Ecology

Whitman, W. B., Coleman, D. C. & Wiebe, W. J. Prokaryotes: the unseen majority. Proc. Natl Acad. Sci. USA 95, 6578–6583 (1998).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Locey, K. J. & Lennon, J. T. Scaling laws predict global microbial diversity. Proc. Natl Acad. Sci. USA 113, 5970–5975 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Louca, S. et al. Function and functional redundancy in microbial systems. Nat. Ecol. Evol. 2, 936–943 (2018).PubMed 

Google Scholar 
Dinsdale, E. A. et al. Functional metagenomic profiling of nine biomes. Nature 452, 629–632 (2008).ADS 
CAS 
PubMed 

Google Scholar 
Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 39, 499–509 (2021).CAS 
PubMed 

Google Scholar 
Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Song, S. J. et al. Comparative analyses of vertebrate gut microbiomes reveal convergence between birds and bats. MBio 11, e02901-19 (2020).Ley, R. E. et al. Evolution of mammals and their gut microbes. Science 320, 1647–1651 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
McDonald, D. et al. American Gut: an open platform for citizen science microbiome research. mSystems 3, e00031-18 (2018).Minich, J. J. et al. Temporal, environmental, and biological drivers of the mucosal microbiome in a wild marine fish, Scomber japonicus. mSphere 5, e00401-20 (2020).Sullam, K. E. et al. Environmental and ecological factors that shape the gut bacterial communities of fish: a meta-analysis. Mol. Ecol. 21, 3363–3378 (2012).PubMed 

Google Scholar 
Youngblut, N. D. et al. Host diet and evolutionary history explain different aspects of gut microbiome diversity among vertebrate clades. Nat. Commun. 10, 2200 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Bosco, N. & Noti, M. The aging gut microbiome and its impact on host immunity. Genes Immun. 22, 289–303 (2021).PubMed 
PubMed Central 

Google Scholar 
McKenzie, V. J. et al. The effects of captivity on the mammalian gut microbiome. Integr. Comp. Biol. 57, 690–704 (2017).PubMed 
PubMed Central 

Google Scholar 
Groussin, M. et al. Unraveling the processes shaping mammalian gut microbiomes over evolutionary time. Nat. Commun. 8, 14319 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ross, A. A., Müller, K. M., Weese, J. S. & Neufeld, J. D. Comprehensive skin microbiome analysis reveals the uniqueness of human skin and evidence for phylosymbiosis within the class Mammalia. Proc. Natl Acad. Sci. USA 115, E5786–E5795 (2018).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hobbie, J. E., Daley, R. J. & Jasper, S. Use of nuclepore filters for counting bacteria by fluorescence microscopy. Appl. Environ. Microbiol. 33, 1225–1228 (1977).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Prussin, A. J. 2nd, Garcia, E. B. & Marr, L. C. Total virus and bacteria concentrations in indoor and outdoor air. Environ. Sci. Technol. Lett. 2, 84–88 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Gomez, D., Sunyer, J. O. & Salinas, I. The mucosal immune system of fish: the evolution of tolerating commensals while fighting pathogens. Fish. Shellfish Immunol. 35, 1729–1739 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lowrey, L., Woodhams, D. C., Tacchi, L. & Salinas, I. Topographical mapping of the Rainbow Trout (Oncorhynchus mykiss) microbiome reveals a diverse bacterial community with antifungal properties in the skin. Appl. Environ. Microbiol. 81, 6915–6925 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Minich, J. J. et al. Microbial ecology of Atlantic Salmon (Salmo salar) hatcheries: impacts of the built environment on fish mucosal microbiota. Appl. Environ. Microbiol. 86, 20 (2020).Minich, J. J. et al. Impacts of the marine hatchery built environment, water and feed on mucosal microbiome colonization across ontogeny in Yellowtail Kingfish, Seriola lalandi. Front. Mar. Sci. 0, 676731 (2021).Minich, J. J. et al. The Southern Bluefin Tuna mucosal microbiome is influenced by husbandry method, net pen location, and anti-parasite treatment. Front. Microbiol. 11, 2015 (2020).PubMed 
PubMed Central 

Google Scholar 
Ruiz-Rodríguez, M. et al. Host species and body site explain the variation in the microbiota associated to wild sympatric Mediterranean teleost fishes. Microb. Ecol. 80, 212–222 (2020).PubMed 

Google Scholar 
Tarnecki, A. M., Burgos, F. A., Ray, C. L. & Arias, C. R. Fish intestinal microbiome: diversity and symbiosis unravelled by metagenomics. J. Appl. Microbiol. 123, 2–17 (2017).CAS 
PubMed 

Google Scholar 
Liu, H. et al. The gut microbiome and degradation enzyme activity of wild freshwater fishes influenced by their trophic levels. Sci. Rep. 6, 24340 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Egerton, S., Culloty, S., Whooley, J., Stanton, C. & Ross, R. P. The gut microbiota of marine fish. Front. Microbiol. 9, 873 (2018).PubMed 
PubMed Central 

Google Scholar 
Woodhams, D. C. et al. Host-associated microbiomes are predicted by immune system complexity and climate. Genome Biol. 21, 23 (2020).PubMed 
PubMed Central 

Google Scholar 
Karachle, P. K. & Stergiou, K. I. Gut length for several marine fish: relationships with body length and trophic implications. Mar. Biodivers. Rec. 3, 1–10 (2010).Ghilardi, M. et al. Phylogeny, body morphology, and trophic level shape intestinal traits in coral reef fishes. Ecol. Evol. 11, 13218–13231 (2021).PubMed 
PubMed Central 

Google Scholar 
Clements, K. D., Angert, E. R., Linn Montgomery, W. & Howard Choat, J. Intestinal microbiota in fishes: what’s known and what’s not. Mol. Ecol. 23, 1891–1898 (2014).PubMed 

Google Scholar 
Zhu, D., Delgado-Baquerizo, M., Ding, J., Gillings, M. R. & Zhu, Y.-G. Trophic level drives the host microbiome of soil invertebrates at a continental scale. Microbiome 9, 189 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Minich, J. J. et al. KatharoSeq enables high-throughput microbiome analysis from low-biomass samples. mSystems 3, e00218-17 (2018).Davis, C. Enumeration of probiotic strains: review of culture-dependent and alternative techniques to quantify viable bacteria. J. Microbiol. Methods 103, 9–17 (2014).CAS 
PubMed 

Google Scholar 
Rastogi, G., Tech, J. J., Coaker, G. L. & Leveau, J. H. J. A PCR-based toolbox for the culture-independent quantification of total bacterial abundances in plant environments. J. Microbiol. Methods 83, 127–132 (2010).CAS 
PubMed 

Google Scholar 
Stämmler, F. et al. Adjusting microbiome profiles for differences in microbial load by spike-in bacteria. Microbiome 4, 28 (2016).PubMed 
PubMed Central 

Google Scholar 
Tourlousse, D. M. et al. Synthetic spike-in standards for high-throughput 16S rRNA gene amplicon sequencing. Nucleic Acids Res. 45, e23 (2017).PubMed 

Google Scholar 
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224 (2017).PubMed 
PubMed Central 

Google Scholar 
Morton, J. T. et al. Establishing microbial composition measurement standards with reference frames. Nat. Commun. 10, 2719 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Louca, S., Doebeli, M. & Parfrey, L. W. Correcting for 16S rRNA gene copy numbers in microbiome surveys remains an unsolved problem. Microbiome 6, 41 (2018).PubMed 
PubMed Central 

Google Scholar 
Smith, N. C., Rise, M. L. & Christian, S. L. A comparison of the innate and adaptive immune systems in cartilaginous fish, ray-finned fish, and lobe-finned fish. Front. Immunol. 10, 2292 (2019).CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).CAS 
PubMed 

Google Scholar 
Chong-Seng, K. M., Mannering, T. D., Pratchett, M. S., Bellwood, D. R. & Graham, N. A. J. The influence of coral reef benthic condition on associated fish assemblages. PLoS ONE 7, e42167 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yahel, G. et al. Fish activity: a major mechanism for sediment resuspension and organic matter remineralization in coastal marine sediments. Mar. Ecol. Prog. Ser. 372, 195–209 (2008).ADS 
CAS 

Google Scholar 
Glover, C. N., Bucking, C. & Wood, C. M. The skin of fish as a transport epithelium: a review. J. Comp. Physiol. B 183, 877–891 (2013).CAS 
PubMed 

Google Scholar 
León-Zayas, R., McCargar, M., Drew, J. A. & Biddle, J. F. Microbiomes of fish, sediment and seagrass suggest connectivity of coral reef microbial populations. PeerJ 8, e10026 (2020).PubMed 
PubMed Central 

Google Scholar 
Hess, S., Wenger, A. S., Ainsworth, T. D. & Rummer, J. L. Exposure of clownfish larvae to suspended sediment levels found on the Great Barrier Reef: impacts on gill structure and microbiome. Sci. Rep. 5, 10561 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sparagon, W. J. et al. Fine scale transitions of the microbiota and metabolome along the gastrointestinal tract of herbivorous fishes. Anim. Microbiome 4, 33 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Edward Stevens, C. & Hume, I. D. Comparative Physiology of the Vertebrate Digestive System (Cambridge University Press, 2004).Wilson, J. M. & Castro, L. F. C. Morphological diversity of the gastrointestinal tract in fishes. Fish Physiol. 1–55 https://doi.org/10.1016/s1546-5098(10)03001-3 (2010).Shirakashi, S. et al. Morphology and distribution of blood fluke eggs and associated pathology in the gills of cultured Pacific bluefin tuna, Thunnus orientalis. Parasitol. Int. 61, 242–249 (2012).PubMed 

Google Scholar 
Ogawa, K. & Fukudome, M. Mass mortality caused by Blood Fluke(Paradeontacylix) among Amberjack(Seriola dumeili) imported to Japan. Fish. Pathol. 29, 265–269 (1994).
Google Scholar 
Wilson, J. M. & Laurent, P. Fish gill morphology: inside out. J. Exp. Zool. 293, 192–213 (2002).PubMed 

Google Scholar 
Huang, Q. et al. Diversity of gut microbiomes in marine fishes is shaped by host-related factors. Mol. Ecol. 29, 5019–5034 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kohl, K. D., Amaya, J., Passement, C. A., Dearing, M. D. & McCue, M. D. Unique and shared responses of the gut microbiota to prolonged fasting: a comparative study across five classes of vertebrate hosts. FEMS Microbiol. Ecol. 90, 883–894 (2014).CAS 
PubMed 

Google Scholar 
Lall, S. P. & Tibbetts, S. M. Nutrition, feeding, and behavior of fish. Vet. Clin. North Am. Exot. Anim. Pract. 12, 361–372 (2009). xi.PubMed 

Google Scholar 
Hacquard, S. et al. Microbiota and host nutrition across plant and animal kingdoms. Cell Host Microbe 17, 603–616 (2015).CAS 
PubMed 

