More stories

  • in

    American mastodon mitochondrial genomes suggest multiple dispersal events in response to Pleistocene climate oscillations

    1.
    Collins, M. et al. In Climate Change 2013—The Physical Science Basis (ed. Intergovernmental Panel on Climate Change) 1029–1136 (Cambridge University Press, Cambridge, 2013).
    2.
    Ackerly, D. D. et al. The geography of climate change: implications for conservation biogeography. Divers. Distrib. 16, 476–487 (2010).
    Google Scholar 

    3.
    Bradshaw, W. E. & Holzapfel, C. M. Evolutionary response to rapid climate change. Science 312, 1477–1478 (2006).
    CAS  PubMed  Google Scholar 

    4.
    Chu, C., Mandrak, N. E. & Minns, C. K. Potential impacts of climate change on the distributions of several common and rare freshwater fishes in Canada. Divers. Distrib. 11, 299–310 (2005).
    Google Scholar 

    5.
    Princé, K. & Zuckerberg, B. Climate change in our backyards: the reshuffling of North America’s winter bird communities. Glob. Change Biol. 21, 572–585 (2015).
    ADS  Google Scholar 

    6.
    Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671 (2016).
    PubMed  Google Scholar 

    7.
    Lisiecki, L. E. & Raymo, M. E. A Pliocene-Pleistocene stack of 57 globally distributed benthic δ 18 O records. Paleoceanography 20, PA1003 (2005).
    ADS  Google Scholar 

    8.
    Dyke, A. S. An outline of the deglaciation of North America with emphasis on central and northern Canada. Quat. Glaciat. Chronol. Part II 2b, 373–424 (2004).
    Google Scholar 

    9.
    Thompson, L. G. et al. Late glacial stage and Holocene tropical ice core records from Huascaran, Peru. Science 269, 46–50 (1995).
    ADS  CAS  Google Scholar 

    10.
    Johnsen, S. J. et al. Oxygen isotope and palaeotemperature records from six Greenland ice-core stations: camp century, dye-3, GRIP, GISP2, Renland and NorthGRIP. J. Quat. Sci. 16, 299–307 (2001).
    Google Scholar 

    11.
    Kawamura, K. et al. Northern Hemisphere forcing of climatic cycles in Antarctica over the past 360,000 years. Nature 448, 912–916 (2007).
    ADS  CAS  PubMed  Google Scholar 

    12.
    Dyke, A. S. Late quaternary vegetation history of Northern North America based on pollen, macrofossil, and faunal remains. Géogr. Phys. Quat. 59, 211–262 (2005).
    Google Scholar 

    13.
    Froese, D. et al. Fossil and genomic evidence constrains the timing of bison arrival in North America. Proc. Natl Acad. Sci. USA 114, 3457–3462 (2017).
    ADS  CAS  PubMed  Google Scholar 

    14.
    Palkopoulou, E. et al. Holarctic genetic structure and range dynamics in the woolly mammoth. Proc. R. Soc. B Biol. Sci. 280, 20131910 (2013).
    Google Scholar 

    15.
    Debruyne, R. et al. Out of America: ancient DNA evidence for a new world origin of late quaternary woolly mammoths. Curr. Biol. 18, 1320–1326 (2008).
    CAS  PubMed  Google Scholar 

    16.
    Shapiro, B. et al. Rise and fall of the Beringian Steppe Bison. Science 306, 1561–1565 (2004).
    ADS  CAS  PubMed  Google Scholar 

    17.
    Campos, P. F. et al. Ancient DNA analyses exclude humans as the driving force behind late Pleistocene musk ox (Ovibos moschatus) population dynamics. Proc. Natl Acad. Sci. USA 107, 5675–5680 (2010).
    ADS  CAS  PubMed  Google Scholar 

    18.
    Chang, D. et al. The evolutionary and phylogeographic history of woolly mammoths: a comprehensive mitogenomic analysis. Sci. Rep. 7, 44585 (2017).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    19.
    Heintzman, P. D. et al. Bison phylogeography constrains dispersal and viability of the ice free corridor in western Canada. Proc. Natl Acad. Sci. USA 113, 8057–8063 (2016).
    CAS  PubMed  Google Scholar 

    20.
    Zazula, G. D. et al. American mastodon extirpation in the Arctic and Subarctic predates human colonization and terminal Pleistocene climate change. Proc. Natl Acad. Sci. USA 2014, 6–11 (2014).
    Google Scholar 

    21.
    Zazula, G. D. et al. A case of early Wisconsinan “over-chill”: New radiocarbon evidence for early extirpation of western camel (Camelops hesternus) in eastern Beringia. Quat. Sci. Rev. 171, 48–57 (2017).
    ADS  Google Scholar 

    22.
    Saunders, J. J. et al. Paradigms and proboscideans in the southern Great Lakes region, USA. Quat. Int. 217, 175–187 (2010).
    Google Scholar 

    23.
    Oltz, D. F. & Kapp, R. O. Plant remains associated with Mastodon and Mammoth remains in central Michigan. Am. Midl. Nat. 70, 339–346 (1963).
    Google Scholar 

    24.
    Dreimanis, A. Extinction of Mastodons in Eastern North America: testing a new climatic-environmental hypothesis. Ohio J. Sci. 68, 257–272 (1968).
    Google Scholar 

    25.
    Shoshani, J. Understanding proboscidean evolution: a formidable task. Trends Ecol. Evol. 13, 480–487 (1998).
    CAS  PubMed  Google Scholar 

    26.
    Teale, C. L. & Miller, N. G. Mastodon herbivory in mid-latitude late-Pleistocene boreal forests of eastern North America. Quat. Res. 78, 72–81 (2012).
    Google Scholar 

    27.
    Green, J. L., DeSantis, L. R. G. & Smith, G. J. Regional variation in the browsing diet of Pleistocene Mammut americanum (Mammalia, Proboscidea) as recorded by dental microwear textures. Palaeogeogr. Palaeoclimatol. Palaeoecol. 487, 59–70 (2017).
    Google Scholar 

    28.
    Birks, H. H. et al. Evidence for the diet and habitat of two late Pleistocene mastodons from the Midwest, USA. Quat. Res. 91, 792–812 (2019).
    CAS  Google Scholar 

    29.
    Owen-Smith, N. Pleistocene extinctions: the pivotal role of megaherbivores. Paleobiology 13, 351–362 (1987).
    Google Scholar 

    30.
    Barnosky, A. D. et al. Variable impact of late-quaternary megafaunal extinction in causing ecological state shifts in North and South America. Proc. Natl Acad. Sci. USA 113, 856–861 (2016).
    ADS  CAS  PubMed  Google Scholar 

    31.
    Widga, C. et al. Late pleistocene proboscidean population dynamics in the North American midcontinent. Boreas 46, 772–782 (2017).
    Google Scholar 

    32.
    Godfrey-Smith, D., Grist, A. & Stea, R. Dosimetric and radiocarbon chronology of a pre-Wisconsinan mastodon fossil locality at East Milford, Nova Scotia, Canada. Quat. Sci. Rev. 22, 1353–1360 (2003).
    ADS  Google Scholar 

    33.
    Enk, J. et al. Mammuthus population dynamics in late pleistocene North America: divergence, phylogeogrpaphy and introgression. Front. Ecol. Evol. 4, 1–13 (2016).
    Google Scholar 

    34.
    Ishida, Y., Georgiadis, N. J., Hondo, T. & Roca, A. L. Triangulating the provenance of African elephants using mitochondrial DNA. Evol. Appl. 6, 253–265 (2013).
    CAS  PubMed  Google Scholar 

    35.
    Fernando, P., Pfrender, M. E., Encalada, S. E. & Lande, R. Mitochondrial DNA variation, phylogeography and population structure of the Asian elephant. Heredity 84, 362–372 (2000).
    CAS  PubMed  Google Scholar 

    36.
    Fisher, D. In The Proboscidea: Evolution and Paleoecology of Elephants andtheir Relatives (eds. Shoshani, J. & Tassy, P.) 296–315 (Oxford University Press, Oxford, 1996).

    37.
    Fisher, D. C. Paleobiology of pleistocene proboscideans. Annu. Rev. Earth Planet. Sci. https://doi.org/10.1146/annurev-earth-060115-012437 (2018).

    38.
    Rohland, N. et al. Genomic DNA sequences from mastodon and woolly mammoth reveal deep speciation of forest and savanna elephants. PLoS Biol. 8, e1000564 (2010).
    CAS  PubMed  PubMed Central  Google Scholar 

    39.
    Muhs, D. R., Ager, T. A. & Begét, J. E. Vegetation and paleoclimate of the last interglacial period, central Alaska. Quat. Sci. Rev. 20, 41–61 (2001).
    ADS  Google Scholar 

    40.
    Jass, C. N. & Barrón-Ortiz, C. I. A review of quaternary proboscideans from Alberta, Canada. Quat. Int. 443, 88–104 (2017).
    Google Scholar 

    41.
    Shapiro, B. et al. A Bayesian phylogenetic method to estimate unknown sequence ages. Mol. Biol. Evol. 28, 879–887 (2011).
    CAS  PubMed  Google Scholar 

    42.
    Drummond, A. J. & Stadler, T. Bayesian phylogenetic estimation of fossil ages. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150129 (2016).
    Google Scholar 

    43.
    Plint, T., Longstaffe, F. J. & Zazula, G. Giant beaver palaeoecology inferred from stable isotopes. Sci. Rep. 9, 7179 (2019).
    ADS  PubMed  PubMed Central  Google Scholar 

    44.
    Yalden, D. W. The history of British mammals 12–27 (T & A D Poyser Ltd, Berkhamsted, 1999).

    45.
    Schreve, D. C. A new record of Pleistocene hippopotamus from River Severn terrace deposits, Gloucester, UK—palaeoenvironmental setting and stratigraphical significance. Proc. Geol. Assoc. 120, 58–64 (2009).
    Google Scholar 

    46.
    Stoffel, C. et al. Genetic consequences of population expansions and contractions in the common hippopotamus (Hippopotamus amphibius) since the late Pleistocene. Mol. Ecol. 24, 2507–2520 (2015).
    PubMed  Google Scholar 

    47.
    Tape, K. D., Gustine, D. D., Ruess, R. W., Adams, L. G. & Clark, J. A. Range expansion of moose in Arctic Alaska linked to warming and increased shrub habitat. PLoS ONE 11, e0152636 (2016).
    PubMed  PubMed Central  Google Scholar 

    48.
    Tape, K. D., Jones, B. M., Arp, C. D., Nitze, I. & Grosse, G. Tundra be dammed: beaver colonization of the Arctic. Glob. Change Biol. 24, 4478–4488 (2018).
    ADS  Google Scholar 

    49.
    Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl Acad. Sci. USA 110, 15758–15763 (2013).
    ADS  CAS  PubMed  Google Scholar 

    50.
    Glocke, I. & Meyer, M. Extending the spectrum of DNA sequences retrieved from ancient bones and teeth. Genome Res. 27, 1–8 (2017).
    Google Scholar 

    51.
    Kircher, M., Sawyer, S. & Meyer, M. Double indexing overcomes inaccuracies in multiplex sequencing on the Illumina platform. Nucleic Acids Res. 40, 1–8 (2012).
    Google Scholar 

    52.
    Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc. 2010, 1–10 (2010).
    Google Scholar 

    53.
    Gansauge, M.-T. & Meyer, M. Single-stranded DNA library preparation for the sequencing of ancient or damaged DNA. Nat. Protoc. 8, 737–748 (2013).
    PubMed  Google Scholar 

    54.
    Gansauge, M.-T. et al. Single-stranded DNA library preparation from highly degraded DNA using T4 DNA ligase. Nucleic Acids Res. 45, 1–10 (2017).
    Google Scholar 

    55.
    Renaud, G., Stenzel, U. & Kelso, J. leeHom: adaptor trimming and merging for Illumina sequencing reads. Nucleic Acids Res. https://doi.org/10.1093/nar/gku699 (2014).

