Philippa Kaur

Fosberg, F. R. & Chapman, V. J. Mangrove Vegetation. Taxon 26, 113 (1977).Article 

Google Scholar 
Vo, Q. T., Kuenzer, C., Vo, Q. M., Moder, F. & Oppelt, N. Review of valuation methods for mangrove ecosystem services. Ecol. Indic. 23, 431–446 (2012).Article 

Google Scholar 
Costanza, R. et al. The value of the world’s ecosystem services and natural capital. Nature 387, 253–260 (1997).ADS 
CAS 
Article 

Google Scholar 
Duke, N. C. et al. A world without mangroves?. Science. 317, 41b–42b (2007).Article 

Google Scholar 
Barbier, E. B. et al. The value of estuarine and coastal ecosystem services. Ecol. Monogr. 81, 169–193 (2011).Article 

Google Scholar 
Saderne, V. et al. Total alkalinity production in a mangrove ecosystem reveals an overlooked Blue Carbon component. Limnol. Oceanogr. Lett. https://doi.org/10.1002/lol2.10170 (2020).Allen, J. R. L. Morphodynamics of Holocene salt marshes: a review sketch from the Atlantic and Southern North Sea coasts of Europe. Quat. Sci. Rev. 19, 1155–1231 (2000).ADS 
Article 

Google Scholar 
Fagherazzi, S. et al. Tidal networks 1. Automatic network extraction and preliminary scaling features from digital terrain maps. Water Resour. Res. 35, 3891–3904 (1999).ADS 
Article 

Google Scholar 
D’Alpaos, A., Lanzoni, S., Mudd, S. M. & Fagherazzi, S. Modeling the influence of hydroperiod and vegetation on the cross-sectional formation of tidal channels. Estuar. Coast. Shelf Sci. 69, 311–324 (2006).ADS 
Article 

Google Scholar 
D’Alpaos, A. & Marani, M. Reading the signatures of biologic-geomorphic feedbacks in salt-marsh landscapes. Adv. Water Resour. 93, 265–275 (2016).ADS 
Article 

Google Scholar 
Schwarz, C. et al. Self-organization of a biogeomorphic landscape controlled by plant life-history traits. Nat. Geosci. 11, 672–677 (2018).ADS 
CAS 
Article 

Google Scholar 
Mariotti, G. & Canestrelli, A. Long-term morphodynamics of muddy backbarrier basins: fill in or empty out? Water Resour. Res. 53, 7029–7054 (2017).ADS 
Article 

Google Scholar 
Stark, J., Van Oyen, T., Meire, P. & Temmerman, S. Observations of tidal and storm surge attenuation in a large tidal marsh. Limnol. Oceanogr. 60, 1371–1381 (2015).ADS 
Article 

Google Scholar 
Montgomery, J., Bryan, K., Horstman, E. & Mullarney, J. Attenuation of tides and surges by mangroves: contrasting case studies from New Zealand. Water 10, 1119 (2018).Article 

Google Scholar 
Temmerman, S. et al. Vegetation causes channel erosion in a tidal landscape. Geology 35, 631–634 (2007).ADS 
Article 

Google Scholar 
van Maanen, B., Coco, G. & Bryan, K. R. On the ecogeomorphological feedbacks that control tidal channel network evolution in a sandy mangrove setting. Proc. R. Soc. A Math. Phys. Eng. Sci. 471, 20150115 (2015).
Google Scholar 
Bij de Vaate, I., Brückner, M. Z. M., Kleinhans, M. G. & Schwarz, C. On the Impact of Salt Marsh Pioneer Species-Assemblages on the Emergence of Intertidal Channel Networks. Water Resour. Res. 56, (2020).Bouma, T. J. et al. Density-dependent linkage of scale-dependent feedbacks: a flume study on the intertidal macrophyte Spartina anglica. Oikos 118, 260–268 (2009).Article 

Google Scholar 
Schwarz, C. et al. Impacts of salt marsh plants on tidal channel initiation and inheritance. J. Geophys. Res. Earth Surf. 119, 385–400 (2014).ADS 
Article 

Google Scholar 
Mcowen, C. J. et al. A global map of saltmarshes. Biodivers. Data J. 5, (2017).Spalding, M. World Atlas of Mangroves. World Atlas of Mangroves https://doi.org/10.4324/9781849776608 (2010).Fromard, F., Vega, C. & Proisy, C. Half a century of dynamic coastal change affecting mangrove shorelines of French Guiana. A case study based on remote sensing data analyses and field surveys. in. Mar. Geol. 208, 265–280 (2004).ADS 
Article 

Google Scholar 
Proisy, C. et al. Mud bank colonization by opportunistic mangroves: a case study from French Guiana using lidar data. Cont. Shelf Res. 29, 632–641 (2009).ADS 
Article 

Google Scholar 
Balke, T. et al. Windows of opportunity: thresholds to mangrove seedling establishment on tidal flats. Mar. Ecol. Prog. Ser. 440, 1–9 (2011).ADS 
Article 

Google Scholar 
Tomlinson, P. B. The botany of mangroves. Bot. Mangroves https://doi.org/10.2307/2996392 (1986).Article 

Google Scholar 
Duke, N. C., Ball, M. C. & Ellison, J. C. Factors influencing biodiversity and distributional gradients in mangroves. Glob. Ecol. Biogeogr. Lett. 7, 27–47 (1998).Article 

Google Scholar 
Swales, A., Bentley, S. J. & Lovelock, C. E. Mangrove-forest evolution in a sediment-rich estuarine system: Opportunists or agents of geomorphic change? Earth Surf. Process. Landf. 40, 1672–1687 (2015).ADS 
Article 

