More stories

  • in

    A signal-like role for floral humidity in a nocturnal pollination system

    Kulahci, I. G., Dornhaus, A. & Papaj, D. R. Multimodal signals enhance decision making in foraging bumble-bees. Proc. Biol. Sci. 275, 797–802 (2008).
    Google Scholar 
    Goldshtein, A. et al. Reinforcement learning enables resource partitioning in foraging bats. Curr. Biol. 30, 4096–4102.e4096 (2020).CAS 

    Google Scholar 
    Skogen, K. A., Overson, R. P., Hilpman, E. T. & Fant, J. B. Hawkmoth pollination facilitates long-distance pollen dispersal and reduces isolation across a gradient of land-use change. Ann. Mo. Bot. Gard. 104, 495–511 (2019). 417.
    Google Scholar 
    Deng, J.-Y., van Noort, S., Compton, S. G., Chen, Y. & Greeff, J. M. Conservation implications of fine scale population genetic structure of Ficus species in South African forests. Ecol. Manag. 474, 118387 (2020).
    Google Scholar 
    Galizia, C. G. et al. Relationship of visual and olfactory signal parameters in a food-deceptive flower mimicry system. Behav. Ecol. 16, 159–168 (2004).
    Google Scholar 
    Gibernau, M., HossaertMcKey, M., Frey, J. & Kjellberg, F. Are olfactory signals sufficient to attract fig pollinators. Ecoscience 5, 306–311 (1998).
    Google Scholar 
    Kapustjansky, A., Chittka, L. & Spaethe, J. Bees use three-dimensional information to improve target detection. Naturwissenschaften 97, 229–233 (2010).ADS 
    CAS 

    Google Scholar 
    Hempel de Ibarra, N., Langridge, K. V. & Vorobyev, M. More than colour attraction: behavioural functions of flower patterns. Curr. Opin. Insect Sci. 12, 64–70 (2015).
    Google Scholar 
    Boff, S., Henrique, J. A., Friedel, A. & Raizer, J. Disentangling the path of pollinator attraction in temporarily colored flowers. Int. J. Trop. Insect Sci. 41, 1305–1311 (2021).
    Google Scholar 
    Leonard, A. S. & Papaj, D. R. ‘X’ marks the spot: the possible benefits of nectar guides to bees and plants. Funct. Ecol. 25, 1293–1301 (2011).
    Google Scholar 
    Dobson, H. E. M. & Bergström, G. The ecology and evolution of pollen odors. Plant Syst. Evol. 222, 63–87 (2000).CAS 

    Google Scholar 
    Raguso, R. A. Why are some floral nectars scented? Ecology 85, 1486–1494 (2004).
    Google Scholar 
    Corbet, S. A., Kerslake, C. J. C., Brown, D. & Morland, N. E. Can bees select nectar-rich flowers in a patch. J. Apic. Res. 23, 234–242 (1984).
    Google Scholar 
    Policha, T. et al. Disentangling visual and olfactory signals in mushroom-mimicking Dracula orchids using realistic three-dimensional printed flowers. N. Phytol. 210, 1058–1071 (2016).CAS 

    Google Scholar 
    Stout, J. C., Goulson, D. & Allen, J. A. Repellent scent-marking of flowers by a guild of foraging bumblebees (Bombus spp.). Behav. Ecol. Sociobiol. 43, 317–326 (1998).
    Google Scholar 
    Howell, A. D. & Alarcón, R. Osmia bees (Hymenoptera: Megachilidae) can detect nectar-rewarding flowers using olfactory cues. Anim. Behav. 74, 199–205 (2007).von Arx, M. Floral humidity and other indicators of energy rewards in pollination biology. Commun. Integr. Biol. 6, e22750–e22750 (2013).
    Google Scholar 
    Goyret, J. The breath of a flower: CO2 adds another channel-and then some-to plant-pollinator interactions. Commun. Integr. Biol. 1, 66–68 (2008).CAS 

    Google Scholar 
    Bradbury, J. W. & Vehrencamp, S. L. Principles of Animal Communication 2nd edn (Sinauer Associates, 2011).McMeniman, C. J., Corfas, R. A., Matthews, B. J., Ritchie, S. A. & Vosshall, L. B. Multimodal integration of carbon dioxide and other sensory cues drives mosquito attraction to humans. Cell 156, 1060–1071 (2014).CAS 

    Google Scholar 
    Smith, J. M. & Harper, D. Animal Signals (Oxford Univ. Press, 2003).Smith, M. J. & Harper, D. G. C. Animal signals: models and terminology. J. Theor. Biol. 177, 305–311 (1995).ADS 

    Google Scholar 
    Laidre, M. E. & Johnstone, R. A. Animal signals. Curr. Biol. 23, R829–R833 (2013).CAS 

    Google Scholar 
    Smith, J. M. Must reliable signals always be costly? Anim. Behav. 47, 1115–1120 (1994).
    Google Scholar 
    Guerenstein, P. G., A.Yepez, E., van Haren, J., Williams, D. G. & Hildebrand, J. G. Floral CO2 emission may indicate food abundance to nectar-feeding moths. Naturwissenschaften 91, 329–333 (2004).ADS 
    CAS 

    Google Scholar 
    Goyret, J., Markwell, P. M. & Raguso, R. A. Context- and scale-dependent effects of floral CO2 on nectar foraging by Manduca sexta. Proc. Natl Acad. Sci. USA 105, 4565–4570 (2008).ADS 
    CAS 

    Google Scholar 
    Thom, C., Guerenstein, P. G., Mechaber, W. L. & Hildebrand, J. G. Floral CO2 reveals flower profitability to moths. J. Chem. Ecol. 30, 1285–1288 (2004).CAS 

    Google Scholar 
    Gilbert, F. S., Haines, N. & Dickson, K. Empty flowers. Funct. Ecol. 5, 29–39 (1991).
    Google Scholar 
    von Arx, M., Goyret, J., Davidowitz, G. & Raguso, R. A. Floral humidity as a reliable sensory cue for profitability assessment by nectar-foraging hawkmoths. Proc. Natl Acad. Sci. USA 109, 9471–9476 (2012).ADS 

    Google Scholar 
    Harrap, M. J. M., Hempel de Ibarra, N., Knowles, H. D., Whitney, H. M. & Rands, S. A. Floral humidity in flowering plants: A preliminary survey. Front. Plant Sci. https://doi.org/10.3389/fpls.2020.00249 (2020).Harrap, M. J. M. & Rands, S. A. The role of petal transpiration in floral humidity generation. Planta 255, 78 (2022).CAS 

    Google Scholar 
    Harrap, M. J. M., Hempel de Ibarra, N., Knowles, H. D., Whitney, H. M. & Rands, S. A. Bumblebees can detect floral humidity. J. Exp. Biol. https://doi.org/10.1242/jeb.240861 (2021).Hebets, E. A. & Papaj, D. R. Complex signal function: developing a framework of testable hypotheses. Behav. Ecol. Sociobiol. 57, 197–214 (2005).
    Google Scholar 
    Bronstein, J. L., Huxman, T., Horvath, B., Farabee, M. & Davidowitz, G. Reproductive biology of Datura wrightii: the benefits of a herbivorous pollinator. Ann. Bot. 103, 1435–1443 (2009).
    Google Scholar 
    Johnson, C. A. et al. Coevolutionary transitions from antagonism to mutualism explained by the co-opted antagonist hypothesis. Nat. Commun. https://doi.org/10.1038/s41467-021-23177-x (2021).Clark, C. J. The role of power versus energy in courtship: what is the ‘energetic cost’ of a courtship display? Anim. Behav. 84, 269–277 (2012).
    Google Scholar 
    Willmott, A. P. & Ellington, C. P. The mechanics of flight in the hawkmoth Manduca sexta. I. Kinematics of hovering and forward flight. J. Exp. Biol. 200, 2705–2722 (1997).CAS 

    Google Scholar 
    Shields, V. D. C. & Hildebrand, J. G. Fine structure of antennal sensilla of the female sphinx moth, Manduca sexta (Lepidoptera: Sphingidae). II. Auriculate, coeloconic, and styliform complex sensilla. Can. J. Zool. 77, 302–313 (1999).
    Google Scholar 
    Lee, J. K. & Strausfeld, N. J. Structure, distribution and number of surface sensilla and their receptor cells on the olfactory appendage of the male moth Manduca sexta. J. Neurocytol. 19, 519–538 (1990).CAS 

    Google Scholar 
    Shields, V. D. & Hildebrand, J. G. Recent advances in insect olfaction, specifically regarding the morphology and sensory physiology of antennal sensilla of the female sphinx moth Manduca sexta. Microsc. Res. Tech. 55, 307–329 (2001).CAS 

    Google Scholar 
    Tichy, H. & Loftus, R. Hygroreceptors in insects and a spider: Humidity transduction models. Naturwissenschaften 83, 255–263 (1996).ADS 
    CAS 

    Google Scholar 
    Ahrens, M., Huang, K.-H., Narayan, S., Mensh, B. & Engert, F. Two-photon calcium imaging during fictive navigation in virtual environments. Front. Neural Circuits https://doi.org/10.3389/fncir.2013.00104 (2013).Lacher, V. Elektrophysiologische untersuchungen an einzelnen rezeptoren für geruch, kohlendioxyd, luftfeuchtigkeit und tempratur auf den antennen der arbeitsbiene und der drohne (Apis mellifica L.). Z. f.ür. Vgl. Physiologie 48, 587–623 (1964).
    Google Scholar 
    Waldow, U. Elektrophysiologische untersuchungen an feuchte-, trocken- und kälterezeptoren auf der antenne der wanderheuschrecke Locusta. Z. f.ür. Vgl. Physiologie 69, 249–283 (1970).
    Google Scholar 
    Yokohari, F. & Tateda, H. Moist and dry hygroreceptors for relative humidity of the cockroach, Periplaneta americana L. J. Comp. Physiol. 106, 137–152 (1976).
    Google Scholar 
    Tichy, H. Low rates of change enhance effect of humidity on the activity of insect hygroreceptors. J. Comp. Physiol. A Neuroethol. Sens Neural Behav. Physiol. 189, 175–179 (2003).CAS 

    Google Scholar 
    Tichy, H., Hellwig, M. & Kallina, W. Revisiting theories of humidity transduction: a focus on electrophysiological data. Front. Physiol. 8, 650 (2017).
    Google Scholar 
    Tichy, H. & Kallina, W. Insect hygroreceptor responses to continuous changes in humidity and air pressure. J. Neurophysiol. 103, 3274–3286 (2010).CAS 

    Google Scholar 
    Wolfin, M. S., Raguso, R. A., Davidowitz, G. & Goyret, J. Context dependency of in-flight responses by Manduca sexta moths to ambient differences in relative humidity. J. Exp. Biol. https://doi.org/10.1242/jeb.177774 (2018).Smith, G., Kim, C. & Raguso, R. A. Pollen accumulation on hawkmoths varies substantially among moth-pollinated flowers. Preprint at bioRxiv https://doi.org/10.1101/2022.07.15.500245 (2022).Haverkamp, A., Bing, J., Badeke, E., Hansson, B. S. & Knaden, M. Innate olfactory preferences for flowers matching proboscis length ensure optimal energy gain in a hawkmoth. Nat. Commun. 7, 11644 (2016).ADS 
    CAS 

    Google Scholar 
    Harrison, A. S. & Rands, S. A. The ability of bumblebees Bombus terrestris (hymenoptera: Apidae) to detect floral humidity is dependent upon environmental humidity. Environ. Entomol. 51, 1010–1019 (2022).
    Google Scholar 
    Kelber, A. What a hawkmoth remembers after hibernation depends on innate preferences and conditioning situation. Behav. Ecol. 21, 1093–1097 (2010).
    Google Scholar 
    Riffell, J. A. et al. Flower discrimination by pollinators in a dynamic chemical environment. Science 344, 1515–1518 (2014).ADS 
    CAS 

    Google Scholar 
    Schellenberg, R. The trouble with humidity: the hidden challenge of RH calibration. Cal. Lab. 9, 40–42 (2002).
    Google Scholar 
    Roddy, A. B., Brodersen, C. R. & Dawson, T. E. Hydraulic conductance and the maintenance of water balance in flowers. Plant Cell Environ. 39, 2123–2132 (2016).CAS 

    Google Scholar 
    Sane, S. P. & Jacobson, N. P. Induced airflow in flying insects. II. Measurement of induced flow. J. Exp. Biol. 209, 43–56 (2006).
    Google Scholar 
    Daly, K. C., Kalwar, F., Hatfield, M., Staudacher, E. & Bradley, S. P. Odor detection in Manduca sexta is optimized when odor stimuli are pulsed at a frequency matching the wing beat during flight. PLoS ONE 8, e81863 (2013).ADS 

    Google Scholar 
    Yokohari, F. Hygroreceptor mechanism in the antenna of the cockroach. Periplaneta. J. Comp. Physiol. 124, 153 (1978).
    Google Scholar 
    Loftus, R. Temperature-dependent dry receptor on antenna of Periplaneta. Tonic response. J. Comp. Physiol. 111, 153–170 (1976).
    Google Scholar 
    Tichy, H. & Kallina, W. Sensitivity of honeybee hygroreceptors to slow humidity changes and temporal humidity variation detected in high resolution by mobile measurements. PLoS ONE 9, e99032 (2014).ADS 

    Google Scholar 
    Galen, C., Sherry, R. A. & Carroll, A. B. Are flowers physiological sinks or faucets? Costs and correlates of water use by flowers of Polemonium viscosum. Oecologia 118, 461–470 (1999).ADS 

