouring defensive symbionts. J. Evol. Biol. 24, 1611–1617 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Vorburger, C., Ganesanandamoorthy, P. & Kwiatkowski, M. Comparing constitutive and induced costs of symbiont-conferred resistance to parasitoids in aphids. Ecol. Evol. 3, 706–713 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
22.Gwynn, D. M., Callaghan, A., Gorham, J., Walters, K. F. A. & Fellowes, M. D. E. Resistance is costly: trade-offs between immunity, fecundity and survival in the pea aphid. Proc. R. Soc. B Biol. Sci. 272, 1803–1808 (2005).CAS 
Article 

Google Scholar 
23.Oliver, K. M., Campos, J., Moran, N. A. & Hunter, M. S. Population dynamics of defensive symbionts in aphids. Proc. R. Soc. B Biol. Sci. 275, 293–299 (2008).Article 

Google Scholar 
24.Wernegreen, J. J. Genome evolution in bacterial endosymbionts of insects. Nat. Rev. Genet. 3, 850–861 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Degnan, P. H., Yu, Y., Sisneros, N., Wing, R. A. & Moran, N. A. Hamiltonella defensa, genome evolution of protective bacterial endosymbiont from pathogenic ancestors. Proc. Natl. Acad. Sci. 106, 9063–9068 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Ankrah, N. Y. D., Luan, J. & Douglas, A. E. Cooperative metabolism in a three-partner insect-bacterial symbiosis revealed by metabolic modeling. J. Bacteriol. 199, e00872-e916 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Herren, J. K. et al. Insect endosymbiont proliferation is limited by lipid availability. Elife 3, e02964 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
28.Hamilton, R. J. Waxes: Chemistry, Molecular Biology and Functions (Insect Waxes. Oily Press, 1995).
Google Scholar 
29.Blailock, T. T., Blomquist, G. J. & Jackson, L. L. Biosynthesis of 2-methylalkanes in the crickets: Nemobiusfasciatus and Grylluspennsylvanicus. Biochem. Biophys. Res. Commun. 68, 841–849 (1976).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Engl, T. et al. Effect of antibiotic treatment and gamma-irradiation on cuticular hydrocarbon profiles and mate choice in tsetse flies (Glossina m. morsitans). BMC Microbiol. 18, 145 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Engl, T. et al. Ancient symbiosis confers desiccation resistance to stored grain pest beetles. Mol. Ecol. 27, 2095–2108 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Schneider, D. I. et al. Symbiont-driven male mating success in the Neotropical Drosophila paulistorum superspecies. Behav. Genet. 49, 83–98 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
33.de Souza, D. J., Devers, S. & Lenoir, A. Blochmannia endosymbionts and their host, the ant Camponotus fellah: cuticular hydrocarbons and melanization. C. R. Biol. 334, 737–741 (2011).Article 
CAS 

Google Scholar 
34.Richard, F.-J. Symbiotic bacteria influence the odor and mating preference of their hosts. Front. Ecol. Evol. 5, 143 (2017).Article 

Google Scholar 
35.Fischer, M. K. & Shingleton, A. W. Host plant and ants influence the honeydew sugar composition of aphids. Funct. Ecol. 15, 544–550 (2001).Article 

Google Scholar 
36.Yao, I. & Akimoto, S. Ant attendance changes the sugar composition of the honeydew of the drepanosiphid aphid Tuberculatus quercicola. Oecologia 128, 36–43 (2001).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Yao, I. & Akimoto, S. Flexibility in the composition and concentration of amino acids in honeydew of the drepanosiphid aphid Tuberculatus quercicola. Ecol. Entomol. 27, 745–752 (2002).Article 

Google Scholar 
38.Offenberg, J. Balancing between mutualism and exploitation: the symbiotic interaction between Lasius ants and aphids. Behav. Ecol. Sociobiol. 49, 304–310 (2001).Article 

Google Scholar 
39.Stadler, B. & Dixon, A. F. G. Ant attendance in aphids: why different degrees of myrmecophily?. Ecol. Entomol. 24, 363–369 (1999).Article 

Google Scholar 
40.Vantaux, A., Van den Ende, W., Billen, J. & Wenseleers, T. Large interclone differences in melezitose secretion in the facultatively ant-tended black bean aphid Aphis fabae. J. Insect. Physiol. 57, 1614–1621 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Moran, N. A., Russell, J. A., Koga, R. & Fukatsu, T. Evolutionary relationships of three new species of Enterobacteriaceae living as symbionts of aphids and other insects. Appl. Environ. Microbiol. 71, 3302–3310 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Molloy, J. C., Sommer, U., Viant, M. R. & Sinkins, S. P. Wolbachia modulates lipid metabolism in Aedes albopictus mosquito cells. Appl. Environ. Microbiol. 82, 3109–3120 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Paredes, J. C., Herren, J. K., Schüpfer, F. & Lemaitre, B. The role of lipid competition for endosymbiont-mediated protection against parasitoid wasps in Drosophila. MBio 7, e01006-e1016 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Chung, H. & Carroll, S. B. Wax, sex and the origin of species: dual roles of insect cuticular hydrocarbons in adaptation and mating. BioEssays 37, 822–830 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
45.Bos, N. et al. Learning and perceptual similarity among cuticular hydrocarbons in ants. J. Insect Physiol. 58, 138–146 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.van Wilgenburg, E. et al. Learning and discrimination of cuticular hydrocarbons in a social insect. Biol. Lett. 8, 17–20 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
47.Oberhauser, F. B., Koch, A. & Czaczkes, T. J. Small differences in learning speed for different food qualities can drive efficient collective foraging in ant colonies. Behav. Ecol. Sociobiol. 72, 164 (2018).Article 

Google Scholar 
48.Erickson, D. M., Wood, E. A., Oliver, K. M., Billick, I. & Abbot, P. The effect of ants on the population dynamics of a protective symbiont of aphids, Hamiltonella defensa. Ann. Entomol. Soc. Am. 105, 447–453 (2012).Article 

Google Scholar 
49.Schmidt, M. H. et al. Relative importance of predators and parasitoids for cereal aphid control. Proc. R. Soc. Lond. Ser. B. Biol. Sci. 270, 1905–1909 (2003).Article 

Google Scholar 
50.Łukasik, P., Dawid, M. A., Ferrari, J. & Godfray, H. C. J. The diversity and fitness effects of infection with facultative endosymbionts in the grain aphid, Sitobion avenae. Oecologia 173, 985–996 (2013).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Oliver, K. M. et al. Parasitic wasp responses to symbiont-based defense in aphids. BMC Biol. 10, 11 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
52.Dennis, A. B., Patel, V., Oliver, K. M. & Vorburger, C. Parasitoid gene expression changes after adaptation to symbiont-protected hosts. Evolution 71, 2599–2617 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
53.Guo, J. et al. Nine facultative endosymbionts in aphids, a review. J. Asia. Pac. Entomol. 20, 794–801 (2017).Article 

Google Scholar 
54.Vorburger, C., Sandrock, C., Gouskov, A., Castañeda, L. E. & Ferrari, J. Genotypic variation and the role of defensive endosymbionts in an all-parthenogenetic host–parasitoid interaction. Evol. Int. J. Org. Evol. 63, 1439–1450 (2009).Article 

Google Scholar 
55.Carlson, D. A., Bernier, U. R. & Sutton, B. D. Elution patterns from capillary GC for methyl-branched alkanes. J. Chem. Ecol. 24, 1845–1865 (1998).CAS 
Article 

Google Scholar 
56.Katritzky, A. R., Chen, K., Maran, U. & Carlson, D. A. QSPR correlation and predictions of GC retention indexes for methyl-branched hydrocarbons produced by insects. Anal. Chem. 72, 101–109 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.R Core Team. R: A Language and Environment for Statistical Computing. (2019).58.Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
59.Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Anderson, M. J. Permutational multivariate analysis of variance (PERMANOVA). Wiley Statsref. Stat. Ref. https://doi.org/10.1002/9781118445112.stat07841 (2014).Article 

Google Scholar 
61.Anderson, M. J. & Walsh, D. C. I. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: what null hypothesis are you testing?. Ecol. Monogr. 83, 557–574 (2013).Article 

Google Scholar 
62.Arbizu, P. M. pairwiseAdonis: Pairwise Multilevel Comparison using Adonis (2017).63.Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47–e47 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar  More

1.Urban, M. C. & Skelly, D. K. Evolving metacommunities: Toward an evolutionary perspective on metacommunities. Ecology 87, 1616–1626 (2006).Article 

