Ecology

Ravindran, S. Coral reefs at a tipping point. Proc. Natl Acad. Sci. 113, 5140–5141 (2016).CAS 

Google Scholar 
Lenton, T. M. et al. Climate tipping points—too risky to bet against. Nature 575, 592–595 (2019).CAS 

Google Scholar 
Veron, J. E. N. et al. Delineating the Coral Triangle. Galaxea J. Coral Reef. Stud. 11, 91–100 (2009).
Google Scholar 
Hoegh-Guldberg, O. et al. Coral Reefs Under Rapid Climate Change and Ocean Acidification. Science 318, 1737–1742 (2007).CAS 

Google Scholar 
Brown, C., Corcoran, E. & Herkenrath, P. Marine and coastal ecosystems and human well-being: a synthesis report based on the findings of the Millennium Ecosystem Assessment. (2006).Heinze, C. et al. The quiet crossing of ocean tipping points. Proc. Natl Acad. Sci. 118, e2008478118 (2021).CAS 

Google Scholar 
Barber, P. H. The challenge of understanding the Coral Triangle biodiversity hotspot. J. Biogeogr. 36, 1845–1846 (2009).
Google Scholar 
Ekman, S. Zoogeography of the Sea. (Sidgwick & Jackson, 1953).Ladd, H. S. Origin of the Pacific island molluscan fauna. Am. J. Sci. 256, 137–150 (1960).
Google Scholar 
Woodland, D. J. Zoogeography of the Siganidae (Pisces): an interpretation of distribution and richness patterns. Bull. Mar. Sci. 33, 713–717 (1983).
Google Scholar 
Loveland, T. R. & Merchant, J. M. Ecoregions and ecoregionalization: geographical and ecological perspectives. Environ. Manag. 34, S1–S13 (2004).
Google Scholar 
Levins, R. Some Demographic and Genetic Consequences of Environmental Heterogeneity for Biological Control. Bull. Entomol. Soc. Am. 15, 237–240 (1969).
Google Scholar 
Obura, D. The Diversity and Biogeography of Western Indian Ocean Reef-Building Corals. PLoS One. 7, e45013 (2012).CAS 

Google Scholar 
Fontoura, L. et al. Protecting connectivity promotes successful biodiversity and fisheries conservation. Science 375, 336–340 (2022).CAS 

Google Scholar 
Roberts, C. M. Connectivity and Management of Caribbean Coral Reefs. Science 278, 1454–1457 (1997).CAS 

Google Scholar 
Ayre, D. J. & Hughes, T. P. Climate change, genotypic diversity and gene flow in reef-building corals: Gene flow in reef building corals. Ecol. Lett. 7, 273–278 (2004).
Google Scholar 
Graham, N. A. et al. Dynamic fragility of oceanic coral reef ecosystems. Proc. Natl Acad. Sci. 103, 8425–8429 (2006).CAS 

Google Scholar 
McClanahan, T. R. et al. Prioritizing Key Resilience Indicators to Support Coral Reef Management in a Changing Climate. PLoS One. 7, e42884 (2012).CAS 

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).
Google Scholar 
Grayson, N., Clements, C. S., Towner, A. A., Beatty, D. S. & Hay, M. E. Did the historic overharvesting of sea cucumbers make coral more susceptible to pathogens? Coral Reefs. 41, 447–453 (2022).
Google Scholar 
Spalding, M. D. et al. Marine Ecoregions of the World: A Bioregionalization of Coastal and Shelf Areas. BioScience 57, 573–583 (2007).
Google Scholar 
Berline, L., Rammou, A.-M., Doglioli, A., Molcard, A. & Petrenko, A. A Connectivity-Based Eco-Regionalization Method of the Mediterranean Sea. PLoS ONE. 9, e111978 (2014).
Google Scholar 
Ser-Giacomi, E., Rossi, V., López, C. & Hernández-García, E. Flow networks: A characterization of geophysical fluid transport. Chaos Interdiscip. J. Nonlinear Sci. 25, 036404 (2015).
Google Scholar 
Thompson, D. M. et al. Variability in oceanographic barriers to coral larval dispersal: Do currents shape biodiversity? Prog. Oceanogr. 165, 110–122 (2018).
Google Scholar 
Treml, E. A., Halpin, P. N., Urban, D. L. & Pratson, L. F. Modeling population connectivity by ocean currents, a graph-theoretic approach for marine conservation. Landsc. Ecol. 23, 19–36 (2008).
Google Scholar 
Liu, G., Bracco, A., Quattrini, A. M. & Herrera, S. Kilometer-Scale Larval Dispersal Processes Predict Metapopulation Connectivity Pathways for Paramuricea biscaya in the Northern Gulf of Mexico. Front. Mar. Sci. 8, 790927 (2021).
Google Scholar 
Fountalis, I., Dovrolis, C., Bracco, A., Dilkina, B. & Keilholz, S. δ-MAPS: from spatio-temporal data to a weighted and lagged network between functional domains. Appl. Netw. Sci. 3, 21 (2018).
Google Scholar 
Falasca, F., Bracco, A., Nenes, A. & Fountalis, I. Dimensionality Reduction and Network Inference for Climate Data Using δ‐MAPS: Application to the CESM Large Ensemble Sea Surface Temperature. J. Adv. Model. Earth Syst. 11, 1479–1515 (2019).
Google Scholar 
Novi, L., Bracco, A. & Falasca, F. Uncovering marine connectivity through sea surface temperature. Sci. Rep. 11, 8839 (2021).CAS 

Google Scholar 
Kleypas, J. A., Castruccio, F. S., Curchitser, E. N. & Mcleod, F. The impact of ENSO on coral heat stress in the western equatorial Pacific. Glob. Change Biol. 21, 2525–2539 (2015).
Google Scholar 
GLOBAL_REANALYSIS_001_030. Global Ocean Physics Reanalysis GLORYS12V1 1/12° product. MERCATOR GLORYS12V1 (global-reanalysis-001-030-monthly). E.U. Copernicus Marine Service Information (CMEMS). https://doi.org/10.48670/moi-00021.Lellouche, J.-M. et al. The Copernicus Global 1/12° Oceanic and Sea Ice GLORYS12 Reanalysis. Front. Earth Sci. 9, 698876 (2021).
Google Scholar 
Treml, E. A. & Halpin, P. N. Marine population connectivity identifies ecological neighbors for conservation planning in the Coral Triangle: Ecological neighbors in conservation. Conserv. Lett. 5, 441–449 (2012).
Google Scholar 
Meyers, G. Variation of Indonesian throughflow and the El Niño-Southern Oscillation. J. Geophys. Res. Oceans 101, 12255–12263 (1996).
Google Scholar 
Wolfram Research (2012), FindGraphCommunities, Wolfram Language function. https://reference.wolfram.com/language/ref/FindGraphCommunities.html (updated 2015).MacArthur, R. H. & Wilson, E. O. The theory of island biogeography. In The Theory of Island Biogeography (Princeton university press, 2016).Brin, S. & Page, L. The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst. 30, 107–117 (1998).
Google Scholar 
Wolfram Research (2010), PageRankCentrality, Wolfram Language function. https://reference.wolfram.com/language/ref/PageRankCentrality.html (Updated 2015).NOAA Coral Reef Watch program, 20180813, NOAA Coral Reef Watch Version 3.1 Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite: NOAA Coral Reef Watch program, College Park, Maryland, USA. https://coralreefwatch.noaa.gov/product/5km/.Liu, G. et al. Reef-Scale Thermal Stress Monitoring of Coral Ecosystems: New 5-km Global Products from NOAA Coral Reef Watch. Remote Sens. 6, 11579–11606 (2014).
Google Scholar 
Liu, G. et al. NOAA Coral Reef Watch’s 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3 and Four-Month Outlook Version 4. 32, 7 (2017).Claar, D. C., Szostek, L., McDevitt-Irwin, J. M., Schanze, J. J. & Baum, J. K. Global patterns and impacts of El Niño events on coral reefs: A meta-analysis. PLOS ONE 13, e0190957 (2018).
Google Scholar 
Sully, S., Burkepile, D. E., Donovan, M. K., Hodgson, G. & van Woesik, R. A global analysis of coral bleaching over the past two decades. Nat. Commun. 10, 1264 (2019).CAS 

Google Scholar 
Darling, E. S. et al. Social–environmental drivers inform strategic management of coral reefs in the Anthropocene. Nat. Ecol. Evol. 3, 1341–1350 (2019).
Google Scholar 
Dance, A. These corals could survive climate change—and help save the world’s reefs. Nature 575, 580–582 (2019).CAS 

Google Scholar 
Renema, W. et al. Hopping Hotspots: Global Shifts in Marine Biodiversity. Science 321, 654–657 (2008).CAS 

Google Scholar 
Weiss, T. L., Denniston, R. F., Wanamaker, A. D., Villarini, G. & von der Heydt, A. S. El Niño–Southern Oscillation–like variability in a late Miocene Caribbean coral. Geology 45, 643–646 (2017).
Google Scholar 
Watanabe, T. et al. Permanent El Niño during the Pliocene warm period not supported by coral evidence. Nature 471, 209–211 (2011).CAS 

Google Scholar 
Von Der Heydt, A. S. & Dijkstra, H. A. The impact of ocean gateways on ENSO variability in the Miocene. Geol. Soc. Lond. Spec. Publ. 355, 305–318 (2011).
Google Scholar 
Yasuhara, M. et al. Past and future decline of tropical pelagic biodiversity. Proc. Natl Acad. Sci. 117, 12891–12896 (2020).CAS 

Google Scholar 
Falasca, F., Crétat, J., Bracco, A., Braconnot, P. & Marti, O. Climate change in the Indo-Pacific basin from mid- to late Holocene. Clim. Dyn. 59, 753–766 (2022).
Google Scholar 
Treml, E. A., Ford, J. R., Black, K. P. & Swearer, S. E. Identifying the key biophysical drivers, connectivity outcomes, and metapopulation consequences of larval dispersal in the sea. Mov. Ecol. 3, 17 (2015).
Google Scholar 
Hackerott, S., Martell, H. A. & Eirin-Lopez, J. M. Coral environmental memory: causes, mechanisms, and consequences for future reefs. Trends Ecol. Evol. 36, 1011–1023 (2021).
Google Scholar 
Ogle, K. et al. Quantifying ecological memory in plant and ecosystem processes. Ecol. Lett. 18, 221–235 (2015).
Google Scholar 
Peterson, G. D. Contagious Disturbance, Ecological Memory, and the Emergence of Landscape Pattern. Ecosystems 5, 329–338 (2002).
Google Scholar 
Thomas, L., López, E. H., Morikawa, M. K. & Palumbi, S. R. Transcriptomic resilience, symbiont shuffling, and vulnerability to recurrent bleaching in reef‐building corals. Mol. Ecol. 28, 3371–3382 (2019).
Google Scholar 
Dziedzic, K. E., Elder, H., Tavalire, H. & Meyer, E. Heritable variation in bleaching responses and its functional genomic basis in reef‐building corals (Orbicella faveolata). Mol. Ecol. 28, 2238–2253 (2019).
Google Scholar 
Ainsworth, T. D. et al. Climate change disables coral bleaching protection on the Great Barrier Reef. Science 352, 338–342 (2016).CAS 

Google Scholar 
Harrison, H. B., Bode, M., Williamson, D. H., Berumen, M. L. & Jones, G. P. A connectivity portfolio effect stabilizes marine reserve performance. Proc. Natl Acad. Sci. 117, 25595–25600 (2020).CAS 

Google Scholar 
Leeuwenburgh, O. & Stammer, D. The Effect of Ocean Currents on Sea Surface Temperature Anomalies. J. Phys. Oceanogr. 31, 2340–2358 (2001).
Google Scholar 
Box, G. E., Jenkins, G. M. & Reinsel, G. C. Time series analysis: forecasting and control. (Wiley, 2011).Falasca, F. & Bracco, A. Exploring the tropical Pacific manifold in models and observations. Phys. Rev. X 12, 021054 (2022).CAS 

Google Scholar 
NOAA (National Oceanic and Atmospheric Administration), (2019a). Nino regions. https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/nino_regions.shtml.NOAA (National Oceanic and Atmospheric Administration), (2019b). Cold and warm episodes by season. https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php.Baird, A. et al. Coral Spawning Database. 10552719 Bytes https://doi.org/10.25405/DATA.NCL.13082333.V1 (2020).UNEP-WCMC, WorldFish Centre, WRI, TNC (2021). Global distribution of warm-water coral reefs, compiled from multiple sources including the Millennium Coral Reef Mapping Project. Version 4.1. Includes contributions from IMaRS-USF and IRD (2005), IMaRS-USF (2005) and Spalding et al. (2001). Cambridge (UK): UN Environment World Conservation Monitoring Centre. Data https://doi.org/10.34892/t2wk-5t34.IMaRS-USF, IRD (Institut de Recherche pour le Developpement) (2005). Millennium Coral Reef Mapping Project. Validated maps. Cambridge (UK): UNEP World Conservation Monitoring Centre.IMaRS-USF (Institute for Marine Remote Sensing-University of South Florida) (2005). Millennium Coral Reef Mapping Project. Unvalidated maps. These maps are unendorsed by IRD, but were further interpreted by UNEP World Conservation Monitoring Centre. Cambridge (UK): UNEP World Conservation Monitoring Centre.Spalding, M., Ravilious, C. & Green, E. World atlas of coral reefs. Choice Rev. Online. 39, 39-2540–39–2540 (2002).
Google Scholar  More