    Google Scholar 
    Elle, E., van Dam, N. M. & Hare, J. D. Cost of glandular trichomes, a “resistance” character in Datura wrightii regel (solanaceae). Evolution 53, 22–35 (1999).
    Google Scholar 
    Elle, E. & Hare, J. D. Environmentally induced variation in floral traits affects the mating system in Datura wrightii. Funct. Ecol. 16, 79–88 (2002).
    Google Scholar 
    Marler, C. A. & Ryan, M. J. Energetic constraints and steroid hormone correlates of male calling behaviour in the túngara frog. J. Zool. 240, 397–409 (1996).
    Google Scholar 
    Bernal, X. E., Rand, A. S. & Ryan, M. J. Acoustic preferences and localization performance of blood-sucking flies (Corethrella Coquillett) to túngara frog calls. Behav. Ecol. 17, 709–715 (2006).
    Google Scholar 
    Raguso, R. A. Flowers as sensory billboards: progress towards an integrated understanding of floral advertisement. Curr. Opin. Plant Biol. 7, 434–440 (2004).
    Google Scholar 
    Peach, D. A. H., Gries, R., Zhai, H., Young, N. & Gries, G. Multimodal floral cues guide mosquitoes to tansy inflorescences. Sci. Rep. 9, 3908 (2019).ADS 

    Google Scholar 
    Riffell, J. A. & Alarcón, R. Multimodal floral signals and moth foraging decisions. PLoS ONE 8, e72809 (2013).ADS 
    CAS 

    Google Scholar 
    van der Kooi, C. J., Kevan, P. G. & Koski, M. H. The thermal ecology of flowers. Ann. Bot. 124, 343–353 (2019).
    Google Scholar 
    Terry, L. I., Roemer, R. B., Walter, G. H., Booth, D. & Lee, K. P. Thrips’ responses to thermogenic associated signals in a cycad pollination system: the interplay of temperature, light, humidity and cone volatiles. Funct. Ecol. 28, 857–867 (2014).
    Google Scholar 
    Bronstein, J. L., Alarcón, R. & Geber, M. The evolution of plant–insect mutualisms. N. Phytol. 172, 412–428 (2006).
    Google Scholar 
    Schaefer, H. M. & Ruxton, G. D. Deception in plants: mimicry or perceptual exploitation. Trends Ecol. Evol. 24, 676–685 (2009).
    Google Scholar 
    Franchi, G. G., Nepi, M. & Pacini, E. Is flower/corolla closure linked to decrease in viability of desiccation-sensitive pollen? Facts and hypotheses: a review of current literature with the support of some new experimental data. Plant Syst. Evol. 300, 577–584 (2014).
    Google Scholar 
    Safavian, D. et al. High humidity partially rescues the Arabidopsis thaliana exo70A1 stigmatic defect for accepting compatible pollen. Plant Reprod. 27, 121–127 (2014).CAS 

    Google Scholar 
    Shivanna, K. R. & Cresti, M. Effects of high humidity and temperature stress on pollen membrane integrity and pollen vigour in Nicotiana tabacum. Sex. Plant Reprod. 2, 137–141 (1989).
    Google Scholar 
    Richman, S. K. et al. The sensory and cognitive ecology of nectar robbing. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2021.698137 (2021).Raguso, R. A. et al. Trumpet flowers of the Sonoran Desert: floral biology of Peniocereus Cacti and Sacred Datura. Int. J. Plant Sci. 164, 877–892 (2003).CAS 

    Google Scholar 
    Carazo, P. & Font, E. ‘Communication breakdown’: the evolution of signal unreliability and deception. Anim. Behav. 87, 17–22 (2014).
    Google Scholar 
    Schemske, D. W. Evolution of floral display in the orchid Brassavola nodosa. Evolution 34, 489–493 (1980).
    Google Scholar 
    Haber, W. A. Pollination by deceit in a mass-flowering tropical tree Plumeria rubra L. (apocynaceae). Biotropica 16, 269–275 (1984).
    Google Scholar 
    Brandenburg, A., Kuhlemeier, C. & Bshary, R. Hawkmoth pollinators decrease seed set of a low-nectar Petunia axillaris line through reduced probing time. Curr. Biol. 22, 1635–1639 (2012).CAS 

    Google Scholar 
    Bye, R. & Sosa, V. Molecular phylogeny of the jimsonweed genus Datura (solanaceae). Syst. Bot. 38, 818–829 (2013).
    Google Scholar 
    Kariñho-Betancourt, E., Agrawal, A. A., Halitschke, R. & Núñez-Farfán, J. Phylogenetic correlations among chemical and physical plant defenses change with ontogeny. N. Phytol. 206, 796–806 (2015).
    Google Scholar 
    Kawahara, A. Y. et al. Evolution of Manduca sexta hornworms and relatives: biogeographical analysis reveals an ancestral diversification in Central America. Mol. Phylogenet. Evol. 68, 381–386 (2013).
    Google Scholar 
    Contreras, H. L. et al. The effect of ambient humidity on the foraging behavior of the hawkmoth Manduca sexta. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 199, 1053–1063 (2013).
    Google Scholar 
    Cardoso, J. C. F., Gonzaga, M. O., Cavalleri, A., Maruyama, P. K. & Alves-Silva, E. The role of floral structure and biotic factors in determining the occurrence of florivorous thrips in a dystilous shrub. Arthropod-Plant Interact. 10, 477–484 (2016).
    Google Scholar 
    Nicolson, S. W. Sweet solutions: nectar chemistry and quality. Philos. Trans. R. Soc. Lond. B Biol. Sci. 377, 20210163 (2022).CAS 

    Google Scholar 
    Pellmyr, O. & Thien, L. B. Insect reproduction and floral fragrances: keys to the evolution of the Angiosperms. Taxon 35, 76–85 (1986).
    Google Scholar 
    Enjin, A. et al. Humidity sensing in Drosophila. Curr. Biol. 26, 1352–1358 (2016).CAS 

    Google Scholar 
    Knecht, Z. A. et al. Distinct combinations of variant ionotropic glutamate receptors mediate thermosensation and hygrosensation in Drosophila. eLife 5, e17879 (2016).
    Google Scholar 
    Knecht, Z. A. et al. Ionotropic receptor-dependent moist and dry cells control hygrosensation in Drosophila. eLife 6, e26654 (2017).
    Google Scholar 
    Croset, V. et al. Ancient protostome origin of chemosensory ionotropic glutamate receptors and the evolution of insect taste and olfaction. PLoS Genet. 6, e1001064–e1001064 (2010).
    Google Scholar 
    Dahake, A. et al. MATLAB codes: a signal-like role for floral humidity in a nocturnal pollination system. Zenodo https://doi.org/10.5281/zenodo.7320037 (2022).Pereira, T. D. et al. Fast animal pose estimation using deep neural networks. Nat. Methods 16, 117–125 (2019).CAS 

    Google Scholar 
    Nilsson, S. R. et al. Simple behavioral analysis (SimBA) – an open source toolkit for computer classification of complex social behaviors in experimental animals. Preprint at bioRxiv https://doi.org/10.1101/2020.04.19.049452 (2020).Casey, T. M. Flight energetics of sphinx moths: power input during hovering flight. J. Exp. Biol. 64, 529–543 (1976).CAS 

    Google Scholar 
    Riffell, J. A. et al. Behavioral consequences of innate preferences and olfactory learning in hawkmoth-flower interactions. Proc. Natl Acad. Sci. USA 105, 3404–3409 (2008).ADS 
    CAS 

    Google Scholar 
    Lott, G. K., Johnson, B. R., Bonow, R. H., Land, B. R. & Hoy, R. R. g-PRIME: a free, windows based data acquisition and event analysis software package for physiology in classrooms and research labs. J. Undergrad. Neurosci. Educ. 8, A50–A54 (2009).
    Google Scholar 
    Chaure, F. J., Rey, H. G. & Quiroga, R. Q. A novel and fully automatic spike-sorting implementation with variable number of features. J. Neurophysiol. 120, 1859–1871 (2018).CAS 

    Google Scholar 
    Tichy, H. Humidity-dependent cold cells on the antenna of the stick insect. J. Neurophysiol. 97, 3851–3858 (2007).
    Google Scholar 
    Campbell, R. raacampbell/shadedErrorBar. https://github.com/raacampbell/shadedErrorBar (2022).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Broadhead, G. T. & Raguso, R. A. Associative learning of non-sugar nectar components: amino acids modify nectar preference in a hawkmoth. J. Exp. Biol. https://doi.org/10.1242/jeb.234633 (2021). More

  • in

    Anthrax hotspot mapping in Kenya support establishing a sustainable two-phase elimination program targeting less than 6% of the country landmass