Google Scholar 
Nardin, W. et al. Dynamics of a fringe mangrove forest detected by Landsat images in the Mekong River Delta, Vietnam. Earth Surf. Process. Landf. 41, 2024–2037 (2016).ADS 
Article 

Google Scholar 
Proffitt, C. E., Travis, S. E. & Edwards, K. R. Genotype and elevation influence Spartina alterniflora colonization and growth in a created salt marsh. Ecol. Appl. 13, 180–192 (2003).Article 

Google Scholar 
van Wesenbeeck, B. K. et al. Potential for sudden shifts in transient systems: distinguishing between local and landscape-scale processes. Ecosystems 11, 1133–1141 (2008).Article 

Google Scholar 
Ranwell, D. S. Spartina salt marshes in southern England 3. Rates of establishment, succession and nutrient supply at Bridgewater Bay, Somerset. J. Ecol. 52, 95–105 (1964).Article 

Google Scholar 
van Wesenbeeck, B. K., van de Koppel, J., Herman, P. M. J. & Bouma, T. J. Does scale dependent feedback explain spatial complexity in salt marsh ecosystems? Oikos 117, 152–159 (2008).Article 

Google Scholar 
Taylor, C. M. & Hastings, A. Finding optimal control strategies for invasive species: a density-structured model for Spartina alterniflora. J. Appl. Ecol. 41, 1049–1057 (2004).Article 

Google Scholar 
Vandenbruwaene, W. et al. Flow interaction with dynamic vegetation patches: Implications for biogeomorphic evolution of a tidal landscape. J. Geophys. Res. Earth Surf. 116, 1–13 (2011).Article 

Google Scholar 
Mobberley, D. G. Taxonomy and distribution of the genus Spartina. (Iowa State University, 1953).Gourgue, O. et al. A Convolution Method to Assess Subgrid-Scale Interactions Between Flow and Patchy Vegetation in Biogeomorphic Models. J. Adv. Model. Earth Syst. 127, 1–25 (2021).Zong, L. & Nepf, H. Spatial distribution of deposition within a patch of vegetation. Water Resour. Res. 47, (2011).Suyadi, Gao, J., Lundquist, C. J. & Schwendenmann, L. Characterizing landscape patterns in changing mangrove ecosystems at high latitudes using spatial metrics. Estuar. Coast. Shelf Sci. 215, 1–10 (2018).ADS 
Article 

Google Scholar 
Best, S. N. et al. Do salt marshes survive sea level rise? Modelling wave action, morphodynamics and vegetation dynamics. Environ. Model. Softw. 109, 152–166 (2018).Article 

Google Scholar 
Chen, Y., Li, Y., Cai, T., Thompson, C. & Li, Y. A comparison of biohydrodynamic interaction within mangrove and saltmarsh boundaries. Earth Surf. Process. Landf. 41, 1967–1979 (2016).ADS 
Article 

Google Scholar 
Xie, D. et al. Mangrove diversity loss under sea-level rise triggered by bio-morphodynamic feedbacks and anthropogenic pressures. Environ. Res. Lett. 15, 114033 (2020).ADS 
Article 

Google Scholar 
Steel, T. J. & Pye, K. The development of salt marsh tidal creek networks: evidence from the UK. In Proceedings of the Canadian Coastal Conference 1, 267–280 (1997).Fagherazzi, S. & Sun, T. A stochastic model for the formation of channel networks in tidal marshes. Geophys. Res. Lett. 31, L21503 (2004).ADS 
Article 

Google Scholar 
D’Alpaos, A., Lanzoni, S., Marani, M., Fagherazzi, S. & Rinaldo, A. Tidal network ontogeny: channel initiation and early development. J. Geophys. Res. 110, F02001 (2005).ADS 

Google Scholar 
Marani, M. et al. On the drainage density of tidal networks. Water Resour. Res. 39, 1040 (2003).ADS 

Google Scholar 
Liu, Z. et al. Efficient tidal channel networks alleviate the drought-induced die-off of salt marshes: Implications for coastal restoration and management. Sci. Total Environ. 749, 141493 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Kearney, W. S. et al. Salt marsh vegetation promotes efficient tidal channel networks. Nat. Commun. 7, 12287 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hood, W. G. Applying tidal landform scaling to habitat restoration planning, design, and monitoring. Estuar. Coast. Shelf Sci. 244, 106060 (2020).Article 

Google Scholar 
Horstman, E., Dohmen-Janssen, C., Geomorphology, T. B.- & 2015, undefined. Tidal-scale flow routing and sedimentation in mangrove forests: Combining field data and numerical modelling. ElsevierCoco, G. et al. Morphodynamics of tidal networks: Advances and challenges. Mar. Geol. 346, 1–16 (2013).ADS 
Article 

Google Scholar 
Geng, L., Gong, Z., Zhou, Z., Lanzoni, S. & D’Alpaos, A. Assessing the relative contributions of the flood tide and the ebb tide to tidal channel network dynamics. Earth Surf. Process. Landf. 45, 237–250 (2020).ADS 
Article 

Google Scholar 
Andutta, F. P., Wang, X. H., Li, L. & Williams, D. Hydrodynamics and Sediment Transport in a Macro-tidal Estuary: Darwin Harbour, Australia. in 111–129 (Springer, Dordrecht, 2014). https://doi.org/10.1007/978-94-007-7019-5_7Elmqvist, T. & Cox, P. A. The Evolution of Vivipary in Flowering Plants. Oikos 77, 3 (1996).Article 

Google Scholar 
Zhang, X., Leonardi, N., Donatelli, C. & Fagherazzi, S. Fate of cohesive sediments in a marsh-dominated estuary. Adv. Water Resour. 125, 32–40 (2019).ADS 
Article 

Google Scholar 
Nardin, W. & Edmonds, D. A. Optimum vegetation height and density for inorganic sedimentation in deltaic marshes. Nat. Geosci. 7, 722–726 (2014).ADS 
CAS 
Article 