Google Scholar 
2.Cortez, M. H. & Ellner, S. P. Understanding rapid evolution in predator-prey interactions using the theory of fast-slow dynamical systems. Am. Nat. 176, E109–E127. https://doi.org/10.1086/656485 (2010).Article 
PubMed 

Google Scholar 
3.Ellner, S. P., Geber, M. A. & Hairston, N. G. Does rapid evolution matter? Measuring the rate of contemporary evolution and its impacts on ecological dynamics. Ecol. Lett. 14, 603–614. https://doi.org/10.1111/j.1461-0248.2011.01616.x (2011).Article 
PubMed 

Google Scholar 
4.Yoder, J. B. et al. Ecological opportunity and the origin of adaptive radiations. J. Evol. Biol. 23, 1581–1596. https://doi.org/10.1111/j.1420-9101.2010.02029.x (2010).CAS 
Article 
PubMed 

Google Scholar 
5.Losos, J. B. Adaptive radiation, ecological opportunity, and evolutionary determinism. Am. Nat. 175, 623–639. https://doi.org/10.1086/652433 (2010).Article 
PubMed 

Google Scholar 
6.Meyer, J. R. & Kassen, R. The effects of competition and predation on diversification in a model adaptive radiation. Nature 446, 432–435. https://doi.org/10.1038/nature05599 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
7.Stroud, J. T. & Losos, J. B. Ecological opportunity and adaptive radiation. Annu. Rev. Ecol. Evol. Syst. 47(47), 507–532. https://doi.org/10.1146/annurev-ecolsys-121415-032254 (2016).Article 

Google Scholar 
8.Keller, I. & Seehausen, O. Thermal adaptation and ecological speciation. Mol. Ecol. 21, 782–799. https://doi.org/10.1111/j.1365-294X.2011.05397.x (2012).CAS 
Article 
PubMed 

Google Scholar 
9.Schluter, D., Price, T. D. & Grant, P. R. ecological character displacement in Darwin Finches. Science 227, 1056–1059. https://doi.org/10.1126/science.227.4690.1056 (1985).ADS 
CAS 
Article 
PubMed 

Google Scholar 
10.Munkemuller, T. & Gallien, L. VirtualCom: A simulation model for eco-evolutionary community assembly and invasion. Methods Ecol. Evol. 6, 735–743. https://doi.org/10.1111/2041-210x.12364 (2015).Article 

Google Scholar 
11.Munoz, F. et al. ecolottery: Simulating and assessing community assembly with environmental filtering and neutral dynamics in R. Methods Ecol. Evol. 9, 693–703. https://doi.org/10.1111/2041-210x.12918 (2018).Article 

Google Scholar 
12.Ruffley, M., Peterson, K., Week, B., Tank, D. C. & Harmon, L. J. Identifying models of trait-mediated community assembly using random forests and approximate Bayesian computation. Ecol. Evol. 9, 13218–13230. https://doi.org/10.1002/ece3.5773 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
13.van der Plas, F. et al. A new modeling approach estimates the relative importance of different community assembly processes. Ecology 96, 1502–1515. https://doi.org/10.1890/14-0454.1 (2015).Article 

Google Scholar 
14.Stegen, J. C. et al. Quantifying community assembly processes and identifying features that impose them. ISME J. 7, 2069–2079. https://doi.org/10.1038/ismej.2013.93 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
15.Dieckmann, U. & Doebeli, M. On the origin of species by sympatric speciation. Nature 400, 354–357 (1999).ADS 
CAS 
Article 

Google Scholar 
16.Geritz, S. A. H., Kisdi, E., Meszena, G. & Metz, J. A. J. Evolutionarily singular strategies and the adaptive growth and branching of the evolutionary tree. Evol. Ecol. 12, 35–57 (1998).Article 

Google Scholar 
17.Vellend, M. Conceptual synthesis in community ecology. Q. R. Biol. 85, 183–206 (2010).Article 

Google Scholar 
18.Urban, M. C. et al. The evolutionary ecology of metacommunities. Trends Ecol. Evol. 23, 311–317 (2008).Article 

Google Scholar 
19.Pausas, J. G. & Verdu, M. The jungle of methods for evaluating phenotypic and phylogenetic structure of communities. Bioscience 60, 614–625. https://doi.org/10.1525/bio.2010.60.8.7 (2010).Article 

Google Scholar 
20.Mouquet, N. et al. Ecophylogenetics: Advances and perspectives. Biol. Rev. 87, 769–785. https://doi.org/10.1111/j.1469-185X.2012.00224.x (2012).Article 
PubMed 

Google Scholar 
21.Kraft, N. J. B., Cornwell, W. K., Webb, C. O. & Ackerly, D. D. Trait evolution, community assembly, and the phylogenetic structure of ecological communities. Am. Nat. 170, 271–283. https://doi.org/10.1086/519400 (2007).Article 
PubMed 

Google Scholar 
22.Wilson, J. B., Weiher, E. & Keddy, P. Assembly Rules in Plant Communities (Cambridge University Press, 1999).Book 

Google Scholar 
23.MacArthur, R. H. & Levins, R. Limiting similarity convergence and divergence of coexisting species. Am. Nat. 101, 377–385 (1967).Article 

Google Scholar 
24.Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505. https://doi.org/10.1146/annurev.ecolysis.33.010802.150448 (2002).Article 

Google Scholar 
25.Pontarp, M., Brännström, A. & Petchey, O. L. Inferring community assembly processes from macroscopic patterns using dynamic eco-evolutionary models and Approximate Bayesian Computation (ABC). Methods Ecol. Evol. 10, 450–460. https://doi.org/10.1111/2041-210x.13129 (2019).Article 

Google Scholar 
26.Mittelbach, G. G. & Schemske, D. W. Ecological and evolutionary perspectives on community assembly. Trends Ecol. Evol. 30, 241–247. https://doi.org/10.1016/j.tree.2015.02.008 (2015).Article 
PubMed 

Google Scholar 
27.Cavender-Bares, J., Kozak, K. H., Fine, P. V. A. & Kembel, S. W. The merging of community ecology and phylogenetic biology. Ecol. Lett. 12, 693–715. https://doi.org/10.1111/j.1461-0248.2009.01314.x (2009).Article 
PubMed 

Google Scholar 
28.Pontarp, M. & Petchey, O. L. Ecological opportunity and predator–prey interactions: Linking eco-evolutionary processes and diversification in adaptive radiations. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2017.2550 (2018).Article 

Google Scholar 
29.Seehausen, O. African cichlid fish: A model system in adaptive radiation research. Proc. R. Soc. B Biol. Sci. 273, 1987–1998. https://doi.org/10.1098/rspb.2006.3539 (2006).Article 

Google Scholar 
30.Schluter, D. The Ecology of Adaptive Radiation (Columbia University Press, 2000).
Google Scholar 
31.Nosil, P. Ecological Speciation (Oxford University Press, 2012).Book 

Google Scholar 
32.Christiansen, F. B. & Loeschcke, V. Evolution and intraspecific exploitative competition I. One-locus theory for small additive gene effects. Theor. Popul. Biol. 18, 297–313 (1980).MathSciNet 
Article 

Google Scholar 
33.Brown, J. S. & Vincent, T. L. A theory for the evolutionary game. Theor. Popul. Biol. 31, 140–166 (1987).MathSciNet 
Article 

Google Scholar 
34.Doebeli, M. & Dieckmann, U. Speciation along environmental gradients. Nature 421, 259–264. https://doi.org/10.1038/Nature01274 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
35.Brose, U., Williams, R. J. & Martinez, N. D. Allometric scaling enhances stability in complex food webs. Ecol. Lett. 9, 1228–1236. https://doi.org/10.1111/j.1461-0248.2006.00978.x (2006).Article 
PubMed 

Google Scholar 
36.Leyequien, E., de Boer, W. F. & Cleef, A. Influence of body size on coexistence of bird species. Ecol. Res. 22, 735–741. https://doi.org/10.1007/s11284-006-0311-6 (2007).Article 

Google Scholar 
37.Yvon-Durocher, G. et al. Across ecosystem comparisons of size structure: Methods, approaches and prospects. Oikos 120, 550–563. https://doi.org/10.1111/j.1600-0706.2010.18863.x (2011).Article 

Google Scholar 
38.Rudolf, V. H. W. Seasonal shifts in predator body size diversity and trophic interactions in size-structured predator-prey systems. J. Anim. Ecol. 81, 524–532. https://doi.org/10.1111/j.1365-2656.2011.01935.x (2012).Article 
PubMed 

Google Scholar 
39.DeLong, J. P. & Vasseur, D. A. A dynamic explanation of size-density scaling in carnivores. Ecology 93, 470–476 (2012).Article 