    Data sourcesThis study builds on two datasets; 666 livestock anthrax outbreaks collected over 60 years (1957–2017) by the Kenya Directorate of Veterinary Services (KDVS), and 13 reported anthrax outbreaks we investigated between 2017 and 201811,13. These datasets were combined with data from targeted active anthrax surveillance we conducted in 2019–2020 (see below) to define anthrax suitable areas in Kenya, including hotspots, and subsequently assessed effectiveness of livestock vaccination as a control strategy.Targeted active surveillance-collected anthrax data, 2019–2020Active anthrax surveillance was conducted for 12 months between 2019 and 2020 in randomly selected areas to ensure representation of all AEZs of the country. AEZs are land units defined based on the patterns of soil, landforms and climatic characteristics. Kenya has seven AEZs that include agro-alpine, high potential, medium potential, semi-arid, arid, very-arid and desert. In 2013, Kenya devolved governance into 47 semi-autonomous counties that are subdivided into 290 subcounties which are in turn divided into 1450 administrative wards, the smallest administrative units in the country. Using a geographic map that condensed Kenya into five AEZs; agro-alpine, high potential, medium potential, semi-arid, and arid/very arid zones, we randomly selected 4 administrative sub-counties from each AEZ (N = 20). To increase geographic spread of the study and enhance detection of anthrax outbreaks, we surveilled the larger administrative county (consisting of 20 to 45 administrative wards) where the randomly selected sub-counties were located. As shown in Fig. S1, we ultimately carried out the active anthrax surveillance in 18 counties, containing 523 administrative wards, the latter being used for measuring spatial association (see below).We conducted the surveillance between April 2019 and June 2020, through 523 animal health practitioners (AHPs), one in each ward, after intensive training to identify anthrax using a standard case definition, and to collect and electronically transmit the data weekly using telephone-based short messaging system (SMS) to a central server hosted by KDVS. Regarding case definition, any livestock death classified as anthrax through clinical or laboratory diagnosis was considered an anthrax event. Using standard guidelines issued by the KDVS, a clinical diagnosis was made by the AHPs across the country as an acute cattle, sheep or goat disease characterized by sudden death with or without bleeding from natural orifices, accompanied by absence of rigor mortis. Further, if the carcass was accidentally opened, failure of blood to clot and/or the presence of splenomegaly were included. In pigs, symptoms included swelling of the face and neck with oedema. A laboratory confirmed anthrax was diagnosed using Gram and methylene blue stains followed by identification of the capsule and typical rod-shaped B. anthracis in clinical specimens that the AHPs submitted to the central or regional veterinary investigation laboratories in Kenya. One case of anthrax in either species was considered an outbreak.During the surveillance, the programmed server sent prompting texts directly to the AHPs’ cell phones every Friday of each week for the 52 weeks. The AHPs interacted with the platform by responding to prompting questions sent via SMS to their telephones. Data were securely stored in an online encrypted platform which was subsequently downloaded into Ms Excel for analysis. This surveillance detected 119 anthrax outbreaks, whose partial data were used to model effects of climate change on future anthrax distribution in Kenya14. Here, we integrated these active surveillance data with other datasets to conduct detailed ENM and kernel-smoothed density mapping with a goal of refining suitable anthrax areas including crystalizing hotspots in the country.Anthrax outbreak incidence per livestock population by countyWe knew the total number of livestock per county and wards by species for the active surveillance period. The counties represented the level of disease management including vaccine distribution while the wards within counties represented the modeling unit for targeting control. Therefore, we estimated the outbreak incidence as the total number of outbreaks per livestock species per 100,000 head of that species.Ecological niche modeling and validationWe used boosted regression tree (BRT) algorithm as previously published13. In those studies, we estimated the geographic distribution of anthrax in southern Kenya using 69 spatially unique outbreak points (thinned from the 86 outbreaks in the records) and 18 environmental variables resampled to 250 m resolution. In this study, the final experiments were run with a learning rate (lr) = 0.001, bagging fraction (br) = 5, and maximum tree = 2500. We then mapped anthrax suitability as the mean output of the 100 experiments and the lower 2.5% and upper 97.5% mapped as confidence intervals. We determined variable contribution and derived partial dependence as previously described13. As BRTs are a random walk and each experiment randomly resamples training and test data, it was necessary to repeat those outputs along with the map predictions.Here, our goal was to evaluate the BRT models built with records data from 2011 to 2017 data and use the predict function to calculate model accuracy metrics using the 2017–2020 outbreaks as presence points and the sub-counties reporting zero outbreaks during the 2019–2020 active surveillance period as absence points. The model of southern Kenya was projected onto all of Kenya using climate variables clipped to the whole of Kenya. We tested the BRT models in two ways; first, evaluating 2011–2017 data models with holdout data using a random resampling and multi-modeling approach. Here, we report the area under curve (AUC) for each of the original training/testing split into the 69 historical points and the 2017–2020 data serving as independent data, the latter representing true model validation. Second, to determine the total percentage of surveillance data predicted and map areas of anthrax suitability to compare with kernel density estimates (see below), we produced a dichotomized map using the Youden index cutoff17 following Otieno et al.14.Outbreak concentrations from kernel density estimation (KDE)To describe the spatial concentration of reported outbreaks, we calculated descriptive spatial statistics, including the spatial mean, standard distance, and standard deviational ellipse of outbreak locations from the prospective surveillance dataset following Blackburn et al.18 These spatial statistics help to differentiate the geographic focus (spatial mean) and dispersion of outbreak reports from year to year and across the sampling period. We then conducted kernel density estimation (KDE) to visualize the concentration of anthrax outbreaks per square kilometer per year and across the study period18. We used the spatstat package for all KDE analyses using the quadratic kernel function19:$$fleft( x right) = frac{1}{{nh^{2} }} mathop sum limits_{i = 1}^{n} Kleft( {frac{{x – X_{i} }}{h}} right)$$where h is the bandwidth, x-Xi is the distance to each anthrax outbreak i. Finally, K is the quadratic kernel function, defined as:$$Kleft( x right) = frac{3}{4}left( {1 – x^{2} } right), left| x right| le 1$$$$Kleft( x right) = 0,x > 1$$This function was employed to estimate anthrax outbreak concentration across space using each outbreak weighted as one. We calculated the bandwidth (kernel) using hopt that uses the sample size (number of outbreaks) and the standard distance to estimate bandwidth. Finally, we estimated bandwidth for each year and then averaged them to apply the same fixed bandwidth for each year under study in Q-GIS version 3.1.8. The resulting outputs were map surfaces representing the spatial concentrations of outbreaks across the country per 1 km2 for each study year and all study years combined. For this study, we used the cutoff criteria of Nelson and Boots19 to identify outbreak hotspots as areas with density values in the upper 25%, 10%, and 5% of outbreak concentrations. The analyses identified these areas by year (2017–2020) and for all surveillance years combined.Local spatial clustering at the ward levelAnthrax outbreak incidence per livestock speciesThe ENM and KDE-derived maps provide a first estimate of potential risk and outbreak concentration, respectively. We were also interested in estimating anthrax outbreak intensity relative to livestock populations at a local level. For the active surveillance period, we knew the total number of outbreaks per ward (the smallest administrative spatial unit) by livestock species. For this two-year period, we estimated the ward-level outbreak incidence as the total number of outbreaks per livestock species per 10,000 head of that species. To estimate livestock population per ward, we extracted the values in the raster file of the areal weighted gridded livestock of the world data using the zonal statistic routine in Q-GIS version 3.1.8, into the polygon consisting of all pixels per ward as the total population19,20. We calculated outbreak incidence as the number of outbreaks per ward cattle population per 10,000 cattle for each administrative ward. We limited this analysis to those 18 counties participating in the active surveillance study (Fig. S1), as we could appropriately assume any ward with no reports was a ‘true zero’ for the estimation. Given that most reported outbreaks were in domestic cattle (see results below), we here report those results involving cattle alone. Given the overall high number of wards and the high number of wards without outbreaks, we performed the empirical Bayes smoothing and spatial Bayes smoothing routines in GeoDa version 1.12.1.161 to reduce the variance in anthrax incidence estimates20,21. To evaluate smoothing routine performance, we box plotted rates per ward and selected the method with the greatest reduction in outliers21. Smoothed rates were mapped as choropleth map in Q-GIS version 3.1.8 using the four equal area bins.Spatial cluster analysisWe used Local Moran’s I16 to test for spatial cluster of livestock anthrax in cattle using the smoothed outbreak incidence estimates. The Local Moran’s I statistic tests whether individual wards are part of spatial cluster, like incidence estimates surrounded by similar estimate (high-high or low-low) or spatial outliers where wards with significantly high or low estimates are surrounded by dissimilar values (high-low or low–high). The local Moran’s I is written as16:$$I_{i} = Z_{i} sum W_{ij} Z_{j}$$where Ii is the statistic for a ward i, Zi is the difference between the incidence at i and the mean anthrax incidence rate for all of wards in the study, Zj is the difference between anthrax risk at ward j and the mean for all wards. Wij is the weights matrix. In this study, the 1st order queen contiguity was employed. Here, Wij equals 1/n if a ward shared a boundary or vertex and 0 if not. For this study, Local Moran’s I was performed on the wards using 999 permutations and p = 0.05 using GeoDa version 1.12.1.161.Assessing effectiveness of cattle vaccination in burden hotspotsAs a first estimate of how we might scale up livestock anthrax vaccination efforts in Kenya, we slightly adjusted a simple published anthrax outbreak simulation model in a cattle population. For this study we applied an early mathematical approach of Funiss and Hahn22 to simulate anthrax at the ward level. While other recent models are available23,24, these are difficult to parameterize or require time series data we could not derive with the surveillance approach in this study. Like the more recent models, Funiss and Hahn22 assumed anthrax transmission was driven by cattle accessing spore-contaminated environments. Here the proportion of infected cattle each day depended on the population of susceptible animals in the population and probability of getting infected. This probability depends on environmental contamination (“a”), and a fraction of anthrax carcasses in the environment on a day (“f,”). Each day, the newly infected cattle are transferred to an incubation period vector, “d,” waiting to die following a probability “p”. In this model, all infected animals, “n,” die following the incubation periods given by the vector, “p”, in which pi is the probability of a cow dying i days after the infection. Following death, the cattle are transferred to a carcass state, providing a direct infection source to the susceptible cattle via environmental contamination. Environmental contamination “a,” is therefore defined as the number of spores ingested by an animal in a day. This environmental contamination depends on spores from carcasses and an assumed spore decay rate γ22.The complete set of difference equations with a daily time step is given by:$${text{S}}_{(t + 1)} = {text{S}}_{(t)} – {text{ S}}_{(t)} *left( {{1} – {text{e}}^{{ – left( {{text{a}}_{t} + gamma {text{f}}_{{{text{t}} + 1}} } right)}} } right)$$$${text{I}}_{(t + 1)} = {text{I}}_{(t)} + {text{ S}}_{(t)} *left( {{1} – {text{e}}^{{ – left( {{text{a}}_{{text{t}}} + gamma {text{f}}_{{{text{t}} + {1}}} } right)}} } right)$$where the expression (left( {{1} – {text{e}}^{{ – left( {{text{a}}_{t} + gamma {text{f}}_{{{text{t}} + 1}} } right)}} } right)) denotes the probability of an animal becoming infected and at + γft+1 is the mean number of spores ingested by a cow in a day. The equation for environmental contamination, a, is given by:$${text{a}}_{t + 1} {-}{text{a}}_{{text{t}}} = alpha {text{a}}_{{text{t}}} + beta {text{c}}_{{{text{t}} + {1}}}$$The newly infected animals die after a certain number of days. The distribution of incubation periods is given by the vector, p. On each day, the new cases are placed in a due-to-die vector, d, and when they die, they are subsequently moved down one step to fresh carcasses, ft. The fresh carcasses provide a direct source of infection to the susceptible cattle via the ‘fresh carcass term’, γ. These carcasses decay or are scavenged or disposed by man. The equation expressing the disseminating carcasses, c, is:$${text{C}}_{t + 1} – {text{c}}_{t} = {text{f}}_{t + 1} – delta {text{c}}_{t}$$The model parameters variables are provided in Table 1 and are similar to those used by Funiss and Hahn22 to generate a standard run. We ran the model for one year and extrapolated to cattle population in the identified hotspot wards.Table 1 Model parameters and variables.Full size table More

  • in

    Silent gene clusters encode magnetic organelle biosynthesis in a non-magnetotactic phototrophic bacterium