Google Scholar 
Swales, A., Bentley, S. J., Lovelock, C. & Bell, R. G. Sediment Processes and Mangrove-Habitat Expansion on a Rapidly-Prograding Muddy Coast, New Zealand. In Coastal Sediments ’07 1441–1454 (American Society of Civil Engineers, 2007). https://doi.org/10.1061/40926(239)111Wang, F., Lu, X., Sanders, C. J. & Tang, J. Tidal wetland resilience to sea level rise increases their carbon sequestration capacity in United States. Nat. Commun. 10, 1–11 (2019).ADS 
Article 
CAS 

Google Scholar 
Kristensen, E., Bouillon, S., Dittmar, T. & Marchand, C. Organic carbon dynamics in mangrove ecosystems: A review. Aquat. Bot. 89, 201–219 (2008).CAS 
Article 

Google Scholar 
Fagherazzi, S. et al. Fluxes of water, sediments, and biogeochemical compounds in salt marshes. Ecol. Process 2, 1–16 (2013).Article 

Google Scholar 
Kirchner, J. W. Statistical inevitability of Horton’s laws and the apparent randomness of stream channel networks. Geology 21, 591–594 (1993).ADS 
Article 

Google Scholar 
Vandenbruwaene, W., Meire, P. & Temmerman, S. Formation and evolution of a tidal channel network within a constructed tidal marsh. Geomorphology (2012).Marani, M. et al. Patterns in tidal environments: salt-marsh channel networks and vegetation. in Geoscience and Remote Sensing Symposium. IEEE 5 3269–3271 (2003).Horstman, E. M., Karin R. B., and Julia C. M. “Drag variations, tidal asymmetry and tidal range changes in a mangrove creek system.” Earth Surf. Process. Landf. (2021).R. Core, Team. R: A language and environment for statistical computing. (2013).Lillesand, T. M. & Kiefer, R. W. Remote Sensing and Image Interpretation. John Willey & Sons. Inc, USA. (1994).Vandenbruwaene, W., Bouma, T. J., Meire, P. & Temmerman, S. Bio-geomorphic effects on tidal channel evolution: impact of vegetation establishment and tidal prism change. Earth Surf. Process. Landforms 38, 122–132 (2013).ADS 
Article 

Google Scholar 
Stefanon, L., Carniello, L., D’Alpaos, A. & Lanzoni, S. Experimental analysis of tidal network growth and development. Cont. Shelf Res. 30, 950–962 (2010).ADS 
Article 

Google Scholar 
Braat, L., Leuven, J. R. F. W., Lokhorst, I. R. & Kleinhans, M. G. Effects of estuarine mudflat formation on tidal prism and large-scale morphology in experiments. Earth Surf. Process. Landf. 44, 417–432 (2019).ADS 
Article 

Google Scholar 
Kleinhans, M. G. et al. Turning the tide: Comparison of tidal flow by periodic sea level fluctuation and by periodic bed tilting in scaled landscape experiments of estuaries. Earth Surf. Dyn. 5, 731–756 (2017).ADS 
Article 

Google Scholar 
Paola, C., Straub, K., Mohrig, D. & Reinhardt, L. The ‘unreasonable effectiveness’ of stratigraphic and geomorphic experiments. Earth-Sci. Rev. 97, 1–43 (2009).ADS 
Article 

Google Scholar 
Kleinhans, M. G., Leuven, J. R. F. W., Braat, L. & Baar, A. Scour holes and ripples occur below the hydraulic smooth to rough transition of movable beds. Sedimentology 64, 1381–1401 (2017).Article 

Google Scholar 
Lokhorst, I. R., Lange, S. I., Buiten, G., Selaković, S. & Kleinhans, M. G. Species selection and assessment of eco‐engineering effects of seedlings for biogeomorphological landscape experiments. Earth Surf. Process. Landf. 44, 2922–2935 (2019).ADS 
Article 

Google Scholar 
Widdows, J. et al. Inter-comparison between five devices for determining erodability of intertidal sediments. Cont. Shelf Res. 27, 1174–1189 (2007).ADS 
Article 

Google Scholar 
Verney, R., Brun-Cottan, J. C., Lafite, R., Deloffre, J. & Taylor, J. A. Tidally-induced shear stress variability above intertidal mudflats in the macrotidal seine estuary. Estuaries and Coasts 29, 653–664 (2006).Article 

Google Scholar 
Wu, W., Perera, C., Smith, J. & Sanchez, A. Critical shear stress for erosion of sand and mud mixtures. J. Hydraul. Res. 56, 96–110 (2018).Article 

Google Scholar 
Wolters, M., Garbutt, A., Bekker, R. M., Bakker, J. P. & Carey, P. D. Restoration of salt-marsh vegetation in relation to site suitability, species pool and dispersal traits. J. Appl. Ecol. 45, 904–912 (2007).Article 

Google Scholar  More

Woollings, T., Hannachi, A. & Hoskins, B. Variability of the North Atlantic eddy-driven jet stream. Q J. R. Meteorol. Soc. 136, 856–868 (2010).ADS 
Article 

Google Scholar 
Coumou, D., Capua, D. I., Vavrus, G., Wang, L. S. & Wang, S. The influence of Arctic amplification on mid-latitude summer circulation. Nat. Commun. 9, 2959 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Belmecheri, S., Babst, F., Hudson, A. R., Betancourt, J. & Trouet, V. Northern Hemisphere jet stream position indices as diagnostic tools for climate and ecosystem dynamics. Earth Interact. 21, 1–23 (2017).Article 