Surface-attached bacteria move towards antibiotics via twitching motilityWe used microfluidic devices and automated cell tracking to quantify the movement of P. aeruginosa cells as they are exposed to well-defined spatial gradients of antibiotics in developing biofilms (Fig. 1). We began with the antibiotic ciprofloxacin, which is widely used to treat P. aeruginosa infections27,28. To set a baseline, we first determined the minimum inhibitory concentration (hereafter MIC) of ciprofloxacin for P. aeruginosa (strain PAO1) in shaking cultures, which agrees with the published MIC of this strain (Fig. S129). We then exposed surface-attached cells to an antibiotic gradient in a microfluidic device where the antibiotic concentration ranged from zero to 10 times the MIC (Fig. 1A, B, Methods). After approximately 5 h of unbiased movement, we were surprised to see that twitching cells began to bias their movement towards increasing concentrations of ciprofloxacin (Fig. 1B, D, Movie 1). The movement bias, β, defined as the number of cells moving up the gradient divided by the number of cells moving down the gradient, peaks after approximately 10 h and then decays as the surface becomes crowded with cells (Movie 1) and tracking becomes difficult (Methods). The flow through the device also has a small influence on the direction of cell movement because it tends to pull cells in the downstream direction (Fig. 1B, C, E). However, this fluid flow is orthogonal to the direction of the antibiotic gradient, and so does not explain the movement towards antibiotics.Fig. 1: Twitching P. aeruginosa cells bias their motility towards increasing antibiotic concentrations.A A dual-inlet microfluidic device generates steady antibiotic gradients (e.g. ciprofloxacin, CMAX = 10X MIC) via molecular diffusion. Isocontours were calculated using mathematical modelling (Methods) and background shading shows approximate ciprofloxacin distribution visualised using fluorescein. B Red (blue) cell trajectories are moving towards (away from) increasing [ciprofloxacin]. Inset: A circular histogram of cell movement direction reveals movement bias towards increasing [ciprofloxacin]. A two-sided binomial test rejects the null hypothesis that trajectories are equally likely to be directed up or down the [ciprofloxacin] gradient (p  More

Franchini, M. & Mannucci, P. M. Air pollution and cardiovascular disease. Thromb. Res. 129, 230–234 (2012).Article 
CAS 

Google Scholar 
Langrish, J. P. et al. Reducing personal exposure to particulate air pollution improves cardiovascular health in patients with coronary heart disease. Environ. Health Perspect. 120, 367–372 (2012).Article 
CAS 

Google Scholar 
Tanwar, V., Katapadi, A., Adelstein, J. M., Grimmer, J. A. & Wold, L. E. Cardiac pathophysiology in response to environmental stress: A current review. Curr. Opin. Physiol. 1, 198–205 (2018).Article 

Google Scholar 
Franchini, M. & Mannucci, P. M. Particulate air pollution and cardiovascular risk: short-term and long-term effects. in Seminars in Thrombosis and Hemostasis. Vol. 35. 665–670 (© Thieme Medical Publishers, 2009).Shah, A. S. V. et al. Global association of air pollution and heart failure: A systematic review and meta-analysis. Lancet 382, 1039–1048 (2013).Article 
CAS 

Google Scholar 
Cesaroni, G. et al. Long term exposure to ambient air pollution and incidence of acute coronary events: Prospective cohort study and meta-analysis in 11 European cohorts from the ESCAPE Project. BMJ 348, f7412 (2014).Article 

Google Scholar 
Brook, R. D. et al. Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association. Circulation 121, 2331–2378 (2010).Article 
CAS 

Google Scholar 
Héroux, M.-E. et al. Quantifying the health impacts of ambient air pollutants: Recommendations of a WHO/Europe project. Int. J. Public Health 60, 619–627 (2015).Article 

Google Scholar 
An, Z., Jin, Y., Li, J., Li, W. & Wu, W. Impact of particulate air pollution on cardiovascular health. Curr. Allergy Asthma Rep. 18, 1–7 (2018).Article 
CAS 

Google Scholar 
Zanobetti, A., Baccarelli, A. & Schwartz, J. Gene-air pollution interaction and cardiovascular disease: A review. Prog. Cardiovasc. Dis. 53, 344–352 (2011).Article 
CAS 

Google Scholar 
Liang, R. et al. Effect of exposure to PM2.5 on blood pressure: A systematic review and meta-analysis. J. Hypertens. 32, 2130–2141 (2014).Article 
CAS 

Google Scholar 
Jerrett, M. et al. Traffic-related air pollution and obesity formation in children: A longitudinal, multilevel analysis. Environ. Heal. 13, 49 (2014).Article 

Google Scholar 
McConnell, R. et al. A longitudinal cohort study of body mass index and childhood exposure to secondhand tobacco smoke and air pollution: The Southern California Children’s Health Study. Environ. Health Perspect. 123, 360–366 (2015).Article 

Google Scholar 
Renzi, M. et al. Air pollution and occurrence of type 2 diabetes in a large cohort study. Environ. Int. 112, 68–76 (2018).Article 
CAS 

Google Scholar 
Jomova, K. et al. Arsenic: Toxicity, oxidative stress and human disease. J. Appl. Toxicol. 31, 95–107 (2011).CAS 

Google Scholar 
Al-Kindi, S. G., Brook, R. D., Biswal, S. & Rajagopalan, S. Environmental determinants of cardiovascular disease: Lessons learned from air pollution. Nat. Rev. Cardiol. 17, 656–672 (2020).Article 

Google Scholar 
Noroozian, M. The elderly population in iran: An ever growing concern in the health system. Iran. J. Psychiatry Behav. Sci. 6, 1 (2012).
Google Scholar 
Chokshi, D. A. & Farley, T. A. The cost-effectiveness of environmental approaches to disease prevention. N. Engl. J. Med. 367, 295–297 (2012).Article 
CAS 

Google Scholar 
Nieuwenhuijsen, M. J. Influence of urban and transport planning and the city environment on cardiovascular disease. Nat. Rev. Cardiol. 15, 432–438 (2018).Article 

Google Scholar 
Barnett, A. G. et al. The effects of air pollution on hospitalizations for cardiovascular disease in elderly people in Australian and New Zealand cities. Environ. Health Perspect. 114, 1018–1023 (2006).Article 
CAS 

Google Scholar 
Koken, P. J. M. et al. Temperature, air pollution, and hospitalization for cardiovascular diseases among elderly people in Denver. Environ. Health Perspect. 111, 1312–1317 (2003).Article 

Google Scholar 
Institute for Health Metrics and Evaluation. GBD 2019. (University of Washington, 2022).IHME. GBD 2019 Data and Tools Overview. (University of Washington, 2020).Dicker, D. et al. Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 392, 1684–1735 (2018).Article 

Google Scholar 
Miller, D. C. & Salkind, N. J. Handbook of Research Design and Social Measurement (Sage, 2002).Book 

Google Scholar 
Rosenthal, F. S., Carney, J. P. & Olinger, M. L. Out-of-hospital cardiac arrest and airborne fine particulate matter: A case–crossover analysis of emergency medical services data in Indianapolis, Indiana. Environ. Health Perspect. 116, 631–636 (2008).Article 

Google Scholar 
Ensor, K. B., Raun, L. H. & Persse, D. A case-crossover analysis of out-of-hospital cardiac arrest and air pollution. Circulation 127, 1192–1199 (2013).Article 
CAS 

Google Scholar 
Forastiere, F. et al. A case-crossover analysis of out-of-hospital coronary deaths and air pollution in Rome, Italy. Am. J. Respir. Crit. Care Med. 172, 1549–1555 (2005).Article 

Google Scholar 
Levy, D. et al. A case-crossover analysis of particulate matter air pollution and out-of-hospital primary cardiac arrest. Epidemiology 12, 193–199 (2001).Article 
CAS 

Google Scholar 
Silverman, R. A. et al. Association of ambient fine particles with out-of-hospital cardiac arrests in New York City. Am. J. Epidemiol. 172, 917–923 (2010).Article 

Google Scholar 
Naghavi, M. et al. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet 390, 1151–1210 (2017).Article 

Google Scholar 
Berend, N. Contribution of air pollution to COPD and small airway dysfunction. Respirology 21, 237–244 (2016).Article 

Google Scholar 
Vahedian, M., Khanjani, N., Mirzaee, M. & Koolivand, A. Associations of short-term exposure to air pollution with respiratory hospital admissions in Arak, Iran. J. Environ. Health Sci. Eng. 15, 17 (2017).Yaser, H. S., Narges, K., Yaser, S. & Rasoul, M. Air pollution and cardiovascular mortality in Kerman from 2006 to 2011. Am. J. Cardiovasc. Dis. Res. 2, 27–30 (2014).
Google Scholar 
Khaefi, M. et al. Association of particulate matter impact on prevalence of chronic obstructive pulmonary disease in Ahvaz, southwest Iran during 2009–2013. Aerosol Air Qual. Res. 17, 230–237 (2017).Article 
CAS 

Google Scholar 
Khaniabadi, Y. O. et al. Exposure to PM10, NO2, and O3 and impacts on human health. Environ. Sci. Pollut. Res. Int. 24, 2781–2789 (2017).Article 
CAS 

Google Scholar 
Momtazan, M. et al. An investigation of particulate matter and relevant cardiovascular risks in Abadan and Khorramshahr in 2014–2016. Toxin Rev. 38, 1–8 (2018).Martinelli, N., Olivieri, O. & Girelli, D. Air particulate matter and cardiovascular disease: A narrative review. Eur. J. Intern. Med. 24, 295–302 (2013).Article 
CAS 

Google Scholar 
Khaniabadi, Y. O. et al. Mortality and morbidity due to ambient air pollution in Iran. Clin. Epidemiol. Glob. Health 7, 222–227 (2019).Article 

Google Scholar 
Almeida-Silva, M. et al. Exposure and dose assessment to particle components among an elderly population. Atmos. Environ. 102, 156–166 (2015).Article 
ADS 
CAS 

Google Scholar 
Suh, H. H., Zanobetti, A., Schwartz, J. & Coull, B. A. Chemical properties of air pollutants and cause-specific hospital admissions among the elderly in Atlanta, Georgia. Environ. Health Perspect. 119, 1421–1428 (2011).Article 

Google Scholar 
Chien, L.-C., Yang, C.-H. & Yu, H.-L. Estimated effects of Asian dust storms on spatiotemporal distributions of clinic visits for respiratory diseases in Taipei children (Taiwan). Environ. Health Perspect. 120, 1215–1220 (2012).Article 

Google Scholar 
Khaniabadi, Y. O. et al. Chronic obstructive pulmonary diseases related to outdoor PM10, O3, SO2, and NO2 in a heavily polluted megacity of Iran. Environ. Sci. Pollut. Res. 25, 17726–17734 (2018).Article 
CAS 

Google Scholar 
Omidi Khaniabadi, Y. et al. Air quality modeling for health risk assessment of ambient PM10, PM2.5 and SO2 in Iran. Hum. Ecol. Risk Assess. Int. J. 25, 1298–1310 (2019).Newell, K., Kartsonaki, C., Lam, K. B. H. & Kurmi, O. P. Cardiorespiratory health effects of particulate ambient air pollution exposure in low-income and middle-income countries: A systematic review and meta-analysis. Lancet Planet. Health 1, e368–e380 (2017).Article 

Google Scholar 
Dominici, F. et al. Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. JAMA 295, 1127–1134 (2006).Article 
CAS 

Google Scholar 
Qiu, H. et al. Inflammatory and oxidative stress responses of healthy elders to solar-assisted large-scale cleaning system (SALSCS) and changes in ambient air pollution: A quasi-interventional study in Xi’an, China. Sci. Total Environ. 806, 151217 (2022).Article 
ADS 
CAS 

Google Scholar 
Fiordelisi, A. et al. The mechanisms of air pollution and particulate matter in cardiovascular diseases. Heart Fail. Rev. 22, 337–347 (2017).Article 
CAS 

Google Scholar 
Yang, D., Yang, X., Deng, F. & Guo, X. Ambient air pollution and biomarkers of health effect. Ambient Air Pollut. Health Impact China 1017, 59–102 (2017).Lim, S. S. et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet 380, 2224–2260 (2012).Article 

Google Scholar 
Newby, D. E. et al. Expert position paper on air pollution and cardiovascular disease. Eur. Heart J. 36, 83–93 (2015).Article 
CAS 

Google Scholar 
Brook, R. D., Newby, D. E. & Rajagopalan, S. Air pollution and cardiometabolic disease: An update and call for clinical trials. Am. J. Hypertens. 31, 1–10 (2018).Article 
CAS 

Google Scholar 
Kang, S.-H. et al. Ambient air pollution and out-of-hospital cardiac arrest. Int. J. Cardiol. 203, 1086–1092 (2016).Article 

Google Scholar 
Thurston, G. D. et al. Ambient particulate matter air pollution exposure and mortality in the NIH-AARP diet and health cohort. Environ. Health Perspect. 124, 484–490 (2016).Article 

Google Scholar 
Gallagher, L. G. et al. Applying a moving total mortality count to the cities in the NMMAPS database to estimate the mortality effects of particulate matter air pollution. Circulation 172, 872–879 (2010).
Google Scholar 
Rodopoulou, S., Samoli, E., Chalbot, M.-C.G. & Kavouras, I. G. Air pollution and cardiovascular and respiratory emergency visits in Central Arkansas: A time-series analysis. Sci. Total Environ. 536, 872–879 (2015).Article 
ADS 
CAS 

Google Scholar 
Teng, T.-H.K. et al. A systematic review of air pollution and incidence of out-of-hospital cardiac arrest. J. Epidemiol. Commun. Health 68, 37–43 (2014).Article 

Google Scholar 
Brook, R. D. et al. Air pollution and cardiovascular disease: A statement for healthcare professionals from the expert panel on population and prevention science of the American Heart Association. Circulation 109, 2655–2671 (2004).Article 

Google Scholar 
Raza, A. et al. Short-term effects of air pollution on out-of-hospital cardiac arrest in Stockholm. Eur. Heart J. 35, 861–868 (2014).Article 
CAS 