    The phototrophic species Rhodovastum atsumiense G2-11 acquired MGCs from an unknown alphaproteobacterial MTB by recent HGTIn a systematic database search for novel MGCs, we identified several orthologs of known magnetosome genes in the recently released draft genome sequence of the culturable anoxygenic phototroph Rhodovastum atsumiense G2-11 [25]. This finding was unexpected as, after isolation of G2-11 from a paddy field more than 20 years ago, no magnetosome formation has been reported [26]. Furthermore, no MTB has been identified so far among phototrophs or within the Acetobacteraceae family to which G2-11 belongs [26] (Fig. 1a).Fig. 1: Phylogeny, chromosome, and MGCs organization of G2-11.a The maximum likelihood phylogenetic tree based on ribosomal proteins demonstrates the position of G2-11 (highlighted in red) within family Acetobacteraceae (highlighted in the yellow box). The Azospirillaceae family was used as an outgroup based on the latest Alphaproteobacteria phylogeny. Branch length represents the number of base substitutions per site. Values at nodes indicate branch support calculated from 500 replicates using non-parametric bootstrap analysis. Bootstrap values 20 genes with no homology to known magnetosome genes (Fig. 1c). In contrast, the compact MGCs in G2-11 include only a few genes that could not be associated with magnetosome biosynthesis.Tetranucleotide usage patterns are frequently employed as a complementary tool to group organisms since they bear a reliable phylogenetic signal [32]. Likewise, deviations of tetranucleotide usage in a certain fragment from the flanking genome regions can indicate HGT [21]. Comparison of the z-normalized tetranucleotide frequencies of the MGCs (27.5 kb) with the flanking upstream (117.7 kb) and downstream (79.5 kb) fragments showed a considerably lower correlation between them (Pearson’s r = 0.88 with both flanking fragments) than between the flanking fragments themselves (Pearson’s r = 0.97, Fig. 1e). This indicates a significant difference in the tetranucleotide composition of the MGCs compared to the flanking genomic regions and supports a foreign origin of the magnetosome genes in G2-11 suggested by the phylogenetic analysis. Besides, the presence of a mobile element (transposase) and position of the MGCs directly downstream of a tRNA gene, a common hotspot for integration of genomic islands [33,34,35], suggests that the MGCs of G2-11 are indeed located on a genomic island, i.e., represent MAI, like in many other MTB [20, 21]. Unfortunately, the lack of other representatives of the genus Rhodovastum makes it impossible to infer whether the MAI was transferred directly to G2-11 or the last common ancestor of the genus. Nonetheless, its compact organization and conspicuous tetranucleotide usage suggest a relatively recent HGT event.G2-11 does not form magnetosomes under laboratory conditionsAlthough magnetosome genes discovered in G2-11 comply with the minimal set required for magnetosome biomineralization in MSR-1 [36], no magnetosomes have been detected in this organism. It might have several explanations: (i) the strain might switch to the magnetotactic lifestyle only under very specific, yet not tested, conditions; (ii) it once was able to synthesize magnetosomes in its natural environment but lost this ability upon subcultivation due to mutations before its characterization; (iii) the strain might naturally not exploit magnetotaxis as its genes might be non-functional or not actively expressed. To clarify which of these explanations is most likely, we first tested whether G2-11 can form magnetosomes under different laboratory conditions. To this end, the strain was cultivated photoheterotrophically, anoxic or microoxic, in a complex medium with potassium lactate and soybean peptone, as commonly used for MSR-1 (FSM) [37], as well as in minimal media with different C-sources previously shown to support growth in G2-11 (glucose, pyruvate, L-glutamine, and ethanol) [26]. All media were supplied with 50 μM ferric citrate to provide sufficient iron for magnetite biomineralization. Since magnetosome biosynthesis is possible only under low oxygen tension, aerobic chemoheterotrophic growth of G2-11 was not tested. The best growth was observed in the complex FSM medium and a minimal medium with glucose or pyruvate, whereas L-glutamine and ethanol supported only weak growth (Supplementary Fig. S3). Irrespective of the growth stage, none of the tested cultures demonstrated magnetic response as measured by a magnetically induced differential light scattering assay (Cmag) [38]. Consistently, micrographs of cells collected from stationary phase cultures did not show any magnetosome-like particles (Supplementary Fig. S3). This confirmed that G2-11 indeed cannot biosynthesize magnetosomes, at least under the conditions available for the laboratory tests. During cultivation, we also noticed that G2-11 cells did not move at any growth stage despite the initial description of this organism as motile using a single polar flagellum [26], and containing several flagellum synthesis operons and other motility-related genes. Moreover, the cells tended to adhere to glass surfaces under all tested conditions and formed a dense clumpy biofilm immersed in a thick extracellular matrix (Supplementary Fig. S3a-ii).Considering that G2-11 generally lacks magnetosomes and appears to have a stationary lifestyle, which is not consistent with magnetotaxis, we assessed whether the maintenance of MGCs comes at fitness costs for the organism. To this end, we deleted the entire region containing the magnetosome genes (in the following, referred to as the MAI region) using the genetic tools we established for G2-11 in this work (Supplementary Fig. S4a, see Materials and Methods for details). After PCR screening, replica plating test, and genome re-sequencing, two of G2-11 ΔMAI mutants were selected for further analysis (Supplementary Fig. S5). These mutants showed no significant differences in the growth behavior compared to the wildtype (WT) when incubated in minimal media supplied with acetate or pyruvate as a sole carbon source (Supplementary Fig. S4b). This finding suggests that the presence of the magnetosome genes neither provides benefits nor poses any substantial metabolic burden for G2-11, at least under the given experimental conditions.RNAseq reveals poor expression levels and antisense transcription in the MGCs of G2-11We set on to determine whether the magnetosome genes are transcribed in G2-11. To this end, we analyzed its whole transcriptome for the photoheterotrophic conditions, under which the best growth was observed, in two biological replicates. The expression levels of all the encoded genes calculated as TPM (transcripts per million) demonstrated a high correlation between the two replicates (Pearson’s r = 0.98). Most genes of the (mms6-like1)(mmsF-like1)mamH1IEKLMOH2 cluster were only poorly or not transcribed at all (Fig. 2a, Supplementary dataset). Transcription of mms6-like1, mamF-like1, mamL, mamH1, mamI, and mamK, for example, did not pass the noise background threshold (TPM ≤ 2) in both replicates and were unlikely to be expressed, whereas mamE, mamM, mamH2, feoAm, and feoBm slightly exceeded the threshold in at least one replicate and might be weakly transcribed (Fig. 2a). Although the TPM of mamO (TPM = 5.67–6.10, Supplementary dataset) exceeded the background threshold, the coverage plot reveals that the number of mapped reads sharply rises at its 3’-end, whereas the 5’-end has low read coverage (Fig. 2b). This indicates the presence of an internal transcription start site (TSS) and its associated promoter within the coding sequence of mamO instead of the full transcription of the gene. Localization of an active promoter within mamO was recently described in MSR-1, suggesting that the transcriptional organization of MGCs may be more broadly conserved across MTB than assumed previously [39].Fig. 2: Transcription of the magnetosome genes in G2-11.a Log10 of the transcript abundances for all genes in the G2-11 genome presented as TPM (transcripts per million). Red dots represent the magnetosome genes. Red rectangle shows genes with TPM below the threshold, and blue rectangle shows genes with expression levels above median. R1 and R2: biological replicates. Pearson’s r and the p value is presented on the graph. b RNAseq coverage of reads mapped on the positive (red) and negative (blue) strands of the genome in the MAI region. The gray balk shows the gene map: genes encoded on the negative strand are colored in black, on the positive – in green. Red arrows indicate the anti-sense transcription in the mamPAQRBST operon. Green arrows indicate the intragenic TSS within mamO. TSS are indicated with dashed lines and black arrowheads that show the direction of transcription.Full size imageTranscription of genes within the mag123, (mms6-like2)(mmsF-like2), and mamAPQRBST clusters significantly exceeded the threshold, with the expression levels of mag1, mamT, and mamS being above the overall median. At the same time, antisense transcription was detected in the mamAPQRBST region, with the coverage considerably exceeding the sense transcription (Fig. 2b). This antisense RNA (asRNA) likely originated from a promoter controlling the tRNA gene positioned on the negative strand downstream of mamT. Such long asRNAs have the potential to interfere with sense transcripts, thereby significantly decreasing the expression of genes encoded on the opposite strand [40].In summary, the RNAseq data revealed extremely low or lack of transcription of several genes that are known to be essential for magnetosome biosynthesis (mamL, mamI, mamM, mamE, and mamO) [27, 41]. Additionally, the detected antisense transcription can potentially attenuate expression of the mamAPQRBST cluster that also comprises essential genes, i.e., mamQ and mamB. Although other factors, like the absence of several accessory genes mentioned above and the potential accumulation of point mutations, might also be involved, the lack or insufficient transcription of the essential magnetosome genes appears to be the primary reason for the absence of magnetosome biosynthesis in G2-11.Magnetosome proteins from G2-11 are functional in a model magnetotactic bacteriumAlthough visual inspection of the G2-11 magnetosome genes did not reveal any frameshifts or other apparent mutations, accumulation of non-obvious functionally deleterious point substitutions in the essential genes could not be excluded. Therefore, we next tested whether at least some of the magnetosome genes from G2-11 still encode functional proteins that can complement isogenic mutants of the model magnetotactic bacterium MSR-1. In addition, we analyzed the intracellular localization of their products in both MSR-1 and G2-11 by fluorescent labeling.One of the key proteins for magnetosome biosynthesis in MSR-1 is MamB, as its deletion mutant is severely impaired in magnetosome vesicle formation and is entirely devoid of magnetite crystals [42, 43]. Here, we observed that expression of MamB[G2-11] partially restored magnetosome chain formation in MSR-1 ΔmamB (Fig. 3a, b-i, b-ii). Consistently, MamB[G2-11] tagged with mNeonGreen (MamB[G2-11]-mNG) was predominantly localized to magnetosome chains in MSR-1, suggesting that the magnetosome vesicle formation was likely restored to the WT levels (Fig. 3b-iii).Fig. 3: Genetic complementation and intracellular localization of magnetosome proteins from G2-11 in MSR-1 isogenic mutants.a TEM micrograph of MSR-1 wildtype (WT). b MSR-1 ΔmamB::mamB[G2-11]. b-i TEM micrograph and b-ii magnetosome chain close-up; b-iii) 3D-SIM Z-stack maximum intensity projection of MSR-1 ΔmamB::mamB[G2-11]-mNG. c MSR-1ΔmamQ::mamQ[G2-11]. c-i TEM micrograph and c-ii close-up of the particles; c-iii 3D-SIM Z-stack maximum intensity projection. d MSR-1 ΔmamK::mamK[G2-11]. d-i TEM micrograph of MSR-1 ΔmamK; d-ii TEM micrograph of MSR-1 ΔmamK::mamK[G2-11]; d-iii 3D-SIM Z-stack maximum intensity projection of MSR-1 ΔmamK::mNG-mamK[G2-11]. e MSR-1 ΔmamKY::mamK[G2-11]. e-i-ii Representative cells of MSR-1 ΔmamKY mutant showing examples of a short chain, cluster (e-i), and ring-shaped chain (e-ii); (e-iii) TEM micrograph of MSR ΔmamKY::mamK[G2-11] mutant showing the complemented phenotype; e-iv distribution of cells with different phenotypes in the populations of MSR-1 ΔmamKY and MSR-1 ΔmamKY::mamK[G2-11] mutants (N  > 50 cells for each strain population); e-v 3D-SIM Z-stack maximum intensity projection of MSR-1 ΔmamKY::mNG-mamK[G2-11]. f MSR-1 ΔmamJ::mamJ-like[G2-11]. f-i TEM micrograph of MSR-1 ΔmamJ; f-ii TEM micrograph of MSR-1 ΔmamJ::mamJ-like[G2-11]; f-iii 3D-SIM Z-stack maximum intensity projection of MSR-1 ΔmamJ::mamJ-like[G2-11]-gfp. g MSR-1 ΔF3::mmsF-like1[G2-11] and ΔF3::mmsF-like2[G2-11]. g-i TEM micrograph of MSR-1 ΔF3; g-ii TEM micrograph of MSR-1 ΔF3::mmsF-like1[G2-11]; g-iii TEM micrograph of MSR-1 ΔF3::mmsF-like2[G2-11]; g-iv magnetosome diameter distribution in MSR-1 ΔF3 and the mutants complemented with mmsF-like1/mmsF-like2. Asterisks indicate points of significance calculated using Kruskal–Wallis test (****p 50 cells for each of two randomly selected insertion mutants MSR-1 ΔmamKY::mamK[G2-11] revealed that the long magnetosome chains were restored in 35-40% of the population (Fig. 3e-iv). Of note, mNG-MamK[G2-11] formed slightly shorter filaments in MSR-1 ΔmamKY than in ΔmamK, which were also characteristically displaced to the outer cell curvature due to the lack of mamY [46] (Fig. 3e-v).MamJ attaches magnetosomes to the MamK filament in MSR-1, mediating their chain-like arrangement. Elimination of mamJ disrupts this linkage, causing magnetosomes to aggregate owing to magnetic interactions [47] (Fig. 3f-i). In MSR-1, MamJ is encoded within the mamAB operon, between mamE and mamK. Within the (mms6-like1)(mmsF-like1)mamH1IEKLMOH2 cluster of G2-11, there is an open reading frame (ORF) encoding a hypothetical protein that is located in a syntenic locus (Fig. 1c). Although the hypothetical protein from G2-11 and MamJ from MSR-1 differ considerably in length (563 vs. 426 aa), share only a low overall sequence similarity (31%), and are not identified as orthologues by reciprocal blast analyses, multiple sequence alignments revealed a few conserved amino acids at their N- and C-termini (Supplementary Fig. S6). Moreover, in both proteins, these conserved residues are separated by a large region rich in acidic residues (pI 3.3 and 3.2) suggesting that the G2-11 protein might be a distant MamJ homolog. To test if it implements the same function as MamJ, we transferred this gene to MSR-1 ΔmamJ. Interestingly, it indeed restored chain-like magnetosome arrangement, which, however, often appeared as closed rings rather than linear chains (Fig. 3f-ii). Despite this difference, it indicated the ability of the hypothetical protein (hereafter referred to as MamJ-like[G2-11]) to attach magnetosomes to MamK, suggesting that in the native context, it can have a function identical to MamJ. Consistently, its fluorescently labeled version was often observed in ring-like structures within the cytoplasm of MSR-1 ΔmamJ, suggesting that it is indeed localized to magnetosomes (Fig. 3f-iii).In magnetospirilla, magnetosome proteins MmsF, MamF, and MmxF share an extensive similarity. Their individual and collective elimination gradually reduces the magnetite crystal size and disrupts the chain formation in MSR-1 (Fig. 3g-i; Paulus, manuscript in preparation). The MAI of G2-11 includes two genes, whose products have high similarity to these proteins, designated here as MmsF-like1[G2-11] and MmsF-like2[G2-11]. Expression of each of them in the MSR-1 ΔmmsFΔmamFΔmmxF triple mutant (ΔF3) partially restored the magnetosome size and led to the formation of short magnetosome chains in MSR ΔF3::mmsF-like1[G2-11] (Fig. 3g-ii) or clusters in MSR-1 ΔF3::mmsF-like2[G2-11] (Fig. 3g-iii, iv). Consistently, fluorescently tagged mNG-MmsF-like1[G2-11] and mNG-MmsF-like2[G2-11] localized to magnetosomes in the pattern resembling that in the TEM micrographs of the complemented corresponding mutants (Fig. 3g-v, vii), or were perfectly targeted to the magnetosome chains in MSR-1 WT (Fig. 3g-vi, viii).In G2-11, MamB[G2-11]-mNG, mNG-MamQ[G2-11], MamJ-like[G2-11]-GFP, mNG-MmsF-like1[G2-11], and mNG-MmsF-like2[G2-11] were patchy-like or evenly distributed in the inner and intracellular membranes (Supplementary Fig. S7). No linear structures that would indicate the formation of aligned magnetosome vesicles were observed in these mutants. As expected, mNG-MamK[G2-11] formed filaments in G2-11 (Supplementary Fig. S7c).Expression of MamM, MamO, MamE, and MamL failed to complement the corresponding deletion mutants of MSR-1 (not shown). Although detrimental mutations in the genes cannot be excluded, this result can be attributed to the lack of their native, cognate interaction partners, likely due to the large phylogenetic distances between the respective orthologues.Transfer of MGCs from MSR-1 endows G2-11 with magnetosome biosynthesis that is rapidly lost upon subcultivationHaving demonstrated the functionality of several G2-11 magnetosome genes in the MSR-1 background, we wondered whether, conversely, the G2-11 background is permissive for magnetosome biosynthesis. To this end, we transferred the well-studied MGCs from MSR-1 into G2-11, thereby mimicking an HGT event under laboratory conditions. The magnetosome genes from MSR-1 were previously cloned on a single vector pTpsMAG1 to enable the one-step transfer and random insertion into the genomes of foreign organisms [23]. Three G2-11 mutants with different positions of the integrated magnetosome cassette were incubated under anoxic phototrophic conditions with iron concentrations (50 μM) sufficient for biomineralization in the donor organism MSR-1. The obtained transgenic strains indeed demonstrated a detectable magnetic response (Cmag = 0.38 ± 0.11) [38], and TEM confirmed the presence of numerous electron-dense particles within the cells (Fig. 4), which, however, were significantly smaller than magnetosome crystals of MSR-1 (ranging 18.5 ± 4.3 nm to 19.9 ± 5.0 nm in three G2-11 MAG insertion mutants vs 35.4 ± 11.5 nm in MSR-1 WT, Fig. 4b) and formed only short chains or were scattered throughout the cells (Fig. 4a, c-i). Mapping of the particle elemental compositions with energy-dispersive X-ray spectroscopy (EDS) in STEM mode revealed iron- and oxygen-dominated compositions, suggesting they were iron oxides. High-resolution TEM (HRTEM) images and their FFT (Fast Fourier Transform) patterns were consistent with the structure of magnetite (Fig. 4c). Thus, G2-11 was capable of genuine magnetosome formation after acquisition of the MGCs from MSR-1.Fig. 4: Magnetosome biosynthesis by G2-11 upon transfer of the MGCs from MSR-1.a A cell with magnetosomes (i) and a close-up of the area with magnetosome chains (ii). Scale bars: 1 µm. b Violin plots displaying magnetosome diameter in three MAG insertion mutants of G2-11 in comparison to MSR-1. Asterisks indicate points of significance calculated using the Kruskal–Wallis test (**** designates p  More

  • in

    Adaptations by the coral Acropora tenuis confer resilience to future thermal stress

    Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).Article 
    CAS 

    Google Scholar 
    Frolicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018).Article 
    CAS 

    Google Scholar 
    Riegl, B. & Purkis, S. Coral population dynamics across consecutive mass mortality events. Glob. Chang Biol. 21, 3995–4005 (2015).Article 

    Google Scholar 
    Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373−+ (2017).Article 

    Google Scholar 
    Hoegh-Guldberg, O. Climate change, coral bleaching and the future of the world’s coral reefs. Mar. Freshw. Res. 50, 839–866 (1999).
    Google Scholar 
    van Hooidonk, R., Maynard, J. A. & Planes, S. Temporary refugia for coral reefs in a warming world. Nat. Clim. Change 3, 508–511 (2013).Article 

    Google Scholar 
    Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidification. Science 318, 1737–1742 (2007).Article 
    CAS 

    Google Scholar 
    Frieler, K. et al. Limiting global warming to 2 degrees C is unlikely to save most coral reefs. Nat. Clim. Change 3, 165–170 (2013).Article 

    Google Scholar 
    Guest, J. R. et al. Contrasting patterns of coral bleaching susceptibility in 2010 suggest an adaptive response to thermal stress. PLoS ONE 7, https://doi.org/10.1371/journal.pone.0033353 (2012).Oliver, T. A. & Palumbi, S. R. Do fluctuating temperature environments elevate coral thermal tolerance? Coral Reefs 30, 429–440 (2011).Article 

    Google Scholar 
    Howells, E. J. et al. Coral thermal tolerance shaped by local adaptation of photosymbionts. Nat. Clim. Change 2, 116–120 (2012).Article 

    Google Scholar 
    Bay, R. A., Rose, N. H., Logan, C. A. & Palumbi, S. R. Genomic models predict successful coral adaptation if future ocean warming rates are reduced. Sci. Adv. 3, https://doi.org/10.1126/sciadv.1701413 (2017).Loya, Y. et al. Coral bleaching: the winners and the losers. Ecol. Lett. 4, 122–131 (2001).Article 

    Google Scholar 
    Edmunds, P. J., Gates, R. D. & Gleason, D. F. The biology of larvae from the reef coral Porites astreoides, and their response to temperature disturbances. Mar. Biol. 139, 981–989 (2001).Article 

    Google Scholar 
    Lager, C. V. A., Hagedorn, M., Rodgers, K. S. & Jokiel, P. L. The impact of short-term exposure to near shore stressors on the early life stages of the reef building coral Montipora capitata. Peerj 8, https://doi.org/10.7717/peerj.9415 (2020).Ross, C., Ritson-Williams, R., Olsen, K. & Paul, V. J. Short-term and latent post-settlement effects associated with elevated temperature and oxidative stress on larvae from the coral Porites astreoides. Coral Reefs 32, 71–79 (2013).Article 