Google Scholar 
Day, R. D., German, D. P. & Tibbetts, I. R. Why can’t young fish eat plants? Neither digestive enzymes nor gut development preclude herbivory in the young of a stomachless marine herbivorous fish. Comp. Biochem. Physiol. Part B: Biochem. Mol. Biol. 158, 23–29 (2011).
Google Scholar 
Lim, S. J. & Bordenstein, S. R. An introduction to phylosymbiosis. Proc. Biol. Sci. 287, 20192900 (2020).PubMed 
PubMed Central 

Google Scholar 
Brucker, R. M. & Bordenstein, S. R. The hologenomic basis of speciation: gut bacteria cause hybrid lethality in the genus Nasonia. Science 341, 667–669 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Mazel, F. et al. Is host filtering the main driver of phylosymbiosis across the tree of life? mSystems 3, e00097-18 (2018).Ross, A. A., Rodrigues Hoffmann, A. & Neufeld, J. D. The skin microbiome of vertebrates. Microbiome 7, 79 (2019).PubMed 
PubMed Central 

Google Scholar 
Javůrková, V. G. et al. Unveiled feather microcosm: feather microbiota of passerine birds is closely associated with host species identity and bacteriocin-producing bacteria. ISME J. 13, 2363–2376 (2019).PubMed 
PubMed Central 

Google Scholar 
Doane, M. P. et al. The skin microbiome of elasmobranchs follows phylosymbiosis, but in teleost fishes, the microbiomes converge. Microbiome 8, 93 (2020).PubMed 
PubMed Central 

Google Scholar 
Chiarello, M. et al. Skin microbiome of coral reef fish is highly variable and driven by host phylogeny and diet. Microbiome 6, 147 (2018).PubMed 
PubMed Central 

Google Scholar 
Sylvain, F.-É. et al. Fish skin and gut microbiomes show contrasting signatures of host species and habitat. Appl. Environ. Microbiol. 86, e00789-20 (2020).Smith, C. C. R., Snowberg, L. K., Gregory Caporaso, J., Knight, R. & Bolnick, D. I. Dietary input of microbes and host genetic variation shape among-population differences in stickleback gut microbiota. ISME J. 9, 2515–2526 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).ADS 

Google Scholar 
Choat, J. H. & Clements, K. D. Vertebrate herbivores in marine and terrestrial environments: a nutritional ecology perspective. Annu. Rev. Ecol. Syst. 29, 375–403 (1998).
Google Scholar 
Sale, P. F. Reef fish communities: open nonequilibrial systems. In The Ecology of Fishes on Coral Reefs. 564–598. https://doi.org/10.1016/b978-0-08-092551-6.50024-6 (Academic Press Inc., San Diego, 1991).Reese, A. T. & Dunn, R. R. Drivers of microbiome biodiversity: a review of general rules, feces, and ignorance. MBio 9, e01294-18 (2018).Press, C. McL & Evensen, Ø. The morphology of the immune system in teleost fishes. Fish Shellfish Immunol. 9, 309–318 (1999).Koppang, E. O., Kvellestad, A. & Fischer, U. Fish mucosal immunity: gill. In Mucosal Health in Aquaculture (eds Beck, B. & Peatman, E.) 93–133. https://doi.org/10.1016/b978-0-12-417186-2.00005-4 (Elsevier Inc., 2015).Esteban, M. Á. & Cerezuela, R. Fish mucosal immunity: skin. In Mucosal Health in Aquaculture (eds Beck, B. & Peatman, E.) 67–92. https://doi.org/10.1016/b978-0-12-417186-2.00004-2 (Elsevier Inc., 2015).Song, S. J. et al. Preservation methods differ in fecal microbiome stability, affecting suitability for field studies. mSystems 1, e00021-16 (2016).Rabosky, D. L. et al. An inverse latitudinal gradient in speciation rate for marine fishes. Nature 559, 392–395 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Love, M. S., Bizzarro, J. J., Maria Cornthwaite, A., Frable, B. W. & Maslenikov, K. P. Checklist of marine and estuarine fishes from the Alaska–Yukon Border, Beaufort Sea, to Cabo San Lucas, Mexico. Zootaxa 5053, 1–285 (2021).PubMed 

Google Scholar 
Allen, L. G. & Horn, M. H. The Ecology of Marine Fishes: California and Adjacent Waters (University of California Press, 2006).Al-Hussaini, A. H. On the functional morphology of the alimentary tract of some fish in relation to differences in their feeding habits; anatomy and histology. Q. J. Microsc. Sci. 90(Pt. 2), 109–139 (1949).PubMed 

Google Scholar 
Maddock, L., Bone, Q. & Rayner, J. M. V. (eds). In Mechanics and Physiology of Animal Swimming (Press Syndicate-of the University of Cambridge, 1994).Gonzalez, A. et al. Qiita: rapid, web-enabled microbiome meta-analysis. Nat. Methods 15, 796–798 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Cruz, G. N. F., Christoff, A. P. & de Oliveira, L. F. V. Equivolumetric protocol generates library sizes proportional to total microbial load in 16S amplicon sequencing. Front. Microbiol. 12, 638231 (2021).PubMed 
PubMed Central 

Google Scholar 
Minich, J. J. et al. Quantifying and understanding well-to-well contamination in microbiome research. mSystems 4, e00186-19 (2019).Minich, J. J. et al. High-throughput miniaturized 16S rRNA amplicon library preparation reduces costs while preserving microbiome integrity. mSystems 3, e00166-18 (2018).Walters, W. et al. Improved bacterial 16S rRNA gene (V4 and V4–5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. mSystems 1, e00009-15 (2016).Amir, A. et al. Deblur rapidly resolves single-nucleotide community sequence patterns. mSystems 2, e00191-16 (2017).Whittaker, R. J., Willis, K. J. & Field, R. Scale and species richness: towards a general, hierarchical theory of species diversity. J. Biogeogr. 28, 453–470 (2001).
Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J. & Knight, R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 5, 169–172 (2011).PubMed 

Google Scholar 
McDonald, D. et al. Striped UniFrac: enabling microbiome analysis at unprecedented scale. Nat. Methods 15, 847–848 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Janssen, S. et al. Phylogenetic placement of exact amplicon sequences improves associations with clinical information. mSystems 3, e00021-18 (2018).Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2001).
Google Scholar 
Minich, J. J. et al. Microbial effects of livestock manure fertilization on freshwater aquaculture ponds rearing tilapia (Oreochromis shiranus) and North African catfish (Clarias gariepinus). Microbiologyopen 7, e00716 (2018).PubMed 
PubMed Central 

Google Scholar 
Van Doan, H. et al. Host-associated probiotics: a key factor in sustainable aquaculture. Rev. Fish. Sci. Aquac. 28, 16–42 (2020).
Google Scholar 
Kumar, S., Stecher, G., Suleski, M. & Hedges, S. B. TimeTree: a resource for timelines, timetrees, and divergence times. Mol. Biol. Evol. 34, 1812–1819 (2017).CAS 
PubMed 

Google Scholar  More

Williams, G. C. Natural selection, the costs of reproduction, and a refinement of lack’s principle. Am. Nat. 100, 687–690 (1966).
Google Scholar 
Stearns, S. C. The evolution of life histories. (Oxford University Press, 1992).Kirkwood, T. B. L. Evolution of ageing. Nature 270, 301–304 (1977).ADS 
CAS 
PubMed 

Google Scholar 
Partridge, L., Prowse, N. & Pignatelli, P. Another set of responses and correlated responses to selection on age at reproduction in Drosophila melanogaster. Proc. R. Soc. B. 266, 255–261 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
Metcalfe, N. Growth versus lifespan: Perspectives from evolutionary ecology. Exp. Gerontol. 38, 935–940 (2003).PubMed 

Google Scholar 
Lee, W.-S., Monaghan, P. & Metcalfe, N. B. Experimental demonstration of the growth rate–lifespan trade-off. Proc. R. Soc. B. 280, 20122370 (2013).PubMed 
PubMed Central 

Google Scholar 
Lemaître, J.-F. et al. Early-late life trade-offs and the evolution of ageing in the wild. Proc. R. Soc. B. 282, 20150209 (2015).PubMed 
PubMed Central 

Google Scholar 
Jehan, C., Sabarly, C., Rigaud, T. & Moret, Y. Late-life reproduction in an insect: Terminal investment, reproductive restraint or senescence. J. Anim. Ecol. 90, 282–297 (2021).PubMed 

Google Scholar 
Pawelec, G. Age and immunity: What is “immunosenescence”?. Exp. Gerontol. 105, 4–9 (2018).CAS 
PubMed 

Google Scholar 
Schwenke, R. A., Lazzaro, B. P. & Wolfner, M. F. Reproduction–immunity trade-offs in insects. Annu. Rev. Entomol. 61, 239–256 (2016).CAS 
PubMed 

Google Scholar 
Maklakov, A. A. & Chapman, T. Evolution of ageing as a tangle of trade-offs: Energy versus function. Proc. R. Soc. B. 286, 20191604 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hamel, S. et al. Fitness costs of reproduction depend on life speed: empirical evidence from mammalian populations: Fitness costs of reproduction in mammals. Ecol. Lett. 13, 915–935 (2010).PubMed 

Google Scholar 
Graham, A. L., Allen, J. E. & Read, A. F. Evolutionary causes and consequences of immunopathology. Annu. Rev. Ecol. Evol. Syst. 36, 373–397 (2005).
Google Scholar 
Sorci, G. & Faivre, B. Inflammation and oxidative stress in vertebrate host–parasite systems. Phil. Trans. R. Soc. B. 364, 71–83 (2009).PubMed 

Google Scholar 
Ashley, N. T., Weil, Z. M. & Nelson, R. J. Inflammation: Mechanisms, costs, and natural variation. Annu. Rev. Ecol. Evol. Syst. 43, 385–406 (2012).
Google Scholar 
Babin, A., Moreau, J. & Moret, Y. Storage of carotenoids in crustaceans as an adaptation to modulate immunopathology and optimize immunological and life history strategies. BioEssays 41, 1800254 (2019).
Google Scholar 
Vasto, S. et al. Inflammatory networks in ageing, age-related diseases and longevity. Mech. Ageing Dev. 128, 83–91 (2007).CAS 
PubMed 

Google Scholar 
Finch, C. E. & Crimmins, E. M. Inflammatory exposure and historical changes in human life-spans. Science 305, 1736–1739 (2004).ADS 
CAS 
PubMed 

Google Scholar 
Licastro, F. et al. Innate immunity and inflammation in ageing: A key for understanding age-related diseases. Immun. Ageing 2, 8 (2005).PubMed 
PubMed Central 

Google Scholar 
Pawelec, G., Goldeck, D. & Derhovanessian, E. Inflammation, ageing and chronic disease. Curr. Opin. Immunol. 29, 23–28 (2014).CAS 
PubMed 