    56.
    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
    CAS  PubMed  PubMed Central  Google Scholar 

    57.
    Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).
    CAS  PubMed  PubMed Central  Google Scholar 

    58.
    Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods 9, 772–772 (2012).
    CAS  PubMed  PubMed Central  Google Scholar 

    59.
    Nguyen, L. T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).
    CAS  PubMed  PubMed Central  Google Scholar 

    60.
    Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).
    CAS  PubMed  PubMed Central  Google Scholar 

    61.
    Baele, G., Lemey, P. & Suchard, M. A. Genealogical working distributions for Bayesian model testing with phylogenetic uncertainty. Syst. Biol. 65, 250–264 (2016).
    PubMed  Google Scholar 

    62.
    Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).
    PubMed  PubMed Central  Google Scholar 

    63.
    Stuiver, M. & Reimer, P. J. Extended 14C database and revised CALIB radiocarbon calibration program. Radiocarbon 35, 215–230 (1993).
    Google Scholar 

    64.
    Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).
    CAS  Google Scholar 

    65.
    Colleoni, F., Wekerle, C., Näslund, J.-O., Brandefelt, J. & Masina, S. Constraint on the penultimate glacial maximum Northern Hemisphere ice topography (≈140 kyrs BP). Quat. Sci. Rev. 137, 97–112 (2016).
    ADS  Google Scholar  More

  • in

    Host age is not a consistent predictor of microbial diversity in the coral Porites lutea

    1.
    Pootakham, W. et al. Dynamics of coral-associated microbiomes during a thermal bleaching event. Microbiologyopen 7, e00604 (2018).
    PubMed  PubMed Central  Google Scholar 
    2.
    Krediet, C. J., Ritchie, K. B., Paul Valerie, J. & Max, T. Coral-associated micro-organisms and their roles in promoting coral health and thwarting diseases. Proc. R. Soc. B Biol. Sci. 280, 20122328 (2013).
    Google Scholar 

    3.
    Ziegler, M., Seneca, F. O., Yum, L. K., Palumbi, S. R. & Voolstra, C. R. Bacterial community dynamics are linked to patterns of coral heat tolerance. Nat. Commun. 8, 14213 (2017).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    4.
    Rädecker, N., Pogoreutz, C., Voolstra, C. R., Wiedenmann, J. & Wild, C. Nitrogen cycling in corals: The key to understanding holobiont functioning?. Trends Microbiol. 23, 490–497 (2015).
    PubMed  Google Scholar 

    5.
    Ritchie, K. B. & Smith, G. W. Microbial communities of coral surface mucopolysaccharide layers. In Coral Health and Disease (eds Rosenberg, E. & Loya, Y.) 259–264 (Springer, Berlin Heidelberg, 2004).
    Google Scholar 

    6.
    Holm, J. B. & Heidelberg, K. B. Microbiomes of Muricea californica and M. fruticosa: Comparative analyses of two co-occurring eastern pacific octocorals. Front. Microbiol. 7, 917 (2016).
    PubMed  PubMed Central  Google Scholar 

    7.
    Sweet, M. J., Brown, B. E., Dunne, R. P., Singleton, I. & Bulling, M. Evidence for rapid, tide-related shifts in the microbiome of the coral Coelastrea aspera. Coral Reefs 36, 815–828 (2017).
    ADS  Google Scholar 

    8.
    Ziegler, M. et al. Coral microbial community dynamics in response to anthropogenic impacts near a major city in the central Red Sea. Mar. Pollut. Bull. 105, 629–640 (2016).
    CAS  PubMed  Google Scholar 

    9.
    Archer, S. D. J. et al. Air mass source determines airborne microbial diversity at the ocean–atmosphere interface of the Great Barrier Reef marine ecosystem. ISME J. https://doi.org/10.1038/s41396-019-0555-0 (2019).
    Article  PubMed  PubMed Central  Google Scholar 

    10.
    Wainwright, B. J., Afiq-Rosli, L., Zahn, G. L. & Huang, D. Characterisation of coral-associated bacterial communities in an urbanised marine environment shows strong divergence over small geographic scales. Coral Reefs https://doi.org/10.1007/s00338-019-01837-1 (2019).
    Article  Google Scholar 

    11.
    Chu, N. D. & Vollmer, S. V. Caribbean corals house shared and host-specific microbial symbionts over time and space. Environ. Microbiol. Rep. 8, 493–500 (2016).
    CAS  PubMed  Google Scholar 

    12.
    Wainwright, B. J., Bauman, A. G., Zahn, G. L., Todd, P. A. & Huang, D. Characterization of fungal biodiversity and communities associated with the reef macroalga Sargassum ilicifolium reveals fungal community differentiation according to geographic locality and algal structure. Mar. Biodivers. https://doi.org/10.1007/s12526-019-00992-6 (2019).
    Article  Google Scholar 

    13.
    Wainwright, B. J., Zahn, G. L., Arlyza, I. S. & Amend, A. S. Seagrass-associated fungal communities follow Wallace’s line, but host genotype does not structure fungal community. J. Biogeogr. 45, 762–770 (2018).
    Google Scholar 

    14.
    Hernandez-Agreda, A., Leggat, W., Bongaerts, P., Herrera, C. & Ainsworth, T. D. Rethinking the coral microbiome: Simplicity exists within a diverse microbial biosphere. mBio 9, e00812 (2018).
    PubMed  PubMed Central  Google Scholar 

    15.
    Williams, A. D., Brown, B. E., Putchim, L. & Sweet, M. J. Age-related shifts in bacterial diversity in a reef coral. PLoS ONE 10, e0144902 (2015).
    PubMed  PubMed Central  Google Scholar 

    16.
    Pollock, F. J. et al. Coral-associated bacteria demonstrate phylosymbiosis and cophylogeny. Nat. Commun. 9, 4921 (2018).
    ADS  PubMed  PubMed Central  Google Scholar 

    17.
    Epstein, H. E., Torda, G., Munday, P. L. & van Oppen, M. J. H. Parental and early life stage environments drive establishment of bacterial and dinoflagellate communities in a common coral. ISME J. 13, 1635–1638 (2019).
    CAS  PubMed  PubMed Central  Google Scholar 

    18.
    Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    19.
    van Dongen, W. F. et al. Age-related differences in the cloacal microbiota of a wild bird species. BMC Ecol. 13, 11 (2013).
    PubMed  PubMed Central  Google Scholar 

    20.
    Huang, D. et al. Extraordinary diversity of reef corals in the South China Sea. Mar. Biodivers. 45, 157–168 (2015).
    Google Scholar 

    21.
    Toda, T. et al. Community structures of coral reefs around Peninsular Malaysia. J. Oceanogr. 63, 113–123 (2007).
    Google Scholar 

    22.
    Tanzil, J. T. I. et al. Regional decline in growth rates of massive Porites corals in Southeast Asia. Glob. Change Biol. 19, 3011–3023 (2013).
    ADS  Google Scholar 

    23.
    Tanzil, J. T. I. et al. Luminescence and density banding patterns in massive Porites corals around the Thai-Malay Peninsula, Southeast Asia. Limnol. Oceanogr. 61, 2003–2026 (2016).
    ADS  Google Scholar 

    24.
    Pootakham, W. et al. High resolution profiling of coral-associated bacterial communities using full-length 16S rRNA sequence data from PacBio SMRT sequencing system. Sci. Rep. 7, 2774 (2017).
    ADS  PubMed  PubMed Central  Google Scholar 

    25.
    Øvreås, L., Daae, F. L., Torsvik, V. & Rodríguez-Valera, F. Characterization of microbial diversity in hypersaline environments by melting profiles and reassociation kinetics in combination with terminal restriction fragment length polymorphism (T-RFLP). Microb. Ecol. 46, 291–301 (2003).
    PubMed  Google Scholar 

    26.
    Baker, B. J. & Banfield, J. F. Microbial communities in acid mine drainage. FEMS Microbiol. Ecol. 44, 139–152 (2003).
    CAS  PubMed  Google Scholar 

    27.
    Li, S.-J. et al. Microbial communities evolve faster in extreme environments. Sci. Rep. 4, 6205 (2014).
    CAS  PubMed  PubMed Central  Google Scholar 

    28.
    Peter, J. et al. A microbial signature of psychological distress in irritable bowel syndrome. Psychosom. Med. 80, 698–709 (2018).
    PubMed  PubMed Central  Google Scholar 

    29.
    Karl, J. P. et al. Effects of psychological, environmental and physical stressors on the gut microbiota. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.02013 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    30.
    Guest, J. R. et al. 27 years of benthic and coral community dynamics on turbid, highly urbanised reefs off Singapore. Sci. Rep. 6, 36260 (2016).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    31.
    Wong, J. S. Y. et al. Comparing patterns of taxonomic, functional and phylogenetic diversity in reef coral communities. Coral Reefs 37, 737–750 (2018).
    ADS  MathSciNet  Google Scholar 

    32.
    Chow, G. S. E., Chan, Y. K. S., Jain, S. S. & Huang, D. Light limitation selects for depth generalists in urbanised reef coral communities. Mar. Environ. Res. 147, 101–112 (2019).
    CAS  PubMed  Google Scholar 

    33.
    Calvani, R. et al. Of microbes and minds: A narrative review on the second brain aging. Front. Med. (Lausanne) https://doi.org/10.3389/fmed.2018.00053 (2018).
    Article  Google Scholar 

    34.
    Nagpal, R. et al. Gut microbiome and aging: Physiological and mechanistic insights. Nutr Healthy Aging 4, 267–285 (2018).
    PubMed  PubMed Central  Google Scholar 

    35.
    Choi, J., Hur, T.-Y. & Hong, Y. Influence of altered gut microbiota composition on aging and aging-related diseases. J. Lifestyle Med. 8, 1–7 (2018).
    PubMed  PubMed Central  Google Scholar 

    36.
    Soong, K., Chen, C. A. & Chang, J.-C. A very large poritid colony at Green Island, Taiwan. Coral Reefs 18, 42–42 (1999).
    Google Scholar 

    37.
    Goodkin, N. et al. Coral communities of Hong Kong: Long-lived corals in a marginal reef environment. Mar. Ecol. Prog. Ser. 426, 185–196 (2011).
    ADS  Google Scholar 

    38.
    Bythell, J. C., Brown, B. E. & Kirkwood, T. B. L. Do reef corals age?. Biol. Rev. 93, 1192–1202 (2018).
    PubMed  Google Scholar 

    39.
    Lee, N. L. Y., Huang, D., Quek, Z. B. R., Lee, J. N. & Wainwright, B. J. Mangrove-associated fungal communities are differentiated by geographic location and host structure. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.02456 (2019).
    Article  PubMed  PubMed Central  Google Scholar 

    40.
    Wainwright, B. J. et al. Seagrass-associated fungal communities show distance decay of similarity that has implications for seagrass management and restoration. Ecol. Evol. 9, 11288–11297 (2019).
    PubMed  PubMed Central  Google Scholar 

    41.
    Röthig, T., Ochsenkühn, M. A., Roik, A., van der Merwe, R. & Voolstra, C. R. Long-term salinity tolerance is accompanied by major restructuring of the coral bacterial microbiome. Mol. Ecol. 25, 1308–1323 (2016).
    PubMed  PubMed Central  Google Scholar 

    42.
    Sin, T. M. et al. The urban marine environment of Singapore. Region. Stud. Mar. Sci. 8, 331–339 (2016).
    Google Scholar 

    43.
    Chénard, C. et al. Temporal and spatial dynamics of bacteria, Archaea and protists in equatorial coastal waters. Sci. Rep. 9, 1–13 (2019).
    Google Scholar 

    44.
    Ford, A. K. et al. Reefs under Siege—The rise, putative drivers, and consequences of benthic cyanobacterial mats. Front. Mar. Sci. https://doi.org/10.3389/fmars.2018.00018 (2018).
    Article  Google Scholar 

    45.
    Charpy, L., Casareto, B. E., Langlade, M. J. & Suzuki, Y. Cyanobacteria in coral reef ecosystems: A review. J. Mar. Biol. 2012, 1–9 (2012).
    Google Scholar 

    46.
    Huang, D., Tun, K., Chou, L. M. & Todd, P. A. An inventory of zooxanthellate scleractinian corals in Singapore, including 33 new records. Raffles Bull. Zool. Suppl. 22, 69 (2009).
    CAS  Google Scholar 

    47.
    Todd, P. A. et al. Towards an urban marine ecology: Characterizing the drivers, patterns and processes of marine ecosystems in coastal cities. Oikos https://doi.org/10.1111/oik.05946 (2019).
    Article  Google Scholar 

    48.
    Rubin, B. E. R. et al. Investigating the impact of storage conditions on microbial community composition in soil samples. PLoS ONE 8, e70460 (2013).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    49.
    Lauber, C. L., Zhou, N., Gordon, J. I., Knight, R. & Fierer, N. Effect of storage conditions on the assessment of bacterial community structure in soil and human-associated samples: Influence of short-term storage conditions on microbiota. FEMS Microbiol. Lett. 307, 80–86 (2010).
    CAS  PubMed  PubMed Central  Google Scholar 