    Google Scholar 
    Putnam, H. M. & Gates, R. D. Preconditioning in the reef-building coral Pocillopora damicornis and the potential for trans-generational acclimatization in coral larvae under future climate change conditions. J. Exp. Biol. 218, 2365–2372 (2015).Article 

    Google Scholar 
    Baird, A. H. & Marshall, P. A. Mortality, growth and reproduction in scleractinian corals following bleaching on the Great Barrier Reef. Mar. Ecol. Prog. Ser. 237, 133–141 (2002).Article 

    Google Scholar 
    Fisch, J., Drury, C., Towle, E. K., Winter, R. N. & Miller, M. W. Physiological and reproductive repercussions of consecutive summer bleaching events of the threatened Caribbean coral Orbicella faveolata. Coral Reefs 38, 863–876 (2019).Article 

    Google Scholar 
    Michalek-Wagner, K. & Willis, B. L. Impacts of bleaching on the soft coral Lobophytum compactum. I. Fecundity, fertilization and offspring viability. Coral Reefs 19, 231–239 (2001).Article 

    Google Scholar 
    Paxton, C. W., Baria, M. V. B., Weis, V. M. & Harii, S. Effect of elevated temperature on fecundity and reproductive timing in the coral Acropora digitifera. Zygote 24, 511–516 (2016).Article 

    Google Scholar 
    Nozawa, Y. & Harrison, P. L. Effects of elevated temperature on larval settlement and post-settlement survival in scleractinian corals, Acropora solitaryensis and Favites chinensis. Mar. Biol. 152, 1181–1185 (2007).Article 

    Google Scholar 
    Hughes, T. P. & Tanner, J. E. Recruitment failure, life histories, and long-term decline of Caribbean corals. Ecology 81, 2250–2263 (2000).Article 

    Google Scholar 
    Edmunds, P. J. Juvenile coral population dynamics track rising seawater temperature on a Caribbean reef. Mar. Ecol. Prog. Ser. 269, 111–119 (2004).Article 

    Google Scholar 
    Davis, K. & Marshall, D. J. Offspring size in a resident species affects community assembly. J. Anim. Ecol. 83, 322–331 (2014).Article 

    Google Scholar 
    Chua, C. M., Leggat, W., Moya, A. & Baird, A. H. Temperature affects the early life history stages of corals more than near future ocean acidification. Mar. Ecol. Prog. Ser. 475, 85–92 (2013).Article 

    Google Scholar 
    Schnitzler, C. E., Hollingsworth, L. L., Krupp, D. A. & Weis, V. M. Elevated temperature impairs onset of symbiosis and reduces survivorship in larvae of the Hawaiian coral, Fungia scutaria. Mar. Biol. 159, 633–642 (2012).Article 

    Google Scholar 
    Ward, S., Harrison, P. & Hoegh-Guldberg, O. in: Proceedings of the Ninth International Coral Reef Symposium, Bali, 23–27 October 2000. 1123–1128 (2000).Foster, T. & Gilmour, J. Egg size and fecundity of biannually spawning corals at Scott Reef. Sci Rep-Uk 10, https://doi.org/10.1038/s41598-020-68289-4 (2020).Negri, A. P., Marshall, P. A. & Heyward, A. J. Differing effects of thermal stress on coral fertilization and early embryogenesis in four Indo Pacific species. Coral Reefs 26, 759–763 (2007).Article 

    Google Scholar 
    Randall, C. J. & Szmant, A. M. Elevated temperature affects development, survivorship, and settlement of the Elkhorn Coral, Acropora palmata (Lamarck 1816). Biol. Bull. 217, 269–282 (2009).Article 

    Google Scholar 
    Anlauf, H., D’Croz, L. & O’Dea, A. A corrosive concoction: the combined effects of ocean warming and acidification on the early growth of a stony coral are multiplicative. J. Exp. Mar. Biol. Ecol. 397, 13–20 (2011).Article 

    Google Scholar 
    Foster, T., Gilmour, J. P., Chua, C. M., Falter, J. L. & McCulloch, M. T. Effect of ocean warming and acidification on the early life stages of subtropical Acropora spicifera. Coral Reefs 34, 1217–1226 (2015).Article 

    Google Scholar 
    Randall, C. J. & Szmant, A. M. Elevated temperature reduces survivorship and settlement of the larvae of the Caribbean scleractinian coral, Favia fragum (Esper). Coral Reefs 28, 537–545 (2009).Article 

    Google Scholar 
    Gilmour, J. P., Smith, L. D., Heyward, A. J., Baird, A. H. & Pratchett, M. S. Recovery of an isolated coral reef system following severe disturbance. Science 340, 69–71 (2013).Article 

    Google Scholar 
    Doropoulos, C., Ward, S., Roff, G., Gonzalez-Rivero, M. & Mumby, P. J. Linking Demographic Processes of Juvenile Corals to Benthic Recovery Trajectories in Two Common Reef Habitats. PLoS ONE 10, https://doi.org/10.1371/journal.pone.0128535 (2015).Donelson, J. M., Munday, P. L., McCormick, M. I. & Pitcher, C. R. Rapid transgenerational acclimation of a tropical reef fish to climate change. Nat. Clim. Change 2, 30–32 (2012).Article 

    Google Scholar 
    Yuyama, I., Nakamura, T., Higuchi, T. & Hidaka, M. Different stress tolerances of juveniles of the Coral Acropora tenuis associated with clades C1 and D symbiodinium. Zool. Stud. 55, https://doi.org/10.6620/Zs.2016.55-19 (2016).Mumby, P. J. Can Caribbean coral populations be modelled at metapopulation scales? Mar. Ecol. Prog. Ser. 180, 275–288 (1999).Article 

    Google Scholar 
    Raymundo, L. J. & Maypa, A. P. Getting bigger faster: mediation of size-specific mortality via fusion in jevenile coral transplants. Ecol. Appl. 14, 281–295 (2004).Article 

    Google Scholar 
    Heyward, A. J. & Negri, A. P. Plasticity of larval pre-competency in response to temperature: observations on multiple broadcast spawning coral species. Coral Reefs 29, 631–636 (2010).Article 

    Google Scholar 
    Figueiredo, J., Baird, A. H., Harii, S. & Connolly, S. R. Increased local retention of reef coral larvae as a result of ocean warming. Nat. Clim. Change 4, 498–502 (2014).Article 

    Google Scholar 
    Ward, S. & Harrison, P. Changes in gametogenesis and fecundity of acroporid corals that were exposed to elevated nitrogen and phosphorus during the ENCORE experiment. J. Exp. Mar. Biol. Ecol. 246, 179–221 (2000).Article 
    CAS 

    Google Scholar 
    Jones, A. M. & Berkelmans, R. Tradeoffs to thermal acclimation: energetics and reproduction of a reef coral with heat tolerant Symbiodinium type-D. J. Mar. Biol. 2011, 185890 (2011).Harriott, V. Reproductive ecology of four scleratinian species at Lizard Island, Great Barrier Reef. Coral Reefs 2, 9–18 (1983).Article 

    Google Scholar 
    Hall, V. R. & Hughes, T. P. Reproductive strategies of modular organisms: Comparative studies of reef-building corals. Ecology 77, 950–963 (1996).Article 

    Google Scholar 
    Smith, C. C. & Fretwell, S. D. The optimal balance between size and number of offspring. Am. Naturalist 108, 499–506 (1974).Article 

    Google Scholar 
    Hoegh-Guldberg, O. & Smith, G. J. Influence of the population density of zooxanthellae and supply of ammonium on the biomass and metabolic characteristics of the reef corals Seriatopora hystrix and Stylophora pistillata. Mar. Ecol. Prog. Ser. 57, 173–186 (1989).Lesser, M. P. Elevated temperatures and ultraviolet radiation cause oxidative stress and inhibit photosynthesis in ymbiotic dinoflagellates. Limnol. Oceanogr. 41, 271–283 (1996).Article 
    CAS 

    Google Scholar 
    Jones, R. J., Hoegh-Guldberg, O., Larkum, A. W. D. & Schreiber, U. Temperature-induced bleaching of corals begins with impairment of the CO2 fixation mechanism in zooxanthellae. Plant Cell Environ. 21, 1219–1230 (1998).Article 
    CAS 

    Google Scholar 
    Warner, M. E., Fitt, W. K. & Schmidt, G. W. Damage to photosystem II in symbiotic dinoflagellates: A determinant of coral bleaching. Proc. Natl Acad. Sci. USA 96, 8007–8012 (1999).Article 
    CAS 

    Google Scholar 
    McRae, C. J., Huang, W. B., Fan, T. Y. & Côté, I. M. Effects of thermal conditioning on the performance of Pocillopora acuta adult coral colonies and their offspring. Coral Reefs 40, 1491–1503 (2021).Article 

    Google Scholar 
    Howells, E. J. et al. Species-specific trends in the reproductive output of corals across environmental gradients and bleaching histories. Mar. Pollut. Bull. 105, 532–539 (2016).Article 
    CAS 

    Google Scholar 
    Galanto, N., Sartor, C., Moscato, V., Lizama, M. & Lemer, S. Effects of elevated temperature on reproduction and larval settlement in Leptastrea purpurea. Coral Reefs 41, 293–302 (2022).Article 

    Google Scholar 
    Hazraty-Kari, S., Masaya, M., Kawachi, M. & Harii, S. The early acquisition of symbiotic algae benefits larval survival and juvenile growth in the coral Acropora tenuis. J. Exp. Zool. A Ecol. Integr. Physiol. https://doi.org/10.1002/jez.2589 (2022).Moran, A. L. & Manahan, D. T. Energy metabolism during larval development of green and white abalone, Haliotis fulgens and H. sorenseni. Biol. Bull. 204, 270–277 (2003).Article 

    Google Scholar 
    Sewell, M. A. Utilization of lipids during early development of the sea urchin Evechinus chloroticus. Mar. Ecol. Prog. Ser. 304, 133–142 (2005).Article 
    CAS 

    Google Scholar 
    Alexander, G., Hancock, J., Huffmyer, A. & Matsuda, S. Larval thermal conditioning does not improve post-settlement thermal tolerance in the dominant reef-building coral, Montipora capitata. Coral Reefs 41, 333–342 (2022).Rivest, E. B., Chen, C. S., Fan, T. Y., Li, H. H. & Hofmann, G. E. Lipid consumption in coral larvae differs among sites: a consideration of environmental history in a global ocean change scenario. Proc. Biol. Sci. 284, https://doi.org/10.1098/rspb.2016.2825 (2017).Graham, E. M., Baird, A. H., Connolly, S. R., Sewell, M. A. & Willis, B. L. Uncoupling temperature-dependent mortality from lipid depletion for scleractinian coral larvae. Coral Reefs 36, 97–104 (2017).Article 

    Google Scholar 
    Graham, E. M., Baird, A. H., Connolly, S. R., Sewell, M. A. & Willis, B. L. Rapid declines in metabolism explain extended coral larval longevity. Coral Reefs 32, 539–549 (2013).Article 

    Google Scholar 
    Reid, E. C. et al. Internal waves influence the thermal and nutrient environment on a shallow coral reef. Limnol. Oceanogr. 64, 1949–1965 (2019).Article 

    Google Scholar 
    Maynard, J. A., Anthony, K. R. N., Marshall, P. A. & Masiri, I. Major bleaching events can lead to increased thermal tolerance in corals. Mar. Biol. 155, 173–182 (2008).Article 

    Google Scholar 
    Siebeck, U. E., Marshall, N. J., Kluter, A. & Hoegh-Guldberg, O. Monitoring coral bleaching using a colour reference card. Coral Reefs 25, 453–460 (2006).Article 

    Google Scholar 
    Veal, C. J., Holmes, G., Nunez, M., Hoegh-Guldberg, O. & Osborn, J. A comparative study of methods for surface area and three-dimensional shape measurement of coral skeletons. Limnol. Oceanogr.-Meth 8, 241–253 (2010).Article 

    Google Scholar 
    Jeffrey, S. W. & Humphrey, G. F. New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton. Biochem. Physiol. Pflanz. 167, 191–194 (1975).Article 
    CAS 

    Google Scholar 
    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).Article 
    CAS 

    Google Scholar 
    R Foundation for Statistical Computing. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria 2021). More

  • in

    Marine phytoplankton community data and corresponding environmental properties from eastern Norway, 1896–2020