Google Scholar 
Pursall, E. R. & Rolff, J. Immune responses accelerate ageing: Proof-of-principle in an insect model. PLoS ONE 6, e19972 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Khan, I., Agashe, D. & Rolff, J. Early-life inflammation, immune response and ageing. Proc. R. Soc. B. 284, 20170125 (2017).PubMed 
PubMed Central 

Google Scholar 
Vigneron, A., Jehan, C., Rigaud, T. & Moret, Y. Immune defenses of a beneficial pest: The mealworm beetle, Tenebrio molitor. Front. Physiol. 10, 138 (2019).PubMed 
PubMed Central 

Google Scholar 
Jehan, C., Chogne, M., Rigaud, T. & Moret, Y. Sex-specific patterns of senescence in artificial insect populations varying in sex-ratio to manipulate reproductive effort. BMC Evol. Biol. 20, 18 (2020).PubMed 
PubMed Central 

Google Scholar 
Jehan, C., Sabarly, C., Rigaud, T. & Moret, Y. Age-specific fecundity under pathogenic threat in an insect: Terminal investment versus reproductive restraint. J. Anim. Ecol. 91, 101–111 (2022).PubMed 

Google Scholar 
Chung, K.-H. & Moon, M.-J. Fine structure of the hemopoietic tissues in the mealworm beetle, Tenebrio molitor. Entomol. Res. 34, 131–138 (2004).
Google Scholar 
Urbański, A., Adamski, Z. & Rosiński, G. Developmental changes in haemocyte morphology in response to Staphylococcus aureus and latex beads in the beetle Tenebrio molitor L.. Micron 104, 8–20 (2018).PubMed 

Google Scholar 
Vommaro, M. L., Kurtz, J. & Giglio, A. Morphological characterisation of haemocytes in the mealworm beetle Tenebrio molitor (Coleoptera, Tenebrionidae). Insects 12, 423 (2021).PubMed 
PubMed Central 

Google Scholar 
Söderhäll, K. & Cerenius, L. Role of the prophenoloxidase-activating system in invertebrate immunity. Curr. Opin. Immunol. 10, 23–28 (1998).PubMed 

Google Scholar 
Siva-Jothy, M. T., Moret, Y. & Rolff, J. Insect immunity: an evolutionary ecology perspective. in Advances in Insect Physiology vol. 32 1–48 (Elsevier, 2005).Nappi, A. J. & Ottaviani, E. Cytotoxicity and cytotoxic molecules in invertebrates. BioEssays 22, 469–480 (2000).CAS 
PubMed 

Google Scholar 
Sadd, B. M. & Siva-Jothy, M. T. Self-harm caused by an insect’s innate immunity. Proc. R. Soc. B. 273, 2571–2574 (2006).PubMed 
PubMed Central 

Google Scholar 
Daukšte, J., Kivleniece, I., Krama, T., Rantala, M. J. & Krams, I. Senescence in immune priming and attractiveness in a beetle: Immunosenescence in a beetle. J. Evol. Biol. 25, 1298–1304 (2012).PubMed 

Google Scholar 
Krams, I. et al. Trade-off between cellular immunity and life span in mealworm beetles Tenebrio molitor. Curr. Zool. 59, 340–346 (2013).
Google Scholar 
Moon, H. J., Lee, S. Y., Kurata, S., Natori, S. & Lee, B. L. Purification and molecular cloning of cDNA for an inducible antibacterial protein from larvae of the coleopteran, Tenebrio molitor. J. Biochem. 116, 53–58 (1994).CAS 
PubMed 

Google Scholar 
Lee, Y. J. et al. Structure and expression of the tenecin 3 gene in Tenebrio molitor. Biochem. Biophys. Res. Comm. 218, 6–11 (1996).CAS 
PubMed 

Google Scholar 
Kim, D. H. et al. Bacterial expression of tenecin 3, an insect antifungal protein isolated from Tenebrio molitor, and its efficient purification. Mol. Cells 8, 786–789 (1998).CAS 
PubMed 

Google Scholar 
Roh, K.-B. et al. Proteolytic cascade for the activation of the insect toll pathway induced by the fungal cell wall component. J. Biol. Chem. 284, 19474–19481 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Park, J.-W. et al. Beetle Immunity. in Invertebrate Immunity (ed. Söderhäll, K.) vol. 708 163–180 (Springer US, 2010).Chae, J.-H. et al. Purification and characterization of tenecin 4, a new anti-Gram-negative bacterial peptide, from the beetle Tenebrio molitor. Dev. Comp. Immunol. 36, 540–546 (2012).CAS 
PubMed 

Google Scholar 
Haine, E. R., Pollitt, L. C., Moret, Y., Siva-Jothy, M. T. & Rolff, J. Temporal patterns in immune responses to a range of microbial insults (Tenebrio molitor). J. Insect Physiol. 54, 1090–1097 (2008).CAS 
PubMed 

Google Scholar 
Dhinaut, J., Chogne, M. & Moret, Y. Immune priming specificity within and across generations reveals the range of pathogens affecting evolution of immunity in an insect. J. Anim. Ecol. 87, 448–463 (2018).PubMed 

Google Scholar 
Hoffmann, J. A., Reichhart, J.-M. & Hetru, C. Innate immunity in higher insects. Curr. Opin. Immunol. 8, 8–13 (1996).CAS 
PubMed 

Google Scholar 
Moret, Y. Explaining variable costs of the immune response: selection for specific versus non-specific immunity and facultative life history change. Oikos 102, 213–216 (2003).
Google Scholar 
Khan, I., Prakash, A. & Agashe, D. Immunosenescence and the ability to survive bacterial infection in the red flour beetle Tribolium castaneum. J. Anim. Ecol. 85, 291–301 (2016).PubMed 

Google Scholar 
Rolff, J. Effects of age and gender on immune function of dragonflies (Odonata, Lestidae) from a wild population. Can. J. Zool. 79, 2176–2180 (2001).
Google Scholar 
Doums, C., Moret, Y., Benelli, E. & Schmid-Hempel, P. Senescence of immune defence in Bombus workers. Ecol. Entomol. 27, 138–144 (2002).
Google Scholar 
Schmid, M. R., Brockmann, A., Pirk, C. W. W., Stanley, D. W. & Tautz, J. Adult honeybees (Apis mellifera L.) abandon hemocytic, but not phenoloxidase-based immunity. J. Insect Physiol. 54, 439–444 (2008).CAS 
PubMed 

Google Scholar 
Moret, Y. & Schmid-Hempel, P. Immune responses of bumblebee workers as a function of individual and colony age: senescence versus plastic adjustment of the immune function. Oikos 118, 371–378 (2009).
Google Scholar 
Armitage, S. A. O. & Boomsma, J. J. The effects of age and social interactions on innate immunity in a leaf-cutting ant. J. Insect Physiol. 56, 780–787 (2010).CAS 
PubMed 

Google Scholar 
Korner, P. & Schmid-Hempel, P. In vivo dynamics of an immune response in the bumble bee Bombus terrestris. J. Invert. Pathol. 87, 59–66 (2004).CAS 

Google Scholar 
Li, T., Yan, D., Wang, X., Zhang, L. & Chen, P. Hemocyte changes during immune melanization in Bombyx Mori infected with Escherichia coli. Insects 10, 301 (2019).PubMed Central 

Google Scholar 
Chase, M. R., Raina, K., Bruno, J. & Sugumaran, M. Purification, characterization and molecular cloning of prophenoloxidases from Sarcophaga bullata. Insect Biochem. Mol. Biol. 30, 953–967 (2000).CAS 
PubMed 

Google Scholar 
Kanost, M. R. & Gorman, M. J. Phenoloxidases in insect immunity. in Insect Immunology 69–96 (Elsevier, 2008).Sadd, B. M. et al. Modulation of sexual signalling by immune challenged male mealworm beetles (Tenebrio molitor L.): Evidence for terminal investment and dishonesty. J. Evol. Biol. 19, 321–325 (2006).CAS 
PubMed 

Google Scholar 
Gálvez, D. & Chapuisat, M. Immune priming and pathogen resistance in ant queens. Ecol. Evol. 4, 1761–1767 (2014).PubMed 
PubMed Central 

Google Scholar 
Armitage, S. A. O. & Siva-Jothy, M. T. Immune function responds to selection for cuticular colour in Tenebrio molitor. Heredity 94, 650–656 (2005).CAS 
PubMed 

Google Scholar 
Armitage, S. A. O., Thompson, J. J. W., Rolff, J. & Siva-Jothy, M. T. Examining costs of induced and constitutive immune investment in Tenebrio molitor. J. Evol. Biol. 16, 1038–1044 (2003).CAS 
PubMed 

Google Scholar 
Kokoza, V. A. et al. Transcriptional regulation of the mosquito vitellogenin gene via a blood meal-triggered cascade. Gene 274, 47–65 (2001).CAS 
PubMed 

Google Scholar 
Isaac, P. G. & Bownes, M. Ovarian and fat-body vitellogenin synthesis in Drosophila melanogaster. Europ. J. Biochem. 123, 527–534 (2005).
Google Scholar 
Hoffmann, J. A. The immune response of Drosophila. Nature 426, 33–38 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Tzou, P. et al. Tissue-specific inducible expression of antimicrobial peptide genes in Drosophila surface epithelia. Immunity 13, 737–748 (2000).CAS 
PubMed 

Google Scholar 
Haine, E. R., Moret, Y., Siva-Jothy, M. T. & Rolff, J. Antimicrobial defense and persistent infection in insects. Science 322, 1257–1259 (2008).ADS 
CAS 
PubMed 

Google Scholar 
Moret, Y. & Siva-Jothy, M. T. Adaptive innate immunity? Responsive-mode prophylaxis in the mealworm beetle, Tenebrio molitor. Proc. R. Soc. B. 270, 2475–2480 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
Du Rand, N. & Laing, M. D. Determination of insecticidal toxicity of three species of entomopathogenic spore-forming bacterial isolates against Tenebrio molitor L. (Coleoptera: Tenebrionidae). Afr. J. Microbiol. Res. 5, 2222–2228 (2011).
Google Scholar 
Jurat-Fuentes, J. L. & Jackson, T. Bacterial entomopathogens. In Insect Pathology 2nd edn (eds Kaya, H. & Vera, F.) 265–349 (Elsevier Academic Press, Cambridge, Mass, 2012).
Google Scholar 
Dhinaut, J., Balourdet, A., Teixeira, M., Chogne, M. & Moret, Y. A dietary carotenoid reduces immunopathology and enhances longevity through an immune depressive effect in an insect model. Sci. Rep. 7, 12429 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Moreau, J., Martinaud, G., Troussard, J.-P., Zanchi, C. & Moret, Y. Trans-generational immune priming is constrained by the maternal immune response in an insect. Oikos 121, 1828–1832 (2012).
Google Scholar 
Lee, H. S. et al. The pro-phenoloxidase of coleopteran insect, Tenebrio molitor, larvae was activated during cell clump/cell adhesion of insect cellular defense reactions. FEBS Lett. 444, 255–259 (1999).CAS 
PubMed 