    50.
    Carruthers, L. V. et al. The impact of storage conditions on human stool 16S rRNA microbiome composition and diversity. PeerJ 7, e8133 (2019).
    PubMed  PubMed Central  Google Scholar 

    51.
    Veron, J. Corals of the World (Australian Institute of Marine Science, Townsville, 2000).
    Google Scholar 

    52.
    Forsman, Z., Wellington, G. M., Fox, G. E. & Toonen, R. J. Clues to unraveling the coral species problem: Distinguishing species from geographic variation in Porites across the Pacific with molecular markers and microskeletal traits. PeerJ 3, e751 (2015).
    PubMed  PubMed Central  Google Scholar 

    53.
    Forsman, Z. H., Barshis, D. J., Hunter, C. L. & Toonen, R. J. Shape-shifting corals: Molecular markers show morphology is evolutionarily plastic in Porites. BMC Evol. Biol. 9, 45 (2009).
    PubMed  PubMed Central  Google Scholar 

    54.
    Terraneo, T. I. et al. Environmental latitudinal gradients and host-specificity shape Symbiodiniaceae distribution in Red Sea Porites corals. J. Biogeogr. https://doi.org/10.1111/jbi.13672 (2019).
    Article  Google Scholar 

    55.
    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. PNAS 108, 4516–4522 (2011).
    ADS  CAS  PubMed  Google Scholar 

    56.
    Lundberg, D. S., Yourstone, S., Mieczkowski, P., Jones, C. D. & Dangl, J. L. Practical innovations for high-throughput amplicon sequencing. Nat. Methods 10, 999–1002 (2013).
    CAS  PubMed  Google Scholar 

    57.
    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).
    Google Scholar 

    58.
    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 

    59.
    Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226 (2018).
    PubMed  PubMed Central  Google Scholar 

    60.
    Cole, J. R. et al. The ribosomal database project (RDP-II): Introducing myRDP space and quality controlled public data. Nucleic Acids Res. 35, D169–D172 (2007).
    CAS  PubMed  Google Scholar 

    61.
    Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).
    CAS  PubMed  PubMed Central  Google Scholar 

    62.
    Oksanen, J. et al. vegan: Community Ecology Package (2019).

    63.
    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 

    64.
    Martin, B. D., Witten, D. & Willis, A. D. Modeling microbial abundances and dysbiosis with beta-binomial regression. Ann. Appl. Stat. 14, 94–115 (2020).
    MathSciNet  MATH  Google Scholar  More

  • in

    Robotic environmental DNA bio-surveillance of freshwater health

    ESP sample processing
    The ESP operated autonomously, needing only power, communications and fluid connections through its waterproof pressure housing (Fig. 1). Prior to sample initiation, the ESP was purged completely with nitrogen to reduce oxidative reactions (i.e., DNA degradation) from occurring. At the initiation of sampling, a puck (Fig. 1A cutout) loaded with filter material was placed within a clamp. Valves open to the outside allowed a syringe to sequentially pull water through the puck. Once the target volume was filtered, or the filter was loaded with biomass (i.e., ‘clogged’), filtering stopped and excess water was cleared. Five mL’s of RNAlater preservative was then added to the puck, soaking the filter for 10 min before the excess was evacuated and the puck was returned to storage. Preserved pucks were stored at the ESP temperature, which were similar to ambient air temperatures. The upper limit on the amount of time that an ESP device can operate in the field before DNA quality on a puck is comprised is not known but is at least 21 days10. A constant humidity kept the pucks moist, allowing for easy filter removal once the instrument was recovered.
    Figure 1

    The ESP is an electro-mechanical robot that can autonomously filter and preserve samples. (A) About the size of a 50-gal barrel, the ESP carries 132 ‘pucks’ (inset), each designed to hold 25 mm filters. (B) The ESP installed in a USGS streamgage station. (C) Streamgage station showing tubing run (white pipe) that contained pump and tubing to deliver stream water to the ESP. The ESP communicated via cell phone, and was powered during the deployment via either line power or portable solar arrays. Photo credits: U.S. Geological Survey.

    Full size image

    To get water to the ESP, we designed an external sampling module from which the ESP drew water11. The sampling module was self-draining, and fed by a submersible pump (WSP-12 V-2 M, Waterra USA Inc., Bellingham, W, USA) installed approximately 0.5 to 2 m below the river water line at each deployment site. To reduce possible carry-over contamination, the sampling pumps, tubing and external sampling modules were flushed with river water for 10 min prior to every sample collection. The sampling port of the ESP itself was cleaned with 10% bleach and a 10% tween-20 solution between samples. At the end of each ESP deployment, pucks were manually removed and filters were aseptically recovered into 2.0 mL screw cap centrifuge tubes and stored at − 80 ºC until molecular analyses were performed.
    Field deployments
    We performed initial ESP feasibility studies in Yellowstone National Park (USA; Fig. 2) in September 2017. Here, our goal was to determine if the ESP could be used to sample DNA of the waterborne protozoa, Naegleria spp., from a freshwater river where these organisms had previously been detected using standard techniques12. We filled 60-L sterilized carboys with water from the confluence of the Boiling and Gardner rivers. Carboys were transported to a lab at Montana State University (Bozeman, Montana) and connected to ESP samplers via tubing and syringe pumps. Water was passed through each filter (5-µm Diapore filters) until the filter became clogged; six samples were filtered.
    Figure 2

    Map of ESP water sampling locations. The inset map shows the location of the Upper Yellowstone River and Upper Snake River in the United States. The larger map shows the sample site locations (filled red circles) on each river relative to Yellowstone National Park and Grand Teton National Park (outlined in green).

    Full size image

    We then integrated the ESPs into two USGS streamgages on the Yellowstone River in 2018 and one USGS streamgage on the Snake River in 2019, (Fig. 1B,C) where we tested for DNA of the fish pathogen, Tetracapsuloides bryosalmonae, the causative agent of salmonid fish Proliferative Kidney Disease (PKD). On the Yellowstone River, we installed ESPs at the streamgage near the upstream and downstream extents of a recent PKD outbreak13, USGS 06191500 Yellowstone River at Corwin Springs MT and USGS 06192500 Yellowstone River near Livingston MT, described below as Corwin Springs and Carters Bridge, respectively (Fig. 2). On the Snake River, we installed one ESP at the streamgage 1.5 km downstream of Palisades Reservoir near the upstream extent of a recent PKD outbreak, USGS 13032500 Snake River near Irwin ID. The ESP pucks were loaded with 1.2-µm cellulose nitrate filters. We ran two negative controls (1 L of molecular grade water) through the ESP prior to and at the conclusion of deployment to assess for contamination.
    Yellowstone River
    The ESPs were programmed to collect 1-L samples every 12 h, from Jul 24 to Aug 26 2018, and every 3 h from Aug 27 to Sep 7 2018. The average (± 1 SE) volume filtered per sample was 639 (± 11) mL, indicating that most filters clogged prior to reaching the 1-L target volume. Filter samples were collected at ambient air temperatures ranging from 9.6 to 35.8 °C ((overline{x})  = 18.9) at Carter’s Bridge and 8.3–29.0 °C ((overline{x})  = 17.1) at Corwin Springs. We compared T. bryosalmonae ESP detections to those from manually collected grab samples from shore (6, 250-mL samples per site filtered through 1.2-µm cellulose nitrate filters) collected at weekly frequencies for the entire length of the ESP deployments and at daily frequencies between Aug 27 and Aug 30. Thus, ESP and manual eDNA samples collected at different temporal intervals (3 h, 12 h or weekly) allowed us to evaluate the added value of higher frequency sampling.
    We also evaluated the utility of automated high frequency sampling to detect a new invasion by introducing novel DNA of Scomber japonicas (mackerel fish) 100 m upstream of each Yellowstone River streamgage. On Aug 27, we introduced 3 kg of canned S. japonicas 100 m upstream of the water sampling inlet for each ESP. S. japonicas was blended with water, frozen and then placed within metal-wire minnow traps and anchored to the river’s bottom with cement pavers. The ESPs were programmed to sample every 3 h from Aug 27 to Sep 7. Manual grab samples (600 mL) were collected 10 m (n = 3), 100 m (n = 6), and 400 m (n = 3) downstream of the S. japonicas in order to test that S. japonicas DNA was transported downstream past the water sampling inlet of each ESP. Manual grab samples were collected immediately prior to S. japonicas introductions, 3 h post-introduction and then every 24 h for 3 days.
    Snake River
    The ESPs were programmed to collect 2-L samples every 12 h from Jul 17 to Sep 09 and then every 4 h from Sep 10 to Oct 1, 2019. Manually collected grab samples (three, 2-L samples filtered through 1.5-µm glass fiber filters) and negative field controls (1, 2-L sample of deionized water filtered through 1.5-µm glass fiber filters) were collected every 2 weeks following methods in Sepulveda et al.7. Filter samples were collected at ambient air temperatures ranging from 3.9 to 30.2 °C ((overline{x})  = 20.6). To broaden our taxonomic assessment, we tested these samples for T. bryosalmonae DNA, and also for kokanee salmon (Oncorhynchus nerka) and dreissenid mussel (Dreissena spp.) DNA. O. nerka only occur upstream in Palisades Reservoir and at such low abundances that they are not captured by resource managers in annual population surveys7. Dreissenid mussels have not yet been observed, but are the principal focus of aquatic invasive species monitoring programs in this region7.
    Molecular analyses
    Filters were removed from the pucks and then shipped frozen to the USGS Upper Midwest Environmental Science Center (LaCrosse, Wisconsin) for DNA extraction and quantitative PCR analyses. Filters were handled and stored in a dedicated room that is physically separated from rooms where high-quantity DNA extraction and PCR product or high-quality DNA is handled. We used the FastDNA SPIN kit for soil to extract DNA on samples from the Boiling River-Gardiner River confluence, following modifications described in Barnhart et al.14. To extract DNA from Yellowstone River and Snake River samples, we used the Investigator Lyse & Spin Basket Kit (Qiagen, Hilden, Germany) in concert with the gMax Mini genomic DNA kit (IBI Scientific), following manufacturer’s instructions, and eluted in 200 µL of buffer. Samples were extracted as site specific batches and one extraction control was collected per batch. We used previously published assays, limits of detection and methods therein for analyses of Naegleria spp.12, T. bryosalmonae13, S. japonicas15, O. nerka7, and Dreissena spp.16 (Table 1).
    Table 1 Primers and probes used in this study.
    Full size table

    We analyzed all samples in four replicate 25 µL reactions containing 2 µL of template DNA, 1 × Perfecta Toughmix (Quantabio), 400 nM forward and reverse primers, and 100 nM probe. Each plate contained 10 no-template PCR controls (one for each sample) using 2 µL of molecular grade water as the template as well as a standard curve with two replicates of 20,000 and 2,000 copy standards and four replicates of 200 and 20 copy standards. The standards were prepared with synthetic gBlocks (Integrated DNA Technologies) containing the amplicon sequences for each assay. Each sample was also analyzed in three replicates with 200 copies of synthetic gBlock spiked in to check for PCR inhibition. Any sample that indicated less than an average of 60 to 70 copies of targeted DNA in these triplicate samples was considered inhibited. Field and extraction negative controls were analyzed as regular samples. No negative controls amplified.
    Analyses
    Samples were scored as positive when one or more PCR replicates amplified for the target DNA. We used McNemar’s Exact Test to compare binary qPCR data (detection/non-detection) of T. bryosalmonae and O. nerka DNA between ESP and manually collected samples in the Yellowstone and Snake rivers. More

  • in

    Hunting strategies to increase detection of chronic wasting disease in cervids

    1.
    Wasserberg, G., Osnas, E. E., Rolley, R. E. & Samuel, M. D. Host culling as an adaptive management tool for chronic wasting disease in white-tailed deer: a modelling study. J. Appl. Ecol. 46, 457–466 (2009).
    PubMed  Google Scholar 
    2.
    Heberlein, T. A. “Fire in the Sistine Chapel”: How Wisconsin responded to chronic wasting disease. Hum. Dimens Wildl. 9, 165–179 (2004).
    Google Scholar 

    3.
    Donnelly, C. A. & Woodroffe, R. Badger-cull targets unlikely to reduce TB. Nature 526, 640 (2015).
    ADS  CAS  PubMed  Google Scholar 

    4.
    Turner, W. C. et al. Fatal attraction: vegetation responses to nutrient inputs attract herbivores to infectious anthrax carcass sites. Proc. R. Soc. Lond. Ser. B 281, https://doi.org/10.1098/rspb.2014.1785 (2014).