    Sampling strategies and dataThe inner Oslofjorden phytoplankton dataset is a compilation of data mostly assembled from the monitoring program, financed since 1978 by a cooperation between the municipalities around the fjord, united in the counsel for technical water and sewage cooperation called “Fagrådet for Vann- og avløpsteknisk samarbeid i Indre Oslofjord”. The monitoring program started in 1973 and is ongoing. The program has sampled environmental parameters and chlorophyll since 1973, but for the first 25 years, phytoplankton data is only reported for the years 1973, 1974, 1988/9, 1990, 1994 and 1995. Since 1998, yearly sampling has been conducted, and from 2006 to 2019, the sampling frequency was approximately monthly. In addition, we have compiled research and monitoring data from researchers at the University of Oslo from 1896 and 1916, 1933–34 and 1962–1965.The records from 1896 and 1897 were collected using zoo-plankton net13. The phytoplankton collection in 1916–1917 used buckets or Nansen flasks for sampling. From 1933 to 1984, phytoplankton samples were collected using Nansen bottles and then from 1985–2020 with Niskin bottles from research vessels. The exception is the period from 2006 to 2018 when samples were also collected with FerryBox- equipped ships of opportunity14 with refrigerated autosamplers (Table 2).Since the 1990s, quantitative phytoplankton samples have mostly been preserved in Lugol’s solution, except for spring and autumn samples in the period 1990–2000 that were preserved in formalin. The records from 1896, 1897 and 1916 were preserved in ethanol, and between 1933 and 1990, samples were preserved in formalin. Sampling strategies and methods are listed in Table 2.The records from 1896 and 1897 were quantified by weight, and taxon abundance is categorised as “rare” (r), “rather common” (+), “common” (c) and “very common” (cc)13. In 1916 and 1917, Grans filtration method15 was used, and the number was given in cell counts per litre. From 1916 to 1993, the data is reported only as phytoplankton abundance (N, number of cells per litre). For most years after 1994, the dataset includes both abundance and biomass (μg C per litre), except for 2003, 2004, 2017 and 2018. Phytoplankton was identified and quantified using the sedimentation method of Utermöhl (1958)16. Biovolume for each species is calculated according to HELCOM 200617 and converted to biomass (μg C) following Menden-Deuer & Lessards (2000)18.Data inventoryThe inner Oslofjorden Phytoplankton dataset was compiled in 2020, comprising quantitative phytoplankton cell counts from inner Oslofjorden since 1896. Previously, parts of the data have been available as handwritten or printed tables in reports and published sources19,20,21 (Fig. 2). All sources are digitally available from the University of Oslo Library, the website for “Fagrådet” (http://www.indre-oslofjord.no/) or the NIVA online report database (https://www.niva.no/rapporter). Data from 1994 and onwards have been accessed digitally from the NIVA’s databases. They are also available from client reports from the monitoring project for inner Oslofjorden from the online sites listed above.The first known, published investigation of hydrography and plankton in the upper water column of the inner Oslofjorden was by Hjort & Gran (1900)13. Samples were collected during a hydrographical and biological investigation covering both the Skagerrak and Oslofjorden. There is only one sampling event from Steilene (Dk 1), but some phytoplankton data were obtained at Drøbak, just south of the shallow sill separating the inner and outer Oslofjorden, from winter 1896 to autumn 1897. Twenty years later, Gran and Gaarder (1927)22 conducted a study that included culture experiments at Drøbak field station (at the border between the inner and outer Oslofjorden) in March – April 1916 and August – September 1917. A higher frequency investigation was carried out from June 1933 to May 1934, covering 12 stations in inner and outer Oslofjorden where phytoplankton was analysed by microscopic examination23. The extensive program (the Oslofjord Project) conducted from 1962–1964 covered many parameters, and we have extracted the data for phytoplankton. From 1973 and onward, the research vessel-based monitoring program was financed by the municipalities around the fjord, and since 2006 NIVA has supplemented the monitoring program using FerryBox ships of opportunity. Samples from 4 m depth were collected using a refrigerated autosampler system (Teledyne ISCO) connected to a FerryBox system on M/S Color Festival and M/S Color Fantasy through cooperation between NIVA and Color Line A/S. Since 2018, the FerryBox has been part of the Norwegian Ships of Opportunity Program research infrastructure funded by the Research Council of Norway.The indicated depth of 3.5–4 m is an estimated average, as the actual sampling depth depends on shipload and sea conditions.Several other research projects have sampled from inner Oslofjorden between 1886 and 2000 with different aims. Data from relevant projects reporting on the whole phytoplankton community have also been included in this database.Data compilationThe data already digitalised were compiled from MS Excel files, and other data were manually entered into the standard format in MS Excel files. All collected data were then integrated into one MS Excel database, and this file was used for upload into GBIF. Data can be downloaded from GBIF in different formats and be linked together by the measurementsorfacts table.Quality control and standardisationAfter compilation, the data were checked for errors that could occur during manual digitalisation or just the compilation process. Duplicates and zero values were removed (Fig. 2). The major quantitative unit is phytoplankton abundance in cells per litre. Due to varying scopes of sampling and the development of gear and instruments, the number of species identified may vary between projects. Some of the earliest records were registered as “present”, indicating the amount in comments.Metadata, such as geographical reference, depth and methodology accessed from papers and reports, were accessible from the data source. When data was accessed from the NIVA internal databases, the metadata information was provided by the database owners/researchers.TaxonomyThe taxonomy of microalgae is in constant revision as new knowledge and techniques for identification are developing. Several historical species names recorded in this database are synonyms of accepted names in 2021. We have used the original names in our database and matched them to accepted names and Aphia ID using the taxon match tool available in the open-access reference system; World Register of Marine Species (Worms)24. The taxon match was conducted in March 2021.The nomenclature in Worms is quality assured by a wide range of taxonomic specialists. The Aphia ID is a unique and stable identifier for each available name in the database24. We also cross-checked the last updated nomenclature in Algaebase25 (March 2022) to assign species to a valid taxon name. When Algaebase and Worms were not in accordance, Algaebase taxonomy was usually chosen except in the case of Class Bacillariophyceae.Before matching the species list, the original species names were cleaned from spelling mistakes or just spelling mismatches like spaces, commas, etc. The original name is, however, left in one column in the database. For registrations where a species identification is uncertain, e.g. Alexandrium cf. tamarense, we used only Alexandrium. For registrations where the full name is uncertain, e.g. cf. Alexandrium tamarense, we used the name and Aphia ID for higher taxa, in this case, order. For others, e.g. “pennate diatoms” or “centric diatoms“, we used the name and Aphia ID for class. When names for, e.g. order and class were not recognised automatically by the matching tool in World Register of Marine Species (WoRMS), these were matched manually. Only very few records, mostly “cysts” and “unidentified monads”, could not be matched neither automatically nor manually but were assigned to general “protists” with affiliated ID. More

  • in

    Composition and toxicity of venom produced by araneophagous white-tailed spiders (Lamponidae: Lampona sp.)

    Schendel, V., Rash, L. D., Jenner, R. A. & Undheim, E. A. The diversity of venom: The importance of behavior and venom system morphology in understanding its ecology and evolution. Toxins 11(11), 666 (2019).Article 
    CAS 

    Google Scholar 
    Casewell, N. R., Wüster, W., Vonk, F. J., Harrison, R. A. & Fry, B. G. Complex cocktails: The evolutionary novelty of venoms. Trends Ecol. Evol. 28(4), 219–229 (2013).Article 

    Google Scholar 
    Pineda, S. S. et al. Structural venomics reveals evolution of a complex venom by duplication and diversification of an ancient peptide-encoding gene. Proc. Natl. Acad. Sci. USA 117(21), 11399–11408 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Chippaux, J. P., Williams, V. & White, J. Snake venom variability: Methods of study, results and interpretation. Toxicon 29(11), 1279–1303 (1991).Article 
    CAS 

    Google Scholar 
    Lyons, K., Dugon, M. M. & Healy, K. Diet breadth mediates the prey specificity of venom potency in snakes. Toxins 12(2), 74 (2020).Article 

    Google Scholar 
    Pekár, S. et al. Venom gland size and venom complexity—essential trophic adaptations of venomous predators: A case study using spiders. Mol. Ecol. 27(21), 4257–4269 (2018).Article 

    Google Scholar 
    Phuong, M. A., Mahardika, G. N. & Alfaro, M. E. Dietary breadth is positively correlated with venom complexity in cone snails. BMC Genom. 17(1), 401 (2016).Article 

    Google Scholar 
    Holding, M. L., Biardi, J. E. & Gibbs, H. L. Coevolution of venom function and venom resistance in a rattlesnake predator and its squirrel prey. Proc. R. Soc. B. 283(1829), 20152841 (2016).Article 

    Google Scholar 
    Pekár, S., Líznarová, E., Bočánek, O. & Zdráhal, Z. Venom of prey-specialized spiders is more toxic to their preferred prey: A result of prey-specific toxins. J. Anim. Ecol. 87(6), 1639–1652 (2018).Article 

    Google Scholar 
    Pekár, S., Coddington, J. A. & Blackledge, T. A. Evolution of stenophagy in spiders (Araneae): Evidence based on the comparative analysis of spider diets. Evolution 66(3), 776–806 (2012).Article 

    Google Scholar 
    Herzig, V., King, G. F. & Undheim, E. A. Can we resolve the taxonomic bias in spider venom research?. Toxicon: X 1, 100005 (2019).Article 
    CAS 

    Google Scholar 
    Platnick, N. A relimitation and revision of the Australasian ground spider family Lamponidae (Araneae: Gnaphosoidea). Bull. Am. Mus. Nat. Hist. 2000(245), 1–328 (2000).Article 

    Google Scholar 
    World Spider Catalog. Version 22.0. Natural History Museum Bern. http://wsc.nmbe.ch. Accessed 15 Mar 2021 (2021).White, J. & Weinstein, S. A. A phoenix of clinical toxinology: White-tailed spider (Lampona spp.) bites. A case report and review of medical significance. Toxicon 87, 76–80 (2014).Article 
    CAS 

    Google Scholar 
    Rash, L. D., King, R. G. & Hodgson, W. C. Sex differences in the pharmacological activity of venom from the white-tailed spider (Lampona cylindrata). Toxicon 38, 1111–1127 (2000).Article 
    CAS 

    Google Scholar 
    Young, A. R. & Pincus, S. J. Comparison of enzymatic activity from three species of necrotising arachnids in Australia: Loxosceles rufescens, Badumna insignis and Lampona cylindrata. Toxicon 39, 391–400 (2001).Article 
    CAS 

    Google Scholar 
    Michálek, O., Petráková, L. & Pekár, S. Capture efficiency and trophic adaptations of a specialist and generalist predator: A comparison. Ecol. Evol. 7(8), 2756–2766 (2017).Article 

    Google Scholar 
    Klint, J. K. et al. Spider-venom peptides that target voltage-gated sodium channels: Pharmacological tools and potential therapeutic leads. Toxicon 60(4), 478–491 (2012).Article 
    CAS 

    Google Scholar 
    Diniz, M. R. et al. An overview of Phoneutria nigriventer spider venom using combined transcriptomic and proteomic approaches. PLoS ONE 13(8), e0200628 (2018).Article 

    Google Scholar 
    Wilson, D. et al. The aromatic head group of spider toxin polyamines influences toxicity to cancer cells. Toxins 9(11), 346 (2017).Article 

    Google Scholar 
    Herzig, V. & King, G. F. The cystine knot is responsible for the exceptional stability of the insecticidal spider toxin ω-hexatoxin-Hv1a. Toxins 7(10), 4366–4380 (2015).Article 
    CAS 

    Google Scholar 
    Wang, X. H. et al. Discovery and characterization of a family of insecticidal neurotoxins with a rare vicinal disulfide bridge. Nat. Struct. Biol. 7(6), 505–513 (2000).Article 
    CAS 

    Google Scholar 
    Yuan, C. H. et al. Discovery of a distinct superfamily of Kunitz-type toxin (KTT) from tarantulas. PLoS ONE 3(10), e3414 (2008).Article 
    ADS 

    Google Scholar 
    Luo, J. et al. Molecular diversity and evolutionary trends of cysteine-rich peptides from the venom glands of Chinese spider Heteropoda venatoria. Sci. Rep. 11, 3211 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Cole, J., Buszka, P. A., Mobley, J. A. & Hataway, R. A. Characterization of the venom proteome for the wandering spider, Ctenus hibernalis (Aranea: Ctenidae). J. Proteom. Bioinform. 9, 196–199 (2016).Article 

    Google Scholar 
    Korolkova, Y. et al. New Insectotoxin from Tibellus Oblongus Spider venom presents novel daptation of ICK Fold. Toxins 13(1), 29 (2021).Article 
    CAS 

    Google Scholar 
    Koua, D. et al. Proteotranscriptomic insights into the venom composition of the wolf spider Lycosa tarantula. Toxins 12(8), 501 (2020).Article 
    CAS 

    Google Scholar 
    Liberato, T., Troncone, L. R. P., Yamashiro, E. T., Serrano, S. M. & Zelanis, A. High-resolution proteomic profiling of spider venom: Expanding the toxin diversity of Phoneutria nigriventer venom. Amino Acids 48(3), 901–906 (2016).Article 
    CAS 

    Google Scholar 
    Oldrati, V. et al. Peptidomic and transcriptomic profiling of four distinct spider venoms. PLoS ONE 12(3), e0172966 (2017).Article 

    Google Scholar 
    King, G. F. & Hardy, M. C. Spider-venom peptides: Structure, pharmacology, and potential for control of insect pests. Annu. Rev. Entomol. 58, 475–496 (2013).Article 
    CAS 

    Google Scholar 
    Turner, A. J., Isaac, R. E. & Coates, D. The neprilysin (NEP) family of zinc metalloendopeptidases: Genomics and function. BioEssays 23(3), 261–269 (2001).Article 
    CAS 

    Google Scholar 
    Casewell, N. R., Harrison, R. A., Wüster, W. & Wagstaff, S. C. Comparative venom gland transcriptome surveys of the saw-scaled vipers (Viperidae: Echis) reveal substantial intra-family gene diversity and novel venom transcripts. BMC Genom. 10(1), 1–12 (2009).Article 

    Google Scholar 
    Tan, C. H., Tan, K. Y., Fung, S. Y. & Tan, N. H. Venom-gland transcriptome and venom proteome of the Malaysian king cobra (Ophiophagus hannah). BMC Genom. 16(1), 1–21 (2015).Article 

    Google Scholar 
    Tan, K. Y., Tan, C. H., Chanhome, L. & Tan, N. H. Comparative venom gland transcriptomics of Naja kaouthia (monocled cobra) from Malaysia and Thailand: Elucidating geographical venom variation and insights into sequence novelty. PeerJ 5, e3142 (2017).Article 

    Google Scholar 
    Undheim, E. A. et al. A proteomics and transcriptomics investigation of the venom from the barychelid spider Trittame loki (brush-foot trapdoor). Toxins. 5(12), 2488–2503 (2013).Article 
    CAS 

    Google Scholar 
    do Nascimento, S. M., de Oliveira, U. C., Nishiyama-Jr, M. Y., Tashima, A. K. & Silva Junior, P. I. D. Presence of a neprilysin on Avicularia juruensis (Mygalomorphae: Theraphosidae) venom. Toxin Rev. 41(2), 370–379 (2021).Article 