Google Scholar 
40.DeLong, J. P. & Vasseur, D. A. Size-density scaling in protists and the links between consumer-resource interaction parameters. J. Anim. Ecol. 81, 1193–1201. https://doi.org/10.1111/j.1365-2656.2012.02013.x (2012).Article 
PubMed 

Google Scholar 
41.Violle, C. et al. Let the concept of trait be functional!. Oikos 116, 882–892. https://doi.org/10.1111/j.2007.0030-1299.15559.x (2007).Article 

Google Scholar 
42.Pontarp, M., Ripa, J. & Lundberg, P. On the origin of phylogenetic structure in competitive metacommunities. Evol. Ecol. Res. 14, 269–284 (2012).
Google Scholar 
43.Pontarp, M., Ripa, J. & Lundberg, P. The biogeography of adaptive radiations and the geographic overlap of sister species. Am. Nat. 186, 565–581 (2015).Article 

Google Scholar 
44.Barabás, G., Pigolotti, S., Gyllenberg, M., Dieckmann, U. & Meszéna, G. Continuous coexistence or discrete species? A new review of an old question. (2012).45.Brännström, A. et al. Modelling the ecology and evolution of communities: A review of past achievements, current efforts, and future promises. Evol. Ecol. Res. 14, 601–625 (2012).
Google Scholar 
46.Emerson, B. C. & Gillespie, R. G. Phylogenetic analysis of community assembly and structure over space and time. Trends Ecol. Evol. 23, 619–630 (2008).Article 

Google Scholar 
47.Vamosi, S. M., Heard, S. B., Vamosi, J. C. & Webb, C. O. Emerging patterns in the comparative analysis of phylogenetic community structure. Mol. Ecol. 18, 572–592. https://doi.org/10.1111/j.1365-294X.2008.04001.x (2009).CAS 
Article 
PubMed 

Google Scholar 
48.Sjödin, H., Ripa, J. & Lundberg, P. Principles of niche expansion. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2018.2603 (2018).Article 

Google Scholar 
49.Ackermann, M. & Doebeli, M. Evolution of niche width and adaptive diversification. Evolution 58, 2599–2612 (2004).Article 

Google Scholar 
50.Urban, M. C. & De Meester, L. Community monopolization: Local adaptation enhances priority effects in an evolving metacommunity. Proc. R. Soc. B. Biol. Sci. 276, 4129–4138 (2009).Article 

Google Scholar 
51.Urban, M. C., De Meester, L., Vellend, M., Stoks, R. & Vanoverbeke, J. A crucial step toward realism: responses to climate change from an evolving metacommunity perspective. Evol. Appl. 5, 154–167. https://doi.org/10.1111/j.1752-4571.2011.00208.x (2012).Article 
PubMed 

Google Scholar 
52.Pontarp, M. & Wiens, J. J. The origin of species richness patterns along environmental gradients: Uniting explanations based on time, diversification rate and carrying capacity. J. Biogeogr. 44, 722–735. https://doi.org/10.1111/jbi.12896 (2017).Article 

Google Scholar 
53.Pontarp, M. & Petchey, O. L. Community trait overdispersion due to trophic interactions: Concerns for assembly process inference. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2016.1602 (2016).Article 

Google Scholar 
54.Pontarp, M. et al. The latitudinal diversity gradient: Novel understanding through mechanistic eco-evolutionary. Trends Ecol. Evol. 34, 211–223. https://doi.org/10.1016/j.tree.2018.11.009 (2019).Article 
PubMed 

Google Scholar 
55.Case, T. J. An Illustrated Guide to Theoretical Ecology (Oxford University Press, Inc, 2000).
Google Scholar 
56.Barabas, G., Michalska-Smith, M. J. & Allesina, S. The effect of intra- and interspecific competition on coexistence in multispecies communities. Am. Nat. 188, E1–E12. https://doi.org/10.1086/686901 (2016).Article 
PubMed 

Google Scholar 
57.Heinz, S. K., Mazzucco, R. & Dieckmann, U. Speciation and the evolution of dispersal along environmental gradients. Evol. Ecol. 23, 53–70. https://doi.org/10.1007/s10682-008-9251-7 (2009).Article 

Google Scholar 
58.Metz, J. A. J., Nisbet, R. M. & Geritz, S. A. H. How should we define fitness for general ecolgical scenarios. Trends Ecol. Evol. 7, 198–202. https://doi.org/10.1016/0169-5347(92)90073-k (1992).CAS 
Article 
PubMed 

Google Scholar 
59.Doebeli, M. & Dieckmann, U. Evolutionary branching and sympatric speciation caused by different types of ecological interactions. Am. Nat. 156, S77–S101. https://doi.org/10.1086/303417 (2000).Article 
PubMed 

Google Scholar 
60.Ito, H. C. & Dieckmann, U. A new mechanism for recurrent adaptive Radiations. Am. Nat. 170, E96–E111. https://doi.org/10.1086/521229 (2007).Article 
PubMed 

Google Scholar 
61.Cressman, R. et al. Unlimited niche packing in a Lotka-Volterra competition game. Theor. Popul. Biol. 116, 1–17 (2017).Article 

Google Scholar 
62.Webb, C. O., Ackerly, D. D. & Kembel, S. W. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics 24, 2098–2100. https://doi.org/10.1093/bioinformatics/btn358 (2008).CAS 
Article 
PubMed 

Google Scholar 
63.Harmon-Threatt, A. N. & Ackerly, D. D. Filtering across spatial scales: Phylogeny, biogeography and community structure in bumble bees. PLoS ONE https://doi.org/10.1371/journal.pone.0060446 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
64.Pybus, O. G. & Harvey, P. H. Testing macro-evolutionary models using incomplete molecular phylogenies. Proc. R. Soc. B Biol. Sci. 267, 2267–2272. https://doi.org/10.1098/rspb.2000.1278 (2000).CAS 
Article 

Google Scholar  More

Figure 1 presents the track of the eight-month cruise, and Table 1 provides the detail of the legs and dates. On a routine basis R/V Hesperides sailed at an average speed of 11 knots from around 3 pm to 4 am (local time). The vessel arrived on station at around 4 am daily to carry out sampling operations at a fixed point for about 11 hours.Fig. 1Cruise track and integrated backscatter at different stations (NASC, daytime 200 to 1000 m).Full size imageTable 1 Dates and starting points of the 7 legs of the Malaspina cruise.Full size tableAcoustic measurements were carried out continuously using a Simrad EK60 echosounder), operating at 38 and 120 kHz (7° beamwidth transducers) with a ping rate of 0.5 Hz. Unfortunately, the 120 kHz failed during the first leg of the cruise and only 38 kHz data were collected. Echosounder observations were recorded down to 1000 m depth. The echosounder files are in the proprietary Simrad raw format and can be read by various softwares (e.g., LSSS, Echoview, Sonar5, MATECHO, ESP3, echopype, pyEcholab). GPS locations and calibration constants are imbedded in each file.Additionally, daytime data integrated over 2 m vertical bins from 200 to 1000 m depth are provided as Nautical Area Scattering Coefficient (NASC). Each “voxel” is the average of all cleaned and validated data recorded over that depth range, in a time period starting 8 hours before the start of the station (defined as start of the CTD cast) and ending 8 hours after the start of the station, with only data recorded in the period between 1 hour after local sunrise and 1 hour prior to local sunset accepted (i.e., during local daytime hours, but removing crepuscular periods when vertical migration of biota is strong). The relatively long interval over which data were accepted around each station was chosen since the station sampling resulted in noisy acoustic data,, a long interval was therefore chosen to ensure valid data on all stations.Finally, summaries of per station daytime and nighttime acoustic data (omitting data recorded within 1 hour of sunrise and sunset) are provided. The data fields in this file are station date, latitude and longitude, and per day and night average NASC 200–1000 m, average NASC 0–1000, weighted mean depth (WMD) of NASC 200–1000 m, migration amplitude, NASC day-to-night ratio and migration ratio. More