Google Scholar 
Trouet, V., Babst, F. & Meko, M. Recent enhanced high-summer North Atlantic Jet variability emerges from three-century context. Nat. Commun. 9, 180 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lehmann, J. & Coumou, D. The influence of mid-latitude storm tracks on hot, cold, dry and wet extremes. Sci. Rep. 5, 17491 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mahlstein, I., Martius, O., Chevalier, C. & Ginsbourger, D. Changes in the odds of extreme events in the Atlantic basin depending on the position of the extratropical jet. Geophys. Res. Lett. 39, 1–6 (2012).
Google Scholar 
Röthlisberger, M., Pfahl, S. & Martius, O. Regional-scale jet waviness modulates the occurrence of midlatitude weather extremes. Geophys. Res. Lett. 43, 10,910–989,997 (2016).Article 

Google Scholar 
Brunner, L., Schaller, N., Anstey, J., Sillmann, J. & Steiner, A. K. Dependence of present and future European temperature extremes on the location of atmospheric blocking. Geophys. Res. Lett. 45, 6311–6320 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Dong, B., Sutton, R. T., Woollings, T. & Hodges, K. Variability of the North Atlantic summer storm track: mechanisms and impacts on European climate. Environ. Res. Lett. 8, 34037 (2013).Article 

Google Scholar 
Mann, M. E. et al. Influence of anthropogenic climate change on planetary wave resonance and extreme weather events. Sci. Rep. 7, 45242 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012).ADS 
Article 

Google Scholar 
Schumacher, D. L. et al. Amplification of mega-heatwaves through heat torrents fuelled by upwind drought. Nat. Geosci. 12, 712–717 (2019).ADS 
CAS 
Article 

Google Scholar 
Zscheischler, J. et al. Future climate risk from compound events. Nat. Clim. Change 8, 469–477 (2018).ADS 
Article 

Google Scholar 
Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Buras, A., Rammig, A. & Zang, C. S. Quantifying impacts of the 2018 drought on European ecosystems in comparison to 2003. Biogeosciences 17, 1655–1672 (2020).ADS 
Article 

Google Scholar 
Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Frank, D. et al. Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts. Glob. Change Biol. 21, 2861–2880 (2015).ADS 
Article 

Google Scholar 
Sillmann, J. et al. Understanding, modeling and predicting weather and climate extremes: challenges and opportunities. Weather Clim. Extrem. 18, 65–74 (2017).Article 

Google Scholar 
Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M. & García-Herrera, R. The hot summer of 2010: redrawing the temperature record map of Europe. Science 332, 220–224 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Bastos, A., Gouveia, C. M., Trigo, R. M. & Running, S. W. Analysing the spatio-temporal impacts of the 2003 and 2010 extreme heatwaves on plant productivity in Europe. Biogeosciences 11, 3421–3435 (2014).ADS 
Article 

Google Scholar 
Fischer, E. M., Seneviratne, S. I., Vidale, P. L., Lüthi, D. & Schär, C. Soil moisture–atmosphere interactions during the 2003 european summer heat wave. J. Clim. 20, 5081–5099 (2007).ADS 
Article 

Google Scholar 
Perkins-Kirkpatrick, S. E. & Lewis, S. C. Increasing trends in regional heatwaves. Nat. Commun. 11, 3357 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).ADS 
Article 

Google Scholar 
Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–472 (1996).ADS 
Article 

Google Scholar 
Rammig, A. et al. Coincidences of climate extremes and anomalous vegetation responses: comparing tree ring patterns to simulated productivity. Biogeosciences 12, 373–385 (2015).ADS 
Article 

Google Scholar 
Spinoni, J., Naumann, G., Vogt, J. V. & Barbosa, P. The biggest drought events in Europe from 1950 to 2012. J. Hydrol. Reg. Stud. 3, 509–524 (2015).Article 

Google Scholar 
Madonna, E., Li, C., Grams, C. M. & Woollings, T. The link between eddy-driven jet variability and weather regimes in the North Atlantic-European sector. Q J. R. Meteorol. Soc. 143, 2960–2972 (2017).ADS 
Article 

Google Scholar 
Grams, C. M., Beerli, R., Pfenninger, S., Staffell, I. & Wernli, H. Balancing Europe’s wind-power output through spatial deployment informed by weather regimes. Nat. Clim. Change 7, 557–562 (2017).Article 

Google Scholar 
Seftigen, K., Frank, D. C., Björklund, J., Babst, F. & Poulter, B. The climatic drivers of normalized difference vegetation index and tree-ring-based estimates of forest productivity are spatially coherent but temporally decoupled in Northern Hemispheric forests. Glob. Ecol. Biogeogr. 27, 1352–1365 (2018).Article 

Google Scholar 
Babst, F. et al. Above-ground woody carbon sequestration measured from tree rings is coherent with net ecosystem productivity at five eddy-covariance sites. N. Phytol. 201, 1289–1303 (2014).CAS 
Article 

Google Scholar 
Zweifel, R. & Sterck, F. A conceptual tree model explaining legacy effects on stem growth. Front. Glob. Change 1, 9 (2018).Article 

Google Scholar 
Fatichi, S., Pappas, C., Zscheischler, J. & Leuzinger, S. Modelling carbon sources and sinks in terrestrial vegetation. N. Phytol. 221, 652–668 (2019).CAS 
Article 

Google Scholar 
Wu, X. et al. Differentiating drought legacy effects on vegetation growth over the temperate Northern Hemisphere. Glob. Chang. Biol. 24, 504–516 (2018).ADS 
PubMed 
Article 

Google Scholar 
Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).Article 

Google Scholar 
Davini, P. & Cagnazzo, C. On the misinterpretation of the North Atlantic Oscillation in CMIP5 models. Clim. Dyn. 43, 1497–1511 (2014).Article 