Google Scholar 
Zanchi, C., Troussard, J.-P., Martinaud, G., Moreau, J. & Moret, Y. Differential expression and costs between maternally and paternally derived immune priming for offspring in an insect. J. Anim. Ecol. 80, 1174–1183 (2011).PubMed 

Google Scholar 
Moret, Y. ‘Trans-generational immune priming’: Specific enhancement of the antimicrobial immune response in the mealworm beetle, Tenebrio molitor. Proc. R. Soc. B. 273, 1399–1405 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dubuffet, A. et al. Trans-generational immune priming protects the eggs only against gram-positive bacteria in the mealworm beetle. PLoS Pathog. 11, e1005178 (2015).PubMed 
PubMed Central 

Google Scholar  More

During recent decades, pathogens that originated in bats have become an increasing public health concern. A major challenge is to identify how those pathogens spill over into human populations to generate a pandemic threat1. Many correlational studies associate spillover with changes in land use or other anthropogenic stressors2,3, although the mechanisms underlying the observed correlations have not been identified4. One limitation is the lack of spatially and temporally explicit data on multiple spillovers, and on the connections among spillovers, reservoir host ecology and behavior, and viral dynamics. We present 25 years of data on land-use change, bat behavior, and spillover of Hendra virus from Pteropodid bats to horses in subtropical Australia. These data show that bats are responding to environmental change by persistently adopting behaviors that were previously transient responses to nutritional stress. Interactions between land-use change and climate now lead to persistent bat residency in agricultural areas, where periodic food shortages drive clusters of spillovers. Pulses of winter flowering of trees in remnant forests appeared to prevent spillover. We developed integrative Bayesian network models based on these phenomena that accurately predicted the presence or absence of clusters of spillovers in each of 25 years. Our long-term study identifies the mechanistic connections among habitat loss, climate, and increased spillover risk. It provides a framework for examining causes of bat virus spillover and for developing ecological countermeasures to prevent pandemics. More

Ringø, E. et al. Probiotics, lactic acid bacteria and bacilli: Interesting supplementation for aquaculture. J. Appl. Microbiol. 129, 116–136 (2020).PubMed 

Google Scholar 
Borges, N. et al. Bacteriome structure, function, and probiotics in fish larviculture: The good, the bad, and the gaps. Annu. Rev. Anim. Biosci. 9, 423–452 (2021).CAS 
PubMed 

Google Scholar 
Aguilar-Toalá, J. E. et al. Postbiotics: An evolving term within the functional foods field. Trends Food Sci. Technol. 75, 105–114 (2018).
Google Scholar 
Salminen, S. et al. The International Scientific Association of Probiotics and Prebiotics (ISAPP) consensus statement on the definition and scope of postbiotics. Nat. Rev. Gastroenterol. Hepatol. 18, 649–667 (2021).PubMed 
PubMed Central 

Google Scholar 
Dash, G. et al. Evaluation of paraprobiotic applicability of Lactobacillus plantarum in improving the immune response and disease protection in giant freshwater prawn, Macrobrachium rosenbergii (de Man, 1879). Fish Shellf. Immunol. 43, 167–174 (2015).CAS 

Google Scholar 
Dawood, M. A. O., Koshio, S., Ishikawa, M. & Yokoyama, S. Effects of partial substitution of fish meal by soybean meal with or without heat-killed Lactobacillus plantarum (LP20) on growth performance, digestibility, and immune response of Amberjack, Seriola dumerili Juveniles. Biomed. Res. Int. 2015, 514196 (2015).PubMed 
PubMed Central 

Google Scholar 
Dawood, M. A. O., Koshio, S., Ishikawa, M. & Yokoyama, S. Immune responses and stress resistance in red sea bream, Pagrus major, after oral administration of heat-killed Lactobacillus plantarum and vitamin C. Fish Shellf. Immunol. 54, 266–275 (2016).CAS 

Google Scholar 
Dawood, M. A. O., Koshio, S., Ishikawa, M. & Yokoyama, S. Interaction effects of dietary supplementation of heat-killed Lactobacillus plantarum and β-glucan on growth performance, digestibility and immune response of juvenile red sea bream, Pagrus major. Fish Shellf. Immunol. 45, 33–42 (2015).CAS 

Google Scholar 
Tung, H. T. et al. Effects of heat-killed Lactobacillus plantarum supplemental diets on growth performance, stress resistance and immune response of juvenile Kuruma shrimp Marsupenaeus japonicus bate. Aquac. Sci. 57, 175–184 (2009).CAS 

Google Scholar 
Van Nguyen, N. et al. Evaluation of dietary heat-killed Lactobacillus plantarum strain L-137 supplementation on growth performance, immunity and stress resistance of Nile tilapia (Oreochromis niloticus). Aquaculture 498, 371–379 (2019).
Google Scholar 
Yang, H. et al. Effects of dietary heat-killed Lactobacillus plantarum L-137 (HK L-137) on the growth performance, digestive enzymes and selected non-specific immune responses in sea cucumber, Apostichopus japonicus Selenka. Aquac. Res. 47, 2814–2824 (2016).CAS 

Google Scholar 
Singh, S. T., Kamilya, D., Kheti, B., Bordoloi, B. & Parhi, J. Paraprobiotic preparation from Bacillus amyloliquefaciens FPTB16 modulates immune response and immune relevant gene expression in Catla catla (Hamilton, 1822). Fish Shelf. Immunol. 66, 35–42 (2017).CAS 

Google Scholar 
Kamilya, D., Baruah, A., Sangma, T., Chowdhury, S. & Pal, P. Inactivated probiotic bacteria stimulate cellular immune responses of catla, Catla catla (Hamilton) in vitro. Probiot. Antimicrob. Proteins 7, 101–106 (2015).CAS 

Google Scholar 
Wang, J. et al. Supplementation of heat-inactivated Bacillus clausii DE 5 in diets for grouper, Epinephelus coioides, improves feed utilization, intestinal and systemic immune responses and not growth performance. Aquac. Nutr. 24, 821–831 (2018).CAS 

Google Scholar 
Giri, S. S. et al. Effects of dietary heat-killed Pseudomonas aeruginosa strain VSG2 on immune functions, antioxidant efficacy, and disease resistance in Cyprinus carpio. Aquaculture 514, 734489 (2020).CAS 

Google Scholar 
Shabanzadeh, S. et al. Growth performance, intestinal histology, and biochemical parameters of rainbow trout (Oncorhynchus mykiss) in response to dietary inclusion of heat-killed Gordonia bronchialis. Fish Physiol. Biochem. 42, 65–71 (2016).CAS 
PubMed 

Google Scholar 
Wu, X. et al. Use of a paraprobiotic and postbiotic feed supplement (HWF™) improves the growth performance, composition and function of gut microbiota in hybrid sturgeon (Acipenser baerii × Acipenser schrenckii). Fish Shelf. Immunol. 104, 36–45 (2020).CAS 

Google Scholar 
Mora-Sánchez, B., Balcázar, J. L. & Pérez-Sánchez, T. Effect of a novel postbiotic containing lactic acid bacteria on the intestinal microbiota and disease resistance of rainbow trout (Oncorhynchus mykiss). Biotechnol. Lett. 42, 1957–1962 (2020).PubMed 

Google Scholar 
Xiong, J. et al. The temporal scaling of bacterioplankton composition: High turnover and predictability during shrimp cultivation. Microb. Ecol. 67, 256–264 (2014).PubMed 

Google Scholar 
Martins, P. et al. Seasonal patterns of bacterioplankton composition in a semi-intensive European seabass (Dicentrarchus labrax) aquaculture system. Aquaculture 490, 240–250 (2018).
Google Scholar 
Offret, C. et al. Protective efficacy of a Pseudoalteromonas strain in European abalone, Haliotis tuberculata, infected with Vibrio harveyi ORM4. Probiot. Antimicrob. Proteins 11, 239–247 (2019).
Google Scholar 
Richards, G. P. et al. Mechanisms for Pseudoalteromonas piscicida-induced killing of vibrios and other bacterial pathogens. Appl. Environ. Microbiol. 83, e00175-e217 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lin, S.-R., Chen, Y.-H., Tseng, F.-J. & Weng, C.-F. The production and bioactivity of prodigiosin: Quo vadis?. Drug Discov. Today 25, 828–836 (2020).CAS 
PubMed 

Google Scholar 
Vynne, N. G., Månsson, M., Nielsen, K. F. & Gram, L. Bioactivity, chemical profiling, and 16S rRNA-based phylogeny of Pseudoalteromonas strains collected on a global research cruise. Mar. Biotechnol. 13, 1062–1073 (2011).CAS 

Google Scholar 
Lovejoy, C., Bowman, J. P. & Hallegraeff, G. M. Algicidal effects of a novel marine Pseudoalteromonas isolate (class Proteobacteria, gamma subdivision) on harmful algal bloom species of the genera Chattonella, Gymnodinium, and Heterosigma. Appl. Environ. Microbiol. 64, 2806–2813 (1998).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Franks, A. et al. Inhibition of fungal colonization by Pseudoalteromonas tunicata provides a competitive advantage during surface colonization. Appl. Environ. Microbiol. 72, 6079–6087 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hjelm, M. et al. Selection and identification of autochthonous potential probiotic bacteria from turbot larvae (Scophthalmus maximus) rearing units. Syst. Appl. Microbiol. 27, 360–371 (2004).PubMed 

Google Scholar 
Sorieul, L. et al. Survival improvement conferred by the Pseudoalteromonas sp. NC201 probiotic in Litopenaeus stylirostris exposed to Vibrio nigripulchritudo infection and salinity stress. Aquaculture 495, 888–898 (2018).
Google Scholar 
Pruitt, K. D., Tatusova, T. & Maglott, D. R. NCBI reference sequence (RefSeq): A curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 33, D501–D504 (2005).CAS 
PubMed 

Google Scholar 
Yan, Y.-Y., Xia, H.-Q., Yang, H.-L., Hoseinifar, S. H. & Sun, Y.-Z. Effects of dietary live or heat-inactivated autochthonous Bacillus pumilus SE5 on growth performance, immune responses and immune gene expression in grouper Epinephelus coioides. Aquac. Nutr. 22, 698–707 (2016).CAS 

Google Scholar 
Louvado, A. et al. Humic substances modulate fish bacterial communities in a marine recirculating aquaculture system. Aquaculture 544, 737121 (2021).CAS 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 1, 5 (2019).
Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 1–17 (2018).
Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-6. 2019. (2020).R. Core Team. R: A Language and Environment for Statistical Computing (Version 2120) (R Foundation for Statistical Computing, 2012).
Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wemheuer, F. et al. Tax4Fun2: Prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences. Environ. Microbiome 15, 11 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Setiyono, E. et al. An Indonesian marine bacterium, Pseudoalteromonas rubra, produces antimicrobial prodiginine pigments. ACS Omega 5, 4626–4635 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Buchan, A., LeCleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: Features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).CAS 
PubMed 