We analyse weekly reported counts of suspected and confirmed human cases and deaths attributed to LF (as defined in Supplementary Table 1), between 1 January 2012 and 30 December 2019, from across the entire of Nigeria. The weekly counts were reported from 774 LGAs in 36 Federal states and the Federal Capital Territory, under Integrated Disease Surveillance and Response (IDSR) protocols, and collated by the NCDC. All suspected cases, confirmed cases and deaths from notifiable infectious diseases (including viral haemorrhagic fevers; VHFs) are reported weekly to the LGA Disease Surveillance and Notification Officer (DSNO) and State Epidemiologist (SE). IDSR routine data on priority diseases are collected from inpatient and outpatient registers in health facilities, and forwarded to each LGA’s DSNO using SMS or paper form. Subsequently, individual LGA DSNOs collate and forward the data to their respective SE, also by SMS and paper form, for weekly and monthly reporting respectively to NCDC. From mid-2017 onwards, data entry in 18 states has been conducted using a mobile phone-based electronic reporting system called mSERS, with the data entered using a customised Excel spreadsheet that is used to manually key into NCDC-compatible spreadsheets. Data from this surveillance regime (WERs) were collated by epidemiologists at NCDC throughout the period 2012 to March 2018 (Supplementary Fig. 1).Throughout the study period, within-country LF surveillance and response has been strengthened under NCDC coordination2,20,33. LGAs are now required to notify immediately any suspected case to the state-level, which in turn reports to NCDC within 24 h, and also sends a cumulative weekly report of all reported cases. A dedicated, multi-sectoral NCDC LF TWG was set up in 2016 with the responsibility of coordinating all LF preparedness and response activities across states. Further capacity building occurred in 2017 to 2019, with the opening of three additional LF diagnostic laboratories in Abuja (Federal Capital Territory), Abakaliki (Ebonyi state) and Owo (Ondo state) (to a total of five; Fig. 2) and the rollout of intensive country-wide training on surveillance, clinical case management and diagnosis. We note that, due to the rapid expansion in a test capacity, the definition of a suspected case in our data has subtly changed over the surveillance period: from 2012 to 2016, suspected cases include probable cases that were not lab-tested, whereas from 2017 to 2019, all suspected cases were tested and confirmed to be negative.In addition to the WERs data, since 2017 LF case reporting data has also been collated by the LF TWG and used to inform the weekly NCDC LF Situation Reports (SitRep data; https://ncdc.gov.ng/diseases/sitreps). This regime includes post hoc follow-ups to ensure more accurate case counts, so our analyses use WER-derived case data from 2012 to 2016, and SitRep-derived case data from 2017 to 2019 (see Fig. 1 for full time series). A visual comparison of the data from each separate time series, including the overlap period (2017 to March 2018) is provided in Supplementary Fig. 1, and all statistical models considered random intercepts for the different surveillance regimes. Where other studies of recent Nigeria LF incidence have been more spatially and temporally restricted34,35, the extended monitoring period and fine spatial granularity of these data provide the opportunity for a detailed empirical perspective on the local drivers of LF at a country-wide scale and their relationship to changes in reporting effort.Recent trends in LF surveillance in NigeriaWe visualised temporal and seasonal trends in suspected and confirmed LF cases within and between years, for both surveillance datasets. Weekly case counts were aggregated to country-level and visualised as both annual case accumulation curves, and aggregated weekly case totals (Fig. 1 and Supplementary Fig. 1). We also mapped annual counts of suspected and confirmed cases across Nigeria at the LGA-level to examine spatial changes in reporting over the surveillance period (Fig. 2). State and LGA shapefiles used for modelling and mapping were obtained from Humanitarian Data Exchange under a CC-BY-IGO license (https://data.humdata.org/dataset/nga-administrative-boundaries).Analyses of aggregated district data are sensitive to differences in scale and shape of aggregation (the modifiable areal unit problem; MAUP36), and LGA geographical areas in Nigeria are highly skewed and vary over >3 orders of magnitude (median 713 km2, mean 1175 km2, range 4–11,255 km2). We therefore also aggregated all LGAs across Nigeria into 130 composite districts with a more even distribution of geographical areas, using distance-based hierarchical clustering on LGA centroids (implemented using hclust in R), with the constraint that each new cluster must contain only LGAs from within the same state (to preserve potentially important state-level differences in surveillance regime). Weekly and annual suspected and confirmed LF case totals were then calculated for each aggregated district. We used these spatially aggregated districts to test for the effects of scale on spatial drivers of LF occurrence and incidence.Statistical analysisWe analysed the full case time series (Fig. 1) to characterise the spatiotemporal incidence and drivers of LF in Nigeria, while controlling for year-on-year increases and expansions of surveillance effort. We firstly modelled annual LF occurrence and incidence at a country-wide scale, to identify the spatial, climatic and socio-ecological correlates of disease risk across Nigeria. Secondly, we modelled seasonal and temporal trends in weekly LF incidence within hyperendemic areas in the north and south of Nigeria, to identify the seasonal climatic conditions associated with LF risk dynamics and evaluate the scope for forecasting. All data processing and modelling was conducted in R v.3.4.1 with the packages R-INLA v.20.03.1737, raster v.3.4.1338 and velox v0.2.039. Statistical modelling was conducted using hierarchical regression in a Bayesian inference framework (integrated nested Laplace approximation (INLA)), which provides fast, stable and accurate posterior approximation for complex, spatially and temporally-structured regression models37,40, and has been shown to outperform alternative methods for modelling environmental phenomena with evidence of spatially biased reporting41.Processing climatic and socio-ecological covariatesWe collated geospatial data on socio-ecological and climatic factors that are hypothesised to influence either M. natalensis distribution and population ecology (rainfall, temperature and vegetation patterns), frequency and mode of human–rodent contact (poverty and improved housing prevalence), both of the above (agricultural and urban land cover) or likelihood of LF reporting (travel time to nearest laboratory with LF diagnostic capacity and travel time to nearest hospital). For each LGA we extracted the mean value for each covariate across the LGA polygon. The full suite of covariates tested across all analyses, data sources and associated hypotheses are described in Supplementary Table 5.We collated climate data spanning the full monitoring period and up until the date of analysis (July 2011 to January 2021). We obtained daily precipitation rasters for Africa42 from the Climate Hazards Infrared Precipitation with Stations (CHIRPS) project; this dataset is based on combining sparse weather station data with satellite observations and interpolation techniques, and is designed to support hydrologic forecasts in areas with poor weather station coverage (such as tropical West Africa)42. A recent study ground-truthing against weather station data showed that CHIRPS provides greater overall accuracy than other gridded precipitation products in Nigeria43. Air temperature daily minimum and maximum rasters were obtained from NOAA and were also averaged to calculate daily mean temperature. EVI, a measure of vegetation quality, was obtained from processing 16-day composite layers from NASA (National Aeronautics and Space Administration) (excluding all grid cells with unreliable observations due to cloud cover and linearly interpolating between observations to give daily values; Supplementary Table 5).We derived several spatial bioclimatic variables to capture conditions across the full monitoring period (Jan 2012 to Dec 2019): mean precipitation of the driest annual month, mean precipitation of the wettest annual month, precipitation seasonality (coefficient of variation), annual mean air temperature, air temperature seasonality, annual mean EVI and EVI seasonality. We also calculated monthly total precipitation, 3-month SPI44, average daily mean (Tmean), minimum (Tmin) and maximum (Tmax) temperature and EVI variables at sequential time lags prior to reporting week for seasonal modelling (described below in Temporal drivers). SPI is a standardised measure of drought or wetness conditions relative to the historical average conditions for a given period of the year. SPI was calculated within a rolling 3-month window across the full 40-year historical CHIRPS rainfall time series (1981–2020) using the R package SPEI v.1.744.We accessed annual human population rasters at 100 m resolution from WorldPop. We accessed the proportion of the population living in poverty in 2010 ( More

1.Pond C. Storage. In: Townsend C, Calow P, editors. Physiological ecology. Oxford: Blackwell Scientific; 1981. p. 190–219.2.Chapin FS, Schulze E, Mooney HA. The ecology and economics of storage in plants. Annu Rev Ecol Syst. 1990;21:423–47.Article 

Google Scholar 
3.Moradali MF, Rehm BHA. Bacterial biopolymers: from pathogenesis to advanced materials. Nat Rev Microbiol. 2020;18:195–210.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Varpe Ø. Life history adaptations to seasonality. Integr Comp Biol. 2017;57:943–60.PubMed 
Article 
PubMed Central 

Google Scholar 
5.Paul EA. Soil microbiology, ecology and biochemistry. 4th ed. Waltham, MA: Academic Press; 2015.6.Becker KW, Collins JR, Durham BP, Groussman RD, White AE, Fredricks HF, et al. Daily changes in phytoplankton lipidomes reveal mechanisms of energy storage in the open ocean. Nat Commun. 2018;9:1–9.Article 
CAS 

Google Scholar 
7.Rothermich MM, Guerrero R, Lenz RW, Goodwin S. Characterization, seasonal occurrence, and diel fluctuation of poly(hydroxyalkanoate) in photosynthetic microbial mats. Appl Environ Microbiol. 2000;66:13.Article 