Google Scholar 
Pfahl, S. & Wernli, H. Quantifying the relevance of atmospheric blocking for co-located temperature extremes in the Northern Hemisphere on (sub-)daily time scales. Geophys. Res. Lett. 39 (2012).Drouard, M. & Woollings, T. Contrasting mechanisms of summer blocking over western Eurasia. Geophys. Res. Lett. 45, 12,040–12,048 (2018).Article 

Google Scholar 
Bastos, A. et al. European land CO2 sink influenced by NAO and East-Atlantic pattern coupling. Nat. Commun. 7, 10315 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ascoli, D. et al. Inter-annual and decadal changes in teleconnections drive continental-scale synchronization of tree reproduction. Nat. Commun. 8, 1–9 (2017).CAS 
Article 

Google Scholar 
Sousa, P. M. et al. Responses of European precipitation distributions and regimes to different blocking locations. Clim. Dyn. 48, 1141–1160 (2017).Article 

Google Scholar 
Hacket-Pain, A. J., Cavin, L., Friend, A. D. & Jump, A. S. Consistent limitation of growth by high temperature and low precipitation from range core to southern edge of European beech indicates widespread vulnerability to changing climate. Eur. J. Res. 135, 897–909 (2016).Article 

Google Scholar 
Cavin, L. & Jump, A. S. Highest drought sensitivity and lowest resistance to growth suppression are found in the range core of the tree Fagus sylvatica L. not the equatorial range edge. Glob. Chang. Biol. 23, 362–379 (2017).ADS 
PubMed 
Article 

Google Scholar 
Leuschner, C. Drought response of European beech (Fagus sylvatica L.): A review. Perspect. Plant Ecol. Evol. Syst. 47, 125576 (2020).Article 

Google Scholar 
Muffler, L. et al. Lowest drought sensitivity and decreasing growth synchrony towards the dry distribution margin of European beech. J. Biogeogr. 47, 1910–1921 (2020).Article 

Google Scholar 
Wang, F. et al. Seedlings from marginal and core populations of European beech (Fagus sylvatica L.) respond differently to imposed drought and shade. Trees 35, 53–67 (2021).CAS 
Article 

Google Scholar 
Hall, R. J., Jones, J. M., Hanna, E., Scaife, A. A. & Erdélyi, R. Drivers and potential predictability of summertime North Atlantic polar front jet variability. Clim. Dyn. 48, 3869–3887 (2017).Article 

Google Scholar 
Screen, J. A. & Simmonds, I. Amplified mid-latitude planetary waves favour particular regional weather extremes. Nat. Clim. Change 4, 704–709 (2014).ADS 
Article 

Google Scholar 
Kornhuber, K. et al. Extreme weather events in early summer 2018 connected by a recurrent hemispheric wave-7 pattern. Environ. Res. Lett. 14, 54002 (2019).Article 

Google Scholar 
Shepherd, T. G. Atmospheric circulation as a source of uncertainty in climate change projections. Nat. Geosci. 7, 703–708 (2014).ADS 
CAS 
Article 

Google Scholar 
Peings, Y., Cattiaux, J., Vavrus, S. J. & Magnusdottir, G. Projected squeezing of the wintertime North-Atlantic jet. Environ. Res. Lett. 13, 74016 (2018).Article 

Google Scholar 
Matsueda, M. & Endo, H. The robustness of future changes in Northern Hemisphere blocking: a large ensemble projection with multiple sea surface temperature patterns. Geophys. Res. Lett. 44, 5158–5166 (2017).ADS 
Article 

Google Scholar 
Kwon, Y. O., Camacho, A., Martinez, C. & Seo, H. North Atlantic winter eddy-driven jet and atmospheric blocking variability in the Community Earth System Model version 1 Large Ensemble simulations. Clim. Dyn. 51, 3275–3289 (2018).Article 

Google Scholar 
Cohen, J. et al. Divergent consensuses on Arctic amplification influence on midlatitude severe winter weather. Nat. Clim. Change 10, 20–29 (2020).ADS 
Article 

Google Scholar 
de Vries, H., Woollings, T., Anstey, J., Haarsma, R. J. & Hazeleger, W. Atmospheric blocking and its relation to jet changes in a future climate. Clim. Dyn. 41, 2643–2654 (2013).Article 

Google Scholar 
Woollings, T. et al. Blocking and its response to climate change. Curr. Clim. Chang. Rep. 4, 287–300 (2018).Article 

Google Scholar 
Anderegg, W. R. L. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Sousa-Silva, R. et al. Tree diversity mitigates defoliation after a drought-induced tipping point. Glob. Chang. Biol. 24, 4304–4315 (2018).ADS 
PubMed 
Article 

Google Scholar 
Magri, D. Patterns of post-glacial spread and the extent of glacial refugia of European beech (Fagus sylvatica). J. Biogeogr. 35, 450–463 (2008).Article 

Google Scholar 
Cailleret, M. et al. A synthesis of radial growth patterns preceding tree mortality. Glob. Chang. Biol. 23, 1675–1690 (2017).ADS 
PubMed 
Article 

Google Scholar 
Dorado-Liñán, I. et al. Geographical adaptation prevails over species-specific determinism in trees’ vulnerability to climate change at Mediterranean rear-edge forests. Glob. Chan. Biol. 25, 1296–1314 (2019).ADS 
Article 

Google Scholar 
DeSoto, L. et al. Low growth resilience to drought is related to future mortality risk in trees. Nat. Commun. 11, 545 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hacket-Pain, A. J., Friend, A. D., Lageard, J. G. A. & Thomas, P. A. The influence of masting phenomenon on growth–climate relationships in trees: explaining the influence of previous summers’ climate on ring width. Tree Physiol. 35, 319–330 (2015).PubMed 
Article 

Google Scholar 
Bréda, N., Huc, R., Granier, A. & Dreyer, E. Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences. Ann. Sci. 63, 625–644 (2006).Article 