Google Scholar 
Bakenhus, I. et al. Composition of total and cell-proliferating bacterioplankton community in early summer in the North Sea—roseobacters are the most active component. Front. Microbiol. 8, 1771 (2017).PubMed 
PubMed Central 

Google Scholar 
Sieber, C. M. K. et al. Unusual metabolism and hypervariation in the genome of a gracilibacterium (BD1-5) from an oil-degrading community. MBio 10, e02128-e2219 (2022).
Google Scholar 
Jaffe, A. L., Castelle, C. J., Matheus Carnevali, P. B., Gribaldo, S. & Banfield, J. F. The rise of diversity in metabolic platforms across the Candidate Phyla Radiation. BMC Biol. 18, 69 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williams, H. N. & Piñeiro, S. Ecology of the predatory Bdellovibrio and like organisms. In Predatory Prokaryotes 213–248 (Springer, 2006).
Google Scholar 
Johnke, J. et al. Multiple micro-predators controlling bacterial communities in the environment. Curr. Opin. Biotechnol. 27, 185–190 (2014).CAS 
PubMed 

Google Scholar 
Rice, T. D., Williams, H. N. & Turng, B.-F. Susceptibility of bacteria in estuarine environments to autochthonous bdellovibrios. Microb. Ecol. 35, 256–264 (1998).CAS 
PubMed 

Google Scholar 
Feng, S. et al. Predation by Bdellovibrio bacteriovorus significantly reduces viability and alters the microbial community composition of activated sludge flocs and granules. FEMS Microbiol. Ecol. 93, fix020 (2017).
Google Scholar 
Duarte, L. N. et al. Bacterial and microeukaryotic plankton communities in a semi-intensive aquaculture system of sea bass (Dicentrarchus labrax): A seasonal survey. Aquaculture 503, 59–69 (2019).
Google Scholar 
Hu, L. et al. Reclassification of the taxonomic framework of orders cellvibrionales, oceanospirillales, pseudomonadales, and alteromonadales in class gammaproteobacteria through phylogenomic tree analysis. mSystems 5, e00543-e620 (2021).
Google Scholar 
Garrity, G. M., Bell, J. A. & Lilburn, T. Oceanospirillalesord. Nov. in Bergey’s Manual® of Systematic Bacteriology 270–323 (Springer, 2005).
Google Scholar 
Thomas, F., Hehemann, J.-H., Rebuffet, E., Czjzek, M. & Michel, G. Environmental and gut bacteroidetes: The food connection. Front. Microbiol. 2, 93 (2011).PubMed 
PubMed Central 

Google Scholar 
Rajilić-Stojanović, M. & De Vos, W. M. The first 1000 cultured species of the human gastrointestinal microbiota. FEMS Microbiol. Rev. 38, 996–1047 (2014).PubMed 

Google Scholar 
Rosenberg, E. The family Chitinophagaceae. In The Prokaryotes: Other Major Lineages of Bacteria and The Archaea (eds Rosenberg, E. et al.) (Springer, 2014).
Google Scholar 
Beckmann, A., Hüttel, S., Schmitt, V., Müller, R. & Stadler, M. Optimization of the biotechnological production of a novel class of anti-MRSA antibiotics from Chitinophaga sancti. Microb. Cell Fact. 16, 1–10 (2017).
Google Scholar 
Loudon, A. H. et al. Interactions between amphibians’ symbiotic bacteria cause the production of emergent anti-fungal metabolites. Front. Microbiol. 5, 441 (2014).PubMed 
PubMed Central 

Google Scholar 
Taylor, J. D. & Cunliffe, M. Coastal bacterioplankton community response to diatom-derived polysaccharide microgels. Environ. Microbiol. Rep. 9, 151–157 (2017).CAS 
PubMed 

Google Scholar 
González, J. M. & Whitman, W. B. Oceanospirillum and related genera. In The Prokaryotes: A Handbook on the Biology of Bacteria Volume 6: Proteobacteria: Gamma Subclass (eds Dworkin, M. et al.) 887–915 (Springer, 2006).
Google Scholar 
Kleindienst, S., Paul, J. H. & Joye, S. B. Using dispersants after oil spills: Impacts on the composition and activity of microbial communities. Nat. Rev. Microbiol. 13, 388–396 (2015).CAS 
PubMed 

Google Scholar 
Williams, T. J. et al. The role of planktonic Flavobacteria in processing algal organic matter in coastal East Antarctica revealed using metagenomics and metaproteomics. Environ. Microbiol. 15, 1302–1317 (2013).CAS 
PubMed 

Google Scholar 
Gobet, A. et al. Evolutionary evidence of algal polysaccharide degradation acquisition by Pseudoalteromonas carrageenovora 9T to adapt to macroalgal niches. Front. Microbiol. 10, 2740 (2018).
Google Scholar 
Beier, S., Rivers, A. R., Moran, M. A. & Obernosterer, I. The transcriptional response of prokaryotes to phytoplankton-derived dissolved organic matter in seawater. Environ. Microbiol. 17, 3466–3480 (2015).CAS 
PubMed 

Google Scholar 
Müller, O., Seuthe, L., Bratbak, G. & Paulsen, M. L. Bacterial response to permafrost derived organic matter input in an Arctic fjord. Front. Mar. Sci. 5, 263 (2018).
Google Scholar 
Fernández-Álvarez, C. & Santos, Y. Identification and typing of fish pathogenic species of the genus Tenacibaculum. Appl. Microbiol. Biotechnol. 102, 9973–9989 (2018).PubMed 

Google Scholar 
Wirth, J. S. & Whitman, W. B. Phylogenomic analyses of a clade within the roseobacter group suggest taxonomic reassignments of species of the genera Aestuariivita, Citreicella, Loktanella, Nautella, Pelagibaca, Ruegeria, Thalassobius, Thiobacimonas and Tropicibacter, and the proposal. Int. J. Syst. Evol. Microbiol. 68, 2393–2411 (2018).CAS 
PubMed 

Google Scholar 
Luo, H. & Moran, M. A. Evolutionary ecology of the marine Roseobacter clade. Microbiol. Mol. Biol. Rev. 78, 573–587 (2014).PubMed 
PubMed Central 

Google Scholar 
Shahina, M. et al. Luteibaculum oceani gen. nov., sp. Nov., a carotenoid-producing, lipolytic bacterium isolated from surface seawater, and emended description of the genus Owenweeksia Lau et al. 2005. Int. J. Syst. Evol. Microbiol. 63, 4765–4770 (2013).CAS 
PubMed 

Google Scholar 
Davidov, Y. & Jurkevitch, E. Diversity and evolution of Bdellovibrio-and-like organisms (BALOs), reclassification of Bacteriovorax starrii as Peredibacter starrii gen. nov., comb. nov., and description of the Bacteriovorax–Peredibacter clade as Bacteriovoracaceae fam. nov. Int. J. Syst. Evol. Microbiol. 54, 1439–1452 (2004).CAS 
PubMed 

Google Scholar 
Müller, F. D., Beck, S., Strauch, E. & Linscheid, M. W. Bacterial predators possess unique membrane lipid structures. Lipids 46, 1129–1140 (2011).PubMed 

Google Scholar 
Kandel, P. P., Pasternak, Z., van Rijn, J., Nahum, O. & Jurkevitch, E. Abundance, diversity and seasonal dynamics of predatory bacteria in aquaculture zero discharge systems. FEMS Microbiol. Ecol. 89, 149–161 (2014).CAS 
PubMed 

Google Scholar 
Cao, H., Wang, H., Yu, J., An, J. & Chen, J. Encapsulated bdellovibrio powder as a potential bio-disinfectant against whiteleg shrimp-pathogenic vibrios. Microorganisms 7, 25 (2019).
Google Scholar 
Jafarian, N., Sepahi, A. A., Naghavi, N. S., Hosseini, F. & Nowroozi, J. Using autochthonous Bdellovibrio as a predatory bacterium for reduction of Gram-negative pathogenic bacteria in urban wastewater and reuse it. Iran. J. Microbiol. 12, 556–564 (2020).PubMed 
PubMed Central 

Google Scholar 
Cosgrove, L., McGeechan, P. L., Handley, P. S. & Robson, G. D. Effect of biostimulation and bioaugmentation on degradation of polyurethane buried in soil. Appl. Environ. Microbiol. 76, 810–819 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Cruz, A. et al. Microbial remediation of organometals and oil hydrocarbons in the marine environment. In Marine Pollution and Microbial Remediation 41–66 (Springer, 2017).
Google Scholar 
Zhao, J., Chen, M., Quan, C. S. & Fan, S. D. Mechanisms of quorum sensing and strategies for quorum sensing disruption in aquaculture pathogens. J. Fish Dis. 38, 771–786 (2015).CAS 
PubMed 

Google Scholar 
Tyc, O., Song, C., Dickschat, J. S., Vos, M. & Garbeva, P. The ecological role of volatile and soluble secondary metabolites produced by soil bacteria. Trends Microbiol. 25, 280–292 (2017).CAS 
PubMed 