Google Scholar 
8.Borzi A. Le comunicazioni intracellulari delle Nostochinee. Malpighia. 1887;1:28–74.
Google Scholar 
9.Sherman LA, Meunier P, Colón-López MS. Diurnal rhythms in metabolism: a day in the life of a unicellular, diazotrophic cyanobacterium. Photosynth Res. 1998;58:25–42.CAS 
Article 

Google Scholar 
10.Stuart RK, Mayali X, Boaro AA, Zemla A, Everroad RC, Nilson D, et al. Light regimes shape utilization of extracellular organic C and N in a cyanobacterial biofilm. mBio. 2016;7:e00650–16.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Allen MM. Cyanobacterial cell inclusions. Annu Rev Microbiol. 1984;38:1–25.CAS 
PubMed 
Article 

Google Scholar 
12.Sanz-Luque E, Bhaya D, Grossman AR. Polyphosphate: a multifunctional metabolite in cyanobacteria and algae. Front Plant Sci. 2020;11:938.PubMed 
PubMed Central 
Article 

Google Scholar 
13.Martin P, Lauro FM, Sarkar A, Goodkin N, Prakash S, Vinayachandran PN. Particulate polyphosphate and alkaline phosphatase activity across a latitudinal transect in the tropical Indian Ocean: polyphosphate in the tropical Indian Ocean. Limnol Oceanogr. 2018;63:1395–406.CAS 
Article 

Google Scholar 
14.Diaz J, Ingall E, Benitez-Nelson C, Paterson D, de Jonge MD, McNulty I, et al. Marine polyphosphate: a key player in geologic phosphorus sequestration. Science. 2008;320:652–5.CAS 
PubMed 
Article 

Google Scholar 
15.Godwin CM, Cotner JB. Aquatic heterotrophic bacteria have highly flexible phosphorus content and biomass stoichiometry. ISME J. 2015;9:2324–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Oehmen A, Lemos P, Carvalho G, Yuan Z, Keller J, Blackall L, et al. Advances in enhanced biological phosphorus removal: From micro to macro scale. Water Res. 2007;41:2271–300.CAS 
PubMed 
Article 

Google Scholar 
17.Dorofeev AG, Nikolaev YuA, Mardanov AV, Pimenov NV. Role of phosphate-accumulating bacteria in biological phosphorus removal from wastewater. Appl Biochem Microbiol. 2020;56:1–14.CAS 
Article 

Google Scholar 
18.Carrondo MA. Ferritins, iron uptake and storage from the bacterioferritin viewpoint. EMBO J. 2003;22:1959–68.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Canessa P, Larrondo LF. Environmental responses and the control of iron homeostasis in fungal systems. Appl Microbiol Biotechnol. 2013;97:939–55.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Harrison PM, Arosio P. The ferritins: molecular properties, iron storage function and cellular regulation. Biochim Biophys Acta. 1996;1275:161–203.PubMed 
Article 
PubMed Central 

Google Scholar 
21.Docampo R, Moreno SNJ. Acidocalcisomes. Cell Calcium. 2011;50:113–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Tsednee M, Castruita M, Salomé PA, Sharma A, Lewis BE, Schmollinger SR, et al. Manganese co-localizes with calcium and phosphorus in Chlamydomonas acidocalcisomes and is mobilized in manganese-deficient conditions. J Biol Chem. 2019;294:17626–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.Mojzeš P, Gao L, Ismagulova T, Pilátová J, Moudříková Š, Gorelova O, et al. Guanine, a high-capacity and rapid-turnover nitrogen reserve in microalgal cells. Proc Natl Acad Sci USA. 2020;117:32722–30.24.Turner BL. Inositol phosphates in soil: Amounts, forms and significance of the phosphorylated inositol stereoisomers. In: Turner BL, Richardson AE, Mullaney EJ, editors. Inositol phosphates: linking agriculture and the environment. 2007. Wallingford: CABI; 2007. p. 186–206.25.Flemming H-C, Wingender J. The biofilm matrix. Nat Rev Microbiol. 2010;8:623–33.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Otero A, Vincenzini M. Nostoc (Cyanophyceae) goes nude: Extracellular polysaccharides serve as a sink for reducing power under unbalanced C/N metabolism. J Phycol. 2004;40:74–81.CAS 
Article 

Google Scholar 
27.Wang J, Yu H-Q. Biosynthesis of polyhydroxybutyrate (PHB) and extracellular polymeric substances (EPS) by Ralstonia eutropha ATCC 17699 in batch cultures. Appl Microbiol Biotechnol. 2007;75:871–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Brangarí AC, Fernàndez-Garcia D, Sanchez-Vila X, Manzoni S. Ecological and soil hydraulic implications of microbial responses to stress—a modeling analysis. Adv Water Resour. 2018;116:178–94.Article 

Google Scholar 
29.Pal S, Manna A, Paul AK. Production of poly(β-hydroxybutyric acid) and exopolysaccharide by Azotobacter beijerinckii WDN-01. World J Microbiol Biotechnol. 1999;15:11–6.Article 

Google Scholar 
30.Kuzyakov Y, Blagodatskaya E. Microbial hotspots and hot moments in soil: Concept & review. Soil Biol Biochem. 2015;83:184–99.CAS 
Article 

Google Scholar 
31.Hauschild P, Röttig A, Madkour MH, Al-Ansari AM, Almakishah NH, Steinbüchel A. Lipid accumulation in prokaryotic microorganisms from arid habitats. Appl Microbiol Biotechnol. 2017;101:2203–16.CAS 
PubMed 
Article 

Google Scholar 
32.Wang JG, Bakken LR. Screening of soil bacteria for poly-β-hydroxybutyric acid production and its role in the survival of starvation. Micro Ecol. 1998;35:94–101.CAS 
Article 

Google Scholar 
33.Hanzlíková A, Jandera A, Kunc F. Poly-3-hydroxybutyrate production and changes of bacterial community in the soil. Folia Microbiologica. 1985;30:58–64.Article 

Google Scholar 
34.Iwahara S, Miki S. Production of α-α-trehalose by a bacterium isolated from soil. Agric Biol Chem. 1988;52:867–8.CAS 

Google Scholar 
35.Treseder KK, Lennon JT. Fungal traits that drive ecosystem dynamics on land. Microbiol Mol Biol Rev. 2015;79:243–62.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.López MF, Männer P, Willmann A, Hampp R, Nehls U. Increased trehalose biosynthesis in Hartig net hyphae of ectomycorrhizas. N Phytol. 2007;174:389–98.Article 
CAS 

Google Scholar 
37.Bünemann EK, Smernik RJ, Doolette AL, Marschner P, Stonor R, Wakelin SA, et al. Forms of phosphorus in bacteria and fungi isolated from two Australian soils. Soil Biol Biochem. 2008;40:1908–15.Article 
CAS 

Google Scholar 
38.Genet P, Prevost A, Pargney JC. Seasonal variations of symbiotic ultrastructure and relationships of two natural ectomycorrhizae of beech (Fagus sylvatica/Lactarius blennius var. viridis and Fagus sylvatica/Lactarius subdulcis). Trees. 2000;14:465–74.Article 

Google Scholar 
39.Frey B, Brunner I, Walther P, Scheidegger C, Zierold K. Element localization in ultrathin cryosections of high-pressure frozen ectomycorrhizal spruce roots. Plant Cell Environ. 1997;20:929–37.CAS 
Article 

Google Scholar 
40.Hanzlíkova A, Jandera A, Kunc F. Formation of poly-3-hydroxybutyrate by a soil microbial community during batch and heterocontinuous cultivation. Folia Microbiol. 1984;29:233–41.Article 

Google Scholar 
41.Mason-Jones K, Banfield CC, Dippold MA. Compound‐specific 13C stable isotope probing confirms synthesis of polyhydroxybutyrate by soil bacteria. Rapid Commun Mass Spectrom. 2019;33:795–802.CAS 
PubMed 
Article 

Google Scholar 
42.Hedlund K. Soil microbial community structure in relation to vegetation management on former agricultural land. Soil Biol Biochem. 2002;34:1299–307.CAS 
Article 

Google Scholar 
43.White PM, Potter TL, Strickland TC. Pressurized liquid extraction of soil microbial phospholipid and neutral lipid fatty acids. J Agric Food Chem. 2009;57:7171–7.CAS 
PubMed 
Article 

Google Scholar 
44.Xu X, Thornton PE, Post WM. A global analysis of soil microbial biomass carbon, nitrogen and phosphorus in terrestrial ecosystems: Global soil microbial biomass C, N and P. Glob Ecol Biogeogr. 2013;22:737–49.Article 