    5.
    Uehlinger, F. D., Johnston, A. C., Bollinger, T. K. & Waldner, C. L. Systematic review of management strategies to control chronic wasting disease in wild deer populations in North America. BMC Vet. Res. 12, 1–16 (2016).
    Google Scholar 

    6.
    Tildesley, M. J., Bessell, P. R., Keeling, M. J. & Woolhouse, M. E. J. The role of pre-emptive culling in the control of foot-and-mouth disease. Proc. R. Soc. Lond. Ser. B 276, 3239 (2009).
    Google Scholar 

    7.
    te Beest, D. E., Hagenaars, T. J., Stegeman, J. A., Koopmans, M. P. & van Boven, M. Risk based culling for highly infectious diseases of livestock. Vet. Res. 42, 81 (2011).
    Google Scholar 

    8.
    Benestad, S. L., Mitchell, G., Simmons, M., Ytrehus, B. & Vikøren, T. First case of chronic wasting disease in Europe in a Norwegian free-ranging reindeer. Vet. Res. 47, 88 (2016).
    PubMed  PubMed Central  Google Scholar 

    9.
    Haley, N. J. & Hoover, E. A. Chronic wasting disease of cervids: current knowledge and future perspectives. Annu. Rev. Anim. Biosci. 3, 305–325 (2015).
    CAS  PubMed  Google Scholar 

    10.
    USGS. Expanding Distribution of Chronic Wasting Disease https://www.usgs.gov/centers/nwhc/science/expanding-distribution-chronic-wasting-disease?qt-science_center_objects=0#qt-science_center_objects (USGS, 2019).

    11.
    Edmunds, D. R. et al. Chronic wasting disease drives population decline of white-tailed deer. PLoS ONE 11, e0161127 (2016).
    PubMed  PubMed Central  Google Scholar 

    12.
    DeVivo, M. T. et al. Endemic chronic wasting disease causes mule deer population decline in Wyoming. PLoS ONE 12, e0186512 (2017).
    PubMed  PubMed Central  Google Scholar 

    13.
    Mysterud, A. & Rolandsen, C. M. A reindeer cull to prevent chronic wasting disease in Europe. Nat. Ecol. Evol. 2, 1343–1345 (2018).
    PubMed  Google Scholar 

    14.
    V. K. M. Ytrehus, et al. Factors that can Contribute to Spread of CWD—An Update on the Situation in Nordfjella, Norway (Opinion of the Panel on biological hazards. Norwegian Scientific Committee for Food and Environment (VKM), Oslo, Norway, 2018).

    15.
    Vors, L. S. & Boyce, M. S. Global declines of caribou and reindeer. Glob. Change Biol. 15, 2626–2633 (2009).
    ADS  Google Scholar 

    16.
    Diefenbach, D. R., Rosenberry, C. S. & Boyd, R. C. From the field: efficacy of detecting chronic wasting disease via sampling hunter-killed white-tailed deer. Wildl. Soc. Bull. 32, 267–272 (2004).
    Google Scholar 

    17.
    Rees, E. E. et al. Targeting the detection of chronic wasting disease using the hunter harvest during early phases of an outbreak in Saskatchewan, Canada. Prev. Vet. Med. 104, 149–159 (2012).
    PubMed  Google Scholar 

    18.
    Belsare, A. V. et al. An agent-based framework for improving wildlife disease surveillance: a case study of chronic wasting disease in Missouri white-tailed deer. Ecol. Model. 417, 108919 (2020).
    Google Scholar 

    19.
    Walsh, D. P. & Miller, M. W. A weighted surveillance approach for detecting chronic wasting disease foci. J. Wildl. Dis. 46, 118–135 (2010).
    PubMed  Google Scholar 

    20.
    Heisey, D. M. et al. Linking process to pattern: estimating spatiotemporal dynamics of a wildlife epidemic from cross-sectional data. Ecol. Monogr. 80, 221–240 (2010).
    Google Scholar 

    21.
    Miller, M. W. & Conner, M. M. Epidemiology of chronic wasting disease in free-ranging mule deer: Spatial, temporal, and demographic influences on observed prevalence patterns. J. Wildl. Dis. 41, 275–290 (2005).
    PubMed  Google Scholar 

    22.
    Samuel, M. D. & Storm, D. J. Chronic wasting disease in white-tailed deer: infection, mortality, and implications for heterogeneous transmission. Ecol. 97, 3195–3205 (2016).
    Google Scholar 

    23.
    Mysterud, A., Coulson, T. & Stenseth, N. C. The role of males in the population dynamics of ungulates. J. Anim. Ecol. 71, 907–915 (2002).
    Google Scholar 

    24.
    Ginsberg, J. R. & Milner-Gulland, E. J. Sex biased harvesting and population dynamics in ungulates: implications for conservation and sustainable use. Cons. Biol. 8, 157–166 (1994).
    Google Scholar 

    25.
    Milner-Gulland, E. J., Coulson, T. N. & Clutton-Brock, T. H. On harvesting a structured ungulate population. Oikos 88, 592–602 (2000).
    Google Scholar 

    26.
    Stärk, K. D. C. et al. Concepts for risk-based surveillance in the field of veterinary medicine and veterinary public health: review of current approaches. BMC Health Serv. Res. 6, 20 (2006).
    PubMed  PubMed Central  Google Scholar 

    27.
    Martin, P. A., Cameron, A. R. & Greiner, M. Demonstrating freedom from disease using multiple complex data sources. Prev. Vet. Med. 79, 71–97 (2007).
    CAS  PubMed  Google Scholar 

    28.
    Cannon, R. M. Demonstrating disease freedom-combining confidence levels. Prev. Vet. Med. 52, 227–249 (2002).
    CAS  PubMed  Google Scholar 

    29.
    Sutherland, W. J. et al. A 2018 horizon scan of emerging Issues for global conservation and biological diversity. Trends Ecol. Evol. 33, 47–58 (2018).
    PubMed  Google Scholar 

    30.
    EFSA Panel on Biological Hazards (BIOHAZ), Ricci, A. et al. Chronic wasting disease (CWD) in cervids. EFSA J. 15, 4667 (2016).
    Google Scholar 

    31.
    Vicente, J. et al. Science-based wildlife disease response. Science 364, 943 (2019).
    ADS  PubMed  Google Scholar 

    32.
    Schalk, G. & Forbes, M. R. Male biases in parasitism of mammals: effects of study type, host age, and parasite taxon. Oikos 78, 67–74 (1997).
    Google Scholar 

    33.
    Córdoba-Aguilar, A. & Munguía-Steyer, R. The sicker sex: understanding male biases in parasitic infection, resource allocation and fitness. Plos One 8, e76246 (2013).
    ADS  PubMed  PubMed Central  Google Scholar 

    34.
    Milner-Gulland, E. J. et al. Reproductive collapse in saiga antelope harems. Nature 422, 135 (2003).
    ADS  CAS  PubMed  Google Scholar 

    35.
    Sargeant, G. A., Weber, D. C. & Roddy, D. E. Implications of chronic wasting disease, cougar predation, and reduced recruitment for elk management. J. Wildl. Manag. 75, 171–177 (2011).
    Google Scholar 

    36.
    Monello, R. J. et al. Survival and population growth of a free-ranging elk population with a long history of exposure to Chronic wasting disease. J. Wildl. Manag. 78, 214–223 (2014).
    Google Scholar 

    37.
    Argue, C. K., Ribble, C., Lees, V. W., McLane, J. & Balachandran, A. Epidemiology of an outbreak of chronic wasting disease on elk farms in Saskatchewan. Can. Vet. J. 48, 1241–1248 (2007).
    PubMed  PubMed Central  Google Scholar 

    38.
    Delahay, R. J. Smith, G. C. & Hutchings, M. R. Management of Disease in Wild Mammals (Springer, Tokyo, Japan, 2009).

    39.
    Almberg, E. S., Cross, P. C., Johnson, C. J., Heisey, D. M. & Richards, B. J. Modeling routes of chronic wasting disease transmission: environmental prion persistence promotes deer population decline and extinction. PLoS ONE 6, e19896 (2011).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    40.
    Keeling, M. J. The effects of local spatial structure on epidemiological invasions. Proc. R. Soc. Lond. Ser. B 266, 859–867 (1999).
    CAS  Google Scholar 

    41.
    Joly, D. O., Samuel, M. D., Langenberg, J. A., Rolley, R. E. & Keane, D. P. Surveillance to detect chronic wasting disease in white-tailed deer in Wisconsin. J. Wildl. Dis. 45, 989–997 (2009).
    PubMed  Google Scholar 

    42.
    Nusser, S. M., Clark, W. R., Otis, D. L. & Huang, L. Sampling considerations for disease surveillance in wildlife populations. J. Wildl. Manag. 72, 52–60 (2008).
    Google Scholar 

    43.
    Osnas, E. E., Heisey, D. M., Rolley, R. E. & Samuel, M. D. Spatial and temporal patterns of chronic wasting disease: fine-scale mapping of a wildlife epidemic in Wisconsin. Ecol. Appl. 19, 1311–1322 (2009).
    PubMed  Google Scholar 

    44.
    Samuel, M. D. et al. Surveillance strategies for detecting chronic wasting disease in free-ranging deer and elk – results of a CWD surveillance workshop. https://pubs.er.usgs.gov/publication/70006758 (U.S. Geological Survey Conference publication, Madison, WI, 2003).

    45.
    Spraker, T. R. et al. Spongiform encephalopathy in free-ranging mule deer (Odocoileus hemionus), white-tailed deer (Odocoileus virginianus) and Rocky Mountain elk (Cervus elaphus nelsoni) in northcentral Colorado. J. Wildl. Dis. 33, 1–6 (1997).
    CAS  PubMed  Google Scholar 

    46.
    Panzacchi, M. et al. Predicting the continuum between corridors and barriers to animal movements using Step Selection Functions and Randomized Shortest Paths. J. Anim. Ecol. 85, 32–42 (2015).
    PubMed  Google Scholar 

    47.
    Ziller, M., Selhorst, T., Teuffert, J., Kramer, M. & Schlüter, H. Analysis of sampling strategies to substantiate freedom from disease in large areas. Prev. Vet. Med. 52, 333–343 (2002).
    CAS  PubMed  Google Scholar 

    48.
    Jongman, R. H. G. Homogenisation and fragmentation of the European landscape: ecological consequences and solutions. Landsc. Urban Plan. 58, 211–221 (2002).
    Google Scholar 

    49.
    Holand, Ø. et al. The effect of sex ratio and male age structure on reindeer calving. J. Wildl. Manag. 67, 25–33 (2003).
    Google Scholar 

    50.
    Sæther, B.-E., Solberg, E. J. & Heim, M. Effects of altering sex ratio structure on the demography of an isolated moose population. J. Wildl. Manag. 67, 455–466 (2003).
    Google Scholar 

    51.
    Morina, D. L., Demarais, S., Strickland, B. K. & Larson, J. E. While males fight, females choose: male phenotypic quality informs female mate choice in mammals. Anim. Behav. 138, 69–74 (2018).
    Google Scholar 

    52.
    Bro-Jørgensen, J. Overt female competition and preference for central males in a lekking antelope. Proc. Natl Acad. Sci. USA 99, 9290–9293 (2002).
    ADS  PubMed  Google Scholar 

    53.
    Andres, D. et al. Sex differences in the consequences of maternal loss in a long-lived mammal, the red deer (Cervus elaphus). Behav. Ecol. Sociobiol. 67, 1249–1258 (2013).
    Google Scholar 

    54.
    Ericsson, G. Reduced cost of reproduction in moose Alces alces through human harvest. Alces 37, 61–69 (2001).
    Google Scholar 

    55.
    Apollonio, M. Andersen, R. & Putman, R. European Ungulates and their Management in the 21st Century (Cambridge University Press, Cambridge, 2010).