Google Scholar 
Baccarelli, A. et al. Effects of exposure to air pollution on blood coagulation. J. Thromb. Haemost. 5, 252–260 (2007).Article 
CAS 

Google Scholar 
Franchini, M. & Mannucci, P. M. Thrombogenicity and cardiovascular effects of ambient air pollution. Blood 118, 2405–2412 (2011).Article 
CAS 

Google Scholar 
Yin, F. et al. Diesel exhaust induces systemic lipid peroxidation and development of dysfunctional pro-oxidant and pro-inflammatory high-density lipoprotein. Arterioscler. Thromb. Vasc. Biol. 33, 1153–1161 (2013).Article 
CAS 

Google Scholar 
Chirinos, J. A. et al. Elevation of endothelial microparticles, platelets, and leukocyte activation in patients with venous thromboembolism. J. Am. Coll. Cardiol. 45, 1467–1471 (2005).Article 
CAS 

Google Scholar 
Adar, S. D. et al. Fine particulate air pollution and the progression of carotid intima-medial thickness: A prospective cohort study from the multi-ethnic study of atherosclerosis and air pollution. PLoS Med. 10, e1001430 (2013).Article 

Google Scholar 
Kampfrath, T. et al. Chronic fine particulate matter exposure induces systemic vascular dysfunction via NADPH oxidase and TLR4 pathways. Circ. Res. 108, 716–726 (2011).Article 
CAS 

Google Scholar 
Sun, Q. et al. Long-term air pollution exposure and acceleration of atherosclerosis and vascular inflammation in an animal model. JAMA 294, 3003–3010 (2005).Article 
CAS 

Google Scholar 
Dennekamp, M. et al. Outdoor air pollution as a trigger for out-of-hospital cardiac arrests. Epidemiology 21, 494–500 (2010).Straney, L. et al. Evaluating the impact of air pollution on the incidence of out-of-hospital cardiac arrest in the Perth Metropolitan Region: 2000–2010. J. Epidemiol. Commun. Health 68, 6–12 (2014).Article 

Google Scholar 
Sullivan, J. et al. Exposure to ambient fine particulate matter and primary cardiac arrest among persons with and without clinically recognized heart disease. Am. J. Epidemiol. 157, 501–509 (2003).Article 
CAS 

Google Scholar 
Barton, T. J. et al. Traditional cardiovascular risk factors strongly underestimate the 5-year occurrence of cardiovascular morbidity and mortality in spinal cord injured individuals. Arch. Phys. Med. Rehabil. 102, 27–34 (2021).Article 

Google Scholar 
Burg, M. M. et al. Risk for incident hypertension associated with PTSD in military veterans, and the effect of PTSD treatment. Psychosom. Med. 79, 181 (2017).Article 

Google Scholar 
Hinojosa, R. Veterans’ likelihood of reporting cardiovascular disease. J. Am. Board Fam. Med. 32, 50–57 (2019).Article 

Google Scholar 
Rush, T., LeardMann, C. A. & Crum-Cianflone, N. F. Obesity and associated adverse health outcomes among US military members and veterans: Findings from the millennium cohort study. Obesity 24, 1582–1589 (2016).Article 

Google Scholar 
Stefanovics, E. A., Potenza, M. N. & Pietrzak, R. H. Smoking, obesity, and their co-occurrence in the US military veterans: Results from the national health and resilience in veterans study. J. Affect. Disord. 274, 354–362 (2020).Article 

Google Scholar 
Brook, R. D. et al. Insights into the mechanisms and mediators of the effects of air pollution exposure on blood pressure and vascular function in healthy humans. Hypertension 54, 659–667 (2009).Article 
CAS 

Google Scholar 
Rajagopalan, S. & Brook, R. D. Air pollution and type 2 diabetes: Mechanistic insights. Diabetes 61, 3037–3045 (2012).Article 
CAS 

Google Scholar 
Franklin, S. S. & Wong, N. D. Hypertension and cardiovascular disease: Contributions of the Framingham Heart Study. Glob. Heart 8, 49–57 (2013).Article 

Google Scholar 
Gu, D. et al. Blood pressure and risk of cardiovascular disease in Chinese men and women. Am. J. Hypertens. 21, 265–272 (2008).Article 

Google Scholar 
Wang, H. et al. Blood pressure, body mass index and risk of cardiovascular disease in Chinese men and women. BMC Public Health 10, 189 (2010).Article 

Google Scholar 
O’Brien, E. The Lancet Commission on hypertension: Addressing the global burden of raised blood pressure on current and future generations. J. Clin. Hypertens. 19, 564–568 (2017).Article 

Google Scholar 
Cai, Y. et al. Associations of short-term and long-term exposure to ambient air pollutants with hypertension: A systematic review and meta-analysis. Hypertension 68, 62–70 (2016).Article 
CAS 

Google Scholar 
Zhang, Z., Laden, F., Forman, J. P. & Hart, J. E. Long-term exposure to particulate matter and self-reported hypertension: A prospective analysis in the Nurses’ Health Study. Environ. Health Perspect. 124, 1414–1420 (2016).Article 

Google Scholar 
Cosselman, K. E., Navas-Acien, A. & Kaufman, J. D. Environmental factors in cardiovascular disease. Nat. Rev. Cardiol. 12, 627 (2015).Article 
CAS 

Google Scholar 
Baccarelli, A. et al. Effects of particulate air pollution on blood pressure in a highly exposed population in Beijing, China: A repeated-measure study. Environ. Heal. 10, 108 (2011).Article 

Google Scholar 
Mordukhovich, I. et al. Black carbon exposure, oxidative stress genes, and blood pressure in a repeated-measures study. Environ. Health Perspect. 117, 1767–1772 (2009).Article 
CAS 

Google Scholar 
Chen, H. et al. Spatial association between ambient fine particulate matter and incident hypertension. Circulation 129, 562–569 (2014).Article 
CAS 

Google Scholar 
Honjo, K. et al. The effects of smoking and smoking cessation on mortality from cardiovascular disease among Japanese: Pooled analysis of three large-scale cohort studies in Japan. Tob. Control 19, 50–57 (2010).Article 

Google Scholar 
Lawlor, D. A., Song, Y.-M., Sung, J., Ebrahim, S. & Smith, G. D. The association of smoking and cardiovascular disease in a population with low cholesterol levels: A study of 648 346 men from the Korean national health system prospective cohort study. Stroke 39, 760–767 (2008).Article 
CAS 

Google Scholar 
Wold, L. E. et al. Cardiovascular remodeling in response to long-term exposure to fine particulate matter air pollution. Circ. Hear. Fail. 5, 452–461 (2012).Article 
CAS 

Google Scholar 
Zoeller, R. T. et al. Endocrine-disrupting chemicals and public health protection: A statement of principles from The Endocrine Society. Endocrinology 153, 4097–4110 (2012).Article 
CAS 

Google Scholar 
Ruiz, D., Becerra, M., Jagai, J. S., Ard, K. & Sargis, R. M. Disparities in environmental exposures to endocrine-disrupting chemicals and diabetes risk in vulnerable populations. Diabetes Care 41, 193–205 (2018).Article 
CAS 

Google Scholar 
Taylor, D. Toxic Communities: Environmental Racism, Industrial Pollution, and Residential Mobility (NYU Press, 2014).
Google Scholar 
Newbold, R. R., Padilla-Banks, E. & Jefferson, W. N. Environmental estrogens and obesity. Mol. Cell. Endocrinol. 304, 84–89 (2009).Article 
CAS 

Google Scholar 
Szyszkowicz, M., Rowe, B. H. & Brook, R. D. Even low levels of ambient air pollutants are associated with increased emergency department visits for hypertension. Can. J. Cardiol. 28, 360–366 (2012).Article 
CAS 

Google Scholar 
van den Hooven, E. H. et al. Air pollution, blood pressure, and the risk of hypertensive complications during pregnancy: The generation R study. Hypertension 57, 406–412 (2011).Article 

Google Scholar 
Vali, M. et al. Effect of meteorological factors and Air Quality Index on the COVID-19 epidemiological characteristics: An ecological study among 210 countries. Environ. Sci. Pollut. Res. 38, 1–11 (2021).Kiani, B. et al. Association between heavy metals and colon cancer: An ecological study based on geographical information systems in North-Eastern Iran. BMC Cancer 21, 1–12 (2021).Article 

Google Scholar 
Cyranoski, D. China tests giant air cleaner to combat smog. Nature 555, 152–154 (2018).Article 
ADS 
CAS 

Google Scholar  More

Neumann, P. & Carreck, N. L. Honey bee colony losses. J. Apic. Res. 49(1), 1–6 (2010).Article 

Google Scholar 
Osterman, J. et al. Global trends in the number and diversity of managed pollinator species. Agr. Ecosyst. Environ. 322, 107653. https://doi.org/10.1016/j.agee.2021.107653 (2021).Article 

Google Scholar 
Potts, S. G. et al. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).Article 

Google Scholar 
Hristov, P., Shumkova, R., Palova, N. & Neov, B. Factors associated with honey bee colony losses: A mini-review. Vet. Sci. 7(4), 166 (2020).Article 

Google Scholar 
Dukas, R. Mortality rates of honey bees in the wild. Insectes Soc. 55, 252–255 (2008).Article 

Google Scholar 
Ellis, J. D., Evans, J. D. & Pettis, J. Colony losses, managed colony population decline, and colony collapse disorder in the United States. J. Apic. Res. 49, 134–136 (2010).Article 

Google Scholar 
Vanengelsdorp, D. & Meixner, M. D. A historical review of managed honey bee populations in Europe and the United States and the factors that may affect them. J. Invertebr. Pathol. 103(Suppl 1), S80-95 (2010).Article 

Google Scholar 
Gallai, N., Salles, J.-M., Settele, J. & Vaissière, B. E. Economic valuation of the vulnerability of world agriculture confronted with pollinator decline. Ecol. Econ. 68, 810–821 (2009).Article 

Google Scholar 
Patel, V., Pauli, N., Biggs, E., Barbour, L. & Boruff, B. Why bees are critical for achieving sustainable development. Ambio 50, 49–59 (2021).Article 

Google Scholar 
Aylanc, V., Falcão, S. I., Ertosun, S. & Vilas-Boas, M. From the hive to the table: Nutrition value, digestibility and bioavailability of the dietary phytochemicals present in the bee pollen and bee bread. Trends Food Sci. Tech. 109, 464–481 (2021).Article 
CAS 

Google Scholar 
Kieliszek, M. et al. Pollen and bee bread as new health-oriented products: A review. Trends Food Sci. Tech. 71, 170–180 (2018).Article 
CAS 

Google Scholar 
Bixby, M. et al. Honey bee queen production: Canadian costing case study and profitability analysis. J. Econ. Entomol. 113, 1618–1627 (2020).Article 

Google Scholar 
Ghosh, S., Jung, C. & Meyer-Rochow, V. B. Nutritional value and chemical composition of larvae, pupae, and adults of worker honey bee, Apis mellifera ligustica as a sustainable food source. J. Asia-Pac. Entomol. 19, 487–495 (2016).Article 
CAS 

Google Scholar 
Ulmer, M., Smetana, S. & Heinz, V. Utilizing honeybee drone brood as a protein source for food products: Life cycle assessment of apiculture in Germany. Resour. Conser. Recy. 154, 104576. https://doi.org/10.1016/j.resconrec.2019.104576 (2020).Article 

Google Scholar 
FAO. Value-added products from beekeeping. FAO Agricultural Services Bulletin. https://www.fao.org/publications/card/en/c/a76265ff-7440-57a6-82da-21976b9fde8d (1996).FAO. Beekeeping and sustainable livelihoods. Diversification booklet 1. https://www.fao.org/3/y5110e/y5110e00.htm (2004).Halvorson, K., Baumung, R., Leroy, G., Chen, C. & Boettcher, P. Protection of honeybees and other pollinators: One global study. Apidologie 52, 535–547 (2021).Article 

Google Scholar 
Moritz, R. F. A. & Erler, S. Lost colonies found in a data mine: Global honey trade but not pests or pesticides as a major cause of regional honeybee colony declines. Agr. Ecosyst. Environ. 216, 44–50 (2016).Article 

Google Scholar 
Naug, D. Nutritional stress due to habitat loss may explain recent honeybee colony collapses. Biol. Conserv. 142, 2369–2372 (2009).Article 

Google Scholar 
Pohorecka, K., Szczęsna, T., Witek, M., Miszczak, A. & Sikorski, P. The exposure of honey bees to pesticide residues in the hive environment with regard to winter colony losses. J. Apicult. Sci. 61, 105 (2017).Article 
CAS 

Google Scholar 
Van Dooremalen, C. et al. Winter survival of individual honey bees and honey bee colonies depends on level of Varroa destructor infestation. PLoS ONE 7, e36285. https://doi.org/10.1371/journal.pone.0036285 (2012).Article 
ADS 
CAS 

Google Scholar 
Steinhauer, N. et al. Drivers of colony losses. Curr. Opin. Insect Sci. 26, 142–148 (2018).Article 

Google Scholar 
Brodschneider, R. et al. Multi-country loss rates of honey bee colonies during winter 2016/2017 from the COLOSS survey. J. Apic. Res. 57, 452–457 (2018).Article 

Google Scholar 
Degrandi-Hoffman, G., Graham, H., Ahumada, F., Smart, M. & Ziolkowski, N. The economics of honey bee (Hymenoptera: Apidae) management and overwintering strategies for colonies used to pollinate almonds. J. Econ. Entomol. 112(6), 2524–2533 (2019).Article 
CAS 

Google Scholar 
Porto, R. G. et al. Pollination ecosystem services: A comprehensive review of economic values, research funding and policy actions. Food Sec. 12, 1425–1442 (2020).Article 