Google Scholar  More

Model validationBased on the time-series validations of water temperature and DO concentration, model accuracy improved gradually, despite several discrepancies at the beginning of the simulation (Supplementary Fig. S1). The model is primarily driven by a set of boundary data, including wind speed, solar radiation, and precipitation data24,25. From this perspective, more high-quality boundary data promotes better numerical reproducibility. However, meteorological data collection was challenging due to the early observation equipment limitations and low observational accuracy compared to current data. The temporal inconsistency of accuracy in observational data has been eliminated to a large extent by fitting a regression curve24. Spatial resolution is the other issue. Possessing spatially constant values for all boundary conditions complicates the numerical reproducibility of variations on finer scales.The relationship between turnovers and the curve shape of water temperature versus DO concentration is theoretically sound27,28. In the last stage of stratification in the lake, water temperature and DO concentration near the bottom are more likely to slightly increase due to thermal diffusion and DO supplies from the upper water. If a turnover occurs, the whole column of water is mixed strongly (Supplementary Fig. S3). Bottom water temperature decreases due to surface water cooling, and DO concentration increases, due to surface water replenishment and increased oxygen solubility. If the turnover fails, only the partial column of water is mixed, causing a delay in the timing of deep-water renewal (Supplementary Fig. S3). However, the upper water in later months, like that in March, has been rapidly warmed, resulting in an increase in the bottom water temperature. For example, in 2007 and 2016, the simulated water temperature and DO concentration fluctuated within a limited range in February and then skyrocketed in March, after mixing with the warmed surface water (blue points in Supplementary Fig. S4). On the other hand, explicit definitions of turnover timing are challenging. The threshold used to judge turnover timing is reliable because the results matched the observation. The turnover timing varied by 36 days in Lake Biwa during the simulation period, which is comparable to that observed in other lakes, such as approximately 21 days in Heiligensee, Germany over a 17-year timespan29, 16 days in Lake Washington over a 40-year timespan30, and 28 days in Blelham Tarn over a 41-year timespan31.Variables affecting the mixing regimeDetermining variables that affect the mixing regime is essential to improve understanding and enable future projections16,17,18. Air temperature, wind speed, cloud cover, precipitation, water density, and lake transparency are all potential variables. We, here, compared the above variables to the turnover timing in Lake Biwa. The meteorological inputs in this study provided data for air temperature, wind speed, cloud cover, and precipitation. Water density and particulate organic carbon (POC) concentration representing lake transparency were the model’s outputs. The annual averages and cold season (November–April) values of the above variables were calculated over the simulation period (Supplementary Fig. S6). Annual averages illustrate general long-term warming trends18, while cold season values particularly determine the timing of turnover17. However, in Lake Biwa, air temperature during the cold season fluctuated greatly compared to the annual averages. A random forest analysis17 has been conducted between the turnover timing and the above two variable sets (cold season values versus annual averages) in Lake Biwa, and the cold season values better explained the turnover timing (35.39% versus 18.48%). The results agree with the conclusion drawn from the previous sensitivity tests, which indicated the relative importance of air temperature and solar radiation during winter based on 40 scenarios32.The importance of variables was estimated based on the random forest analysis using the cold season data (Fig. 4a). Wind speed dominates the timing of turnover, which is consistent with the previous studies17,25. The POC concentration, the difference in water density between the surface and bottom, and cloud cover have moderate effects on the timing of turnover. However, air temperature is less important, which is contrary to the turnover mechanism17,24,32. A re-confirmation was conducted of the relationship between turnover timing and air temperature (Fig. 4b and Supplementary Fig. S7). The cool air generally encourages an early turnover, albeit with several anomalies. The turnover timing between 1976 and 1990 remained constant independent of climate change, and the period coincidently had a substantial nutrient fluctuation (Fig. 3). As a result, it is essential to investigate the nutrient status further.Figure 4Analysis results of the relationship between potential variables and turnover timing: (a) the importance of variables importance using a random forest analysis, and (b) the relationship between the cold season air temperature and the timing of turnover. Variable importance is calculated using the percentage increase in mean square error (MSE) and the increase in node purity. Higher values illustrate the greater importance of the variable. Variables include air temperature (AT), precipitation (pptn.), cloud cover (CC), the difference in density (DD), POC, and wind speed (WS).Full size imageLake nutrient concentrationsBecause phosphorus is the limiting nutrient in Lake Biwa and DIP concentrations can be effectively limited by regulating external loadings as practiced (Fig. 3), DIP concentrations become the focus of this discussion for nutrient status. However, the DIP concentrations disproportionately responded to the external loadings of total phosphorus (TP) in Lake Biwa. Although external TP loading itself fails to determine lake phosphorus concentrations due to the hydrodynamics of lakes33, Lake Biwa exhibited insignificant changes in the inflow rate or the retention time (and see an example of the surface flow in Supplementary Fig. S8). Therefore, it can be assumed that the hydraulic loading remained constant, and the input nutrient concentrations were proportionate to the external nutrient loadings in Lake Biwa. This finding contradicts a recent meta-analysis that highlighted a deterministic relationship between input nutrient concentrations and lake nutrient concentrations, based on steady-state mass balance models6. The possible reason is the dynamics of the lake’s ecosystem22, which have been considered in this study. For example, the surface DIP concentrations were almost nonexistent regardless of the external TP loadings in Lake Biwa, supporting that phosphorus is the limiting nutrient in Lake Biwa34,35. The low DIP concentrations at the surface may be caused by the rapid recycling of phosphorus because the amount of phosphorus available for phytoplankton is easily affected by the feedback mechanism between phytoplankton photosynthesis and the phosphorus released from the water35,36.Hypoxia and strategiesThe variations in DO concentration are the public’s top concern as it relates to hypoxia, a key indicator of water quality. Lake bottom, among all water depths, is more sensitive to small changes in oxygen conditions12. In Lake Biwa, the annual minimum DO concentrations ranged from 2 to 5.5 mg/L over the last 60 years (Supplementary Fig. S9). The decrease in DO concentrations in the early period, typically till the 1980s, was mainly caused by nutrient enrichments (Fig. 3). The nutrient enrichment-induced heavy eutrophication eventually accelerates the rate of DO depletion2. After eutrophication was controlled in the 1980s, climate change became the dominant stressor23. There remains much uncertainty surrounding the relationship between climatic variables-related turnover timing and hypoxia in Lake Biwa12. We, therefore, first investigate the relationship between hypoxia and turnover timing, and then concentrate on nutrients to alleviate hypoxia.Although the relationship between turnover timing and DO concentrations is quite weak (R2 = 0.10), there is a general decrease in DO concentrations with increasing turnover timing (Fig. 5a). On the other hand, a linear relationship has been found between DIP concentrations and DO concentrations, with an R2 of 0.67 (Fig. 5b). The slope of –0.841 μgP/mgDO means an increase in DIP concentrations by approximately 0.841 μgP/L causes a decrease in DO concentrations by 1 mg/L. Note that the simulation results were compared over the whole period, and eutrophication-induced hypoxia differs theoretically from climate-induced hypoxia. Additional testing has been conducted to distinguish the effects of two stressors (eutrophication- and climate-induced hypoxia; Supplementary Fig. S10). Before 1980 when eutrophication progressed, the annual minimum DO concentrations and the DIP concentrations had a stronger linear relationship (R2 = 0.89). Although waste-water treatment has improved conditions in the lake, climate change induced alteration of turnover timing may adversely influence water quality. However, the relationship weakened dramatically with an R2 of 0.10 after 1980, when climate change dominated hypoxia. The lower R2 value indicates that climate-related hypoxia is more complex as concluded previously37,38. The two possibilities are as follows. First, there can be a legacy of hypoxia related to eutrophication. The DO recovery at the bottom of Lake Biwa was complicated by the low DO concentration in 1980 and the delayed timing of turnover; similar phenomena have been observed in the Lake of Zurich22. Second, ecosystem dynamics could help explain the difficulty in predicting hypoxia at the bottom. Phytoplankton fully exploits phosphorus at the surface, as explained above, then the death and sinking of the surface phytoplankton are accompanied by the sedimentation of phosphorus to the bottom as modeled. Bacteria break down the sinking phytoplankton, releasing phosphorus and consuming DO in the process. Additional DO consumption lowers the bottom DO concentration, which in turn encourages phosphorus release from the sediment in a low DO environment22. Such unfavorable feedback between DIP and DO concentrations are strengthened by prolonged stratification and eventually accelerates the development of hypoxia. However, future research is necessary because this numerical model simplified the relationship between water and sediment. The sinking of organic carbon into sediment is integrated in the model, and due to the decomposition of organic carbon in the sediment, nutrients are released into and oxygen is depleted in the water. Despite that, the trends between DO and DIP concentrations stay the same under climate change (Fig. 5b), and thus controlling lake phosphorus is beneficial to the Lake Biwa hypoxia.Figure 5The linear regression results of the relationship: (a) between turnover timing and annual minimum concentration of DO, (b) between the annual minimum concentration of DO and annual average concentration of DIP. The simulation results at the monitoring station were used for analysis.Full size image More

The effects of extreme drought on soil biochemical propertiesAs shown in Fig. 1A, the range of SOC during the early, midterm and late extreme drought experiments, were 73.53–251.44 g kg−1, 54.75–256.16 g kg−1, and 66.37–282.16 g kg−1, respectively. Concomitantly, DOC was 171.85–323.74 mg kg−1, 158.15 – 504.62 mg kg−1, and 166.63–418.43 mg kg−1, MBC was 247.80 – 461.69 mg kg−1, 257.90–450.98 mg kg−1, and 264.10–458.15 mg kg−1, respectively (Fig. 1B, C). The variation ranges of soil TN were 3.50–16.60 g kg−1, 4.70–34.5 g kg−1, and 6.70–32.50 g kg−1, respectively (Fig. 1D). Similarly, the variation ranges of NH4+ were 5.96–12.03 g kg−1, 5.39–12.59 g kg−1, and 5.74–13.03 g kg−1, NO3− were 2.27–8.79 mg kg−1, 5.07–9.62 mg kg−1, and 5.09–9.52 mg kg−1, respectively (Fig. 1E, F). The changes of SOC and NH4+ with soil depth were significantly different in different extreme drought periods and decreased significantly with the increase of soil depth (Table 1, P  More