    56.
    Mawson, P. R., Hampton, J. O. & Dooley, B. Subsidized commercial harvesting for cost-effective wildlife management in urban areas: a case study with kangaroo sharpshooting. Wildl. Soc. Bull. 40, 251–260 (2016).
    Google Scholar 

    57.
    Manjerovic, M. B., Green, M. L., Mateus-Pinilla, N. & Novakofski, J. The importance of localized culling in stabilizing chronic wasting disease prevalence in white-tailed deer populations. Prev. Vet. Med. 113, 139–145 (2014).
    PubMed  Google Scholar 

    58.
    Mateus-Pinilla, N., Weng, H. Y., Ruiz, M. O., Shelton, P. & Novakofski, J. Evaluation of a wild white-tailed deer population management program for controlling chronic wasting disease in Illinois, 2003-2008. Prev. Vet. Med. 110, 541–548 (2013).
    PubMed  Google Scholar 

    59.
    Vaske, J. J. Lessons learned from human dimensions of chronic wasting disease research. Hum. Dimens Wildl. 15, 165–179 (2010).
    Google Scholar 

    60.
    Mysterud, A., Strand, O. & Rolandsen, C. M. Efficacy of recreational hunters and marksmen for host culling to combat chronic wasting disease in reindeer. Wildl. Soc. Bull. 43, 683–692 (2019).
    Google Scholar 

    61.
    Gaydos, D. A., Petrasova, A., Cobb, R. C. & Meentemeyer, R. K. Forecasting and control of emerging infectious forest disease through participatory modelling. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20180283 (2019).
    PubMed  PubMed Central  Google Scholar 

    62.
    Strand, O., Nilsen, E. B., Solberg, E. J. & Linnell, J. D. C. Can management regulate the population size of wild reindeer (Rangifer tarandus) through harvest? Can. J. Zool. 90, 163–171 (2012).
    Google Scholar 

    63.
    Nilsen, E. B. & Strand, O. Integrating data from several sources for increased insight into demographic processes: Simulation studies and proof of concept for hierarchical change in ratio models. PLoS ONE 13, e0194566 (2018).
    PubMed  PubMed Central  Google Scholar 

    64.
    Viljugrein, H. et al. A method that accounts for differential detectability in mixed samples of long-term infections with applications to the case of chronic wasting disease in cervids. Methods Ecol. Evol. 10, 134–145 (2019).
    Google Scholar 

    65.
    Mysterud, A. et al. The demographic pattern of infection with chronic wasting disease in reindeer at an early epidemic stage. Ecosphere 10, e02931 (2019).
    Google Scholar 

    66.
    MacDiarmid, S. C. A theoretical basis for the use of a skin test for brucellosis surveillance in extensively-managed cattle herds. Rev. Sci. Tech. Int Epiz 6, 1029–1035 (1987).
    CAS  Google Scholar 

    67.
    Viljugrein, H. Accompanying Code for the Paper “Hunting Wildlife to Increase Disease Detection” Version v1.0.0, August 4-2020) https://doi.org/10.5281/zenodo.3972037 (Zenodo, 2020). More

  • in

    Habitat preference and diverse migration in threespine sticklebacks, Gasterosteus aculeatus and G. nipponicus

    Effects of salinity on otolith Sr:Ca ratios
    The mean total lengths (TLs) in the four experimental salinities of 0, 10, 20 and 30 psu were 17.3 ± 0.5 (± SD) mm, 18.2 ± 1.3 mm, 18.6 ± 0.7 mm and 17.5 ± 0.8 mm, respectively. There were significant differences in TL among the four salinities (Kruskal–Wallis test, n = 40, H = 13.577, p  More

  • in

    New insights into the food web of an Australian tropical river to inform water resource management

    1.
    Albert, J. S. et al. Scientists’ warning to humanity on the freshwater biodiversity crisis. Ambio https://doi.org/10.1007/s13280-020-01318-8 (2020).
    Article  PubMed  Google Scholar 
    2.
    Poff, N. L. et al. The natural flow regime. Bioscience 47, 769–784. https://doi.org/10.2307/1313099 (1997).
    Article  Google Scholar 

    3.
    Dudgeon, D. et al. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol. Rev. 81, 163–182. https://doi.org/10.1017/S1464793105006950 (2006).
    Article  PubMed  Google Scholar 

    4.
    Sparks, R. E. Need for ecosystem management of large rivers and their floodplains. Bioscience 45, 168–182. https://doi.org/10.2307/1312556 (1995).
    Article  Google Scholar 

    5.
    Hancock, P. J. Human impacts on the stream-groundwater exchange zone. Environ. Manage. 29, 763–781. https://doi.org/10.1007/s00267-001-0064-5 (2002).
    Article  PubMed  Google Scholar 

    6.
    Pringle, C. What is hydrologic connectivity and why is it ecologically important?. Hydrol. Process. 17, 2685–2689. https://doi.org/10.1002/hyp.5145 (2003).
    ADS  Article  Google Scholar 

    7.
    Reid, M. A., Delong, M. D. & Thoms, M. C. The influence of hydrological connectivity on food web structure in floodplain lakes. River Res. Appl. 28, 827–844. https://doi.org/10.1002/rra.1491 (2012).
    Article  Google Scholar 

    8.
    Tickner, D. et al. Bending the curve of global freshwater biodiversity loss: an emergency recovery plan. Bioscience 70, 330–342. https://doi.org/10.1093/biosci/biaa002 (2020).
    Article  PubMed  PubMed Central  Google Scholar 

    9.
    Australian Government. Our North, Our Future: White Paper on Developing Northern Australia. https://industry.gov.au/ONA/WhitePaper/Documents/northern_australia_white_paper.pdf (2015).

    10.
    Petheram, C., Bruce, C., Chilcott, C. & Watson, I. Water resource assessment for the Fitzroy catchment. A report to the Australian Government from the CSIRO Northern Australia Water Resource Assessment, part of the National Water Infrastructure Development Fund: Water Resource Assessments (CSIRO, Australia, 2018).
    Google Scholar 

    11.
    Pusey, B. Aquatic Biodiversity in Northern Australia: Patterns Threats and Future (Charles Darwin University Press, Darwin, 2011).
    Google Scholar 

    12.
    Jackson, S., Finn, M. & Featherston, P. Aquatic resource use by Indigenous Australians in two tropical river catchments: the Fitzroy River and Daly River. Hum. Ecol. 40, 893–908. https://doi.org/10.1007/s10745-012-9518-z (2012).
    Article  Google Scholar 

    13.
    Douglas, M. M. et al. Conceptualizing hydro-socio-ecological relationships to enable more integrated and inclusive water allocation planning. One Earth 1, 361–373. https://doi.org/10.1016/j.oneear.2019.10.021 (2019).
    Article  Google Scholar 

    14.
    Lear, K. O. et al. Recruitment of a critically endangered sawfish into a riverine nursery depends on natural flow regimes. Sci. Rep. 9, 17071. https://doi.org/10.1038/s41598-019-53511-9 (2019).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    15.
    Fry, B. & Sherr, E. B. in Stable Isotopes in Ecological Research Vol. 68 (eds P.W. Rundel, J. R. Ehleringer, & K. A. Nagy) (Springer, New York, 1989).

    16.
    Jardine, T. D. et al. Consumer-resource coupling in wet-dry tropical rivers. J. Anim. Ecol. 81, 310–322. https://doi.org/10.1111/j.1365-2656.2011.01925.x (2012).
    Article  PubMed  Google Scholar 

    17.
    Fellman, J. B., Pettit, N. E., Kalic, J. & Grierson, P. F. Influence of stream–floodplain biogeochemical linkages on aquatic foodweb structure along a gradient of stream size in a tropical catchment. Freshw. Sci. 32, 217–229. https://doi.org/10.1899/11-117.1 (2013).
    Article  Google Scholar 

    18.
    Burford, M. A., Cook, A. J., Fellows, C. S., Balcombe, S. R. & Bunn, S. E. Sources of carbon fuelling production in an arid floodplain river. Mar. Freshw. Res. 59, 224–234 (2008).
    CAS  Article  Google Scholar 

    19.
    Hunt, R. J. et al. Temporal and spatial variation in ecosystem metabolism and food web carbon transfer in a wet-dry tropical river. Freshw. Biol. 57, 435–450. https://doi.org/10.1111/j.1365-2427.2011.02708.x (2012).
    CAS  Article  Google Scholar 

    20.
    Jardine, T. D. et al. Carbon from periphyton supports fish biomass in waterholes of a wet-dry tropcical river. River Res. Appl. 29, 560–573. https://doi.org/10.1002/rra.2554 (2013).
    Article  Google Scholar 

    21.
    Junk, W. J., Bayley, P. B. & Sparks, R. E. The Flood Pulse Concept In River-Floodplain Systems (Canadian Special Publication of Fisheries and Aquatic Sciences, Toronto, 1989).
    Google Scholar 

    22.
    Zeug, S. C. & Winemiller, K. O. Evidence supporting the importance of terrestrial carbon in a large-river food web. Ecology 89, 1733–1743. https://doi.org/10.1890/07-1064.1 (2008).
    Article  PubMed  Google Scholar 

    23.
    Karim, F. et al. Floodplain Inundation Mapping and Modelling for the Fitzroy, Darwin and Mitchell Catchments (CSIRO, Australia, 2018).
    Google Scholar 

    24.
    Burrows, R., Beesley, L., Douglas, M., Pusey, B. & Kennard, M. Water velocity and groundwater upwelling control benthic algae biomass in a sandy tropical river during base flow: implications for water resource development. Hydrobiologia 847, 1207–1219 (2020).
    Article  Google Scholar 

    25.
    Bunn, S. E. & Arthington, A. H. Basic principles and ecological consequences of altered flow regimes for aquatic biodiversity. Environ. Manage. 30, 492–507. https://doi.org/10.1007/s00267-002-2737-0 (2002).
    Article  PubMed  Google Scholar 

    26.
    Staunton-Smith, J., Robins, J. B., Mayer, D. G., Sellin, M. J. & Halliday, I. A. Does the quantity and timing of fresh water flowing into a dry tropical estuary affect year-class strength of barramundi (Lates calcarifer)?. Mar. Freshw. Res. 55, 787–797. https://doi.org/10.1071/MF03198 (2004).
    Article  Google Scholar 

    27.
    Morgan, D. L., Allen, M. G., Bedford, P. & Horstman, M. Fish fauna of the Fitzroy River in the Kimberley region of Western Australia: including the Bunuba, Gooniyandi, Ngarinyin, Nyikina and Walmajarri Aboriginal names. Rec. West. Austral. Museum 22, 147–161 (2004).
    Article  Google Scholar 

    28.
    Jardine, T. D. et al. Fish mediate high food web connectivity in the lower reaches of a tropical floodplain river. Oecologia 168, 829–838. https://doi.org/10.1007/s00442-011-2148-0 (2012).
    ADS  Article  PubMed  Google Scholar 

    29.
    Burnham, K. P. & Anderson, D. R. A practical information-theoretic approach. in Model Selection and Multimodel Inference 2nd edn (Springer, New York, 2002).
    Google Scholar 

    30.
    Stock, B. C. et al. Analyzing mixing systems using a new generation of Bayesian tracer mixing models. PeerJ 6, e5096. https://doi.org/10.7717/peerj.5096 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    31.
    MacAvoy, S. E., Macko, S. A. & Garman, G. C. Tracing marine biomass into tidal freshwater ecosystems using stable sulfur isotopes. Sci. Nat. 85, 544–546. https://doi.org/10.1007/s001140050546 (1998).
    CAS  Article  Google Scholar 

    32.
    Douglas, M. M., Bunn, S. E. & Davies, P. M. River and wetland food webs in Australias wet-dry tropics: general principles and implications for management. Mar. Freshw. Res. 56, 329–342. https://doi.org/10.1071/MF04084 (2005).
    Article  Google Scholar 

    33.
    Hill, W. R., Rinchard, J. & Czesny, S. Light, nutrients and the fatty acid composition of stream periphyton. Freshw. Biol. 56, 1825–1836. https://doi.org/10.1111/j.1365-2427.2011.02622.x (2011).
    CAS  Article  Google Scholar 

    34.
    Guo, F., Kainz, M. J., Sheldon, F. & Bunn, S. E. The importance of high-quality algal food sources in stream food webs: current status and future perspectives. Freshw. Biol. 61, 815–831. https://doi.org/10.1111/fwb.12755 (2016).
    CAS  Article  Google Scholar 

    35.
    Pettit, N. E. et al. Productivity and connectivity in tropical riverscapes of northern Australia: ecological insights for management. Ecosystems 20, 492–514. https://doi.org/10.1007/s10021-016-0037-4 (2017).
    Article  Google Scholar 

    36.
    Medeiros, E. S. F. & Arthington, A. H. The importance of zooplankton in the diets of three native fish species in floodplain waterholes of a dryland river, the Macintyre River, Australia. Hydrobiologia 614, 19–31. https://doi.org/10.1007/s10750-008-9533-7 (2008).
    Article  Google Scholar 

    37.
    Hladyz, S., Nielsen, D. L., Suter, P. J. & Krull, E. S. Temporal variations in organic carbon utilization by consumers in a lowland river. River Res. Appl. 28, 513–528. https://doi.org/10.1002/rra.1467 (2012).
    Article  Google Scholar 