Google Scholar 
45.Bååth E. The use of neutral lipid fatty acids to indicate the physiological conditions of soil fungi. Micro Ecol. 2003;45:373–83.Article 
CAS 

Google Scholar 
46.Soliman AH, Radwan SS. Degradation of sterols, triacylglycerol, and phospholipids by soil microorganisms. Zbl Bakt II Abt. 1981;136:420–6.CAS 

Google Scholar 
47.Diakhaté S, Gueye M, Chevallier T, Diallo NH, Assigbetse K, Abadie J, et al. Soil microbial functional capacity and diversity in a millet-shrub intercropping system of semi-arid Senegal. J Arid Environ. 2016;129:71–9.Article 

Google Scholar 
48.Bölscher T, Wadsö L, Börjesson G, Herrmann AM. Differences in substrate use efficiency: impacts of microbial community composition, land use management, and substrate complexity. Biol Fertil Soils. 2016;52:547–59.Article 
CAS 

Google Scholar 
49.Mason-Jones K, Schmücker N, Kuzyakov Y. Contrasting effects of organic and mineral nitrogen challenge the N-Mining Hypothesis for soil organic matter priming. Soil Biol Biochem. 2018;124:38–46.CAS 
Article 

Google Scholar 
50.Muhammadi S, Afzal M, Hameed S. Bacterial polyhydroxyalkanoates-eco-friendly next generation plastic: Production, biocompatibility, biodegradation, physical properties and applications. Green Chem Lett Rev. 2015;8:56–77.Article 
CAS 

Google Scholar 
51.Jose NA, Lau R, Swenson TL, Klitgord N, Garcia-Pichel F, Bowen BP, et al. Flux balance modeling to predict bacterial survival during pulsed-activity events. Biogeosciences. 2018;15:2219–29.CAS 
Article 

Google Scholar 
52.Medeiros PM, Fernandes MF, Dick RP, Simoneit BRT. Seasonal variations in sugar contents and microbial community in a ryegrass soil. Chemosphere. 2006;65:832–9.CAS 
PubMed 
Article 

Google Scholar 
53.Žifčáková L, Větrovský T, Lombard V, Henrissat B, Howe A, Baldrian P. Feed in summer, rest in winter: microbial carbon utilization in forest topsoil. Microbiome 2017;5:1–12.Article 

Google Scholar 
54.Ratcliff WC, Denison RF. Individual-level bet hedging in the bacterium Sinorhizobium meliloti. Curr Biol. 2010;20:1740–4.CAS 
PubMed 
Article 

Google Scholar 
55.Schoch CL, Ciufo S, Domrachev M, Hotton CL, Kannan S, Khovanskaya R, et al. NCBI Taxonomy: a comprehensive update on curation, resources and tools. Database. 2020;2020:baaa062.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Choi J, Kim S-H. A genome tree of life for the fungi kingdom. Proc Natl Acad Sci USA. 2017;114:9391–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Jun S-R, Sims GE, Wu GA, Kim S-H. Whole-proteome phylogeny of prokaryotes by feature frequency profiles: An alignment-free method with optimal feature resolution. Proc Natl Acad Sci USA. 2010;107:133–8.CAS 
PubMed 
Article 

Google Scholar 
58.Elbahloul Y, Krehenbrink M, Reichelt R, Steinbuchel A. Physiological conditions conducive to high cyanophycin content in biomass of Acinetobacter calcoaceticus strain ADP1. Appl Environ Microbiol. 2005;71:858–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Lillie SH, Pringle JR. Reserve carbohydrate metabolism in Saccharomyces cerevisiae: responses to nutrient limitation. J Bacteriol. 1980;143:1384–94.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Hall KD, Guo J. Obesity energetics: Body weight regulation and the effects of diet composition. Gastroenterology. 2017;152:1718–27.e3.PubMed 
Article 

Google Scholar 
61.Sala A, Woodruff DR, Meinzer FC. Carbon dynamics in trees: feast or famine? Tree Physiol. 2012;32:764–75.CAS 
PubMed 
Article 

Google Scholar 
62.Varpe Ø, Ejsmond MJ. Trade-offs between storage and survival affect diapause timing in capital breeders. Evol Ecol. 2018;32:623–41.Article 

Google Scholar 
63.Heilmeier H, Freund M, Steinlein T, Schulze E-D, Monson RK. The influence of nitrogen availability on carbon and nitrogen storage in the biennial Cirsium vulgare (Savi) Ten. I. Storage capacity in relation to resource acquisition, allocation and recycling. Plant Cell Environ. 1994;17:1125–31.CAS 
Article 

Google Scholar 
64.Pond CM. Ecology of storage. In: Levin SA, editor. Encyclopedia of biodiversity, 2nd ed. Amsterdam: Academic Press; 2013. p. 23–38.65.McCue MD. Starvation physiology: reviewing the different strategies animals use to survive a common challenge. Comp Biochem Physiol A Mol Integr Physiol. 2010;156:1–18.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
66.Donald J, Pannabecker TL. Osmoregulation in desert-adapted mammals. In: Hyndman KA, Pannabecker TL, editors. Sodium and water homeostasis. New York: Springer New York; 2015. p. 191–211.67.Röttig A, Hauschild P, Madkour MH, Al-Ansari AM, Almakishah NH, Steinbüchel A. Analysis and optimization of triacylglycerol synthesis in novel oleaginous Rhodococcus and Streptomyces strains isolated from desert soil. J Biotechnol. 2016;225:48–56.PubMed 
Article 
CAS 

Google Scholar 
68.Bailey AP, Koster G, Guillermier C, Hirst EMA, MacRae JI, Lechene CP, et al. Antioxidant role for lipid droplets in a stem cell niche of Drosophila. Cell. 2015;163:340–53.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Jenni-Eiermann S, Jenni L. Fasting in birds: general patterns and the special case of endurance flight. In: McCue MD, editor. Comparative physiology of fasting, starvation, and food limitation. 2012. Berlin: Springer; 2012. p. 171–92.70.Fischer B, Dieckmann U, Taborsky B. When to store energy in a stochastic environment. Evolution. 2011;65:1221–32.PubMed 
Article 
PubMed Central 

Google Scholar 
71.Bonnet X, Bradshaw D, Shine R. Capital versus income breeding: An ectothermic perspective. Oikos. 1998;83:333.Article 

Google Scholar 
72.de Mazancourt C, Schwartz MW. Starve a competitor: evolution of luxury consumption as a competitive strategy. Theor Ecol. 2012;13:37–49.Article 

Google Scholar 
73.Ejsmond MJ, Varpe Ø, Czarnoleski M, Kozłowski J. Seasonality in offspring value and trade-offs with growth explain capital breeding. Am Nat. 2015;186:E111–25.Article 

Google Scholar 
74.Kourmentza C, Plácido J, Venetsaneas N, Burniol-Figols A, Varrone C, Gavala HN, et al. Recent advances and challenges towards sustainable polyhydroxyalkanoate (PHA) production. Bioengineering. 2017;4:55.PubMed Central 
Article 
CAS 
PubMed 

Google Scholar 
75.Wilson WA, Roach PJ, Montero M, Baroja-Fernández E, Muñoz FJ, Eydallin G, et al. Regulation of glycogen metabolism in yeast and bacteria. FEMS Microbiol Rev. 2010;34:952–85.CAS 
PubMed 
Article 

Google Scholar 
76.Doi Y, Kawaguchi Y, Koyama N, Nakamura S, Hiramitsu M, Yoshida Y, et al. Synthesis and degradation of polyhydroxyalkanoates in Alcaligenes eutrophus. FEMS Microbiol Lett. 1992;103:103–8.CAS 
Article 

Google Scholar 
77.Alvarez AHM, Kalscheuer R, Steinbüchel A. Accumulation of storage lipids in species of Rhodococcus and Nocardia and effect of inhibitors and polyethylene glycol. Lipid. 1997;99:239–46.CAS 
Article 

Google Scholar 
78.Parrou JL, Enjalbert B, Plourde L, Bauche A, Gonzalez B, François J. Dynamic responses of reserve carbohydrate metabolism under carbon and nitrogen limitations in Saccharomyces cerevisiae. Yeast. 1999;15:191–203.CAS 
PubMed 
Article 

Google Scholar 
79.Gebremariam SY, Beutel MW, Christian D, Hess TF. Research advances and challenges in the microbiology of enhanced biological phosphorus removal-A critical review. Water Environ Res. 2011;83:195–219.CAS 
PubMed 
Article 