We investigated whether ricefield mosquito larval control and/or rice cultivation practices are associated with malaria vector densities through a systematic review and meta-analysis. Forty-seven experimental studies were eligible for inclusion in the qualitative analysis and thirty-three studies were eligible for the meta-analysis. It was demonstrated that the use of fish, chemical and biological larvicides in rice fields were effective in controlling larval malaria vector densities at all developmental stages. Intermittent irrigation, however, could only significantly reduce late-stage larvae. Based on a limited number of studies, meta-analyses on other forms of larval control such as monomolecular surface films (MSFs), neem, copepods and Azolla failed to demonstrate any consistent reduction in anopheline numbers. Similarly, rice cultivation practices such as plant variety and density, type of levelling and pesticide application were not generally associated with reduced malaria vectors. Nonetheless, in one study, minimal tillage was observed to reduce average numbers of larvae throughout a cropping season. In another study, herbicide application increased larval abundance over a 4-week period, as did one-time drainage in a third study.
Despite their different modes of action, the use of chemical and bacterial larvicides and MSFs were all relatively effective measures of larval control in rice fields, varying between a 57% to 76% reduction in vector abundance compared to no larviciding. Their effects were highest (often reaching 100% reduction) only shortly following application but did not persist for longer than two weeks. These larvicides mostly had short residual half-lives because they were applied to paddy water which was naturally not completely stagnant: there was a small but constant process of water loss (through drainage, evapotranspiration and percolation) and replacement through irrigation. Hence, even with a residual formulation, weekly re-application would be needed for sustained control47,40,41,50. This would be very labour- and cost-intensive to scale-up, to ensure that larvicides are evenly distributed across vast areas (even at plot/sub-plot level) throughout at least one 5-month long rice-growing season per year42,51. Aerial application (including unmanned aerial vehicles), although widely used in the US and Europe, is unlikely to be a feasible delivery system for smallholders in SSA, even in large irrigation schemes26,27,48,49. Furthermore, if synthetic organic chemicals were to be considered for riceland malaria vector control, their management in the current landscape of insecticide resistance across Africa must be considered.Biological control using fish was found to be, in general, slightly more effective than (chemical, bacterial and MSF) larviciding. The degree of effectiveness was dependent on the fish species and their feeding preferences: surface-feeding, larvivorous species provided better anopheline control than bottom-feeding selective feeders4,43. Selecting the most suitable fish for local rice fields is not straightforward; many criteria need to be considered4,52,53. Generally, fish were well-received by rice farmers, perceived to contribute to increased yield by reducing weeds and pests and providing fertiliser through excrement43,44. This was reportedly also observed in Guangxi, China, where a certain proportion of the field had to be deepened into a side-trench where the fish could take shelter when the fields were drained. Even with this reduction in rice production area, carp rearing still increased yields by 10% and farmer’s income per hectare by 70%53. Unfortunately, none of the eligible studies in this review had included yield or water use as an outcome. Future entomological studies need to measure these critical agronomic variables so that studies of vector control in rice can be understood by, and transferred to, agronomists. In SSA, irrigated rice-fish farming can be scaled up provided that an inventory of fish species suitable for specific locations is available and that water is consistently available in fields (an important limiting factor in African irrigation schemes)54. Lessons can be learnt from successful large-scale rice-fish systems in Asia, where they have served as win–win solutions for sustainable food production and malaria control16,55.Overall, there was only limited evidence that intermittent irrigation is effective at reducing late-instar anopheline larvae in rice fields. This finding contrasts with prior reviews, which found mixed results (regardless of larval stage) but emphasised that success was site-specific4,17,56. This contrast is presumably due to the inclusion criteria of our systematic review. These reviews excluded studies in various geographical settings and some older studies that reported successful anopheline control with intermittent irrigation but lacked either a contemporaneous control arm, adequate replication or adequate differentiation between culicines and anophelines16,57,50,51,52,61. It seems, from our review, that intermittent irrigation does not prevent the recruitment of early instars (and in one case, may have encouraged oviposition31) but tends to prevent their development into late-stage immatures. This important conclusion is, however, based only on four studies; more evidence is urgently needed where future trials should consider the basic principles of modern trials with adequate replication, controls and differentiation between larval instars and species.Generally, it is observed that drainage, passive or active, did not reliably reduce overall numbers of mosquito immatures. In India and Kenya, closer inspection revealed that soils were not drying sufficiently, so any stranded larvae were not killed31,46. Highlighted by van der Hoek et al.29 and Keiser et al.17, water management in rice fields is very dependent on the physical characteristics of the soil and the climate and is most suited to places that not only favour rapid drying, but also have a good control of water supply17,56. Moreover, repeated drainage, although directed against mosquitoes, can also kill their aquatic predators62. Since mosquitoes can re-establish themselves in a newly flooded rice field more quickly than their predators, intermittent irrigation with more than a week between successive drying periods can permit repeated cycles of mosquito breeding without any predation pressure. Its efficacy against malaria vectors is therefore highly reliant on the timing of the wetting and drying periods. Further site-specific research on timing, especially with regards to predator–prey interactions within the rice agroecosystem, is required to find the perfect balance.Another limitation in intermittent irrigation is that it cannot be applied during the first two to three weeks following transplanting, because rice plants must remain flooded to recover from transplanting shock. Unfortunately, this time coincides with peak vector breeding. Thus, other methods of larval control would be required to fill this gap. To agronomists, intermittent irrigation provides benefits to farmers, as it does not penalise yield but significantly reduces water consumption. Nonetheless, farmer compliance seems to be variable, especially in areas where water availability is inconsistent and intermittent irrigation would potentially require more labour31,32,39. Importantly, rice farmers doubted their ability to coordinate water distribution evenly amongst themselves, suggesting that there may be sharing issues, as in the “tragedy of the commons”63. Instead, they said that they preferred to have an agreed authority to regulate water46.No general conclusions could be made on the effect on malaria vectors of other rice cultivation practices (apart from water management) because only one study was eligible for each practice. Nevertheless, these experiments on pesticide application, tillage and weed control, as well as another study on plant spacing (not eligible since glass rods were used to simulate rice plants), do illustrate that small changes in agronomic inputs and conditions can have considerable effects on mosquito densities, not just rice yield36,38,64. Moreover, in partially- or shallowly-flooded plots, the larvae are often concentrated in depressions (usually footprints), suggesting that rice operations which leave or remove footprints (e.g. hand-weeding, drum seeders, levelling) will influence vector breeding4.Our study has some important limitations. First, in most trials, the units of intervention were replicate plots of rice, and success was measured as a reduction in larval densities within treated plots. This design focuses on the identification of effective and easy-to-implement ways of growing rice without growing mosquitoes, on the assumption that higher vector densities are harmful. However, from a public health perspective, the need for epidemiological outcomes is often, and reasonably, stressed22,65. Nonetheless, from a farmers’ perspective, it is also important to consider whether the vectors emerging from their rice fields significantly contribute to the local burden of malaria and to determine how this contribution can be minimised. There is evidence that riceland vectors do increase malaria transmission, since human biting rates are much higher in communities living next to rice schemes than their non-rice counterparts66 and that additional riceland vectors may intensify transmission and malaria prevalence in rice communities15. Hence, when investigating how rice-attributed malaria risk can be minimised, mosquito abundance as measured in the experimental rice trials is a useful indicator of potential impact on epidemiological outcomes.Second, larval density was not always separated into larval developmental stages. This can be misleading because some interventions work by reducing larval survival (but not by preventing oviposition) and development to late instars and pupae. Therefore, an intervention could completely eliminate late-stage larvae and pupae but have little effect on the total number of immatures. This was illustrated in our meta-analyses of intermittent irrigation in Table 3 and Supplementary Table 5, and could have been the case for some studies that failed to demonstrate consistent reductions in overall anopheline numbers but did not differentiate between larval instars34,45,67,60,69. We infer that when monitoring mosquito immatures in rice trials, it is important to distinguish between larval instars and pupae. Pupae should always be counted separately since its abundance is the most direct indicator of adult productivity70.Third, experimental trials rarely reported the timing of intervention application or accounted for different rice-growing phases, or “days after transplantation”, in the outcome. Both aspects are important to consider since an intervention may be suited to control larvae during certain growth phases but not others. This is illustrated by Djegbe et al.38, where, compared to deep tillage, minimal tillage could significantly reduce larvae during the early stages of rice cultivation but not during tillering and maturation38. In contrast, other interventions, such as Azolla and predatory copepods, took time to grow and accumulate, and were more effective during the later stages of a rice season45,67,71. This differentiation is important because it can identify components that could potentially form a complementary set of interventions against riceland malaria vectors, each component being effective at different parts of the season. Since rice fields, and hence the dynamics of riceland mosquito populations, vary from place to place, this set of interventions must also be robust. Special attention must be paid to the early stages of rice cultivation, particularly the first few weeks after transplanting (or sowing), since, with many vector species, a large proportion of adult mosquitoes are produced during this time.Fourth, the analysis of entomological counts is often inadequate. Many studies failed to provide the standard deviation (or any other measure of error) for larval counts and could not be included in the quantitative analysis. Often, due to the extreme (and not unexpected) variability of larval numbers, sample sizes were insufficient to calculate statistically significant differences between treatments. Fifth, a high risk of bias was found across both CTS and CITS studies, including high heterogeneity and some publication bias. Study quality was, in general, a shortcoming and limited the number of eligible studies for certain interventions, including intermittent irrigation. Moreover, there are conspicuous a priori reasons for bias in such experimental trials: trial locations are frequently chosen to maximise the probability of success.Finally, few studies were conducted in African countries, where the relationship between rice and malaria is most important because of the efficiency, and the “rice-philic” nature, of the vector An. gambiae s.l.15. In particular, there was a lack of studies on the effectiveness and scalability of biological control and rice cultivation practices. There is also very little information (particularly social science studies) on the views and perspectives of African rice farmers on mosquitoes in rice and interventions to control them72,73.In the future, as malaria declines (particularly across SSA), the contribution of rice production to increased malaria transmission is likely to become more conspicuous15. Unless this problem is addressed, rice growing will probably become an obstacle to malaria elimination. Current default methods of rice production provide near-perfect conditions for the larvae of African malaria vectors. Therefore, we need to develop modified rice-growing methods that are unfavourable to mosquitoes but still favourable for the rice. Although larviciding and biological control may be appropriate, their unsustainable costs remain the biggest barrier to uptake amongst smallholder farmers. Future investigations into riceland vector control should pay more attention to interventions that may be useful to farmers.Supported by medical entomologists, agronomists should lead the research task of identifying cultivation methods that achieve high rice productivity whilst suppressing vector productivity. Rice fields are a major global source of greenhouse gases, and agronomists have responded by successfully developing novel cultivation methods that minimise these emissions while maintaining yield. We need the same kind of response from agronomists, to achieve malaria control co-benefits within rice cultivation. At present, only a few aspects of rice cultivation have been investigated for their effects on mosquitoes, and the potential of many other practices for reducing anopheline numbers are awaiting study. Due to the spatial and temporal heterogeneity of rice agroecosystems, it is likely that no single control method can reduce mosquito numbers throughout an entire cropping season and in all soil types and irrigation methods. Thus, effective overall control is likely to come from a combination of local, site-specific set of complementary methods, each of which is active and effective during a different phase of the rice-growing season. More

Meat, Milk and More: Policy Innovations to Shepherd Inclusive and Sustainable Livestock Systems in Africa (Malabo Montpellier Panel, 2020).Value of Agricultural Production (FAO, accessed August 25, 2022); https://www.fao.org/faostat/en/#data/QVJayne, T. & Sanchez, P. A. Agricultural productivity must improve in sub-Saharan Africa. Science 372, 1045–1047 (2021).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Dangal, S. R. S. et al. Methane emission from global livestock sector during 1890–2014: magnitude, trends and spatiotemporal patterns. Glob. Change Biol. 23, 4147–4161 (2017).Article 
ADS 

Google Scholar 
Mottet, A. et al. Climate change mitigation and productivity gains in livestock supply chains: insights from regional case studies. Reg. Env. Change 17, 129–141 (2016).Article 

Google Scholar 
Valin, H. et al. Agricultural productivity and greenhouse gas emissions: trade-offs or synergies between mitigation and food security? Environ. Res. Lett. 8, 035019 (2013).Article 
ADS 
CAS 

Google Scholar 
González-Quintero, R. et al. Yield gap analysis to identify attainable milk and meat productivities and the potential for greenhouse gas emissions mitigation in cattle systems of Colombia. Agric. Syst. 195, 103303 (2022).Article 

Google Scholar 
Crops and Livestock Products (FAO, accessed August 17,2022); https://www.fao.org/faostat/en/#data/QCLLedo, J. et al. Persistent challenges in safety and hygiene control practices in emerging dairy chains: the case of Tanzania. Food Control 105, 164–173 (2019).Article 

Google Scholar 
Häsler, B. et al. Integrated food safety and nutrition assessments in the dairy cattle value chain in Tanzania. Glob. Food Sec. 18, 102–113 (2018).Article 