Google Scholar 
Hacket-Pain, A. J. et al. Climatically controlled reproduction drives interannual growth variability in a temperate tree species. Ecol. Lett. 21, 1833–1844 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Popkin, G. How much can forests fight climate change? Nature 565, 280–282 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Davini, P. & D’Andrea, F. Northern Hemisphere atmospheric blocking representation in global climate models: twenty years of improvements? J. Clim. 29, 8823–8840 (2016).ADS 
Article 

Google Scholar 
Barton, N. P. & Ellis, A. W. Variability in wintertime position and strength of the North Pacific jet stream as represented by re-analysis data. Int. J. Climatol. 29, 851–862 (2009).Article 

Google Scholar 
Doblas-Reyes, F. J., Casado, M. J. & Pastor, M. A. Sensitivity of the Northern Hemisphere blocking frequency to the detection index. J. Geophys. Res. Atmos. 107, D2 (2002).Article 

Google Scholar 
Cook, E. R. & Peters, K. The smoothing spline: a new approach to standardizing forest interior tree-ring width series for dendroclimatic studies. Tree-Ring Bull. 41, 45–53 (1981).
Google Scholar 
Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).ADS 
Article 

Google Scholar 
Team, R. Core (2020). R A Lang. Environ. Stat. Comput. R Found. Stat. Comput. Vienna, Austria. URL https://www.R-project.org (2020).Bunn, A. G. A dendrochronology program library in R (dplR). Dendrochronologia 26, 115–124 (2008).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, (2015).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Barton, K. Mu-MIn: Multi-model inference. R Package Version 0.12.2/r18, (2009) http://R-Forge.R-project.org/projects/mumin/ More

AffiliationsDepartment of Earth and Environmental Sciences, Macquarie University, Sydney, New South Wales, AustraliaErnest F. Asamoah & Joseph M. MainaDepartment of Biological Sciences, Macquarie University, Sydney, New South Wales, AustraliaLinda J. BeaumontAuthorsErnest F. AsamoahLinda J. BeaumontJoseph M. MainaCorresponding authorCorrespondence to
Ernest F. Asamoah. More

As well as the humanitarian catastrophe it is inflicting, Russia’s invasion of Ukraine in February is disrupting global flows of vital commodities such as fuel, food and fertilizer. This will affect biodiversity and the environment far beyond the war zones, with implications for sustainability and well-being worldwide.
Competing Interests
The authors declare no competing interests. More

Kåhrström CT, Pariente N, Weiss U. Intestinal microbiota in health and disease. Nature 2016;535:47–47.Article 

Google Scholar 
El Aidy S, van Baarlen P, Derrien M, Lindenbergh-Kortleve DJ, Hooiveld G, Levenez F, et al. Temporal and spatial interplay of microbiota and intestinal mucosa drive establishment of immune homeostasis in conventionalized mice. Mucosal Immunol. 2012;5:567–79.CAS 
Article 

Google Scholar 
Okumura R, Takeda K. Roles of intestinal epithelial cells in the maintenance of gut homeostasis. Exp Mol Med. 2017;49:e338–e338.CAS 
Article 

Google Scholar 
Mahata SK, O’Connor DT, Mahata M, Yoo SH, Taupenot L, Wu H, et al. Novel autocrine feedback control of catecholamine release. A discrete chromogranin a fragment is a noncompetitive nicotinic cholinergic antagonist. J Clin Invest. 1997;100:1623–33.CAS 
Article 

Google Scholar 
Briolat J, Wu SD, Mahata SK, Gonthier B, Bagnard D, Chasserot-Golaz S, et al. New antimicrobial activity for the catecholamine release-inhibitory peptide from chromogranin A. Cell Mol Life Sci. 2005;62:377–85.CAS 
Article 

Google Scholar 
Lugardon K, Raffner R, Goumon Y, Corti A, Delmas A, Bulet P, et al. Antibacterial and antifungal activities of vasostatin-1, the N-terminal fragment of chromogranin A. J Biol Chem. 2000;275:10745–53.CAS 
Article 

Google Scholar 
Aslam R, Atindehou M, Lavaux T, Haïkel Y, Schneider F, Metz-Boutigue M-H. Chromogranin A-derived peptides are involved in innate immunity. Curr Med Chem. 2012;19:4115–23.CAS 
Article 

Google Scholar 
El-Salhy M, Patcharatrakul T, Hatlebakk JG, Hausken T, Gilja OH, Gonlachanvit S. Chromogranin A cell density in the large intestine of Asian and European patients with irritable bowel syndrome. Scand J Gastroenterol. 2017;52:691–7.CAS 
Article 

Google Scholar 
Bartolomucci A, Possenti R, Mahata SK, Fischer-Colbrie R, Loh YP, Salton SR. The extended granin family: structure, function, and biomedical implications. Endocr Rev. 2011;32:755–97.CAS 
Article 

Google Scholar 
Mahata SK, Corti A. Chromogranin A and its fragments in cardiovascular, immunometabolic, and cancer regulation. Ann N Y Acad Sci. 2019;1455:34–58.CAS 
Article 

Google Scholar 
Corti A, Marcucci F, Bachetti T. Circulating chromogranin A and its fragments as diagnostic and prognostic disease markers. Pflugers Archiv Eur J Physiol. 2018;470:199–210.Mahata SK, Mahata M, Fung MM, O’Connor DT. Catestatin: a multifunctional peptide from chromogranin A. Regul Pept. 2010;162:33–43.CAS 
Article 

Google Scholar 
Ying W, Mahata S, Bandyopadhyay GK, Zhou Z, Wollam J, Vu J, et al. Catestatin inhibits obesity-induced macrophage infiltration and inflammation in the liver and suppresses hepatic glucose production, leading to improved insulin sensitivity. Diabetes. 2018;67:841–8.CAS 
Article 