    Google Scholar 
    Zobel-Thropp, P. A. et al. Not so dangerous after all? Venom composition and potency of the Pholcid (daddy long-leg) spider Physocyclus mexicanus. Front. Ecol. Evol. 7, 256 (2019).Article 

    Google Scholar 
    Diniz, M. R. et al. An overview of Phoneutria nigriventer spider venom using combined transcriptomic and proteomic approaches. PLoS ONE 13(8), e0200628 (2018).Article 

    Google Scholar 
    He, Q. et al. The venom gland transcriptome of Latrodectus tredecimguttatus revealed by deep sequencing and cDNA library analysis. PLoS ONE 8(11), e81357 (2013).Article 
    ADS 

    Google Scholar 
    Haney, R. A., Ayoub, N. A., Clarke, T. H., Hayashi, C. Y. & Garb, J. E. Dramatic expansion of the black widow toxin arsenal uncovered by multi-tissue transcriptomics and venom proteomics. BMC Genom. 15(1), 1–18 (2014).Article 

    Google Scholar 
    Haney, R. A., Matte, T., Forsyth, F. S. & Garb, J. E. Alternative transcription at venom genes and its role as a complementary mechanism for the generation of venom complexity in the common house spider. Front. Ecol. Evol. 7, 85 (2019).Article 

    Google Scholar 
    Lüddecke, T. et al. An economic dilemma between molecular weapon systems may explain an arachno-atypical venom in wasp spiders (Argiope bruennichi). Biomolecules 10(7), 978 (2020).Article 

    Google Scholar 
    Fainzilber, M., Gordon, D., Hasson, A., Spira, M. E. & Zlotkin, E. Mollusc-specific toxins from the venom of Conus textile neovicarius. Eur. J. Biochem. 202(2), 589–595 (1991).Article 
    CAS 

    Google Scholar 
    Pawlak, J. et al. Denmotoxin, a three-finger toxin from the colubrid snake Boiga dendrophila (Mangrove Catsnake) with bird-specific activity. J. Biol. Chem. 281(39), 29030–29041 (2006).Article 
    CAS 

    Google Scholar 
    Krasnoperov, V. G., Shamotienko, O. G. & Grishin, E. V. Isolation and properties of insect and crustacean-specific neurotoxins from the venom of the black widow spider (Latrodectus mactans tredecimguttatus). J. Nat. Toxins 1, 17–23 (1992).CAS 

    Google Scholar 
    Xu, X. et al. A comparative analysis of the venom gland transcriptomes of the fishing spiders Dolomedes mizhoanus and Dolomedes sulfurous. PLoS ONE 10(10), e0139908 (2015).Article 

    Google Scholar 
    Kuzmenkov, A. I., Sachkova, M. Y., Kovalchuk, S. I., Grishin, E. V. & Vassilevski, A. A. Lachesana tarabaevi, an expert in membrane-active toxins. Biochem. J. 473(16), 2495–2506 (2016).Article 
    CAS 

    Google Scholar 
    Pekár, S. & Toft, S. Trophic specialisation in a predatory group: The case of prey-specialised spiders (Araneae). Biol. Rev. 90(3), 744–761 (2015).Article 

    Google Scholar 
    Nyffeler, M. & Pusey, B. J. Fish predation by semi-aquatic spiders: A global pattern. PLoS ONE 9(6), e99459 (2014).Article 
    ADS 

    Google Scholar 
    Pekár, S. & Lubin, Y. Prey and predatory behavior of two zodariid species (Araneae, Zodariidae). J. Arachnol. 37(1), 118–121 (2009).Article 

    Google Scholar 
    Michálek, O., Kuhn-Nentwig, L. & Pekár, S. High specific efficiency of venom of two prey-specialized spiders. Toxins 11(12), 687 (2019).Article 

    Google Scholar 
    Modahl, C. M., Mrinalini, Frietze, S. & Mackessy, S. P. Adaptive evolution of distinct prey-specific toxin genes in rear-fanged snake venom. Proc. R. Soc. B. 285(1884), 20181003 (2018).Article 

    Google Scholar 
    Harris, R. J., Zdenek, C. N., Harrich, D., Frank, N. & Fry, B. G. An appetite for destruction: Detecting prey-selective binding of α-neurotoxins in the venom of Afro-Asian elapids. Toxins 12(3), 205 (2020).Article 
    CAS 

    Google Scholar 
    Duran, L. H., Rymer, T. L. & Wilson, D. T. Variation in venom composition in the Australian funnel-web spiders Hadronyche valida. Toxicon: X 8, 100063 (2020).Article 
    CAS 

    Google Scholar 
    Kuhn-Nentwig, L., Schaller, J. & Nentwig, W. Purification of toxic peptides and the amino acid sequence of CSTX-1 from the multicomponent venom of Cupiennius salei (Araneae: Ctenidae). Toxicon 32(3), 287–302 (1994).Article 
    CAS 

    Google Scholar 
    Friedel, T. & Nentwig, W. Immobilizing and lethal effects of spider venoms on the cockroach and the common mealbeetle. Toxicon 27(3), 305–316 (1989).Article 
    CAS 

    Google Scholar 
    Eggs, B., Wolff, J. O., Kuhn-Nentwig, L., Gorb, S. N. & Nentwig, W. Hunting without a web: How lycosoid spiders subdue their prey. Ethology 121(12), 1166–1177 (2015).Article 

    Google Scholar 
    Andrews, S. FastQC: A quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc (2015).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30(15), 2114–2120 (2014).Article 
    CAS 

    Google Scholar 
    Song, L. & Florea, L. Rcorrector: Efficient and accurate error correction for Illumina RNA-seq reads. GigaScience 4(1), s13742–s14015 (2015).Article 

    Google Scholar 
    Grabherr, M. G. et al. Trinity: Reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nat. Biotechnol. 29(7), 644 (2011).Article 
    CAS 

    Google Scholar 
    Gilbert, D. EvidentialGene: Evidence directed gene predictions for eukaryotes. Available online at: http://arthropods.eugenes.org/EvidentialGene/ (2010).Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10(3), 1–10 (2009).Article 

    Google Scholar 
    Seppey, M., Manni, M. & Zdobnov, E. M. BUSCO: Assessing genome assembly and annotation completeness. In Gene Prediction (ed. Kollmar, M.) 227–245 (Humana, 2019).
    Google Scholar 
    Haas, B. TransDecoder. Available online at: https://github.com/TransDecoder/TransDecoder (2015).Petersen, T. N., Brunak, S., Von Heijne, G. & Nielsen, H. SignalP 4.0: Discriminating signal peptides from transmembrane regions. Nat. Methods 8(10), 785–786 (2011).Article 
    CAS 

    Google Scholar 
    Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 25(17), 3389–3402 (1997).Article 
    CAS 

    Google Scholar 
    UniProt. The universal protein knowledgebase in 2021. Nucleic Acids Res. 49(1), 480–489 (2021).
    Google Scholar 
    Eddy, S. R. A probabilistic model of local sequence alignment that simplifies statistical significance estimation. PLoS Comput. Biol. 4(5), e1000069 (2008).Article 
    ADS 
    MathSciNet 

    Google Scholar 
    Finn, R. D. et al. Pfam: The protein families database. Nucleic Acids Res. 42(1), 222–230 (2014).Article 

    Google Scholar 
    Wong, E. S., Hardy, M. C., Wood, D., Bailey, T. & King, G. F. SVM-based prediction of propeptide cleavage sites in spider toxins identifies toxin innovation in an Australian tarantula. PLoS ONE 8(7), e66279 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    King, G. F., Gentz, M. C., Escoubas, P. & Nicholson, G. M. A rational nomenclature for naming peptide toxins from spiders and other venomous animals. Toxicon 52(2), 264–276 (2008).Article 
    CAS 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available online at: https://www.R-project.org/ (2019).Venables, W. N. & Ripley, B. D. Random and mixed effects in Modern Applied Statistics with S 271–300 (Springer, New York, 2002).Pekár, S. & Brabec, M. Modern Analysis of Biological Data: Generalized Linear Models in R (Masaryk University Press, 2016).
    Google Scholar 
    Halekoh, U., Højsgaard, S. & Yan, J. The R package geepack for generalized estimating equations. J. Stat. Softw. 15(2), 1–11 (2006).Article 

    Google Scholar 
    Pekár, S. & Brabec, M. Generalized estimating equations: A pragmatic and flexible approach to the marginal GLM modelling of correlated data in the behavioural sciences. Ethology 124(2), 86–93 (2018).Article 

    Google Scholar  More

  • in

    Dynamics of rumen microbiome in sika deer (Cervus nippon yakushimae) from unique subtropical ecosystem in Yakushima Island, Japan

    Gruninger, R. J., Ribeiro, G. O., Cameron, A. & McAllister, T. A. Invited review: Application of meta-omics to understand the dynamic nature of the rumen microbiome and how it responds to diet in ruminants. Animal 13, 1843–1854 (2019).CAS 

    Google Scholar 
    Morgavi, D. P., Kelly, W. J., Janssen, P. H. & Attwood, G. T. Rumen microbial (meta)genomics and its application to ruminant production. Animal 7, 184–201 (2013).CAS 

    Google Scholar 
    Bergman, E. N. Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiol. Rev. 70, 567–590 (1990).CAS 

    Google Scholar 
    Flint, H. J. The rumen microbial ecosystem—Some recent developments. Trends Microbiol. 5, 483–488 (1997).CAS 

    Google Scholar 
    Hobson, P. N. & Stewart, C. S. The Rumen Microbial Ecosystem. (Springer, 2012).Moraïs, S. & Mizrahi, I. The road not taken: The rumen microbiome, functional groups, and community states. Trends Microbiol. 27, 538–549 (2019).
    Google Scholar 
    Cheng, K. J., Forsberg, C. W., Minato, H. & Costerton, J. W. in Physiological Aspects of Digestion and Metabolism in Ruminants (eds T. Tsuda, Y. Sasaki, & R. Kawashima) 595–624 (Academic Press, 1991).McSweeney, C. S., Palmer, B., McNeill, D. M. & Krause, D. O. Microbial interactions with tannins: Nutritional consequences for ruminants. Anim. Feed Sci. Technol. 91, 83–93 (2001).CAS 

    Google Scholar 
    Skene, I. K. & Brooker, J. D. Characterization of tannin acylhydrolase activity in the ruminal bacterium Selenomonas ruminantium. Anaerobe 1, 321–327 (1995).CAS 

    Google Scholar 
    Khanbabaee, K. & van Ree, T. Tannins: Classification and definition. Nat. Prod. Rep. 18, 641–649 (2001).CAS 

    Google Scholar 
    Makkar, H. P. S. & Becker, K. Isolation of tannins from leaves of some trees and shrubs and their properties. J. Agric. Food Chem. 42, 731–734 (1994).CAS 

    Google Scholar 
    Bhat, T. K., Kannan, A., Singh, B. & Sharma, O. P. Value addition of feed and fodder by alleviating the antinutritional effects of tannins. Agr. Res. 2, 189–206 (2013).CAS 

    Google Scholar 
    Shimada, T. Salivary proteins as a defense against dietary tannins. J. Chem. Ecol. 32, 1149–1163 (2006).CAS 

    Google Scholar 
    Zhu, J., Filippich, L. J. & Alsalami, M. T. Tannic acid intoxication in sheep and mice. Res. Vet. Sci. 53, 280–292 (1992).CAS 

    Google Scholar 
    Kohl, K. D., Stengel, A. & Dearing, M. D. Inoculation of tannin-degrading bacteria into novel hosts increases performance on tannin-rich diets. Environ. Microbiol. 18, 1720–1729 (2016).CAS 

    Google Scholar 
    Kumar, K., Chaudhary, L. C., Agarwal, N. & Kamra, D. N. Isolation and characterization of tannin-degrading bacteria from the rumen of goats fed oak (Quercus semicarpifolia) leaves. Agr. Res. 3, 377–385 (2014).
    Google Scholar 
    Odenyo, A. A. et al. Characterization of tannin-tolerant bacterial isolates from East African ruminants. Anaerobe 7, 5–15 (2001).CAS 

    Google Scholar 
    Grilli, D. J. et al. Analysis of the rumen bacterial diversity of goats during shift from forage to concentrate diet. Anaerobe 42, 17–26 (2016).
    Google Scholar 
    Tong, J. et al. Illumina sequencing analysis of the ruminal microbiota in high-yield and low-yield lactating dairy cows. PLoS ONE 13, e0198225 (2018).
    Google Scholar 
    Pope, P. B. et al. Metagenomics of the Svalbard reindeer rumen microbiome reveals abundance of polysaccharide utilization loci. PLoS ONE 7, e38571 (2012).ADS 
    CAS 

    Google Scholar 
    Østbye, K., Wilson, R. & Rudi, K. Rumen microbiota for wild boreal cervids living in the same habitat. FEMS Microbiol. Lett. 363, fnw233 (2016).
    Google Scholar 
    Gruninger, R. J., Sensen, C. W., McAllister, T. A. & Forster, R. J. Diversity of rumen bacteria in Canadian cervids. PLoS ONE 9, e89682 (2014).ADS 

    Google Scholar 
    Henderson, G. et al. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci. Rep. 5, 14567 (2015).CAS 

    Google Scholar 
    Reese, A. T. & Kearney, S. M. Incorporating functional trade-offs into studies of the gut microbiota. Curr. Opin. Microbiol. 50, 20–27 (2019).CAS 

    Google Scholar 
    Moeller, A. H. et al. Social behavior shapes the chimpanzee pan-microbiome. Sci. Adv. 2, e1500997 (2016).ADS 

    Google Scholar 
    Okano, T. & Matsuda, H. Biocultural diversity of Yakushima Island: Mountains, beaches, and sea. J. Mar. Isl. Cult. 2, 69–77 (2013).
    Google Scholar 
    Agetsuma, N., Agetsuma-Yanagihara, Y. & Takafumi, H. Food habits of Japanese deer in an evergreen forest: Litter-feeding deer. Mamm. Biol. 76, 201–207 (2011).
    Google Scholar 
    Higashi, Y., Hirota, S. K., Suyama, Y. & Yahara, T. Geographical and seasonal variation of plant taxa detected in faces of Cervus nippon yakushimae based on plant DNA analysis in Yakushima Island. Ecol. Res. 37, 582–597 (2022).CAS 