Google Scholar 
80.Ratledge C. Fatty acid biosynthesis in microorganisms being used for single cell oil production. Biochimie. 2004;86:807–15.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Matin A, Veldhuis C, Stegeman V, Veenhuis M. Selective advantage of a Spirillum sp. in a carbon-limited environment. Accumulation of poly-β-hydroxybutyric acid and its role in starvation. J Gen Microbiol. 1979;112:349–55.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
82.Poblete-Castro I, Escapa IF, Jäger C, Puchalka J, Chi Lam C, Schomburg D, et al. The metabolic response of P. putida KT2442 producing high levels of polyhydroxyalkanoate under single- and multiple-nutrient-limited growth: Highlights from a multi-level omics approach. Micro Cell Fact. 2012;11:34.CAS 
Article 

Google Scholar 
83.Wilkinson JF, Munro ALS. The influence of growth limiting conditions on the synthesis of possible carbon and energy storage polymers in Bacillus megaterium. In: Powell EO, Evans CGT, Strange RE, Tempest DW, editors. Microbial physiology and continuous culture, Proceedings of the Third International Symposium. Salisbury, United Kingdom: Her Majesty’s Stationery Office; 1967. p. 173–85.84.Alvarez HM, Mayer F, Fabritius D, Steinbüchel A. Formation of intracytoplasmic lipid inclusions by Rhodococcus opacus strain PD630. Arch Microbiol. 1996;165:377–86.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Orchard ED, Benitez-Nelson CR, Pellechia PJ, Lomas MW, Dyhrman ST. Polyphosphate in Trichodesmium from the low-phosphorus Sargasso Sea. Limnol Oceanogr. 2010;55:2161–9.CAS 
Article 

Google Scholar 
86.Li J, Mara P, Schubotz F, Sylvan JB, Burgaud G, Klein F, et al. Recycling and metabolic flexibility dictate life in the lower oceanic crust. Nature. 2020;579:250–5.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
87.Preiss J, Romeo T. Molecular biology and regulatory aspects of glycogen biosynthesis in bacteria. Prog Nucleic Acid Res Mol Biol. 1994;47:299–329.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
88.Mackerras AH, de Chazal NM, Smith GD. Transient accumulations of cyanophycin in Anabaena cylindrica and Synechocystis 6308. J Gen Microbiol. 1990;136:2057–65.CAS 
Article 

Google Scholar 
89.Parnas H, Cohen D. The optimal strategy for the metabolism of reserve materials in micro-organisms. J Theor Biol. 1976;56:19–55.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
90.Dijkstra P, Salpas E, Fairbanks D, Miller EB, Hagerty SB, van Groenigen KJ, et al. High carbon use efficiency in soil microbial communities is related to balanced growth, not storage compound synthesis. Soil Biol Biochem. 2015;89:35–43.CAS 
Article 

Google Scholar 
91.Empadinhas N, da Costa MS. Osmoadaptation mechanisms in prokaryotes: distribution of compatible solutes. Int Microbiol. 2008;11:151–61.CAS 
PubMed 
PubMed Central 

Google Scholar 
92.Albi T, Serrano A. Inorganic polyphosphate in the microbial world. Emerging roles for a multifaceted biopolymer. World J Microbiol Biotechnol. 2016;32:27.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
93.Sekar K, Linker SM, Nguyen J, Grünhagen A, Stocker R, Sauer U. Bacterial glycogen provides short-term benefits in changing environments. Appl Environ Microbiol. 2020;86:e00049–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
94.Silljé HH, Paalman JW, ter Schure EG, Olsthoorn SQ, Verkleij AJ, Boonstra J, et al. Function of trehalose and glycogen in cell cycle progression and cell viability in Saccharomyces cerevisiae. J Bacteriol. 1999;181:396–400.PubMed 
PubMed Central 
Article 

Google Scholar 
95.Jahid IK, Silva AJ, Benitez JA. Polyphosphate stores enhance the ability of Vibrio cholerae to overcome environmental stresses in a low-phosphate environment. Appl Environ Microbiol. 2006;72:7043–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
96.Ramírez-Trujillo JA, Dunn MF, Suárez-Rodríguez R, Hernández-Lucas I. The Sinorhizobium meliloti glyoxylate cycle enzyme isocitrate lyase (AceA) is required for the utilization of poly-β-hydroxybutyrate during carbon starvation. Ann Microbiol. 2016;66:921–4.Article 
CAS 

Google Scholar 
97.Vagabov VM, Trilisenko LV, Kulaev IS. Dependence of inorganic polyphosphate chain length on the orthophosphate content in the culture medium of the yeast Saccharomyces cerevisiae. Biochemistry. 2000;65:6.
Google Scholar 
98.Schimz K-L, Irrgang K, Overhoff B. Glycogen, a cytoplasmic reserve polysaccharide of Cellulomonas sp. (DSM20108): Its identification, carbon source-dependent accumulation, and degradation during starvation. FEMS Microbiol Lett. 1985;30:165–9.CAS 
Article 

Google Scholar 
99.Kalscheuer R, Stöveken T, Malkus U, Reichelt R, Golyshin PN, Sabirova JS, et al. Analysis of storage lipid accumulation in Alcanivorax borkumensis: Evidence for alternative triacylglycerol biosynthesis routes in bacteria. J Bacteriol. 2007;189:918–28.CAS 
PubMed 
Article 

Google Scholar 
100.Busuioc M, Mackiewicz K, Buttaro BA, Piggot PJ. Role of intracellular polysaccharide in persistence of Streptococcus mutans. J Bacteriol. 2009;191:7315–22.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
101.Ruiz JA, Lopez NI, Fernandez RO, Mendez BS. Polyhydroxyalkanoate degradation Is associated with nucleotide accumulation and enhances stress resistance and survival of Pseudomonas oleovorans in natural water microcosms. Appl Environ Microbiol. 2001;67:225–30.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
102.Klotz A, Georg J, Bučinská L, Watanabe S, Reimann V, Januszewski W, et al. Awakening of a dormant cyanobacterium from nitrogen chlorosis reveals a genetically determined program. Curr Biol. 2016;26:2862–72.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
103.Elbein AD. New insights on trehalose: a multifunctional molecule. Glycobiology. 2003;13:17R–27R.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
104.Obruca S, Sedlacek P, Koller M. The underexplored role of diverse stress factors in microbial biopolymer synthesis. Bioresour Technol. 2021;326:124767.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
105.Ayub ND, Tribelli PM, López NI. Polyhydroxyalkanoates are essential for maintenance of redox state in the Antarctic bacterium Pseudomonas sp. 14-3 during low temperature adaptation. Extremophiles. 2009;13:59–66.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
106.Grime JP. Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. Am Nat. 1977;111:1169–94.Article 

Google Scholar 
107.Ho A, Kerckhof F-M, Luke C, Reim A, Krause S, Boon N, et al. Conceptualizing functional traits and ecological characteristics of methane-oxidizing bacteria as life strategies: Functional traits of methane-oxidizing bacteria. Environ Microbiol Rep. 2013;5:335–45.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
108.Santillan E, Seshan H, Constancias F, Wuertz S. Trait‐based life‐history strategies explain succession scenario for complex bacterial communities under varying disturbance. Environ Microbiol. 2019;21:3751–64.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
109.Chesson P. Mechanisms of maintenance of species diversity. Annu Rev Ecol Syst. 2000;31:343–66.Article 

Google Scholar 
110.Loreau M, de Mazancourt C. Biodiversity and ecosystem stability: a synthesis of underlying mechanisms. Ecol Lett. 2013;16:106–15.PubMed 
Article 
PubMed Central 

Google Scholar 
111.Geyer KM, Kyker-Snowman E, Grandy AS, Frey SD. Microbial carbon use efficiency: accounting for population, community, and ecosystem-scale controls over the fate of metabolized organic matter. Biogeochemistry. 2016;127:173–88.CAS 
Article 

Google Scholar 
112.Manzoni S, Porporato A. Soil carbon and nitrogen mineralization: Theory and models across scales. Soil Biol Biochem. 2009;41:1355–79.CAS 
Article 

Google Scholar 
113.Schultz P, Urban NR. Effects of bacterial dynamics on organic matter decomposition and nutrient release from sediments: a modeling study. Ecol Model. 2008;210:1–14.CAS 
Article 

Google Scholar 
114.Torres-Dorante LO, Claassen N, Steingrobe B, Olfs H-W. Polyphosphate determination in calcium acetate-lactate (CAL) extracts by an indirect colorimetric method. J Plant Nutr Soil Sci. 2004;167:701–3.CAS 
Article 