    38.
    Thorburn, D. C., Gill, H. & Morgan, D. L. Predator and prey interactions of fishes of a tropical Western Australia river revealed by dietary and stable isotope analyses. J. R. Soc. West. Austral. 97, 363–387 (2014).
    Google Scholar 

    39.
    Davis, A. M. et al. Trophic ecology of northern Australia’s terapontids: ontogenetic dietary shifts and feeding classification. J. Fish Biol. 78, 265–286. https://doi.org/10.1111/j.1095-8649.2010.02862.x (2011).
    CAS  Article  PubMed  Google Scholar 

    40.
    Jardine, T. D. et al. Body size drives allochthony in food webs of tropical rivers. Oecologia 183, 505–517. https://doi.org/10.1007/s00442-016-3786-z (2017).
    ADS  Article  PubMed  Google Scholar 

    41.
    Peterson, B. J. & Fry, B. Stable isotopes in ecosystem studies. Annu. Rev. Ecol. Syst. 18, 293–320 (1987).
    Article  Google Scholar 

    42.
    Roberts, B. H. et al. Migration to freshwater increases growth rates in a facultatively catadromous tropical fish. Oecologia 191, 253–260 (2019).
    ADS  Article  Google Scholar 

    43.
    Crook, D. A. et al. Tracking the resource pulse: movement responses of fish to dynamic floodplain habitat in a tropical river. J. Anim. Ecol. 89, 795–807. https://doi.org/10.1111/1365-2656.13146 (2020).
    Article  PubMed  Google Scholar 

    44.
    Kwak, T. J. Lateral movement and use of floodplain habitat by fishes of the Kankakee River, Illinois. Am. Midl. Nat. 120, 241–249. https://doi.org/10.2307/2425995 (1988).
    Article  Google Scholar 

    45.
    Jackson, S., Finn, M., Woodward, E. & Featherston, P. Indigenous Socio-Economic Values and River Flows (CSIRO Ecosystem Sciences, Australia, 2011).
    Google Scholar 

    46.
    Townsend, S. A. & Padovan, A. V. The seasonal accrual and loss of benthic algae (Spirogyra) in the Daly River, an oligotrophic river in tropical Australia. Mar. Freshw. Res. 56, 317–327. https://doi.org/10.1071/MF04079 (2005).
    CAS  Article  Google Scholar 

    47.
    Fellman, J. B. et al. Dissolved organic carbon biolability decreases along with its modernization in fluvial networks in an ancient landscape. Ecology 95, 2622–2632. https://doi.org/10.1890/13-1360.1 (2014).
    Article  Google Scholar 

    48.
    Pace, M. L. et al. Whole-lake carbon-13 additions reveal terrestrial support of aquatic food webs. Nature 427, 240–243. https://doi.org/10.1038/nature02227 (2004).
    ADS  CAS  Article  PubMed  Google Scholar 

    49.
    Baldwin, D. S., Colloff, M. J., Mitrovic, S. M., Bond, N. R. & Wolfenden, B. Restoring dissolved organic carbon subsidies from floodplains to lowland river food webs: a role for environmental flows?. Mar. Freshw. Res. 67, 1387–1399. https://doi.org/10.1071/MF15382 (2016).
    CAS  Article  Google Scholar 

    50.
    Kennard, M. J. et al. Classification of natural flow regimes in Australia to support environmental flow management. Freshw. Biol. 55, 171–193. https://doi.org/10.1111/j.1365-2427.2009.02307.x (2010).
    Article  Google Scholar 

    51.
    Taylor, C. F. H. in Limnology of the Fitzroy River, Western Australia: a technical workshop. (eds A Storey & L Beesley).

    52.
    Hesslein, R. H., Capel, M. J., Fox, D. E. & Hallard, K. A. Stable isotopes of sulfur, carbon, and nitrogen as indicators of trophic level and fish migration in the lower Mackenzie River Basin, Canada. Can. J. Fish. Aquat. Sci. 48, 2258–2265. https://doi.org/10.1139/f91-265 (1991).
    Article  Google Scholar 

    53.
    Herzka, S. Z. Assessing connectivity of estuarine fishes based on stable isotope ratio analysis. Estuar. Coast. Shelf Sci. 64, 58–69. https://doi.org/10.1016/j.ecss.2005.02.006 (2005).
    ADS  Article  Google Scholar 

    54.
    Pusey, B. J. et al. Carbon sources supporting Australia’s most widely distributed freshwater fish, Nematalosa erebi (Günther) (Clupeidae: Dorosomatinae). Mar. Freshw. Res. https://doi.org/10.1071/MF20014 (2020).
    Article  Google Scholar 

    55.
    Hecky, R. & Hesslein, R. Contributions of benthic algae to lake food webs as revealed by stable isotope analysis. J. N. Am. Benthol. Soc. 14, 631–653 (1995).
    Article  Google Scholar 

    56.
    Yoshii, K. et al. Stable isotope analyses of the pelagic food web in Lake Baikal. Limnol. Oceanogr. 44, 502–511 (1999).
    ADS  Article  Google Scholar 

    57.
    Pinnegar, J. K. & Polunin, N. V. C. Differential fractionation of δ13C and δ15N among fish tissues: implications for the study of trophic interactions. Funct. Ecol. 13, 225–231. https://doi.org/10.1046/j.1365-2435.1999.00301.x (1999).
    Article  Google Scholar 

    58.
    Skrzypek, G. Normalization procedures and reference material selection in stable HCNOS isotope analyses: an overview. Anal. Bioanal. Chem. 405, 2815–2823. https://doi.org/10.1007/s00216-012-6517-2 (2013).
    CAS  Article  PubMed  Google Scholar 

    59.
    Logan, J. M. et al. Lipid corrections in carbon and nitrogen stable isotope analyses: comparison of chemical extraction and modelling methods. J. Anim. Ecol. 77, 838–846. https://doi.org/10.1111/j.1365-2656.2008.01394.x (2008).
    Article  PubMed  Google Scholar 

    60.
    Skinner, M. M., Martin, A. A. & Moore, B. C. Is lipid correction necessary in the stable isotope analysis of fish tissues?. Rapid Commun. Mass Spectrom. 30, 881–889. https://doi.org/10.1002/rcm.7480 (2016).
    CAS  Article  PubMed  Google Scholar 

    61.
    Parnell, A. C., Inger, R., Bearhop, S. & Jackson, A. L. Source partitioning using stable isotopes: coping with too much variation. PLoS ONE 5, e9672. https://doi.org/10.1371/journal.pone.0009672 (2010).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    62.
    Sauer, J. R. & Link, W. A. Hierarchical modeling of population stability and species group attributes from survey data. Ecology 83, 1743–1751. https://doi.org/10.1890/0012-9658(2002)083[1743:Hmopsa]2.0.Co;2 (2002).
    Article  Google Scholar 

    63.
    McCutchan, J. H. Jr., Lewis, W. M. Jr., Kendall, C. & McGrath, C. C. Variation in trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos 102, 378–390. https://doi.org/10.1034/j.1600-0706.2003.12098.x (2003).
    CAS  Article  Google Scholar 

    64.
    Parnell, A. C. et al. Bayesian stable isotope mixing models. Environmetrics 24, 387–399. https://doi.org/10.1002/env.2221 (2013).
    MathSciNet  Article  Google Scholar 

    65.
    Blanchette, M. L., Davis, A. M., Jardine, T. D. & Pearson, R. G. Omnivory and opportunism characterize food webs in a large dry-tropics river system. Freshw. Sci. 33, 142–158. https://doi.org/10.1086/674632 (2014).
    Article  Google Scholar 

    66.
    Bunn, S. E., Leigh, C. & Jardine, T. D. Diet-tissue fractionation of δ15N by consumers from streams and rivers. Limnol. Oceanogr. 58, 765–773. https://doi.org/10.4319/lo.2013.58.3.0765 (2013).
    ADS  CAS  Article  Google Scholar 

    67.
    Pusey, B., Kennard, M. & Arthington, A. Freshwater Fishes of North-Eastern Australia (CSIRO publishing, Clayton, 2004).
    Google Scholar 

    68.
    Post, D. M. Using stable isotopes to estimate trophic position: models, methods and assumptions. Ecology 83, 703–718. https://doi.org/10.1890/0012-9658(2002)083[0703:Usitet]2.0.Co;2 (2002).
    Article  Google Scholar  More

  • in

    Ecological pest control fortifies agricultural growth in Asia–Pacific economies

    1.
    Griggs, D. et al. Policy: sustainable development goals for people and planet. Nature 495, 305–307 (2013).
    CAS  Article  Google Scholar 
    2.
    Bernhardt, E. S., Rosi, E. J. & Gessner, M. O. Synthetic chemicals as agents of global change. Front. Ecol. Environ. 15, 84–90 (2017).
    Google Scholar 

    3.
    Springmann, M. et al. Options for keeping the food system within environmental limits. Nature 562, 519–525 (2018).
    CAS  PubMed  Google Scholar 

    4.
    Pretty, J. et al. Global assessment of agricultural system redesign for sustainable intensification. Nat. Sustain. 1, 441–446 (2018).
    Google Scholar 

    5.
    Rockström, J. et al. A safe operating space for humanity. Nature 461, 472–475 (2009).
    PubMed  Google Scholar 

    6.
    Alston, J. M. & Pardey, P. G. Agriculture in the global economy. J. Econ. Perspect. 28, 121–146 (2014).
    Google Scholar 

    7.
    Johnston, B. F. & Mellor, J. W. The role of agriculture in economic development. Am. Econ. Rev. 51, 566–593 (1961).
    Google Scholar 

    8.
    de Janvry, A. Agriculture for development: new paradigm and options for success. Agric. Econ. 41, 17–36 (2010).
    Google Scholar 

    9.
    Pingali, P. L. Green revolution: impacts, limits, and the path ahead. Proc. Natl. Acad. Sci. USA 109, 12302–12308 (2012).
    CAS  PubMed  Google Scholar 

    10.
    Sayer, J. & Cassman, K. G. Agricultural innovation to protect the environment. Proc. Natl Acad. Sci. USA 110, 8345–8348 (2013).
    CAS  PubMed  Google Scholar 

    11.
    Bale, J. S., Van Lenteren, J. C. & Bigler, F. Biological control and sustainable food production. Phil. Trans. R. Soc. B 363, 761–776 (2008).
    CAS  PubMed  Google Scholar 

    12.
    Naranjo, S. E., Ellsworth, P. C. & Frisvold, G. B. Economic value of biological control in integrated pest management of managed plant systems. Annu. Rev. Entomol. 60, 621–645 (2015).
    CAS  PubMed  Google Scholar 

    13.
    Heimpel, G. E. & Cock, M. J. W. Shifting paradigms in the history of classical biological control. BioControl 63, 27–37 (2018).
    Google Scholar 

    14.
    Simmonds, F. J., Franz, J. M., & Sailer, R. I. in Theory and Practice of Biological Control (eds Huffaker, C. B. & Messenger, P. S.) 17–39 (Academic Press, 1976).

    15.
    Maredia, M. K. & Raitzer, D. A. Estimating overall returns to international agricultural research in Africa through benefit–cost analysis: a “best‐evidence” approach. Agric. Econ. 41, 81–100 (2010).
    Google Scholar 

    16.
    Nghiem, L. T. et al. Economic and environmental impacts of harmful non-indigenous species in Southeast Asia. PLoS ONE 8, e71255 (2013).
    CAS  PubMed Central  Google Scholar 

    17.
    Bradshaw, C. J. et al. Massive yet grossly underestimated global costs of invasive insects. Nat. Commun. 7, 12986 (2016).
    CAS  PubMed  PubMed Central  Google Scholar 

    18.
    Huffaker, C. B. & Caltagirone, L. E. The impact of biological control on the development of the Pacific. Agric. Ecosyst. Environ. 15, 95–107 (1986).
    Google Scholar 

    19.
    Waterhouse, D. F., Dillon, B. & Vincent, D. P. Economic Benefits to Papua New Guinea and Australia from the Biological Control of Banana Skipper (Erionota thrax) No. 434-2016-33658 (ACIAR, 1998).

    20.
    DeBach, P. & Rosen, D. Biological Control by Natural Enemies (Cambridge Univ. Press, 1991).

    21.
    Heimpel, G. E. & Mills, N. J. Biological Control (Cambridge Univ. Press, 2017).

    22.
    Spennemann, D. H. Introduction of coccinellid beetles to control the coconut scale insect Aspidiotus destructor Signoret in Micronesia 1901–1914. Oriental Insects 54, 197–215 (2019).