    Google Scholar 
    Kuroiwa, A. Nutritional ecology of the Yakushika (Cervus nippon yakushimae) population under high density Ph.D. thesis, Kyushu University, (2017).Koda, R., Agetsuma, N., Agetsuma-Yanagihara, Y., Tsujino, R. & Fujita, N. A proposal of the method of deer density estimate without fecal decomposition rate: A case study of fecal accumulation rate technique in Japan. Ecol. Res. 26, 227–231 (2011).
    Google Scholar 
    Yahara, T. in Deer eats world heritages: Ecology of deer and forets (eds T. Yumoto & H. Matsuda) 168–187 (Bunichi-Sogo-Shuppan, 2006).Onoda, Y. & Yahara, T. in Challenges for Conservation Ecology in Space and Time. (eds T. Miyashita & J. Nishihiro) 126–149 (University of Tokyo Press, 2015).Kagoshima Prefecture Nature Conservation Division. The current status of Yakusika in FY 2020, available at https://www.rinya.maff.go.jp/kyusyu/fukyu/shika/attach/pdf/yakushikaWG_R3_2-23.pdf (2020).Kuroiwa, A., Kuroe, M. & Yahara, T. Effects of density, season, and food intake on sika deer nutrition on Yakushima Island, Japan. Ecol. Res. 32, 369–378 (2017).
    Google Scholar 
    Hiura, T., Hashidoko, Y., Kobayashi, Y. & Tahara, S. Effective degradation of tannic acid by immobilized rumen microbes of a sika deer (Cervus nippon yesoensis) in winter. Anim. Feed Sci. Technol. 155, 1–8 (2010).CAS 

    Google Scholar 
    Kawarai, S. et al. Seasonal and geographical differences in the ruminal microbial and chloroplast composition of sika deer (Cervus nippon) in Japan. Sci. Rep. 12, 6356 (2022).ADS 
    CAS 

    Google Scholar 
    Li, Z. et al. Response of the rumen microbiota of sika deer Cervus nippon fed different concentrations of tannin rich plants. PLoS ONE 10, e0123481 (2015).
    Google Scholar 
    McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).CAS 

    Google Scholar 
    Kim, M., Morrison, M. & Yu, Z. Status of the phylogenetic diversity census of ruminal microbiomes. FEMS Microbiol. Ecol. 76, 49–63 (2011).CAS 

    Google Scholar 
    Weimer, P. J. Redundancy, resilience, and host specificity of the ruminal microbiota: Implications for engineering improved ruminal fermentations. Front. Microbiol. 6, 296 (2015).
    Google Scholar 
    Scott, K. P., Gratz, S. W., Sheridan, P. O., Flint, H. J. & Duncan, S. H. The influence of diet on the gut microbiota. Pharmacol. Res. 69, 52–60 (2013).CAS 

    Google Scholar 
    Tapio, I. et al. Taxon abundance, diversity, co-occurrence and network analysis of the ruminal microbiota in response to dietary changes in dairy cows. PLoS ONE 12, e0180260 (2017).
    Google Scholar 
    Ohara, M. in Agriculture in Hokkaido v2 (ed K. Iwama, Ohara, M., Araki, H., Yamada, T., Nakatsuji, H., Kataoka, T., Yamamoto, Y.) 1–18(Faculty of Agriculture, Hokkaido Univ., 2009).Igota, H., Sakuragi, M. & Uno, H. in Sika Deer: Biology and Management of Native and Introduced Populations (eds. Dale R. McCullough, Seiki Takatsuki, & Koichi Kaji) 251–272 (Springer Japan, 2009).Fernando, S. C. et al. Rumen microbial population dynamics during adaptation to a high-grain diet. Appl. Environ. Microbiol. 76, 7482–7490 (2010).ADS 
    CAS 

    Google Scholar 
    Hu, X. et al. High-throughput analysis reveals seasonal variation of the gut microbiota composition within forest musk deer (Moschus berezovskii). Front. Microbiol. 9, (2018).Artzi, L., Morag, E., Shamshoum, M. & Bayer, E. A. Cellulosomal expansion: Functionality and incorporation into the complex. Biotechnol. Biofuels 9, 61 (2016).
    Google Scholar 
    Biddle, A., Stewart, L., Blanchard, J. & Leschine, S. Untangling the genetic basis of fibrolytic specialization by Lachnospiraceae and Ruminococcaceae in diverse gut communities. Diversity 5, (2013).Eisenhauer, N., Scheu, S. & Jousset, A. Bacterial diversity stabilizes community productivity. PLoS ONE 7, e34517 (2012).ADS 
    CAS 

    Google Scholar 
    Miller, A. W., Oakeson, K. F., Dale, C. & Dearing, M. D. Effect of dietary oxalate on the gut microbiota of the mammalian herbivore Neotoma albigula. Appl. Environ. Microbiol. 82, 2669–2675 (2016).ADS 
    CAS 

    Google Scholar 
    Adams, J. M., Rehill, B., Zhang, Y. & Gower, J. A test of the latitudinal defense hypothesis: Herbivory, tannins and total phenolics in four North American tree species. Ecol. Res. 24, 697–704 (2009).CAS 

    Google Scholar 
    Nabeshima, E., Murakami, M. & Hiura, T. Effects of herbivory and light conditions on induced defense in Quercus crispula. J. Plant Res. 114, 403–409 (2001).
    Google Scholar 
    Yang, C.-M., Yang, M.-M., Hsu, J.-M. & Jane, W.-N. Herbivorous insect causes deficiency of pigment–protein complexes in an oval-pointed cecidomyiid gall of Machilus thunbergii leaf. Bot. Bull. Acad. Sin. 44, 315–321 (2003).
    Google Scholar 
    Agetsuma, N., Agetsuma-Yanagihara, Y., Takafumi, H. & Nakaji, T. Plant constituents affecting food selection by sika deer. J. Wildl. Manag. 83, 669–678 (2019).
    Google Scholar 
    Couch, C. E. et al. Diet and gut microbiome enterotype are associated at the population level in African buffalo. Nat. Commun. 12, 2267 (2021).ADS 
    CAS 

    Google Scholar 
    Goel, G., Puniya, A. K. & Singh, K. Tannic acid resistance in ruminal streptococcal isolates. J. Basic Microbiol. 45, 243–245 (2005).CAS 

    Google Scholar 
    Jiménez, N. et al. Genetic and biochemical approaches towards unravelling the degradation of gallotannins by Streptococcus gallolyticus. Microb. Cell Fact. 13, 154 (2014).
    Google Scholar 
    Nelson, K. E., Thonney, M. L., Woolston, T. K., Zinder, S. H. & Pell, A. N. Phenotypic and phylogenetic characterization of ruminal tannin-tolerant bacteria. Appl. Environ. Microbiol. 64, 3824–3830 (1998).ADS 
    CAS 

    Google Scholar 
    Selwal, M. K. et al. Optimization of cultural conditions for tannase production by Pseudomonas aeruginosa IIIB 8914 under submerged fermentation. World J. Microbiol. Biotechnol. 26, 599–605 (2010).CAS 

    Google Scholar 
    Kohl, K. D., Weiss, R. B., Cox, J., Dale, C. & Denise Dearing, M. Gut microbes of mammalian herbivores facilitate intake of plant toxins. Ecol. Lett. 17, 1238–1246 (2014).
    Google Scholar 
    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Method 7, 335–336 (2010).CAS 

    Google Scholar 
    Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).CAS 

    Google Scholar 
    Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2009).
    Google Scholar 
    Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).ADS 

    Google Scholar 
    R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2020).Osawa, R. Formation of a clear zone on tannin-treated brain heart infusion agar by a Streptococcus sp. isolated from feces of koalas. Appl. Environ. Microbiol. 56, 829–831 (1990).ADS 
    CAS 

    Google Scholar 
    Hamamura, N., Olson, S. H., Ward, D. M. & Inskeep, W. P. Diversity and functional analysis of bacterial communities associated with natural hydrocarbon seeps in acidic soils at Rainbow Springs, Yellowstone National Park. Appl. Environ. Microbiol. 71, 5943–5950 (2005).ADS 
    CAS 

    Google Scholar 
    Benson, D. A. et al. GenBank. Nucleic Acids Res. 41, D36–D42 (2012).ADS 

    Google Scholar 
    Chen, I.-M. A. et al. The IMG/M data management and analysis system v.6.0: new tools and advanced capabilities. Nucleic Acids Res. 49, D751–D763 (2020)Suzuki, M. T., Taylor, L. T. & Delong, E. F. Quantitative analysis of small-subunit rRNA genes in mixed microbial populations via 5 ’-nuclease assays. Appl. Environ. Microbiol. 66, 4605–4614 (2000).ADS 
    CAS 

    Google Scholar  More

  • in

    Edaphic controls of soil organic carbon in tropical agricultural landscapes

    Study area and soil collectionTwenty NRCS map units were selected across Hawaii Commercial & Sugar Company (HC&S) in central Maui that represented seven soil orders, 10 NRCS soil series, and approximately 77% of the total plantation area (Fig. 1). Soil heterogeneity across the landscape allowed for the comparison of a continuum of soil and soil properties that have experienced the same C4 grass inputs and agricultural treatment under sugarcane production for over 100 years. Conventional sugarcane production involved 2-year growth followed by harvest burn, collection of remaining stalks by mechanical ripper, deep tillage to 40 cm, no crop rotations, and little to no residue return. The sampled soils, collected from September-August 2015, thus represent a baseline of SOC after input-intensive tropical agriculture and long-term soil disturbance. Elemental analyses from this work show consistent agricultural disturbances led to degraded SOC content ranging from 0.23 to 2.91% SOC of soil mass with an average of only 1.16% SOC across all locations and depths.Figure 1Hawaiian Commercial and Sugar in central Maui with main Hawaiian Islands inset (left). Soil series identified by NRCS across HC&S fields (right) with black dots indicating 20 locations where soils were sampled to test landscape level differences in topical soil kinetics and associated soil properties under conventional sugarcane. Maps from Ref.19 created using ESRI ArcGIS with soil series data from: Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture, Web Soil Survey, Available online at http://websoilsurvey.nrcs.usda.gov/. Accessed [07/30/2016]19.Full size imageThe homogenized land use history allowed focused investigation of soil property effects on SOC storage across heterogenous soils (Table 1). Though soil inputs (e.g. water, nutrients, root inputs, residue removal) and disturbance regimes (e.g. burn, rip, till, compaction, no crop rotation) were consistent across the 20 field locations, average annual surface temperatures varied from 22.9 to 25.1 °C with a mean of 24.4 °C, average annual relative humidity varied from 70.4 to 79.2% with a mean of 73.4%, and average annual rainfall varied from 306 to 1493 mm with a mean of 575 mm. Gradients of rainfall, relative humidity, and elevation across the site generally increase in an east/north-east direction towards the prevailing winds and up the western slope of Haleakalā. In contrast, surface temperatures increase in the opposite direction towards Kihei and the southern tip of the West Maui Mountains.Table 1 NRCS soil classification and environmental conditions at 20 field sites.Full size tableaSoil descriptions26.bInterpolated estimates from Ref.25.Soil sampling and analysisPit locations were identified with a handheld GPS and were sampled using NRCS Rapid Carbon Assessment methods27. A total of 75 horizons were identified from the 20 selected map units to a depth of 1 m28,29. The central depth of each horizon was sampled using volumetric bulk density cores up to 50 cm. After 50 cm, a hand auger was used to check for any further horizon changes. The bulk density of horizons past 50 cm were estimated using collected soil mass and known auger size. Collected soils were air dried, processed through a 2 mm sieve, and analyzed for total C and nitrogen percent, SOC percent, soil texture, iron (Fe) and aluminum (Al) minerals, pH, cation and anion exchange capacity, extractable cations, wet and dry size classes, aggregate stability, and soil water potential at -15 kPa. Total C and nitrogen were measured by elemental analysis (Costech, ECS 4010, Valencia, CA), with SOC content determined by elemental analysis after hydrochloric acid digestion to remove carbonates. Soil texture was measured using sedimentary separation, while a 10:1 soil slurry in water was used to test soil pH. Soil pressure plates were used to measure soil water potential at -15 kPa.Fe and Al oxides were quantified in mineral phases using selective dissolutions of collected soils, including: (1) a 20:1 sodium citrate to sodium dithionite extraction, shaken 16 h, to quantify total free minerals30, (2) 0.25 M hydroxylamine hydrochloride and hydrochloric acid extraction, shaken 16 h, to quantify amorphous minerals31, and (3) 0.1 M sodium pyrophosphate (pH 10), shaken 16 h and centrifuged at 20,000g, to quantify organo-bound metals30. Extracted Fe, Al, and Si from al extractions were measured by inductively coupled plasma analysis (PerkinElmer, Optima ICP-OES, Norwalk, CT). Exploratory ratios of Fe/Al, Fe/Si, and Al/Si for the citrate/dithionite (c), hydroxylamine (h), and pyrophosphate (p) extractions were calculated. Crystalline Fe, operationally-defined as the difference between the citrate dithionite and hydroxylamine extraction, and Al + ½ Fe32 were calculated for each extraction.Plant-available phosphorus was extracted by the Olsen method using 0.5 M sodium bicarbonate adjusted to pH 8.5 and measured by continuous flow colorimetry (Hach, Lachat Quickchem 8500, Loveland, CO). Exchangeable cations (i.e. calcium, magnesium, potassium, and sodium), effective cation exchange capacity, and anion exchange capacity were measured by compulsive exchange using barium chloride and magnesium sulfate33. Cations were quantified by continuous flow colorimetry and flame-spectroscopy (Hach, Lachat Quickchem 8500, Loveland, CO). Field soils were air dried and initially passed through a 2 mm sieve before size classes of macroaggregate (2 mm – 250 µm) and microaggregate ( More