Google Scholar 
Supply Utilization Accounts (FAO, accessed August 26, 2022); https://www.fao.org/faostat/en/#data/SCLMichael, S. et al. Tanzania Livestock Master Plan (International Livestock Research Institute, 2018).Tanzania Livestock Sector Analysis (2016/2017–2030/2031) (United Republic of Tanzania Ministry of Livestock and Fisheries, 2017); https://www.mifugouvuvi.go.tz/uploads/projects/1553602287-LIVESTOCK%20SECTOR%20ANALYSIS.pdfNicholson, C. et al. Assessment of Investment Priorities for Tanzania’s Dairy Sector: Report on Activities and Accomplishments (International Livestock Research Institute, 2021).Chagunda, M. G. C., Romer, D. A. M. & Roberts, D. J. Effect of genotype and feeding regime on enteric methane, non-milk nitrogen and performance of dairy cows during the winter feeding period. Livest. Sci. 122, 323–332 (2009).Article 

Google Scholar 
Notenbaert, A. et al. Towards environmentally sound intensification pathways for dairy development in the Tanga region of Tanzania. Reg. Environ. Change 20, 138 (2020).Yesuf, G. A. et al. Embedding stakeholders’ priorities into the low-emission development of the East African dairy sector. Env. Res. Lett. 16, 064032 (2021).Article 
CAS 

Google Scholar 
GLS (Greening Livestock Survey) (International Livestock Research Institute, 2019); https://data.ilri.org/portal/dataset/greeninglivestockIntended Nationally Determined Contributions (United Republic of Tanzania, 2021); https://unfccc.int/sites/default/files/NDC/2022-06/TANZANIA_NDC_SUBMISSION_30%20JULY%202021.pdfNdung’u, P. W. et al. Farm-level emission intensities of smallholder cattle (Bos indicus; B. indicus–B. taurus crosses) production systems in highlands and semi-arid regions. Animal 16, 100445 (2022).Article 
PubMed 

Google Scholar 
Goopy, J. P. et al. Severe below-maintenance feed intake increases methane yield from enteric fermentation in cattle. Br. J. Nutr. 123, 1239–1246 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Goopy, J. P. et al. A new approach for improving emission factors for enteric methane emissions of cattle in smallholder systems of East Africa—results for Nyando, Western Kenya. Agric. Syst. 161, 72–80 (2018).Article 

Google Scholar 
Supporting Low Emissions Development in the Tanzanian Dairy Cattle Sector—Reducing Enteric Methane for Food Security and Livelihoods (FAO, 2019).Gerssen-Gondelach, S. J. et al. Intensification pathways for beef and dairy cattle production systems: impacts on GHG emissions, land occupation and land use change. Agric. Ecosyst. Environ. 240, 135–147 (2017).Article 

Google Scholar 
Havlik, P. et al. Climate change mitigation through livestock system transitions. Proc. Natl Acad. Sci. USA 111, 3709–3714 (2014).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Herrero, M. et al. Greenhouse gas mitigation potentials in the livestock sector. Nat. Clim. Change 6, 452–461 (2016).Article 
ADS 

Google Scholar 
Dizyee, K., Baker, D. & Omore, A. Upgrading the smallholder dairy value chain: a system dynamics ex-ante impact assessment in Tanzania’s Kilosa district. J. Dairy Res. 86, 440–449 (2019).Article 
CAS 
PubMed 

Google Scholar 
Simões, A. R. P., Nicholson, C. F., Novakovicc, A. M. & Protil, R. M. Dynamic impacts of farm-level technology adoption on the Brazilian dairy supply chain. Int. Food Agribus. Manag. Rev. 23, 71–84 (2020).Article 

Google Scholar 
Rahimi, J. et al. Heat stress will detrimentally impact future livestock production in East Africa. Nat. Food. 2, 88–96 (2021).Article 

Google Scholar 
Mbululo, Y. & Nyihirani, F. Climate characteristics over southern highlands Tanzania. Atmos. Clim. Sci. 2, 454–463 (2012).
Google Scholar 
Kihoro, E. M., Schoneveld, G. C. & Crane, T. A. Pathways toward inclusive low-emission dairy development in Tanzania: producer heterogeneity and implications for intervention design. Agric. Syst. 190, 103073 (2021).Mruttu, H. et al. Animal Genetics Strategy and Vision for Tanzania (Tanzania Ministry of Agriculture, Livestock and Fisheries and ILRI, 2016).Agricultural Sample Survey 2018/19 Report on Livestock and Livestock Characteristics (Private Peasant Holdings) (Central Statistical Agency, 2019).2019/20 National Sample Census of Agriculture Main Report (Tanzania National Bureau of Statistics, 2022).Robinson, T. P. et al. Global Livestock Production Systems (FAO, 2011).Herrero, M. et al. Biomass use, production, feed efficiencies and greenhouse gas emissions from global livestock systems. Proc. Natl Acad. Sci. USA 110, 20888–20893 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Baseline Study of the Tanzania Dairy Value Chain (United Republic of Tanzania Ministry of Agriculture, Livestock and Fisheries, 2016).Mbwambo, N., Nandonde, S., Ndomba, C. & Desta, S. Assessment of Animal Feed Resources in Tanzania (Tanzania Ministry of Agriculture, Livestock and Fisheries and ILRI, 2016).Hartung, C., Lerer, A., Anokwa, Y., Tseng, C., Brunette, W., & Borriello, G. Open data kit: tools to build information services for developing regions. Proc. 4th ACM/IEEE International Conference on Information and Communication Technologies and Development (Association for Computing Machinery, 2010).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).https://www.r-project.orgRufino, M. C. et al. Lifetime productivity of dairy cows in smallholder farming systems of the central highlands of Kenya. Animal 3, 1044–1056 (2009).Article 
CAS 
PubMed 

Google Scholar 
Hawkins, J. et al. Feeding efficiency gains can increase the greenhouse gas mitigation potential of the Tanzanian dairy sector. Sci. Rep. 11, 4190 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Python Software Foundation (Python Software Foundation, 2019); https://www.python.org/psf/Kashoma, I. P. B. et al. Predicting body weight of Tanzania shorthorn zebu cattle using heart girth measurements. Livest. Res. Rural. Dev. 23, Table 1 (2011).Galukande, E. B., Mahadevan, P. & Black, J. G. Milk production in East African zebu cattle. Anim. Sci. 4, 329–336 (1962).Article 

Google Scholar 
Gillah, K. A., Kifaro, G. C. & Madsen, J. Effects of pre partum supplementation on milk yield, reproduction and milk quality of crossbred dairy cows raised in a peri urban farm of Morogoro town Tanzania. Livest. Res. Rural. Dev. 26 (2014).Njau, F. B. C., Lwelamira, J. & Hyandye, C. Ruminant livestock production and quality of pastures in the communal grazing land of semi-arid central Tanzania. Livest. Res. Rural. Dev. 8, Table 4 (2013).Mwambene, P. L. et al. Selecting indigenous cattle populations for improving dairy production in the Southern Highlands and Eastern Tanzania. Livest. Res. Rural. Dev. 26 (2014).Rege, J. E. O. et al. Cattle of Kenya: Uses, Performance, Farmer Preferences, Measures of Genetic Diversity and Options for Improved Use (International Livestock Research Institute, 2001).Beffa, L. M. Genotype × Environment Interaction in Afrikaner Cattle. PhD thesis, Univ. of the Free State (2005).Meaker, H. J., Coetsee, T. P. N. & Lishman, A. W. The effects of age at 1st calving on the productive and reproductive-performance of beef-cows. S. Afr. J. Anim. Sci. 10, 105–113 (1980).
Google Scholar 
Chenyambuga, S. W. & Mseleko, K. F. Reproductive and lactation performances of Ayrshire and Boran crossbred cattle kept in smallholder farms in Mufindi district, Tanzania. Livest. Res. Rural. Dev. 21, 100 (2009).
Google Scholar 
Ojango, J. M. K. et al. Dairy production systems and the adoption of genetic and breeding technologies in Tanzania, Kenya, India and Nicaragua. Anim. Genet. Resour. 59, 81–95 (2016).Article 

Google Scholar 
Feedipedia—Animal Feed Resources Information System (FAO, accessed 2021); https://www.feedipedia.org/Lukuyu, B. et al. (eds) Feeding Dairy Cattle in East Africa (East Africa Dairy Development Project, 2012).Rubanza, C. D. K. et al. Biomass production and nutritive potential of conserved forages in silvopastoral traditional fodder banks (Ngitiri) of Meatu District of Tanzania. Asian-Aust. J. Anim. Sci. 19, 978–983 (2006).Article 

Google Scholar 
Food Balances (2010-) (FAO, accessed September 29, 2021); http://www.fao.org/faostat/en/#data/FBSCrop Data for the United Republic of Tanzania (FAO, accessed September 22, 2021); http://www.fao.org/faost at/en/#data/QCGilbert, M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci. Data. 5, 180227 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
2014/15 Annual Agricultural Sample Survey Report (The United Republic of Tanzania, 2016).Basic Data for Livestock and Fisheries (The United Republic of Tanzania Ministry of Livestock and Fisheries, 2013).IPCC Guidelines for National Greenhouse Gas Inventories Vol. 4 Agriculture, Forestry and Other Land Use (IPCC, 2006).2019 Refinement to the IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2019).Fertilizers by Nutrient (FAO, accessed July 6, 2022); https://www.fao.org/faostat/en/#data/RFNHutton, M. O. et al. Toward a nitrogen footprint calculator for Tanzania. Env. Res. Lett. 12, 034016 (2017).Article 

Google Scholar 
Tanzania Fertilizer Assessment (International Fertilizer Development Center, 2012); http://tanzania.countrystat.org/fileadmin/user_upload/countrystat_fenix/congo/docs/Tanzania%20Fertilizer%20Assessment%202012.pdfA Common Carbon Footprint Approach for the Dairy Sector: The IDF Guide to Standard Life Cycle Methodology (International Dairy Federation, 2015); https://www.fil-idf.org/wp-content/uploads/2016/09/Bulletin479-2015_A-common-carbon-footprint-approach-for-the-dairy-sector.CAT.pdfBruzzone, L., Bovolo, F. & Arino, O. European Space Agency land cover climate change initiative. ESA LC CCI data: high resolution land cover data via Centre for Environmental Data Analysis; https://climate.esa.int/en/projects/high-resolution-land-cover/ (2021)Characteristics of Markets for Animal Feeds Raw Materials in the East African Community: Focus on Maize Bran and Sunflower Seed Cake (Kilimo Trust, 2017).Ngunga, D. & Mwendia, S. Forage Seed System in Tanzania: A Review Report (Alliance of Biodiversity and CIAT, 2020).Nkombe, B.M. Investigation of the Potential for Forage Species to Enhance the Sustainability of Degraded Rangeland and Cropland Soils. MSc thesis, Ohio State Univ. (2016).Producer Prices (FAO, accessed 2021); http://www.fao.org/faostat/en/#data/PP More