Google Scholar 
Mahata SK, Kiranmayi M, Mahapatra NR. Catestatin: a master regulator of cardiovascular functions. Curr Med Chem. 2018;25:1352–74.CAS 
Article 

Google Scholar 
Muntjewerff EM, Tang K, Lutter L, Christoffersson G, Nicolasen MJT, Gao H, et al. Chromogranin A regulates gut permeability via the antagonistic actions of its proteolytic peptides. Acta Physiol. 2021;232:e13655.Rabbi MF, Munyaka PM, Eissa N, Metz-Boutigue MH, Khafipour E, Ghia JE. Human catestatin alters gut microbiota composition in mice. Front Microbiol. 2017;7:1–12.Article 

Google Scholar 
Radek KA, Lopez-Garcia B, Hupe M, Niesman IR, Elias PM, Taupenot L, et al. The neuroendocrine peptide catestatin is a cutaneous antimicrobial and induced in the skin after injury. J Invest Dermatol. 2008;128:1525–34.CAS 
Article 

Google Scholar 
Bevins CL, Salzman NH. Paneth cells, antimicrobial peptides and maintenance of intestinal homeostasis. Nat Rev Microbiol. 2011;9:356–68.CAS 
Article 

Google Scholar 
Dupont A, Heinbockel L, Brandenburg K, Hornef MW. Antimicrobial peptides and the enteric mucus layer act in concert to protect the intestinal mucosa. Gut Microbes. 2014;5:761–5.Article 

Google Scholar 
Tsukuda N, Yahagi K, Hara T, Watanabe Y, Matsumoto H, Mori H, et al. Key bacterial taxa and metabolic pathways affecting gut short-chain fatty acid profiles in early life. ISME J. 2021;15:2574–90.CAS 
Article 

Google Scholar 
Nuri R, Shprung T, Shai Y. Defensive remodeling: How bacterial surface properties and biofilm formation promote resistance to antimicrobial peptides. Biochim Biophys Acta Biomembr. 2015;1848:3089–100.CAS 
Article 

Google Scholar 
Jakobsson HE, Rodríguez‐Piñeiro AM, Schütte A, Ermund A, Boysen P, Bemark M, et al. The composition of the gut microbiota shapes the colon mucus barrier. EMBO Rep. 2015;16:164–77.CAS 
Article 

Google Scholar 
Samantha A, Vrielink A. Lipid A Phosphoethanolamine Transferase: regulation, structure and immune response. J Mol Biol. 2020;432:5184–96.CAS 
Article 

Google Scholar 
Gottesman S. Proteases and their targets in Escherichia coli. Annu Rev Genet. 1996;30:465–506.CAS 
Article 

Google Scholar 
Mirsepasi-Lauridsen HC, Vallance BA, Krogfelt KA, Petersen AM. Escherichia coli pathobionts associated with inflammatory bowel disease. Clin Microbiol Rev. 2019;32:1–16.Article 

Google Scholar 
Nayfach S, Fischbach MA, Pollard KS. MetaQuery: a web server for rapid annotation and quantitative analysis of specific genes in the human gut microbiome. Bioinformatics. 2015;31:3368–70.CAS 
Article 

Google Scholar 
Ying W, Tang K, Avolio E, Schilling JM, Pasqua T, Liu MA, et al. Immunosuppression of macrophages underlies the cardioprotective effects of CST (Catestatin). Hypertension. 2021;77:1670–82.CAS 
Article 

Google Scholar 
Stojanov S, Berlec A, Štrukelj B. The influence of probiotics on the firmicutes/bacteroidetes ratio in the treatment of obesity and inflammatory bowel disease. Microorganisms. 2020;8:1–16.Article 

Google Scholar 
Indiani CMDSP, Rizzardi KF, Castelo PM, Ferraz LFC, Darrieux M, Parisotto TM. Childhood obesity and firmicutes/bacteroidetes ratio in the gut microbiota: a systematic review. Child Obes. 2018;14:501–9.Article 

Google Scholar 
Lam YY, Ha CWY, Campbell CR, Mitchell AJ, Dinudom A, Oscarsson J, et al. Increased gut permeability and microbiota change associate with mesenteric fat inflammation and metabolic dysfunction in diet-induced obese mice. PLoS One. 2012;7:1–10.
Google Scholar 
Herp S, Durai Raj AC, Salvado Silva M, Woelfel S, Stecher B. The human symbiont Mucispirillum schaedleri: causality in health and disease. Med Microbiol Immunol. 2021;210:173–9.Article 

Google Scholar 
Parker BJ, Wearsch PA, Veloo ACM, Rodriguez-Palacios A. The genus alistipes: gut bacteria with emerging implications to inflammation, cancer, and mental health. Front Immunol. 2020;11:1–15.Article 

Google Scholar 
Hiippala K, Barreto G, Burrello C, Diaz-Basabe A, Suutarinen M, Kainulainen V, et al. Novel Odoribacter splanchnicus strain and its outer membrane vesicles exert immunoregulatory effects in vitro. Front Microbiol. 2020;11:1–14.Article 

Google Scholar 
McPhee JB, Small CL, Reid-Yu SA, Brannon JR, Moual H LE, Coombes BK. Host defense peptide resistance contributes to colonization and maximal intestinal pathology by Crohn’s disease-associated adherent-invasive Escherichia coli. Infect Immun. 2014;82:3383–93.Article 

Google Scholar 
Xu Y, Wei W, Lei S, Lin J, Srinivas S, Feng Y. An evolutionarily conserved mechanism for intrinsic and transferable polymyxin resistance. MBio. 2018;9:1–18.Article 