Google Scholar 
Kielmanowicz, M. G. et al. Prospective large-scale field study generates predictive model identifying major contributors to colony losses. PLoS Pathog. 11, e1004816. https://doi.org/10.1371/journal.ppat.1004816 (2015).Article 
CAS 

Google Scholar 
Kulhanek, et al. A national survey of managed honey bee 2015–2016 annual colony losses in the USA. J. Apic. Res. 56(4), 328–340 (2017).Article 

Google Scholar 
van Engelsdorp, D. & Meixner, M. D. A historical review of managed honey bee populations in Europe and the United States and the factors that may affect them. J. Invertebr. Pathol. 103, S80–S95 (2010).Article 

Google Scholar 
Caron, D. M., Burgett, M., Rucker, R. & Thurman, W. Honey bee colony mortality in the Pacific Northwest winter 2008/2009. Am. Bee J. 150, 265–269 (2010).
Google Scholar 
Mashilingi, S. K., Zhang, H., Garibaldi, L. A. & An, J. Honeybees are far too insufficient to supply optimum pollination services in agricultural systems worldwide. Agric. Ecosyst. Environ. 335, 108003. https://doi.org/10.1016/j.agee.2022.108003 (2022).Article 

Google Scholar 
Kohsaka, R., Park, M. S. & Uchiyama, Y. Beekeeping and honey production in Japan and South Korea: Past and present. J. Ethn. Foods 4(2), 72–79 (2017).Article 

Google Scholar 
Walker, M. J., Cowen, S., Gray, K., Hancock, P. & Burns, D. T. Honey authenticity: The opacity of analytical reports – part 1 defining the problem. npj Sci. Food 6(1), 1–9 (2022).
Google Scholar 
Fakhlaei, R. et al. The toxic impact of honey adulteration: A review. Foods 9(11), 1538. https://doi.org/10.3390/foods9111538 (2020).Article 
CAS 

Google Scholar 
Rogers, R., Hassler, E., Carey, Q. & Cazier, J. More time to fly: With a warming climate the Western honey bee (Apis mellifera, Linnaeus) now has more temperature-eligible flight hours than 40 years ago. J. Apic. Res. https://doi.org/10.1080/00218839.2022.2073633 (2022).Article 

Google Scholar 
Aizen, M. A. & Harder, L. D. The global stock of domesticated honey bees is growing slower than agricultural demand for pollination. Curr. Biol. 19(11), 915–918 (2009).Article 
CAS 

Google Scholar 
FAO. Data collection. Food and Agriculture Statistics. https://www.fao.org/food-agriculture-statistics/data-collection/en/ (2022).Le Conte, Y. & Navajas, M. Climate change: Impact on honey bee populations and diseases. Rev. Sci. Tech. 27(2), 499–510 (2008).
Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/ (2022).FAO. Crops and livestock products. FAOSTAT. https://www.fao.org/faostat/en/#data/QCL (2022).Global Change Data Lab. Global and regional population estimates (US Census Bureau vs. UN), World. Our World in Data. https://ourworldindata.org/grapher/global-and-regional-population-estimates-us-census-bureau-vs-un (2021).van Brakel, J. Peak signal detection in realtime timeseries data: Robust peak detection algorithm (using z-scores). Stack Overflow. https://stackoverflow.com/questions/22583391/ (2014).Rykov, Y., Thach, T.-Q., Bojic, I., Christopoulos, G. & Car, J. Digital biomarkers for depression screening with wearable devices: Cross-sectional study with machine learning modeling. JMIR Mhealth Uhealth 9, e24872 (2021).Article 

Google Scholar  More

Schneider-Crease, I. et al. Identifying wildlife reservoirs of neglected taeniid tapeworms: Non-invasive diagnosis of endemic Taenia serialis infection in a wild primate population. PLoS Negl Trop Dis 11, e0005709 (2017).Article 

Google Scholar 
Schneider-Crease, I. et al. Ecology eclipses phylogeny as a major driver of nematode parasite community structure in a graminivorous primate. Funct. Ecol. 34, 1898–1906 (2020).Article 

Google Scholar 
Gillespie, T. R. Noninvasive assessment of gastrointestinal parasite infections in free-ranging primates. Int. J. Primatol. 27, 1129 (2006).Article 

Google Scholar 
Budischak, S. A., Hoberg, E. P., Abrams, A., Jolles, A. E. & Ezenwa, V. O. A combined parasitological molecular approach for noninvasive characterization of parasitic nematode communities in wild hosts. Mol. Ecol. Resour. 15, 1112–1119 (2015).Article 

Google Scholar 
Ghalehnoei, H., Bagheri, A., Fakhar, M. & Mishan, M. A. Circulatory microRNAs: promising non-invasive prognostic and diagnostic biomarkers for parasitic infections. Eur. J. Clin. Microbiol. Infect. Dis. 39, 395–402 (2020).Article 
CAS 

Google Scholar 
Hing, S., Narayan, E. J., Andrew Thompson, R. C. & Godfrey, S. S. The relationship between physiological stress and wildlife disease: consequences for health and conservation. Wildl Res. 43, 51–60 (2016).Article 

Google Scholar 
Kersey, D. C. & Dehnhard, M. The use of noninvasive and minimally invasive methods in endocrinology for threatened mammalian species conservation. Gen. Comp. Endocrinol. 203, 296–306 (2014).Article 
CAS 

Google Scholar 
Behringer, V. & Deschner, T. Non-invasive monitoring of physiological markers in primates. Horm. Behav. 91, 3–18 (2017).Article 
CAS 

Google Scholar 
Higham, J. P., Stahl-Hennig, C. & Heistermann, M. Urinary suPAR: A non-invasive biomarker of infection and tissue inflammation for use in studies of large free-ranging mammals. R Soc Open Sci. 7, 191825 (2020).Article 
ADS 
CAS 

Google Scholar 
Heistermann, M. & Higham, J. P. Urinary neopterin, a non-invasive marker of mammalian cellular immune activation, is highly stable under field conditions. Sci Rep. 5, 16308 (2015).Article 
ADS 
CAS 

Google Scholar 
Higham, J. P. et al. Evaluating noninvasive markers of nonhuman primate immune activation and inflammation. Am. J. Phys. Anthropol. 158, 673–684 (2015).Article 

Google Scholar 
Behringer, V. et al. Elevated neopterin levels in wild, healthy chimpanzees indicate constant investment in unspecific immune system. BMC Zool. 4, 1–7 (2019).Article 

Google Scholar 
Dibakou, S. E., Basset, D., Souza, A., Charpentier, M. & Huchard, E. Determinants of variations in fecal neopterin in free-ranging mandrills. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2019.00368 (2019).Article 

Google Scholar 
Löhrich, T., Behringer, V., Wittig, R. M., Deschner, T. & Leendertz, F. H. The use of neopterin as a noninvasive marker in monitoring diseases in wild chimpanzees. EcoHealth 15, 792–803 (2018).Article 

Google Scholar 
Behringer, V., Stevens, J. M. G., Leendertz, F. H., Hohmann, G. & Deschner, T. Validation of a method for the assessment of urinary neopterin levels to monitor health status in nonhuman primate species. Front Physiol. 8, 51 (2017).Article 

Google Scholar 
Negrey, J. D., Behringer, V., Langergraber, K. E. & Deschner, T. Urinary neopterin of wild chimpanzees indicates that cell-mediated immune activity varies by age, sex, and female reproductive status. Sci. Rep. 11, 9298 (2021).Article 
ADS 
CAS 

Google Scholar 
Behringer, V. et al. Cell-mediated immune ontogeny is affected by sex but not environmental context in a long-lived primate species. Front. Ecol. Evol. 9, 272 (2021).Article 

Google Scholar 
Müller, N., Heistermann, M., Strube, C., Schülke, O. & Ostner, J. Age, but not anthelmintic treatment, is associated with urinary neopterin levels in semi-free ranging Barbary macaques. Sci. Rep. 7, 41973 (2017).Article 
ADS 

Google Scholar 
Dibakou, S. E. et al. Ecological, parasitological and individual determinants of plasma neopterin levels in a natural mandrill population. Int. J. Parasitol. Parasites Wildl. 11, 198–206 (2020).Article 

Google Scholar 
Eisenhut, M. Neopterin in diagnosis and monitoring of infectious diseases. J. Biomark. 2013, 196432 (2013).Article 

Google Scholar 
Franceschi, C. & Campisi, J. Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. J Gerontol. A Biol. Sci. Med Sci. 69(Suppl 1), S4–9 (2014).Article 

Google Scholar 
Basha, S., Surendran, N. & Pichichero, M. Immune responses in neonates. Expert. Rev. Clin. Immunol. 10, 1171–1184 (2014).Article 
CAS 

Google Scholar 
Werner, E. R. et al. Determination of neopterin in serum and urine. Clin. Chem. 33, 62–66 (1987).Article 
CAS 

Google Scholar 
Lucore, J. M., Marshall, A. J., Brosnan, S. F. & Benítez, M. E. Validating urinary neopterin as a biomarker of immune response in captive and wild capuchin monkeys. Front. Vet. Sci. 9, 918036. https://doi.org/10.3389/fvets.2022.918036 (2022).Article 

Google Scholar 
Berdowska, A. & Zwirska-Korczala, K. Neopterin measurement in clinical diagnosis. J. Clin. Pharm. Ther. 26, 319–329 (2001).Article 
CAS 

Google Scholar 
Denz, H. et al. Value of urinary neopterin in the differential diagnosis of bacterial and viral infections. Klin. Wochenschr. 68, 218–222 (1990).Article 
CAS 

Google Scholar 
Reibnegger, G. et al. Urinary neopterin reflects clinical activity in patients with rheumatoid arthritis. Arthritis Rheum. 29, 1063–1070 (1986).Article 
CAS 

Google Scholar 
Huber, C. et al. Immune response-associated production of neopterin. Release from macrophages primarily under control of interferon-gamma. J. Exp. Med. 160, 310–316 (1984).Article 
CAS 

Google Scholar 
Horak, E., Gassner, I., Sölder, B., Wachter, H. & Fuchs, D. Neopterin levels and pulmonary tuberculosis in infants. Lung 176, 337–344 (1998).Article 
CAS 

Google Scholar 
Fendrich, C. et al. Urinary neopterin concentrations in rhesus monkeys after infection with simian immunodeficiency virus (SIVmac 251). AIDS 3, 305–307 (1989).Article 
CAS 

Google Scholar 
Chan, C. P. Y. et al. Detection of serum neopterin for early assessment of dengue virus infection. J. Infect. 53, 152–158 (2006).Article 

Google Scholar 
Wu, D. F., Behringer, V., Wittig, R. M., Leendertz, F. H. & Deschner, T. Urinary neopterin levels increase and predict survival during a respiratory outbreak in wild chimpanzees (Taï National Park, Côte d’Ivoire). Sci. Rep. 8, 13346 (2018).Article 
ADS 

Google Scholar 
Maizels, R. M. & McSorley, H. J. Regulation of the host immune system by helminth parasites. J. Allergy Clin. Immunol. 138, 666–675 (2016).Article 
CAS 

Google Scholar 
Maizels, R. M. & Yazdanbakhsh, M. Immune regulation by helminth parasites: Cellular and molecular mechanisms. Nat. Rev. Immunol. 3, 733–744 (2003).Article 
CAS 

Google Scholar 
Yazdanbakhsh, M., Kremsner, P. G. & van Ree, R. Allergy, parasites, and the hygiene hypothesis. Science 296, 490–494 (2002).Article 
ADS 
CAS 

Google Scholar 
Faz-López, B., Morales-Montor, J. & Terrazas, L. I. Role of macrophages in the repair process during the tissue migrating and resident helminth infections. Biomed. Res. Int. 2016, 8634603 (2016).Article 

Google Scholar 
Garcia, H. H., Rodriguez, S., Friedland, J. S. Cysticercosis Working Group in Peru. Immunology of Taenia solium taeniasis and human cysticercosis. Parasite Immunol. 36, 388–396. https://doi.org/10.1111/pim.12126 (2014)Article 
CAS 

Google Scholar 
Thaiss, C. A., Zmora, N., Levy, M. & Elinav, E. The microbiome and innate immunity. Nature 535, 65–74 (2016).Article 
ADS 
CAS 

Google Scholar 
Schneider-Crease, I. A., Griffin, R. H., Gomery, M. A., Bergman, T. J. & Beehner, J. C. High mortality associated with tapeworm parasitism in geladas (Theropithecus gelada) in the Simien Mountains National Park, Ethiopia. Am. J. Primatol. https://doi.org/10.1002/ajp.22684 (2017).Article 

Google Scholar 
Nguyen, N. et al. Fitness impacts of tapeworm parasitism on wild gelada monkeys at Guassa, Ethiopia. Am. J. Primatol. 77, 579–594 (2015).Article 

Google Scholar 
Schneider-Crease, I. A. et al. Helminth infection is associated with dampened cytokine responses to viral and bacterial stimulations in Tsimane forager-horticulturalists. Evol. Med. Public Health 9, 349–359 (2021).Article 

Google Scholar 
Roberts, E. K., Lu, A., Bergman, T. J. & Beehner, J. C. Female reproductive parameters in wild geladas (Theropithecus gelada). Int. J. Primatol. 38, 1–20 (2017).Article 

Google Scholar 
Beehner, J. C. et al. Corrigendum to “Testosterone related to age and life-history stages in male baboons and geladas” [Horm. Behav. 56/4 (2009) 472-480]. Horm Behav. 80, 149 (2016).Article 

Google Scholar 
Erb, R. E., Tillson, S. A., Hodgen, G. D. & Plotka, E. D. Urinary creatinine as an index compound for estimating rate of excretion of steroids in the domestic sow. J. Anim. Sci. 30, 79–85 (1970).Article 
CAS 