    23.
    Zalucki, M. P. et al. Estimating the economic cost of one of the world’s major insect pests, Plutella xylostella (Lepidoptera: Plutellidae): just how long is a piece of string? J. Econ. Entomol. 105, 1115–1129 (2012).
    PubMed  Google Scholar 

    24.
    Hoddle, M. S. Restoring balance: using exotic species to control invasive exotic species. Conserv. Biol. 18, 38–49 (2004).
    Google Scholar 

    25.
    Van Driesche, R. G. et al. Classical biological control for the protection of natural ecosystems. Biol. Control 54, S2–S33 (2010).
    Google Scholar 

    26.
    Ricciardi, A., Palmer, M. E. & Yan, N. D. Should biological invasions be managed as natural disasters? BioScience 61, 312–317 (2011).
    Google Scholar 

    27.
    Bellard, C. et al. A global picture of biological invasion threat on islands. Nat. Ecol. Evol. 1, 1862–1869 (2017).
    PubMed  Google Scholar 

    28.
    Naranjo, S. E., Frisvold, G. B. & Ellsworth, P. C. in The Economics of Integrated Pest Management of Insects (eds Onstad, D. W. & Crain, P. R.) 49–85 (CAB International, 2019).

    29.
    Liu, J. et al. Systems integration for global sustainability. Science 347, 1258832 (2015).
    PubMed  Google Scholar 

    30.
    Gordon, L. J. et al. Rewiring food systems to enhance human health and biosphere stewardship. Environ. Res. Lett. 12, 100201 (2017).
    Google Scholar 

    31.
    Ruttan, V. W. Productivity growth in world agriculture: sources and constraints. J. Econ. Perspect. 16, 161–184 (2002).
    Google Scholar 

    32.
    Ruttan, V. W. & Hayami, Y. in International Agricultural Development (eds Eicher, C. K. & Staatz, J. M.) Ch. 10 (Johns Hopkins Univ. Press, 1998).

    33.
    Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 5, eaax012 (2019).
    Google Scholar 

    34.
    Keane, R. M. & Crawley, M. J. Exotic plant invasions and the enemy release hypothesis. Trends Ecol. Evol. 17, 164–170 (2002).
    Google Scholar 

    35.
    Thancharoen, A. et al. Effective biological control of an invasive mealybug pest enhances root yield in cassava. J. Pest Sci. 91, 1199–1211 (2018).
    Google Scholar 

    36.
    Raitzer, D. A. & Kelley, T. G. Benefit–cost meta-analysis of investment in the International Agricultural Research Centers of the CGIAR. Agric. Syst. 96, 108–123 (2008).
    Google Scholar 

    37.
    van der Ploeg, J. D. et al. The economic potential of agroecology: empirical evidence from Europe. J. Rural Stud. 71, 46–61 (2019).
    Google Scholar 

    38.
    Haggblade, S., Hazell, P. B. & Dorosh, P. A. in Transforming the Rural Nonfarm Economy: Opportunities and Threats in the Developing World (eds Haggblade, S. et al.) Ch. 7 (IFPRI, 2007).

    39.
    Wiggins, S., Kirsten, J. & Llambí, L. The future of small farms. World Dev. 38, 1341–1348 (2010).
    Google Scholar 

    40.
    Timmer, P. C. in International Agricultural Development (eds Eicher, C. K. & Staatz, J. M.) Ch. 32 (Johns Hopkins Univ. Press, 1998).

    41.
    Folke, C. et al. Transnational corporations and the challenge of biosphere stewardship. Nat. Ecol. Evol. 3, 1396–1403 (2019).
    PubMed  Google Scholar 

    42.
    Yletyinen, J. et al. Understanding and managing social-ecological tipping points in primary industries. BioScience 69, 335–347 (2019).
    Google Scholar 

    43.
    Bottrell, D. G. & Schoenly, K. G. Resurrecting the ghost of green revolutions past: the brown planthopper as a recurring threat to high-yielding rice production in tropical Asia. J. Asia-Pacific Entomol. 15, 122–140 (2012).
    Google Scholar 

    44.
    Cock, M. J. W. et al. Trends in the classical biological control of insect pests by insects: an update of the BIOCAT database. Biocontrol 61, 349–363 (2016).
    CAS  Google Scholar 

    45.
    Reardon, T. The hidden middle: the quiet revolution in the midstream of agrifood value chains in developing countries. Oxford Rev. Econ. Policy 31, 45–63 (2015).
    Google Scholar 

    46.
    Wyckhuys, K. A. G. Biological control of an invasive pest eases pressures on global commodity markets. Environ. Res. Lett. 13, 094005 (2018).
    Google Scholar 

    47.
    Martin, E. A. et al. Assessing the resilience of 2 biodiversity-driven functions in agroecosystems under environmental change. Adv. Ecol. Res. 60, 59–123 (2019).
    Google Scholar 

    48.
    Warner, K. D. et al. The decline of public interest agricultural science and the dubious future of crop biological control in California. Agric. Human Values 28, 483–496 (2011).
    Google Scholar 

    49.
    Benelli, G., Jeffries, C. L. & Walker, T. Biological control of mosquito vectors: past, present, and future. Insects 7, 52 (2016).
    PubMed Central  Google Scholar 

    50.
    Dangour, A. D., Mace, G. & Shankar, B. Food systems, nutrition, health and the environment. Lancet Planet. Health 1, e8–e9 (2017).
    PubMed  Google Scholar 

    51.
    Rasmussen, L. V. et al. Social-ecological outcomes of agricultural intensification. Nat. Sustain. 1, 275–282 (2018).
    Google Scholar 

    52.
    Henneman, M. L. & Memmott, J. Infiltration of a Hawaiian community by introduced biological control agents. Science 293, 1314–1316 (2001).
    CAS  PubMed  Google Scholar 

    53.
    Stiling, P. & Cornelissen, T. What makes a successful biocontrol agent? A meta-analysis of biological control agent performance. Biol. Control 34, 236–246 (2005).
    Google Scholar 

    54.
    Sparger, J. A. et al. Is the share of agricultural maintenance research rising in the United States? Food Policy 38, 126–135 (2013).
    Google Scholar 

    55.
    Trewavas, A. Malthus foiled again and again. Nature 418, 668–670 (2002).
    CAS  PubMed  Google Scholar 

    56.
    Greathead, D. J. & Greathead, A. H. Biological control of insect pests by insect parasitoids and predators: the BIOCAT database. Biocontrol News Inf. 13, 61N–68N (1992).
    Google Scholar 

    57.
    Wyckhuys, K. A. G. et al. Data from: Ecological pest control fortifies agricultural growth in Asia-Pacific economies (Dryad Digital Repository, 2020); https://doi.org/10.5061/dryad.547d7wm45

    58.
    Zeddies, J., Schaab, R. P., Neuenschwander, P. & Herren, H. R. Economics of biological control of cassava mealybug in Africa. Agric. Econ. 24, 209–219 (2001).
    Google Scholar 

    59.
    Hayami, Y. & Ruttan, V. W. Agricultural Development: An International Perspective (Johns Hopkins Univ. Press, 1971). More

  • in

    Novel bacterial clade reveals origin of form I Rubisco

    1.
    Nisbet, E. G. et al. The age of Rubisco: the evolution of oxygenic photosynthesis. Geobiology 5, 311–335 (2007).
    CAS  Google Scholar 
    2.
    Tabita, F. R. et al. Function, structure, and evolution of the RubisCO-like proteins and their RubisCO homologs. Microbiol. Mol. Biol. Rev. 71, 576–599 (2007).
    CAS  PubMed  PubMed Central  Google Scholar 

    3.
    Tabita, F. R., Satagopan, S., Hanson, T. E., Kreel, N. E. & Scott, S. S. Distinct form I, II, III, and IV Rubisco proteins from the three kingdoms of life provide clues about Rubisco evolution and structure/function relationships. J. Exp. Bot. 59, 1515–1524 (2007).
    Google Scholar 

    4.
    Andrews, T. J. Catalysis by cyanobacterial ribulose-bisphosphate carboxylase large subunits in the complete absence of small subunits. J. Biol. Chem. 263, 12213–12219 (1988).
    CAS  PubMed  Google Scholar 

    5.
    Morell, M. K., Wilkin, J. M., Kane, H. J. & Andrews, T. J. Side reactions catalyzed by ribulose-bisphosphate carboxylase in the presence and absence of small subunits. J. Biol. Chem. 272, 5445–5451 (1997).
    CAS  PubMed  Google Scholar 

    6.
    Spreitzer, R. J. Role of the small subunit in ribulose-1,5-bisphosphate carboxylase/oxygenase. Arch. Biochem. Biophys. 414, 141–149 (2003).
    CAS  PubMed  Google Scholar 

    7.
    Joshi, J., Mueller-Cajar, O., Tsai, Y.-C. C., Hartl, F. U. & Hayer-Hartl, M. Role of small subunit in mediating assembly of red-type form I rubisco. J. Biol. Chem. 290, 1066–1074 (2015).
    CAS  PubMed  Google Scholar 

    8.
    Liu, C. et al. Coupled chaperone action in folding and assembly of hexadecameric Rubisco. Nature 463, 197–202 (2010).
    CAS  PubMed  Google Scholar 

    9.
    Grabsztunowicz, M., Górski, Z., Luciński, R. & Jackowski, G. A reversible decrease in ribulose 1,5-bisphosphate carboxylase/oxygenase carboxylation activity caused by the aggregation of the enzyme’s large subunit is triggered in response to the exposure of moderate irradiance-grown plants to low irradiance. Physiol. Plant. 154, 591–608 (2015).
    CAS  PubMed  Google Scholar 

    10.
    Kusian, B. & Bowien, B. Organization and regulation of cbb CO2 assimilation genes in autotrophic bacteria. FEMS Microbiol. Rev. 21, 135–155 (1997).
    CAS  PubMed  Google Scholar 

    11.
    Tabita, F. R. Microbial ribulose 1,5-bisphosphate carboxylase/oxygenase: a different perspective. Photosynth. Res. 60, 1–28 (1999).
    CAS  Google Scholar 

    12.
    Whitney, S. M. & Andrews, T. J. The gene for the ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) small subunit relocated to the plastid genome of tobacco directs the synthesis of small subunits that assemble into Rubisco. Plant Cell 13, 193–205 (2001).
    CAS  PubMed  PubMed Central  Google Scholar 

    13.
    Bryant, D. A. & Liu, Z. in Advances in Botanical Research (ed. Beatty, J. T.) 99–150 (Academic Press, 2013).

    14.
    Shih, P. M., Ward, L. M. & Fischer, W. W. Evolution of the 3-hydroxypropionate bicycle and recent transfer of anoxygenic photosynthesis into the Chloroflexi. Proc. Natl Acad. Sci. USA 114, 10749–10754 (2017).
    CAS  PubMed  Google Scholar 

    15.
    Ward, L. M., Hemp, J., Shih, P. M., McGlynn, S. E. & Fischer, W. W. Evolution of phototrophy in the Chloroflexi phylum driven by horizontal gene transfer. Front. Microbiol. 9, 260 (2018).
    PubMed  PubMed Central  Google Scholar 

    16.
    Fischer, W. W., Hemp, J. & Johnson, J. E. Evolution of oxygenic photosynthesis. Annu. Rev. Earth Planet. Sci. 44, 647–683 (2016).
    CAS  Google Scholar 

    17.
    Roy, H. Rubisco assembly: a model system for studying the mechanism of chaperonin action. Plant Cell 1, 1035–1042 (1989).
    CAS  PubMed  PubMed Central  Google Scholar 

    18.
    Hayer-Hartl, M. From chaperonins to Rubisco assembly and metabolic repair. Protein Sci. 26, 2324–2333 (2017).
    CAS  PubMed  PubMed Central  Google Scholar 

    19.
    Aigner, H. et al. Plant RuBisCo assembly in E. coli with five chloroplast chaperones including BSD2. Science 358, 1272–1278 (2017).
    CAS  PubMed  Google Scholar 

    20.
    Wilson, R. H. & Hayer-Hartl, M. Complex chaperone dependence of Rubisco biogenesis. Biochemistry 57, 3210–3216 (2018).
    CAS  PubMed  Google Scholar 

    21.
    Saschenbrecker, S. et al. Structure and function of RbcX, an assembly chaperone for hexadecameric Rubisco. Cell 129, 1189–1200 (2007).
    CAS  PubMed  Google Scholar 

    22.
    Gunn, L. H., Valegård, K. & Andersson, I. A unique structural domain in Methanococcoides burtonii ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) acts as a small subunit mimic. J. Biol. Chem. 292, 6838–6850 (2017).
    CAS  PubMed  PubMed Central  Google Scholar 

    23.
    Goloubinoff, P., Christeller, J. T., Gatenby, A. A. & Lorimer, G. H. Reconstitution of active dimeric ribulose bisphosphate carboxylase from an unfolded state depends on two chaperonin proteins and Mg-ATP. Nature 342, 884–889 (1989).
    CAS  PubMed  Google Scholar 

    24.
    Parry, M. A. J., Keys, A. J. & Gutteridge, S. Variation in the specificity factor of C3 higher plant Rubiscos determined by the total consumption of ribulose-P2. J. Exp. Bot. 40, 317–320 (1989).
    CAS  Google Scholar 

    25.
    Tcherkez, G. G. B., Farquhar, G. D. & Andrews, T. J. Despite slow catalysis and confused substrate specificity, all ribulose bisphosphate carboxylases may be nearly perfectly optimized. Proc. Natl Acad. Sci. USA 103, 7246–7251 (2006).
    CAS  PubMed  Google Scholar 

    26.
    Flamholz, A. I. et al. Revisiting trade-offs between Rubisco kinetic parameters. Biochemistry 58, 3365–3376 (2019).
    CAS  PubMed  PubMed Central  Google Scholar 

    27.
    Yamada, T. & Sekiguchi, Y. Cultivation of uncultured Chloroflexi subphyla: significance and ecophysiology of formerly uncultured Chloroflexi ‘subphylum i’ with natural and biotechnological relevance. Microbes Environ. 24, 205–216 (2009).
    PubMed  Google Scholar 

    28.
    Hemp, J., Ward, L. M., Pace, L. A. & Fischer, W. W. Draft genome sequence of Ornatilinea apprima P3M-1, an anaerobic member of the Chloroflexi class Anaerolineae. Genome Announc. 3, e01353-15 (2015).