Google Scholar 
115.Micić V, Köster J, Kruge MA, Engelen B, Hofmann T. Bacterial wax esters in recent fluvial sediments. Org Geochem. 2015;89–90:44–55.Article 
CAS 

Google Scholar 
116.Mooshammer M, Wanek W, Zechmeister-Boltenstern S, Richter A. Stoichiometric imbalances between terrestrial decomposer communities and their resources: mechanisms and implications of microbial adaptations to their resources. Front Microbiol 2014;5:1–10.Article 

Google Scholar 
117.Op De Beeck M, Troein C, Siregar S, Gentile L, Abbondanza G, Peterson C, et al. Regulation of fungal decomposition at single-cell level. ISME J. 2020;14:896–905.PubMed 
PubMed Central 
Article 

Google Scholar 
118.Liang C, Amelung W, Lehmann J, Kästner M. Quantitative assessment of microbial necromass contribution to soil organic matter. Glob Chang Biol. 2019;25:3578–90.PubMed 
Article 
PubMed Central 

Google Scholar 
119.Ducklow H, Steinberg D, Buesseler K. Upper ocean carbon export and the biological pump. Oceanography. 2001;14:50–8.Article 

Google Scholar 
120.Wieder WR, Allison SD, Davidson EA, Georgiou K, Hararuk O, He Y, et al. Explicitly representing soil microbial processes in Earth system models: Soil microbes in Earth system models. Glob Biogeochem Cycles. 2015;29:1782–800.CAS 
Article 

Google Scholar 
121.Schimel J, Weintraub MN. The implications of exoenzyme activity on microbial carbon and nitrogen limitation in soil: a theoretical model. Soil Biol Biochem. 2003;35:549–63.CAS 
Article 

Google Scholar 
122.Ni B-J, Fang F, Rittmann BE, Yu H-Q. Modeling microbial products in activated sludge under feast-famine conditions. Environ Sci Technol. 2009;43:2489–97.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
123.Godwin CM, Cotner JB. Stoichiometric flexibility in diverse aquatic heterotrophic bacteria is coupled to differences in cellular phosphorus quotas. Front Microbiol 2015;6:1–15.Article 

Google Scholar 
124.Camenzind T, Philipp Grenz K, Lehmann J, Rillig MC. Soil fungal mycelia have unexpectedly flexible stoichiometric C:N and C:P ratios. Ecol Lett. 2021;24:208–18.PubMed 
Article 
PubMed Central 

Google Scholar 
125.Fatichi S, Manzoni S, Or D, Paschalis A. A mechanistic model of microbially mediated soil biogeochemical processes: a reality check. Glob Biogeochem Cycles. 2019;33:620–48.CAS 
Article 

Google Scholar 
126.Sistla SA, Rastetter EB, Schimel JP. Responses of a tundra system to warming using SCAMPS: a stoichiometrically coupled, acclimating microbe–plant–soil model. Ecol Monogr. 2014;84:151–70.Article 

Google Scholar 
127.Lashermes G, Gainvors-Claisse A, Recous S, Bertrand I. Enzymatic strategies and carbon use efficiency of a litter-decomposing fungus grown on maize leaves, stems, and roots. Front Microbiol 2016;7:1–14.Article 

Google Scholar 
128.Lee ZM, Schmidt TM. Bacterial growth efficiency varies in soils under different land management practices. Soil Biol Biochem. 2014;69:282–90.CAS 
Article 

Google Scholar 
129.Camenzind T, Lehmann A, Ahland J, Rumpel S, Rillig MC. Trait‐based approaches reveal fungal adaptations to nutrient‐limiting conditions. Environ Microbiol. 2020;22:3548–60.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
130.Manzoni S, Čapek P, Mooshammer M, Lindahl BD, Richter A, Šantrůčková H. Optimal metabolic regulation along resource stoichiometry gradients. Ecol Lett. 2017;20:1182–91.PubMed 
Article 
PubMed Central 

Google Scholar 
131.Tang J, Riley WJ. Weaker soil carbon–climate feedbacks resulting from microbial and abiotic interactions. Nat Clim Chang. 2015;5:5.CAS 
Article 

Google Scholar 
132.Lee KS, Pereira FC, Palatinszky M, Behrendt L, Alcolombri U, Berry D, et al. Optofluidic Raman-activated cell sorting for targeted genome retrieval or cultivation of microbial cells with specific functions. Nat Protoc. 2021;16:634–76.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
133.Günther S, Trutnau M, Kleinsteuber S, Hause G, Bley T, Röske I, et al. Dynamics of polyphosphate-accumulating bacteria in wastewater treatment plant microbial communities detected via DAPI (4′,6′-diamidino-2-phenylindole) and tetracycline labeling. Appl Environ Microbiol. 2009;75:2111–21.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
134.Singleton CM, Petriglieri F, Kristensen JM, Kirkegaard RH, Michaelsen TY, Andersen MH, et al. Connecting structure to function with the recovery of over 1000 high-quality metagenome-assembled genomes from activated sludge using long-read sequencing. Nat Commun. 2021;12:2009.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
135.Link H, Fuhrer T, Gerosa L, Zamboni N, Sauer U. Real-time metabolome profiling of the metabolic switch between starvation and growth. Nat Methods. 2015;12:1091–7.CAS 
PubMed 
Article 

Google Scholar 
136.Warren CR. Altitudinal transects reveal large differences in intact lipid composition among soils. Soil Res. 2021;59:644–59.CAS 
Article 

Google Scholar 
137.Wilkinson J. The problem of energy-storage compounds in bacteria. Exp Cell Res. 1959;7:111–30.Article 

Google Scholar 
138.Nickels JS, King JD, White DC. Poly-β-hydroxybutyrate accumulation as a measure of unbalanced growth of the estuarine detrital microbiota. Appl Environ Microbiol. 1979;37:459–65.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
139.Murphy DJ. The dynamic roles of intracellular lipid droplets: from archaea to mammals. Protoplasma. 2012;249:541–85.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
140.Alvarez HM. Triacylglycerol and wax ester-accumulating machinery in prokaryotes. Biochimie. 2016;120:28–39.CAS 
PubMed 
Article 

Google Scholar 
141.Koller M, Maršálek L, de Sousa Dias MM, Braunegg G. Producing microbial polyhydroxyalkanoate (PHA) biopolyesters in a sustainable manner. N Biotechnol. 2017;37:24–38.CAS 
PubMed 
Article 

Google Scholar 
142.Obruca S, Sedlacek P, Slaninova E, Fritz I, Daffert C, Meixner K, et al. Novel unexpected functions of PHA granules. Appl Microbiol Biotechnol. 2020;104:4795–810.CAS 
PubMed 
Article 

Google Scholar 
143.Roach PJ, Depaoli-Roach AA, Hurley TD, Tagliabracci VS. Glycogen and its metabolism: some new developments and old themes. Biochem J. 2012;441:763–87.CAS 
PubMed 
Article 

Google Scholar 
144.Wang L, Wang M, Wise MJ, Liu Q, Yang T, Zhu Z, et al. Recent progress in the structure of glycogen serving as a durable energy reserve in bacteria. World J Microbiol Biotechnol. 2020;36:14.PubMed 
Article 

Google Scholar 
145.Ruhal R, Kataria R, Choudhury B. Trends in bacterial trehalose metabolism and significant nodes of metabolic pathway in the direction of trehalose accumulation: Trehalose metabolism in bacteria. Micro Biotechnol. 2013;6:493–502.Article 
CAS 

Google Scholar 
146.Kalscheuer R. Genetics of wax ester and triacylglycerol biosynthesis in bacteria. In: Timmis KN, editor. Handbook of hydrocarbon and lipid microbiology. Berlin, Heidelberg: Springer Berlin Heidelberg; 2010. p. 527–35.147.Rao NN, Gómez-García MR, Kornberg A. Inorganic polyphosphate: essential for growth and survival. Annu Rev Biochem. 2009;78:605–47.CAS 
PubMed 
Article 

Google Scholar 
148.Denoncourt A, Downey M. Model systems for studying polyphosphate biology: a focus on microorganisms. Curr Genet. 2021;67:331–46.CAS 
PubMed 
Article 

Google Scholar 
149.Füser G, Steinbüchel A. Analysis of genome sequences for genes of cyanophycin metabolism: Identifying putative cyanophycin metabolizing prokaryotes. Macromol Biosci. 2007;7:278–96.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
150.Watzer B, Forchhammer K. Cyanophycin: a nitrogen-rich reserve polymer. In: Tiwari A, editor. Cyanobacteria. London: InTech; 2018. More