Google Scholar 
Tinsley Johnson, E., Snyder-Mackler, N., Lu, A., Bergman, T. J. & Beehner, J. C. Social and ecological drivers of reproductive seasonality in geladas. Behav. Ecol. 29, 574–588 (2018).Article 

Google Scholar 
Kaushik, S. & Kaur, J. Effect of chronic cold stress on intestinal epithelial cell proliferation and inflammation in rats. Stress 8, 191–197 (2005).Article 
CAS 

Google Scholar 
Jarvey, J. C., Low, B. S., Pappano, D. J. & Bergman, T. J. Graminivory and fallback foods: annual diet profile of geladas (Theropithecus gelada) living in the Simien Mountains National Park, Ethiopia. Int. J. Primatol. https://doi.org/10.1007/s10764-018-0018-x (2018).Article 

Google Scholar 
Gowda, C., Hadley, C. & Aiello, A. E. The association between food insecurity and inflammation in the US adult population. Am. J. Public Health. 102, 1579–1586 (2012).Article 

Google Scholar 
Becker, D. J. et al. Macroimmunology: The drivers and consequences of spatial patterns in wildlife immune defence. J. Anim. Ecol. 89, 972–995 (2020).Article 

Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. lme4: Linear mixed-effects models using Eigen and S4. R package version 1, 1–7 (2014).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2021. https://www.R-project.org/.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

Google Scholar 
Simon, A. K., Hollander, G. A. & McMichael, A. Evolution of the immune system in humans from infancy to old age. Proc. Biol. Sci. 282, 20143085 (2015).
Google Scholar 
Heinonen, S. et al. Infant immune response to respiratory viral infections. Immunol. Allergy Clin. North Am. 39, 361–376 (2019).Article 

Google Scholar 
Teran, R. et al. Immune system development during early childhood in tropical Latin America: Evidence for the age-dependent down regulation of the innate immune response. Clin. Immunol. 138, 299–310 (2011).Article 
CAS 

Google Scholar 
van de Pol, M. & Verhulst, S. Age-dependent traits: a new statistical model to separate within- and between-individual effects. Am. Nat. 167, 766–773 (2006).Article 

Google Scholar 
Furman, D. et al. Chronic inflammation in the etiology of disease across the life span. Nat. Med. 25, 1822–1832 (2019).Article 
CAS 

Google Scholar 
Petrovsky, N., McNair, P. & Harrison, L. C. Diurnal rhythms of pro-inflammatory cytokines: regulation by plasma cortisol and therapeutic implications. Cytokine 10, 307–312 (1998).Article 
CAS 

Google Scholar 
Lasselin, J., Rehman, J.-U., Åkerstedt, T., Lekander, M. & Axelsson, J. Effect of long-term sleep restriction and subsequent recovery sleep on the diurnal rhythms of white blood cell subpopulations. Brain Behav. Immun. 47, 93–99 (2015).Article 

Google Scholar 
Auzéby, A., Bogdan, A., Krosi, Z. & Touitou, Y. Time-dependence of urinary neopterin, a marker of cellular immune activity. Clin Chem. 34, 1866–1867 (1988).Article 

Google Scholar 
Ansari, A. & Williams, J. F. The eosinophilic response of the rat to infection with Taenia taeniaeformis. J. Parasitol. 62, 728–736 (1976).Article 
CAS 

Google Scholar 
Schneider-Crease, I. A., Snyder-Mackler, N., Jarvey, J. C. & Bergman, T. J. Molecular identification of Taenia serialis coenurosis in a wild Ethiopian gelada (Theropithecus gelada). Vet. Parasitol. 198, 240–243 (2013).Article 
CAS 

Google Scholar 
Terrazas, L. I., Bojalil, R., Govezensky, T. & Larralde, C. Shift from an early protective Th1-type immune response to a late permissive Th2-type response in murine cysticercosis (Taenia crassiceps). J. Parasitol. 84, 74–81 (1998).Article 
CAS 

Google Scholar 
Toenjes, S. A., Spolski, R. J., Mooney, K. A. & Kuhn, R. E. The systemic immune response of BALB/c mice infected with larval Taenia crassiceps is a mixed Th1/Th2-type response. Parasitology 118(Pt 6), 623–633 (1999).Article 
CAS 

Google Scholar 
Gaze, S. et al. Characterising the mucosal and systemic immune responses to experimental human hookworm infection. PLoS Pathog. 8, e1002520 (2012).Article 
CAS 

Google Scholar 
Johnston, M. J. G., MacDonald, J. A. & McKay, D. M. Parasitic helminths: a pharmacopeia of anti-inflammatory molecules. Parasitology 136, 125–147 (2009).Article 
CAS 

Google Scholar 
Cortés, A., Muñoz-Antoli, C., Esteban, J. G. & Toledo, R. Th2 and Th1 responses: Clear and hidden sides of immunity against intestinal helminths. Trends Parasitol. 33, 678–693 (2017).Article 

Google Scholar 
White, M. P. J., McManus, C. M. & Maizels, R. M. Regulatory T-cells in helminth infection: induction, function and therapeutic potential. Immunology 160, 248–260 (2020).Article 
CAS 

Google Scholar 
Maizels, R. M. & Holland, M. J. Parasite immunity: Pathways for expelling intestinal helminths. Curr Biol. 8, R711–R714 (1998).Article 
CAS 

Google Scholar 
Zhang, D. & Frenette, P. S. Cross talk between neutrophils and the microbiota. Blood 133, 2168–2177 (2019).Article 
CAS 

Google Scholar 
Wang, J., Chen, W.-D. & Wang, Y.-D. The relationship between gut microbiota and inflammatory diseases: The role of macrophages. Front. Microbiol. 11, 1065 (2020).Article 

Google Scholar 
Pallikkuth, S. et al. Age associated microbiome and microbial metabolites modulation and its association with systemic inflammation in a rhesus macaque model. Front. Immunol. 12, 748397 (2021).Article 
CAS 

Google Scholar 
Pierce, Z. et al. The infant gut microbiome is associated with stool markers of macrophage and neutrophil activity. FASEB J. 30, 668–9 (2016).
Google Scholar 
Levast, B. et al. Impact on the gut microbiota of intensive and prolonged antimicrobial therapy in patients with bone and joint infection. Front. Med. https://doi.org/10.3389/fmed.2021.586875 (2021).Article 

Google Scholar 
Hooper, L. V., Littman, D. R. & Macpherson, A. J. Interactions between the microbiota and the immune system. Science 336, 1268–1273 (2012).Article 
ADS 
CAS 

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

Google Scholar 
Libertucci, J. & Young, V. B. The role of the microbiota in infectious diseases. Nat. Microbiol. 4, 35–45 (2019).Article 
CAS 

Google Scholar 
Huttenhower, C. et al. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).Article 
ADS 
CAS 

Google Scholar 
Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).Article 
ADS 
CAS 

Google Scholar 
Blaser, M. J. & Falkow, S. What are the consequences of the disappearing human microbiota?. Nat. Rev. Microbiol. 7, 887–894 (2009).Article 
CAS 

Google Scholar 
Brown, E. M., Kenny, D. J. & Xavier, R. J. Gut microbiota regulation of T cells during inflammation and autoimmunity. Annu. Rev. Immunol. 37, 599–624 (2019).Article 
CAS 

Google Scholar 
Gollwitzer, E. S. & Marsland, B. J. Impact of early-life exposures on immune maturation and susceptibility to disease. Trends Immunol. 36, 684–696 (2015).Article 
CAS 

Google Scholar 
Ravi, A. et al. Loss of microbial diversity and pathogen domination of the gut microbiota in critically ill patients. Microbial. Genomics https://doi.org/10.1099/mgen.0.000293 (2019).McLaren, M. R. & Callahan, B. J. Pathogen resistance may be the principal evolutionary advantage provided by the microbiome. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190592 (2020).Article 

Google Scholar 
Ragonnaud, E. & Biragyn, A. Gut microbiota as the key controllers of “healthy” aging of elderly people. Immun Ageing 18, 2 (2021).Article 

Google Scholar 
Wilmanski, T. et al. Gut microbiome pattern reflects healthy ageing and predicts survival in humans. Nat. Metab. 3, 274–286 (2021).Article 

Google Scholar 
Cattadori, I. M. et al. Impact of helminth infections and nutritional constraints on the small intestine microbiota. PLoS ONE 11, e0159770 (2016).Article 

Google Scholar 
Houlden, A. et al. Chronic Trichuris muris infection in C57BL/6 mice causes significant changes in host microbiota and metabolome: Effects reversed by pathogen clearance. PLoS ONE 10, e0125945 (2015).Article 

Google Scholar 
Holm, J. B. et al. Chronic Trichuris muris infection decreases diversity of the intestinal microbiota and concomitantly increases the abundance of Lactobacilli. PLoS ONE 10, e0125495 (2015).Article 

Google Scholar 
Peachey, L. E., Jenkins, T. P. & Cantacessi, C. This gut ain’t big enough for both of us. Or is it? Helminth–microbiota interactions in veterinary species. Trends Parasitol. 33, 619–632 (2017).Article 

Google Scholar  More

World Health Organization. World Health Statistics 2018. (WHO, 2018).Wynne, N. E., Lorenzo, M. G. & Vinauger, C. Mechanism and plasticity of vectors’ host-seeking behavior. Curr. Opin. Insect Sci. 40, 1–5 (2020).Article 

Google Scholar 
Carlile, P. A., Peters, R. A. & Evans, C. S. Detection of a looming stimulus by the Jacky dragon: Selective sensitivity to characteristics of an aerial predator. Anim. Behav. 72, 553–562 (2006).Article 

Google Scholar 
Ingle, D. J. Visually elicited evasive behavior in frogs. Bioscience 40, 284–291 (1990).Article 

Google Scholar 
Yilmaz, M. & Meister, M. Rapid innate defensive responses of mice to looming visual stimuli. Curr. Biol. 23, 2011–2015 (2013).Article 
CAS 

Google Scholar 
Temizer, I., Donovan, J. C., Baier, H. & Semmelhack, J. L. A visual pathway for looming-evoked escape in larval zebrafish. Curr. Biol. 25, 1823–1834 (2015).Article 
CAS 

Google Scholar 
Scarano, F., Tomsic, D. & Sztarker, J. Direction selective neurons responsive to horizontal motion in a crab reflect an adaptation to prevailing movements in flat environments. J. Neurosci. https://doi.org/10.1523/JNEUROSCI.0372-20.2020 (2020).Article 

Google Scholar 
Scarano, F. & Tomsic, D. Escape response of the crab Neohelice to computer generated looming and translational visual danger stimuli. J. Physiol. Paris 108, 141–147 (2014).Article 

Google Scholar 
Santer, R. D., Rind, F. C., Stafford, R. & Simmons, P. J. Role of an identified looming-sensitive neuron in triggering a flying locust’s escape. J. Neurophysiol. 95, 3391–3400 (2006).Article 

Google Scholar 
Simmons, P. J., Rind, F. C. & Santer, R. D. Escapes with and without preparation: The neuroethology of visual startle in locusts. J. Insect Physiol. 56, 876–883 (2010).Article 
CAS 

Google Scholar 
Dupuy, F., Casas, J., Body, M. & Lazzari, C. R. Danger detection and escape behaviour in wood crickets. J. Insect Physiol. 57, 865–871 (2011).Article 
CAS 

Google Scholar 
Muijres, F. T., Elzinga, M. J., Melis, J. M. & Dickinson, M. H. Flies evade looming targets by executing rapid visually directed banked turns. Science 344, 172–177 (2014).Article 
ADS 
CAS 

Google Scholar 
Ache, J. M. et al. Neural basis for looming size and velocity encoding in the Drosophila giant fiber escape pathway. Curr. Biol. 29, 1073-1081.e4 (2019).Article 
CAS 

Google Scholar 
Domenici, P., Booth, D., Blagburn, J. M. & Bacon, J. P. Cockroaches keep predators guessing by using preferred escape trajectories. Curr. Biol. 18, 1792–1796 (2008).Article 
CAS 

Google Scholar 
Smolka, J., Zeil, J. & Hemmi, J. M. Natural visual cues eliciting predator avoidance in fiddler crabs. Proc. Biol. Sci. 278, 3584–3592 (2011).
Google Scholar 
Card, G. & Dickinson, M. Performance trade-offs in the flight initiation of Drosophila. J. Exp. Biol. 211, 341–353 (2008).Article 

Google Scholar 
Sun, Y. A. & Wyman, R. J. Neurons of the Drosophila giant fiber system: I. Dorsal longitudinal motor neurons. J. Comp. Neurol. 387, 157–166 (1997).Article 
CAS 

Google Scholar 
von Reyn, C. R. et al. Feature integration drives probabilistic behavior in the Drosophila escape response. Neuron 94, 1190-1204.e6 (2017).Article 

Google Scholar 
Fotowat, H., Fayyazuddin, A., Bellen, H. J. & Gabbiani, F. A novel neuronal pathway for visually guided escape in Drosophila melanogaster. J. Neurophysiol. 102, 875–885 (2009).Article 

Google Scholar 
Card, G. & Dickinson, M. H. Visually mediated motor planning in the escape response of Drosophila. Curr. Biol. 18, 1300–1307 (2008).Article 
CAS 

Google Scholar 
Matherne, M. E., Cockerill, K., Zhou, Y., Bellamkonda, M. & Hu, D. L. Mammals repel mosquitoes with their tails. J. Exp. Biol. 221, 178905 (2018).Article 

Google Scholar 
Cribellier, A. et al. Diurnal and nocturnal mosquitoes escape looming threats using distinct flight strategies. Curr. Biol. 32, 1232-1246.e5 (2022).Article 
CAS 