    29.
    Ward, L. M., Hemp, J., Pace, L. A. & Fischer, W. W. Draft genome sequence of Leptolinea tardivitalis YMTK-2, a mesophilic anaerobe from the Chloroflexi class Anaerolineae. Genome Announc. 3, e01356-15 (2015).

    30.
    Alonso, H., Blayney, M. J., Beck, J. L. & Whitney, S. M. Substrate-induced assembly of Methanococcoides burtonii d-ribulose-1,5-bisphosphate carboxylase/oxygenase dimers into decamers. J. Biol. Chem. 284, 33876–33882 (2009).
    CAS  PubMed  PubMed Central  Google Scholar 

    31.
    Knott, G. J. et al. Structural basis for AcrVA4 inhibition of specific CRISPR-Cas12a. eLife 8, e49110 (2019).

    32.
    Duff, A. P., Andrews, T. J. & Curmi, P. M. The transition between the open and closed states of Rubisco is triggered by the inter-phosphate distance of the bound bisphosphate. J. Mol. Biol. 298, 903–916 (2000).
    CAS  PubMed  Google Scholar 

    33.
    Newman, J., Branden, C. I. & Jones, T. A. Structure determination and refinement of ribulose 1,5-bisphosphate carboxylase/oxygenase from Synechococcus PCC6301. Acta Crystallogr. D. Biol. Crystallogr. 49, 548–560 (1993).
    CAS  PubMed  Google Scholar 

    34.
    Lu, Z., Zhao, Z. & Fu, B. Efficient protein alignment algorithm for protein search. BMC Bioinf. 11, S34 (2010).
    Google Scholar 

    35.
    Cleland, W. W., Andrews, T. J., Gutteridge, S., Hartman, F. C. & Lorimer, G. H. Mechanism of Rubisco: the carbamate as general base. Chem. Rev. 98, 549–562 (1998).
    CAS  PubMed  Google Scholar 

    36.
    Andersson, I. & Backlund, A. Structure and function of Rubisco. Plant Physiol. Biochem. 46, 275–291 (2008).
    CAS  PubMed  Google Scholar 

    37.
    van Lun, M., van der Spoel, D. & Andersson, I. Subunit interface dynamics in hexadecameric Rubisco. J. Mol. Biol. 411, 1083–1098 (2011).
    PubMed  Google Scholar 

    38.
    Schneider, G. et al. Comparison of the crystal structures of L2 and L8S8 Rubisco suggests a functional role for the small subunit. EMBO J. 9, 2045–2050 (1990).
    CAS  PubMed  PubMed Central  Google Scholar 

    39.
    Huynh, K. & Partch, C. L. Analysis of protein stability and ligand interactions by thermal shift assay. Curr. Protoc. Protein Sci. 79, 28.9.1–28.9.14 (2015).
    Google Scholar 

    40.
    Greene, D. N., Whitney, S. M. & Matsumura, I. Artificially evolved Synechococcus PCC6301 Rubisco variants exhibit improvements in folding and catalytic efficiency. Biochem. J. 404, 517–524 (2007).
    CAS  PubMed  PubMed Central  Google Scholar 

    41.
    DePristo, M. A., Weinreich, D. M. & Hartl, D. L. Missense meanderings in sequence space: a biophysical view of protein evolution. Nat. Rev. Genet. 6, 678–687 (2005).
    CAS  PubMed  Google Scholar 

    42.
    Tokuriki, N., Stricher, F., Serrano, L. & Tawfik, D. S. How protein stability and new functions trade off. PLoS Comput. Biol. 4, e1000002 (2008).
    PubMed  PubMed Central  Google Scholar 

    43.
    Tokuriki, N. & Tawfik, D. S. Protein dynamism and evolvability. Science 324, 203–207 (2009).
    CAS  PubMed  Google Scholar 

    44.
    Erb, T. J. & Zarzycki, J. A short history of RubisCO: the rise and fall (?) of Nature’s predominant CO2 fixing enzyme. Curr. Opin. Biotechnol. 49, 100–107 (2018).
    CAS  PubMed  Google Scholar 

    45.
    Badger, M. R., Hanson, D. & Dean Price, G. Evolution and diversity of CO2 concentrating mechanisms in cyanobacteria. Funct. Plant Biol. 29, 161–173 (2002).
    CAS  PubMed  Google Scholar 

    46.
    Studer, R. A., Christin, P.-A., Williams, M. A. & Orengo, C. A. Stability–activity tradeoffs constrain the adaptive evolution of RubisCO. Proc. Natl Acad. Sci. USA 111, 2223–2228 (2014).
    CAS  PubMed  Google Scholar 

    47.
    Zhou, Y. & Whitney, S. Directed evolution of an improved Rubisco; in vitro analyses to decipher fact from fiction. Int. J. Mol. Sci. 20, 5019 (2019).

    48.
    Wilson, R. H., Alonso, H. & Whitney, S. M. Evolving Methanococcoides burtonii archaeal Rubisco for improved photosynthesis and plant growth. Sci. Rep. 6, 22284 (2016).
    CAS  PubMed  PubMed Central  Google Scholar 

    49.
    Frey, S. & Görlich, D. A new set of highly efficient, tag-cleaving proteases for purifying recombinant proteins. J. Chromatogr. A 1337, 95–105 (2014).
    CAS  PubMed  Google Scholar 

    50.
    Kane, H. J., Wilkin, J. M., Portis, A. R. & John Andrews, T. Potent inhibition of ribulose-bisphosphate carboxylase by an oxidized impurity in ribulose-1,5-bisphosphate. Plant Physiol. 117, 1059–1069 (1998).
    CAS  PubMed  PubMed Central  Google Scholar 

    51.
    Pierce, J., Tolbert, N. E. & Barker, R. Interaction of ribulosebisphosphate carboxylase/oxygenase with transition-state analogues. Biochemistry 19, 934–942 (1980).
    CAS  PubMed  Google Scholar 

    52.
    Pereira, J. H., McAndrew, R. P., Tomaleri, G. P. & Adams, P. D. Berkeley Screen: a set of 96 solutions for general macromolecular crystallization. J. Appl. Crystallogr. 50, 1352–1358 (2017).
    CAS  PubMed  PubMed Central  Google Scholar 

    53.
    Winter, G., Lobley, C. M. C. & Prince, S. M. Decision making in xia2. Acta Crystallogr. D. Biol. Crystallogr. 69, 1260–1273 (2013).
    CAS  PubMed  PubMed Central  Google Scholar 

    54.
    McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).
    CAS  PubMed  PubMed Central  Google Scholar 

    55.
    Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D 66, 213–221 (2010).
    CAS  PubMed  Google Scholar 

    56.
    Afonine, P. V. et al. Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallogr. D 68, 352–367 (2012).
    CAS  PubMed  Google Scholar 

    57.
    Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D 60, 2126–2132 (2004).
    PubMed  Google Scholar 

    58.
    Davis, I. W. et al. MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Res. 35, W375–W383 (2007).
    PubMed  PubMed Central  Google Scholar 

    59.
    Dyer, K. N. et al. High-throughput SAXS for the characterization of biomolecules in solution: a practical approach. Methods Mol. Biol. 1091, 245–258 (2014).
    CAS  PubMed  PubMed Central  Google Scholar 

    60.
    Hura, G. L. et al. Robust, high-throughput solution structural analyses by small angle X-ray scattering (SAXS). Nat. Methods 6, 606–612 (2009).
    CAS  PubMed  PubMed Central  Google Scholar 

    61.
    Rambo, R. P. & Tainer, J. A. Accurate assessment of mass, models and resolution by small-angle scattering. Nature 496, 477–481 (2013).
    CAS  PubMed  PubMed Central  Google Scholar 

    62.
    Sali, A. & Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).
    CAS  PubMed  Google Scholar 

    63.
    Schneidman-Duhovny, D., Hammel, M. & Sali, A. FoXS: a web server for rapid computation and fitting of SAXS profiles. Nucleic Acids Res. 38, W540–W544 (2010).
    CAS  PubMed  PubMed Central  Google Scholar 

    64.
    Schneidman-Duhovny, D., Hammel, M., Tainer, J. A. & Sali, A. Accurate SAXS profile computation and its assessment by contrast variation experiments. Biophys. J. 105, 962–974 (2013).
    CAS  PubMed  PubMed Central  Google Scholar 

    65.
    Prins, A. et al. Rubisco catalytic properties of wild and domesticated relatives provide scope for improving wheat photosynthesis. J. Exp. Bot. 67, 1827–1838 (2016).
    CAS  PubMed  PubMed Central  Google Scholar 

    66.
    Sharwood, R. E., Ghannoum, O. & Whitney, S. M. Prospects for improving CO2 fixation in C3-crops through understanding C4-Rubisco biogenesis and catalytic diversity. Curr. Opin. Plant Biol. 31, 135–142 (2016).
    CAS  PubMed  Google Scholar 

    67.
    Pei, J., Kim, B.-H. & Grishin, N. V. PROMALS3D: a tool for multiple protein sequence and structure alignments. Nucleic Acids Res. 36, 2295–2300 (2008).
    CAS  PubMed  PubMed Central  Google Scholar 

    68.
    Katoh, K., Rozewicki, J. & Yamada, K. D. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief. Bioinform. 20, 1160–1166 (2017).
    PubMed Central  Google Scholar 

    69.
    Potterton, E., Briggs, P., Turkenburg, M. & Dodson, E. A graphical user interface to the CCP4 program suite. Acta Crystallogr. D 59, 1131–1137 (2003).
    PubMed  Google Scholar 

    70.
    Krissinel, E. & Henrick, K. Inference of macromolecular assemblies from crystalline state. J. Mol. Biol. 372, 774–797 (2007).
    CAS  PubMed  PubMed Central  Google Scholar 

    71.
    Krissinel, E. Crystal contacts as nature’s docking solutions. J. Comput. Chem. 31, 133–143 (2010).
    CAS  PubMed  Google Scholar 

    72.
    Pettersen, E. F. et al. UCSF Chimera-a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).
    CAS  Google Scholar 

    73.
    Diamond, S. et al. Mediterranean grassland soil C-N compound turnover is dependent on rainfall and depth, and is mediated by genomically divergent microorganisms. Nat. Microbiol. 4, 1356–1367 (2019).
    CAS  PubMed  PubMed Central  Google Scholar 

    74.
    Lavy, A. et al. Microbial communities across a hillslope–riparian transect shaped by proximity to the stream, groundwater table, and weathered bedrock. Ecol. Evol. 9, 6869–6900 (2019).
    PubMed  PubMed Central  Google Scholar 

    75.
    Knight, S., Andersson, I. & Brändén, C. I. Crystallographic analysis of ribulose 1,5-bisphosphate carboxylase from spinach at 2.4 A resolution. Subunit interactions and active site. J. Mol. Biol. 215, 113–160 (1990).
    CAS  PubMed  Google Scholar  More