Google Scholar 
Cribellier, A., Spitzen, J., Straw, A. D., van Leeuwen, J. L. & Muijres, F. T. Escape flight performances of night-active malaria mosquitoes: the role of visual and airflow cues of an approaching object. in Integrative and Comparative Biology. Vol. 61. E170–E171 (Oxford University Press Inc Journals Dept, 2021).Reid, J. A. Anopheline Mosquitoes of Malaya and Borneo. Studies from the Institute for Medical Research, Malaysia. (1968).Clements, A. N. The Biology of Mosquitoes. Volume 2: Sensory Reception and Behaviour (CABI Publishing, 1999).
Google Scholar 
Tuno, N., Tsuda, Y., Takagi, M. & Swonkerd, W. Pre- and postprandial mosquito resting behavior around cattle hosts. J. Am. Mosq. Control Assoc. 19, 211–219 (2003).
Google Scholar 
Day, J. F. & Edman, J. D. Mosquito engorgement on normally defensive hosts depends on host activity Patterns. J. Med. Entomol. 21, 732–740 (1984).Article 
CAS 

Google Scholar 
Edman, J. D., Webber, L. A. & Kale, H. W. Effect of mosquito density on the interrelationship of host behavior and mosquito feeding success. Am. J. Trop. Med. Hyg. 21, 487–491 (1972).Article 
CAS 

Google Scholar 
Christophers, S. R. Aedes aegypti: The Yellow Fever Mosquito. (1960).Ponlawat, A. & Harrington, L. C. Blood feeding patterns of Aedes aegypti and Aedes albopictus in Thailand. J. Med. Entomol. 42, 844–849 (2005).Article 

Google Scholar 
Walilko, T. J., Viano, D. C. & Bir, C. A. Biomechanics of the head for Olympic boxer punches to the face. Br. J. Sports Med. 39, 710–719 (2005).Article 
CAS 

Google Scholar 
Reiser, M. B. & Dickinson, M. H. A modular display system for insect behavioral neuroscience. J. Neurosci. Methods 167, 127–139 (2008).Article 

Google Scholar 
Cribellier, A. Biomechanics of Flying Mosquitoes During Capture and Escape. Doctoral Dissertation. (Wageningen University, 2021).Hu, X., Leming, M. T., Whaley, M. A. & O’Tousa, J. E. Rhodopsin coexpression in UV photoreceptors of Aedes aegypti and Anopheles gambiae mosquitoes. J. Exp. Biol. 217, 1003–1008 (2014).
Google Scholar 
Tammero, L. F., Frye, M. A. & Dickinson, M. H. Spatial organization of visuomotor reflexes in Drosophila. J. Exp. Biol. 207, 113–122 (2004).Article 

Google Scholar 
Tammero, L. F. & Dickinson, M. H. Collision-avoidance and landing responses are mediated by separate pathways in the fruit fly, Drosophila melanogaster. J. Exp. Biol. 205, 2785–2798 (2002).Article 

Google Scholar 
Muijres, F. T. et al. Escaping blood-fed malaria mosquitoes minimize tactile detection without compromising on take-off speed. J. Exp. Biol. 220, 3751–3762 (2017).Article 
CAS 

Google Scholar 
van Veen, W. G., van Leeuwen, J. L. & Muijres, F. T. Malaria mosquitoes use leg push-off forces to control body pitch during take-off. J. Exp. Zool. A Ecol. Integr. Physiol. 333, 38–49 (2020).Article 

Google Scholar 
Caro, T. et al. Benefits of zebra stripes: Behaviour of tabanid flies around zebras and horses. PLoS ONE 14, e0210831 (2019).Article 
CAS 

Google Scholar 
Edman, J. D., Webber, L. A. & Schmid, A. A. Effect of host defenses on the feeding pattern of Culex nigripalpus when offered a choice of blood sources. J. Parasitol. 60, 874–883 (1974).Article 
CAS 

Google Scholar 
Walker, E. D. & Edman, J. D. The influence of host defensive behavior on mosquito (Diptera: Culicidae) biting persistence1. J. Med. Entomol. 22, 370–372 (1985).Article 
CAS 

Google Scholar 
Warnes, M. L. & Finlayson, L. H. Effect of host behaviour on host preference in Stomoxys calcitrans. Med. Vet. Entomol. 1, 53–57 (1987).Article 
CAS 

Google Scholar 
Vinauger, C. et al. Modulation of host learning in Aedes aegypti mosquitoes. Curr. Biol. 28, 333-344.e8 (2018).Article 
CAS 

Google Scholar 
Wolff, G. H. & Riffell, J. A. Olfaction, experience and neural mechanisms underlying mosquito host preference. J. Exp. Biol. 221, 157131 (2018).Article 

Google Scholar 
Alonso San Alberto, D. et al. The olfactory gating of visual preferences to human skin and visible spectra in mosquitoes. Nat. Commun. 13, 1–14 (2022).Article 

Google Scholar 
van Breugel, F., Riffell, J., Fairhall, A. & Dickinson, M. H. Mosquitoes use vision to associate odor plumes with thermal targets. Curr. Biol. 25, 2123–2129 (2015).Article 

Google Scholar 
Vinauger, C. et al. Visual-olfactory integration in the human disease vector mosquito, Aedes aegypti. Curr. Biol. 29, 2509-2516.e5 (2019).Article 
CAS 

Google Scholar 
Grant, A. J. & O’Connell, R. J. Age-related changes in female mosquito carbon dioxide detection. J. Med. Entomol. 44, 617–623 (2007).Article 
CAS 

Google Scholar 
Tallon, A. K., Hill, S. R. & Ignell, R. Sex and age modulate antennal chemosensory-related genes linked to the onset of host seeking in the yellow-fever mosquito, Aedes aegypti. Sci. Rep. 9, 43 (2019).Article 
ADS 

Google Scholar 
Eilerts, D. F., VanderGiessen, M., Bose, E. A., Broxton, K. & Vinauger, C. Odor-specific daily rhythms in the olfactory sensitivity and behavior of Aedes aegypti mosquitoes. Insects 9, 147 (2018).Article 

Google Scholar 
Taylor, B. & Jones, M. D. The circadian rhythm of flight activity in the mosquito Aedes aegypti (L). The phase-setting effects of light-on and light-off. J. Exp. Biol. 51, 59–70 (1969).Article 
CAS 

Google Scholar 
Peirce, J. et al. PsychoPy2: Experiments in behavior made easy. Behav. Res. Methods 51, 195–203 (2019).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. arXiv [stat.CO] (2014).Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).Article 
MathSciNet 
MATH 

Google Scholar 
Lund, U., & Agostinelli, C. Package “Circular”. Repository CRAN (2017).Bunn, A. G. A dendrochronology program library in R (dplR). Dendrochronologia 26, 115–124 (2008).Article 

Google Scholar 
Walker, J. A. Estimating velocities and accelerations of animal locomotion: A simulation experiment comparing numerical differentiation algorithms. J. Exp. Biol. 201, 981–995 (1998).Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).Book 
MATH 

Google Scholar  More

Narayan, S. et al. The effectiveness, costs and coastal protection benefits of natural and nature-based defences. PLoS ONE 11, e0154735 (2016).Article 

Google Scholar 
Schürch, M., Rapaglia, J., Liebetrau, V., Vafeidis, A. T. & Reise, K. Salt marsh accretion and storm tide variation: An example from a barrier island in the North Sea. ESCO 35, 486–500 (2012).
Google Scholar 
de Groot, A. V., Veeneklaas, R. M., Kuijper, D. P. & Bakker, J. P. Spatial patterns in accretion on barrier-island salt marshes. Geomorphology 134, 280–296 (2011).Article 
ADS 

Google Scholar 
Temmerman, S. et al. Ecosystem-based coastal defence in the face of global change. Nature 504, 79–83 (2013).Article 
ADS 
CAS 

Google Scholar 
Barbier, E. B. et al. Coastal ecosystem: Based management with nonlinear ecologial functions and values. Science 319, 321–323 (2008).Article 
ADS 
CAS 

Google Scholar 
Schoonees, T. et al. Hard structures for coastal protection, towards greener designs. Estuaries Coasts 21, 755 (2019).
Google Scholar 
IPCC. Summary for Policymakers. in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (2021).Lenssen, G. M., Lamers, J., Stroetenga, M. & Rozema, J. CO2 and biosphere 379–390 (Kluwer Academic Publishers, 1993).Book 

Google Scholar 
Cherry, J. A., McKee, K. L. & Grace, J. B. Elevated CO2 enhances biological contributions to elevation change in coastal wetlands by offsetting stressors associated with sea-level rise. J. Ecol. 97, 67–77 (2009).Article 

Google Scholar 
Arp, W. J., Drake, B. G., Pockman, W. T., Curtis, P. S. & Whigham, D. F. CO2 and Biosphere 133–143 (Kluwer Academic Publishers, 1993).Book 

Google Scholar 
Cao, H. et al. Wave effects on seedling establishment of three pioneer marsh species: survival, morphology and biomechanics. Ann. Bot. 125, 345–352 (2020).Article 

Google Scholar 
Puijalon, S. et al. Plant resistance to mechanical stress: Evidence of an avoidance-tolerance trade-off. New Phytol. 191, 1141–1149 (2011).Article 
CAS 

Google Scholar 
Niklas, K. Plant Biomechanics: An Engineering Approach to Plant Form and Function (University of Chicago Press, 1992).
Google Scholar 
Silinski, A. et al. Effects of wind waves versus ship waves on tidal marsh plants: A flume study on different life stages of Scirpus maritimus. PLoS ONE 10, e0118687 (2015).Article 

Google Scholar 
Rupprecht, F., Möller, I., Evans, B. R., Spencer, T. & Jensen, K. Biophysical properties of salt marsh canopies: Quantifying plant stem flexibility and above ground biomass. Coast. Eng. 100, 48–57 (2015).Article 

Google Scholar 
Paul, M. & de los Santos, C. B. Variation in flexural, morphological, and biochemical leaf properties of eelgrass (Zostera marina) along the European Atlantic climate regions. Mar. Biol. 166, 2187 (2019).Article 

Google Scholar 
Carus, J., Paul, M. & Schröder, B. Vegetation as self-adaptive coastal protection: Reduction of current velocity and morphologic plasticity of a brackish marsh pioneer. Ecol. Evol. 6, 1579–1589 (2016).Article 

Google Scholar 
Callaghan, F. M. et al. A submersible device for measuring drag forces on aquatic plants and other organisms. NZ J. Mar. Freshw. Res. 41, 119–127 (2007).Article 

Google Scholar 
Paul, M., Bouma, T. J. & Amos, C. L. Wave attenuation by submerged vegetation: combining the effect of organism traits and tidal current. Mar. Ecol. Prog. Ser. 444, 31–41 (2012).Article 
ADS 

Google Scholar 
Taphorn, M., Villanueva, R., Paul, M., Visscher, J. H. & Schlurmann, T. Flow field and wake structure characteristics imposed by single seagrass blade surrogates. J. Ecohydraul. 1, 1–13 (2021).
Google Scholar 
Lightbody, A. F. & Nepf, H. M. Prediction of velocity profiles and longitudinal dispersion in emergent salt marsh vegetation. Limnol. Oceangr 51, 218–228 (2006).Article 
ADS 

Google Scholar 
Kobayashi, N., Raichle, A. W. & Asano, T. Wave attenuation by vegetation. J. Waterway Port Coastal Ocean Eng. 119, 30–48 (1993).Article 

Google Scholar 
Villanueva, R., Thom, M., Visscher, J. H., Paul, M. & Schlurmann, T. Wake length of an artificial seagrass meadow: A study of shelter and its feasibility for restoration. J. Ecohydraul. 1, 1–15 (2021).
Google Scholar 
Paul, M. & Amos, C. L. Spatial and seasonal variation in wave attenuation over Zostera noltii. J. Geophys. Res. 116, C08019 (2011).ADS 

Google Scholar 
Marjoribanks, T. I. & Paul, M. Modelling flow-induced reconfiguration of variable rigidity aquatic vegetation. J. Hydraul. Res. 1, 1–16 (2021).
Google Scholar 
Schulze, D., Rupprecht, F., Nolte, S. & Jensen, K. Seasonal and spatial within-marsh differences of biophysical plant properties: Implications for wave attenuation capacity of salt marshes. Aquat. Sci. 81, 82 (2019).Article 

Google Scholar 
Gillis, L. G. et al. Living on the edge: How traits of ecosystem engineers drive bio-physical interactions at coastal wetland edges. Adv. Water Resour. 166, 104257 (2022).Article 

Google Scholar 
Zhao, H. & Chen, Q. Modeling attenuation of storm surge over deformable vegetation: methodology and verification. J. Eng. Mech. 140, 4014090 (2014).
Google Scholar 
Möller, I. et al. Wave attenuation over coastal salt marshes under storm surge conditions. Nat. Geosci 7, 727–731 (2014).Article 
ADS 

Google Scholar 
Maza, M. et al. Large-scale 3-D experiments of wave and current interaction with real vegetation. Part 2. Experimental analysis. Coast. Eng. 106, 73–86 (2015).Article 

Google Scholar 
Gray, A. J. & Mogg, R. J. Climate impacts on pioneer saltmarsh plants. Clim. Res. 18, 105–112 (2001).Article 

Google Scholar 
Novaes, E., Kirst, M., Chiang, V., Winter-Sederoff, H. & Sederoff, R. Lignin and biomass: A negative correlation for wood formation and lignin content in trees. Plant Physiol. 154, 555–561 (2010).Article 
CAS 

Google Scholar 
Redfield, A. C. Development of a New England salt marsh. Ecol. Monogr. 42, 201–237 (1972).Article 

Google Scholar 
Kirwan, M. L. et al. Limits on the adaptability of coastal marshes to rising sea level. Geophys. Res. Lett. 37, 1–10 (2010).Article 

Google Scholar 
Idier, D., Dumas, F. & Muller, H. Tide-surge interaction in the English Channel. Nat. Hazards Earth Syst. Sci. 12, 3709–3718 (2012).Article 
ADS 

Google Scholar 
Weisse, R., von Storch, H., Niemeyer, H. D. & Knaack, H. Changing North Sea storm surge climate: An increasing hazard?. Ocean Coast. Manag. 68, 58–68 (2012).Article 

Google Scholar 
Idier, D., Paris, F., Le Cozannet, G., Boulahya, F. & Dumas, F. Sea-level rise impacts on the tides of the European Shelf. Cont. Shelf Res. 137, 56–71 (2017).Article 
ADS 

Google Scholar 
Marcos, M., Calafat, F. M., Berihuete, Á. & Dangendorf, S. Long-term variations in global sea level extremes. J. Geophys. Res. Oceans 120, 8115–8134 (2015).Article 
ADS 

Google Scholar 
Dangendorf, S., Mudersbach, C., Jensen, J., Anette, G. & Heinrich, H. Seasonal to decadal forcing of high water level percentiles in the German Bight throughout the last century. Ocean Dyn. 46, 277 (2013).
Google Scholar 
de Winter, R. C., Sterl, A. & Ruessink, B. G. Wind extremes in the North Sea Basin under climate change: An ensemble study of 12 CMIP5 GCMs. J. Geophys. Res. Atmos. 118, 1601–1612 (2013).Article 
ADS 

Google Scholar 
Arns, A. et al. Sea-level rise induced amplification of coastal protection design heights. Sci. Rep. 7, 40171 (2017).Article 
ADS 
CAS 

Google Scholar 
Pansch, A., Winde, V., Asmus, R. & Asmus, H. Tidal benthic mesocosms simulating future climate change scenarios in the field of marine ecology. Limnol. Oceanogr. Methods 14, 257–267 (2016).Article 

Google Scholar 
Meehl, G. A. et al. Climate Change 2007: The Physical Science Basis: Summary for Policymakers. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2007).
Google Scholar 
Miler, O., Albayrak, I., Nikora, V. I. & O’Hare, M. T. Biomechanical properties of aquatic plants and their effects on plant–flow interactions in streams and rivers. Aquat. Sci. 74, 31–44 (2012).Article 

Google Scholar  More

Chemotaxonomic characteristicsThe predominant respiratory quinone for all novel strains was ubiquinone 8 (Q-8), consistent with other Ramlibacter species. C16:0 and summed feature 3 (consisting of C16:1 ω7c and/or C16:1 ω6c) were identified as the common major fatty acids ( > 10%) of the novel strains USB13T, AW1T, GTP1T, and HM2T. Other than the aforementioned fatty acids, strain USB13T had C10:0 3-OH additionally as its major fatty acid, whereas strains AW1T and HM2T shared C17:0 cyclo and summed feature 8 (consisting of C18:1 ω7c and/or C18: 1 ω6c) as its additional fatty acids. Detailed comparisons of the fatty acid profiles of the novel strains and their reference strains are summarized in Table S1.Strains USB13T, AW1T, GTP1T, and HM2T shared major polar lipids diphosphatidylglycerol (DPG), phosphatidylglycerol (PG), and phosphatidylethanolamine (PE), which was consistent with the major polar lipids of the reference strains. Additionally, the polar lipid profile of USB13T consisted of one unidentified phosphoaminolipid, two unidentified phosphoglycoaminolipids, and six unidentified polar lipids while the polar lipid profile of AW1T had one unidentified lipid, one unidentified phosphoglycolipid, and three unidentified glycolipids in addition. The polar lipid profile of strain GTP1T additionally consisted of two unidentified phosphoaminolipids, and that of strain HM2T additionally had one unidentified phosphoaminolipid, one unidentified phosphoglycolipid, one unidentified phosphoglycoaminolipid, and two unidentified phospholipids. Polar lipid profiles of the novel strains USB13T, AW1T, GTP1T, and HM2T are shown in Figure S1.Physiological, morphological characteristics, and screening of cellulose-degrading strainsWhen grown on R2A agar, strain USB13T produced reddish white and flat colonies while strain AW1T produced orange, convex colonies, strain GTP1T produced white, convex colonies, and strain HM2T produced cream-colored, flat, transparent colonies. Under TEM, monotrichous flagella were observed only in strain HM2T, and when tested for motility, strain USB13T and AW1T showed gliding motility, whereas strain GTP1T was non-motile. Strains USB13T and HM2T showed positive results for both catalase and oxidase activities; strain AW1T showed positive results for catalase and negative results for oxidase activity, and strain GTP1T showed negative results for catalase and positive results for oxidase activity. All strains were identified to be strictly aerobic, while showing negative results for urea, gelatin, starch, chitin, and DNA hydrolysis and positive results for hydrolysis of Tween 80. In addition, strain USB13T was the only strain to produce iron-chelating siderophores. When tested for NaCl tolerance, growth of strain USB13T was observed in NaCl concentrations of 0–7% (w/v), possibly due to the fact the strain was isolated from a marine environment. A detailed comparison of physiological and morphological characteristics between the novel species and its closely related Ramlibacter strains is presented in Table 1, while TEM images of the novel strains are shown in Figure S2. Results of the reference strains in Table 1 coincided with the data from the original literature1,3,4,5,7,8.Table 1 Characteristics differentiating strains USB13T, AW1T, GTP1T, and HM2T from closely related strains of the genus Ramlibacter.Full size tableStrains: 1, USB13T; 2, AW1T; 3, GTP1T; 4, HM2T; R. monticola KACC 19175T; 6, R. alkalitolerans KACC 19305T; 7, R. ginsenosidimutans KACC 17527T; 8, R. humi KCTC 52922T; 9, R. henchirensis KACC 11925T; 10, R. tataouinensis KACC 11924T; 11, R. rhizophilus KCTC 52083T. All strains are positive for esterase lipase (C8), while all strains are negative for chitin hydrolysis. All data were obtained from this study unless indicated otherwise. + , Positive; w + , weakly positive; -, negative.R2A agar plates supplemented with 1% (w/v) CMC were stained with Congo red dye after 7 days of incubation. Clear zones only formed around colonies of strain USB13T, indicating that strain USB13T solely possessed CMC-hydrolyzing activity among the four novel strains. When inoculated in basal salt medium, filter paper from the USB13T sample underwent degradation, whereas samples containing strains AW1T, GTP1T, and HM2T did not show any signs of degradation.Phylogenetic and genomic analysesEzBioCloud search results and BLASTn searches revealed that the novel strains belonged to the family Comamonadaceae and genus Ramlibacter. Using BLASTn, 16S rRNA gene sequence similarities were determined where strain USB13T was closest to strain GTP1T (98.5%), followed by strain HM2T (98.1%) and strain AW1T (97.1%). Strain AW1T shared the highest similarity with strain GTP1T (97.3%), followed by strain HM2T (97.1%), while strain GTP1T shared a similarity of 98.2% with strain HM2T. Phylogenetic analysis based on the MP method (Fig. 1) showed the clustering of the novel strains USB13T, AW1T, GTP1T, and HM2T with strains such as R. monticola G-3-2T, R. ginsenosidimutans BXN5-27T, R. alkalitolerans CJ661T, and R. rhizophilus YS3.2.7T. Similar topologies were observed in trees reconstructed by ML (Figure S3) and MP methods. The UBCG phylogenomic tree (Fig. 2), which was reconstructed using whole genome sequences, also showed close clustering of the selected reference strains and novel strains.Figure 1Maximum-parsimony (MP) tree reconstructed based on 16S rRNA gene sequences, showing the relationship between strains USB13T, AW1T, GTP1T, and HM2T and other closely related type strains. Bootstrap values based on 1000 replications are listed as percentages at branching points. Only bootstrap values exceeding 50% are shown. Bar, 50 substitutions per nucleotide position.Full size imageFigure 2Phylogenomic tree of strains USB13T, AW1T, GTP1T, and HM2T and their closely related taxa was reconstructed based on core genomes using UBCG version 3.0 pipeline42. NCBI GenBank accession numbers are shown in parentheses. Bootstrap analysis was carried out using 1000 replications. Percentage bootstrap values ( > 50%) are given at branching points. Bar, 0.050 substitution per position.Full size imageDraft genome sequences of the novel strains USB13T, AW1T, GTP1T, and HM2T were deposited in the GenBank database under the accession numbers JACORT000000000, JAEQNA000000000, JACORU000000000, and JADDIV000000000, respectively. In addition, the draft genome sequences of R. monticola KACC 19175T, R. alkalitolerans KACC 19305T, and R. ginsenosidimutans KACC 17527T were also deposited in GenBenk under the accession numbers JAEQNE000000000, JAEQND000000000, and JAEPWM000000000, respectively. The assembled genome size of the novel strains USB13T, AW1T, GTP1T, and HM2T was 5.53 Mbp, 5.11 Mbp, 6.15 Mbp, 4.31 Mbp, respectively. G + C content ranged from 67.9% to 69.9%, which was similar to those of the reference strains. The genomic features of the novel strains and their closely related Ramlibacter strains are presented in Table S2. CheckM analysis showed the following estimations for each strain: USB13T, had a 99.84% completeness and 0.68% contamination; AWIT, had a 99.84% completeness and 0.86% contamination; GTP1T, had a 99.38% completeness and 1.32% contamination; HM2T, had a 97.51% completeness and 0.16% contamination. These results indicated that the draft genome results for all strains were reliable. ANI values between the novel strains and reference strains ranged from 76.5–83.4% while dDDH values ranged from 20.7–26.7%, and AAI values ranged from 65.7–80.4%. All values were below the threshold for delineation of a new species54. ANI values between the novel strains and their reference strains are presented in Fig. 3, while a detailed comparison of GGDC and AAI values are shown in Table 2.Figure 3Heatmap of strains USB13T, AW1T, GTP1T, and HM2T and other closely related strains within the genus Ramlibacter, generated with OrthoANI values calculated using OAT software45. Bacterial strains and accession numbers are indentical to those of Fig. 2.Full size imageTable 2 Average amino acid identity (AAI) and digital DNA-DNA hybridization (dDDH) value comparisons between the closely related Ramlibacter type species and the novel strains, USB13T, AW1T, GTP1T, and HM2T. AAI values were calculated by two-way AAI, while dDDH values were calculated based on formula 246.Full size tableBased on NCBI PGAP annotation and CAZyme prediction results, strain USB13T, which was the only strain to show cellulolytic activity, possessed a total of four protein CDs encoding CAZymes, namely, two GH15 proteins, one glycosyl hydrolase protein, and one GH99-like domain-containing protein. Despite not showing any cellulolytic activity, strain AW1T possessed eight CAZyme CDs; the most amount among the novel strains. The enzymes include, two GH2 proteins, one GH5 protein, three GH15 proteins, one glycoside hydrolase protein, and one cellulase family glycosyl hydrolase. Strain GTP1T possessed two CDs encoding one GH15 protein and one GH16 protein; strain HM2T possessed three CDs encoding one GH2, one GH15, and one GH18 protein. All strains possessed GH15, which is known for its glucoamylase activity in fungi55. A detailed summary of the novel strains CAZymes are presented in Table S3 and a comparison of CAZyme numbers between strains USB13T, AW1T, GTP1T, and HM2T is summarized in Table S4. The presence of these genes may suggest the cellulolytic activity of strain USB13T, while it is uncertain why GH families responsible for endoglucanase (GH 5–8, 12, 16, 44, 45, 48, 51, 64, 71, 74, 81, 87, 124, and 128), exoglucanase (GH 5–7, and 48), and β-glucosidase (GH 1, 3, 4, 17, 30, and 116) were not present in the genome11.COG predictions (Fig. 4) revealed that the majority of the core genes of the four novel strains accounted for genes belonging to the functional categories C (energy production and conversion), E (amino acid transport and metabolism), I (lipid transport and metabolism), T (signal transduction mechanisms), and K (transcription). Meanwhile, the number of core genes belonging in category G, carbohydrate transport and metabolism, was the highest for strain USB13T (258), followed by GTP1T (230), HM2T (212), and AW1T (181). The high number of genes in strain USB13T may be a contributing factor in the strain’s cellulolytic activity. A comparison of COG gene count distribution of the novel strains is presented in Table S5.Figure 4Comparison of total number of matched genes of strains USB13T, AW1T, GTP1T, and HM2T according to functional classes based on Cluster of Orthologous Groups of proteins (COG) predictions48.Full size imageAntiSMASH analysis results showed four gene clusters within the genome of strain USB13T: ribosomally synthesized and post-translationally modified peptides (RIPP)-like cluster (989,516–1,000,916 nt; JACORT010000001), terpene synthesis (8,622–30,347 nt; JACORT010000003), RIPP precursor peptide recognition element (RRE)-containing cluster (311,469–333,619 nt; JACORT010000004), and redox-cofactor (281,860–303,948 nt; JACORT010000007). Among the clusters, the RRE-containing cluster showed 11% similarity to streptobactin, a tricatechol-type siderophore isolated from Streptomyces sp. YM5-79956. Strain AW1T had a total of eight gene clusters which encoded for: arylpolyene (165,946–207,130 nt), terpene (618,322–640,854 nt), RIPP-like proteins (804,411–819,137 nt), non-ribosomal peptide synthetase cluster (NRPS)-like (61,798–104,764 nt), betalactone (323,399–348,739 nt), N-acetylglutaminylglutamine amide (NAGGN; 106,834–121,648 nt), type I polyketide synthase (T1PKS; 56,584–107,578 nt), and heterocyst glycolipid synthase-like polyketide synthase (hglE-KS; 75,419–113,566 nt). Strain GTP1T possessed four gene clusters that encoded for RRE-containing cluster (175,155–199,102 nt), homoserine lactone (110,293–130,892 nt), a signaling molecule known for its involvement in bacterial quorum sensing, the RIPP-like cluster (38,002–48,856 nt), and terpene synthesis (47,942–69,701 nt). Strain HM2T had two gene clusters that encoded for resorcinol (403,967–445,901 nt), an organic compound known for its antiseptic properties, and terpene (697,660–721,242 nt), which showed 100% similarity for carotenoid synthesis. BRIG analysis results showed that a majority of the regions within the four analyzed genomes were conserved with at least 70% similarity (Figure S4).Cellulolytic potential and FE-SEM analysis of strain USB13T
A USB13T-inoculated basal salt medium sample containing degraded filter paper was examined under FE-SEM to observe the morphological interactions between cellulose fibers and USB13T cells. Images in Fig. 5 show individual rod cells of strain USB13T surrounding filter paper fibers, indicating bacterial adherence.Figure 5Field emission-scanning electron microscopy (FE-SEM) images of adhesion of strain USB13T to degraded filter paper fibers. Arrows indicate filter paper fibers. (A) low magnification (5000(times)) and (B), high magnification (20,000(times)) images of strain USB13T surrounding filter paper fibers.Full size imageThe enzymatic assay results showed endoglucanase, exoglucanase, β-glucosidase, and filter paper cellulase (FPCase) activities of strain USB13T, wherein activities for endoglucanase was the highest and β-glucosidase was the lowest in all experiments. As seen in Fig. 6A, enzyme activity for all cellulolytic enzymes increased along with its cultivation time. In addition, enzyme activities showed the highest results when tested on buffer solutions of pH 6.0 (Fig. 6B), indicating the enzymes’ resistance to moderately acidic conditions. The pH of the buffer solution seemed to be an important factor in enzyme activity, as activity of endoglucanase, exoglucanase, and FPCase drastically decreased when the pH was altered from pH 6.0 to pH 7.0. Meanwhile, β-glucosidase activity was relatively resistant to pH change as its activity decreased less than 50%. On day 7, enzyme activities were measured as 1.91 IU/mL for endoglucanase, 1.77 IU/mL for exoglucanase, 0.76 IU/mL for β-glucosidase, and 1.12 IU/mL for FPCase at pH 6.0. When measured at pH 8.0, where enzyme activity was the lowest, enzyme activities were measured as 0.51 IU/mL for endoglucanase, 0.25 IU/mL for exoglucanase, 0.45 IU/mL for β-glucosidase, and 0.23 IU/mL for FPCase; all values were less than half of the measured activity at pH 6.0. The results of strain USB13T are comparable to FPCase results of other species such as Mucilaginibacter polytrichastri RG4-7T (0.98 U/mL) isolated from the moss Polytrichastrum formosum14, Paenibacillus lautus BHU3 (2.9 U/mL) isolated from a landfill site57, and Serratia rubidaea DBT4 (0.5 U/mL) isolated from the gastrointestinal tract of a black Bengal goat58.Figure 6Cellulolytic enzyme activity of strain USB13T. Enzyme activity was defined in international units (IU); one unit of enzymatic activity was defined as the amount of enzyme that releases 1 μmol of glucose per mL per 1 min of reaction. (A) cellulase activity results under different cultivation time; (B) cellulase activity under different buffer solution pH. Values in the figure are mean values of triplicate data with standard deviation.Full size imageDespite the absence of the main three cellulolytic enzymes, endoglucanase, exoglucanase, and β-glucosidase, the cellulolytic activity of strain USB13T was confirmed through SEM images, CMC agar screening, and enzymatic assay results. However, because PGAP annotation results showed that other non-cellulolytic strains also possessed CAZymes, in some cases more than strain USB13T, further research is necessary to understand the mechanics of how CAZymes and other cellulases interact to degrade cellulose, and how these genes are expressed under certain conditions. Furthermore, the cellulolytic activity of strain USB13T can be further optimized for commercial use by adjusting growth conditions such as pH, temperature, and growth media.While cellulolytic bacteria are known to inhabit animal intestinal tracts, the rumen, and soil, they can be found almost everywhere, such as ocean floors, municipal landfills, and even extreme environments such as hot springs59. In these habitats, cellulolytic bacteria utilize cellulose while cohabiting with non-cellulolytic bacteria. There have been many studies suggesting the synergistic role non-cellulolytic bacteria play in cellulose degradation, where non-cellulolytic bacteria aid cellulose degradation by neutralizing pH or removing harmful metabolites60,61,62.Bacterial cellulases have shown immense value in various industries such as animal feed processing, food and brewery production, and agriculture, not to mention biofuel synthesis through biomass utilization11. Due to the versatile uses of bacterial cellulases, the cellulolytic strain USB13T has the potential to become an invaluable resource. However, further research of the novel strain’s cellulose-degradation mechanisms is necessary to develop and commercially make use of its bacterial cellulases in the future. In addition, research regarding co-culturing non-cellulolytic bacteria and strain USB13T may also help in developing effective methods to use an otherwise underutilized bioresource.Taxonomy of novel Ramlibacter speciesWhile phylogenetic analyses indicated that the novel strains USB13T, AW1T, GTP1T, and HM2T should be assigned to the genus Ramlibacter, differences in fatty acid compositions, polar lipid profiles, and physiological characteristics suggested that the four novel strains are noticeably distinct from other validly published species of the genus. Additionally, genomic characteristics such as ANI, dDDH, and AAI values further supported the novel strains’ position as a distinct species within the genus Ramlibacter. Therefore, we propose that the strains USB13T, AW1T, GTP1T, and HM2T represent novel species within the genus Ramlibacter.Description of the novel Ramlibacter speciesThe descriptions of the novel species are given according to the standards of the Judicial Commission of the International Committee on Systematic Bacteriology63.Description of Ramlibacter cellulosilyticus sp. nov
Ramlibacter cellulosilyticus (cel.lu.lo.si.ly’ti.cus. N.L. n. cellulosum, cellulose; N.L. adj. lyticus from Gr. lytikos, dissolving; N.L. masc. adj. cellulosilyticus, cellulose-dissolving).Cells of strain USB13T are Gram-negative, rod-shaped, non-flagellated and motile by gliding. The strain is positive for both oxidase and catalase activity, while cells have a width of 0.3–0.5 μm and length of 2.0–2.4 μm. When observed on R2A agar, colonies are reddish white, flat with entire margins, and have a diameter of 1–2 mm. Growth of strain USB13T is observed at 7–50 °C (optimum, 28–30 °C), at pH 5.0–10.0 (optimum, pH 6.0), and at NaCl concentrations of 0–7% (optimum, 0–3%). The strain is unable to grow in anaerobic conditions. Produces siderophores and hydrolyzes Tween 20, Tween 80, CMC, and esculin. According to the API ZYM results, the strain showed positive results for alkaline phosphatase, esterase lipase (C8), leucine arylamidase, acid phosphatase, β-galactosidase, α-glucosidase, and β-glucosidase. In the API 20NE assay, strain USB13T showed positive results only for β-galactosidase. The predominant respiratory quinone is ubiquinone 8 (Q-8). The major fatty acids are C16:0, C10:0 3-OH, and summed feature 3 (consisting of C16:1 ω7c and/or C16:1 ω6c). The polar lipid profile consists of diphosphatidylglycerol (DPG), phosphatidylglycerol (PG), phosphatidylethanolamine (PE), one unidentified phosphoaminolipid, two unidentified phosphoglycoaminolipids, and six unidentified polar lipids. The G + C content is 69.7%. The GenBank/EMBL/DDBJ accession numbers for the 16S rRNA gene sequence and the assembled genome sequence of strain USB13T are MN603953 and JACORT000000000, respectively.The type strain USB13T (= KACC 21656T = NBRC 114839T) was isolated from shallow coastal water at Haeundae Beach, Busan, Republic of Korea.Description of Ramlibacter aurantiacus sp. nov
Ramlibacter aurantiacus (au.ran.ti’a.cus. L. masc. adj. aurantiacus, orange-colored, referring to the orange colonies of the strain).Cells of strain AW1T are Gram-negative, coccoid to short rod-shaped, non-flagellated, and motile by gliding. The strain is negative for oxidase activity, and positive for catalase activity. When observed on R2A agar, colonies are orange, convex, with entire margins, and 0.5–1.0 mm in diameter. Under TEM cells have and approximate width of 0.3–0.5 μm and length of 0.6–0.8 μm. Growth of strain AW1T can be observed at 7–45 °C (optimum, 30 °C), at pH 7.0–10.0 (optimum, 7.0–8.0), and at NaCl concentrations of 0–3% (optimum, 0–1%). The strain does not grow under anaerobic conditions but is able to hydrolyze Tween 80. In addition, AW1T is not able to produce siderophores. In the API ZYM assay, positive for alkaline phosphatase, esterase (C4), esterase lipase (C8), leucine arylamidase, and β-glucosidase. In the API 20NE assay, positive for esculin hydrolysis. The predominant respiratory quinone is ubiquinone 8 (Q-8). The major fatty acids are C16:0, C17:0 cyclo, summed feature 3 (consisting of C16:1 ω7c and/or C16:1 ω6c), and summed feature 8 (consisting of C18:1 ω7c and/or C18:1 ω6c). The polar lipid profile consists of diphosphatidylglycerol (DPG), phosphatidylglycerol (PG), phosphatidylethanolamine (PE), one unidentified phosphoglycolipid, one unidentified lipid, and three unidentified glycolipids. The G + C content is 68.6%. The GenBank/EMBL/DDBJ accession numbers for the 16S rRNA gene sequence and the assembled genome sequence of strain AW1T are MN498045 and JAEQNA000000000, respectively.The type strain AW1T (= KACC 21544T = NBRC 114862T) was isolated from soil at Aewol, Jeju Island, Republic of Korea.Description of Ramlibacter albus sp. nov
Ramlibacter albus (al’bus. L. masc. adj. albus, white, referring to the white colonies of the strain).Strain GTP1T is non-motile, Gram-negative, strictly aerobic, positive for oxidase activity, and negative for catalase activity. When observed on R2A, colonies are white, convex, with entire margins, and 1–2 mm in diameter. Under TEM, cells lack flagella, are rod-shaped, and have a width of 0.7–0.8 μm and length of 1.6–1.9 μm. Growth of strain GTP1T can be observed at 10–45 °C (optimum, 30 °C), at pH 5.0–8.0 (optimum, pH 7.0), and at NaCl concentrations of 0–2% (optimum, 0%). The strain shows positive results for Tween 20 and Tween 80 hydrolysis. GTP1T does not produce siderophores when tested on CAS-blue agar. According to API ZYM results, strain GTP1T is positive for alkaline phosphatase, esterase (C4), esterase lipase (C8), and leucine arylamidase, while the API 20NE assay results show negative results for all substrates. The predominant respiratory quinone is ubiquinone 8 (Q-8). The major fatty acids are C16:0 and summed feature 3 (consisting of C16:1 ω7c and/or C16:1 ω6c). The polar lipid profile consists of diphosphatidylglycerol (DPG), phosphatidylglycerol (PG), phosphatidylethanolamine (PE), and two unidentified phosphoaminolipids. The predominant respiratory quinone is ubiquinone 8 (Q-8). The major fatty acids are C16:0, C17:0 cyclo, summed feature 3 (consisting of C16:1 ω7c and/or C16:1 ω6c), and summed feature 8 (consisting of C18:1 ω7c and/or C18:1 ω6c). The polar lipid profile consists of diphosphatidylglycerol (DPG), phosphatidylglycerol (PG), phosphatidylethanolamine (PE), one unidentified phosphoaminolipid, one unidentified phosphoglycolipid, one unidentified phosphoglycoaminolipid, and two unidentified polar lipids. The G + C content is 67.9%. The GenBank/EMBL/DDBJ accession numbers for the 16S rRNA gene sequence and the assembled genome sequence of strain GTP1T are MN498046 and JACORU000000000, respectively.The type strain GTP1T (= KACC 21702T = NBRC 114488T) was isolated from soil at Seogwipo, Jeju Island, Republic of Korea.Description of Ramlibacter pallidus sp. nov
Ramlibacter pallidus (pal’li.dus. L. masc. adj. pallidus, pale, referring to the color of the colonies).Cells of strain HM2T are Gram-negative, and positive for both oxidase and catalase activities. When observed on R2A agar, colonies are cream-colored, transparent, 1.0–2.5 mm in diameter, and flat with entire margins. Under TEM, monotrichous flagella are observed, and cells are rod-shaped with a width of 0.4–0.78 μm and length of 1.7–1.8 μm. The strain shows the fastest growth at a temperature range of 25–35 °C and at pH values between 8.0 and 9.0. When NaCl is present, growth is observed at concentrations of 0–3% (w/v), with optimal growth was observed at concentrations of 0–1% (w/v). The strain is not able to tolerate anaerobic conditions. Strain HM2T hydrolyzes Tween 80 and weakly hydrolyzes casein. However, siderophore production cannot be observed when tested on CAS-blue agar. According to API ZYM tests, strain HM2T shows positive results for alkaline phosphatase, esterase (C4), esterase lipase (C8), leucine arylamidase, valine arylamidase, acid phosphatase, and naphthol-AS-BI-phosphohydrolase. In addition, API 20NE tests show positive results for nitrate (NO3) to nitrite (NO2-) reduction and esculin hydrolysis. The G + C content is 69.9%. The GenBank/EMBL/DDBJ accession numbers for the 16S rRNA gene sequence and the assembled genome sequence of strain HM2T are MN498047 and JADDIV000000000, respectively.The type strain HM2T (= KCTC 82557T = NBRC 114489T) was isolated from soil at Seopjikoji, Jeju Island, Republic of Korea. More