Google Scholar 
Thomassin JL, Brannon JR, Gibbs BF, Gruenheid S, Le Moual H. OmpT outer membrane proteases of enterohemorrhagic and enteropathogenic Escherichia coli contribute differently to the degradation of human LL-37. Infect Immun. 2012;80:483–92.CAS 
Article 

Google Scholar 
Desloges I, Taylor JA, Leclerc JM, Brannon JR, Portt A, Spencer JD, et al. Identification and characterization of OmpT-like proteases in uropathogenic Escherichia coli clinical isolates. Microbiologyopen. 2019;8:1–36.Article 

Google Scholar 
McCarter JD, Stephens D, Shoemaker K, Rosenberg S, Kirsch JF, Georgiou G. Substrate specificity of the Escherichia coli outer membrane protease OmpT. J Bacteriol. 2004;186:5919–25.CAS 
Article 

Google Scholar 
Kulkarni HM, Nagaraj R, Jagannadham MV. Protective role of E. coli outer membrane vesicles against antibiotics. Microbiol Res. 2015;181:1–7.CAS 
Article 

Google Scholar 
Muntjewerff EM, Dunkel G, Nicolasen MJT, Mahata SK, van den Bogaart G. Catestatin as a Target for Treatment of Inflammatory Diseases. Front Immunol. 2018;9:2199.Santella RM. Approaches to DNA/RNA extraction and whole genome amplification: table 1. Cancer Epidemiol Biomark Prev. 2006;15:1585–7.CAS 
Article 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
Article 

Google Scholar 
R Core Team. R: a language and environment for statistical computing. Vienna, Austria; 2019. https://www.r-project.org/.Lahti L, Shetty S. microbiome R package. http://microbiome.github.io.McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217.Paulson JN, Colin Stine O, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nat Methods. 2013;10:1200–2.CAS 
Article 

Google Scholar 
Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60.Article 

Google Scholar 
Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38:685–8.CAS 
Article 

Google Scholar 
Parks DH, Tyson GW, Hugenholtz P, Beiko RG. STAMP: Statistical analysis of taxonomic and functional profiles. Bioinformatics. 2014;30:3123–4.CAS 
Article 

Google Scholar 
Beresford-Jones BS, Forster SC, Stares MD, Notley G, Viciani E, Browne HP, et al. The Mouse Gastrointestinal Bacteria Catalogue enables translation between the mouse and human gut microbiotas via functional mapping. Cell Host Microbe. 2022;30:124–138.e8.CAS 
Article 

Google Scholar 
Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.CAS 
Article 

Google Scholar 
Price MN, Dehal PS, Arkin AP. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5:e9490.Article 

Google Scholar 
Menardo F, Loiseau C, Brites D, Coscolla M, Gygli SM, Rutaihwa LK, et al. Treemmer: a tool to reduce large phylogenetic datasets with minimal loss of diversity. BMC Bioinforma. 2018;19:1–8.Article 

Google Scholar 
Haider SR, Reid HJ, Sharp BL. Tricine-SDS-PAGE. In: Kurien B., Scofield R. editors. Protein electrophoresis. Methods in Molecular Biology (Methods and Protocols). Totowa, NJ: Humana Press; 2012. p. 81–91.Schägger H. Tricine-SDS-PAGE. Nat Protoc. 2006;1:16–22.Article 

Google Scholar 
Zoetendal EG, Booijink CCGM, Klaassens ES, Heilig HGHJ, Kleerebezem M, Smidt H, et al. Isolation of RNA from bacterial samples of the human gastrointestinal tract. Nat Protoc. 2006;1:954–9.CAS 
Article 

Google Scholar 
Zhou K, Zhou L, Lim Q, Zou R, Stephanopoulos G, Too HP. Novel reference genes for quantifying transcriptional responses of Escherichia coli to protein overexpression by quantitative PCR. BMC Mol Biol. 2011;12:18.CAS 
Article 

Google Scholar 
Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods. 2001;25:402–8.CAS 
Article 

Google Scholar  More

Phylogenetic placement of Saccharibacteria rhodopsins (SacRs) shows that these sequences form a sibling clade to characterized light-driven inward and outward H+ pumps (Fig. 1a). We selected three phylogenetically diverse SacRs from freshwater lakes (Table S1) and two related, previously uncharacterized sequences from the Gammaproteobacteria (Kushneria aurantia and Halomonas sp.) for synthesis and functional characterization (highlighted in Fig. 1a). All sequences have Asp–Thr–Ser (DTS) residues at the positions of D85–T96–D96 of bacteriorhodopsin (BR) in the third transmembrane helix (Fig. S1). These residues are known as the triplet DTD motif and represent key residues for proton pumping function in BR [6].Fig. 1: Characteristics of Saccharibacteria rhodopsins (SacRs).a Rhodopsin protein tree indicating that SacRs from freshwater lakes form a broad clade of proton pumps. b The ion-pumping activity of SacRs. Blue and green lines indicate the pH change with and without 10 μM CCCP, respectively. Yellow bars indicate the period of light illumination. c Time evolution of transient absorption changes of SacRNC335 in 100 mM NaCl, 20 mM HEPES–NaOH, pH 7.0, and POPE/POPG (molar ratio 3:1) vesicles with a lipid to protein molar ratio = 50. Time evolution at 406 nm (blue, representing the M accumulation), 561 nm (green, representing the bleaching of the initial state and the L accumulation), and 638 nm (red, representing the K and O accumulations). Yellow lines indicate fitting curves by a multi-exponential function. Inset: The photocycle of SacRNC335 based on the fitting in (c) and a kinetic model assuming a sequential photocycle. The lifetime (τ) of each intermediate is indicated by numbers as follow (mean ± S.D., fraction of the intermediate decayed with each lifetime in its double exponential decay is indicated in parentheses): I: τ = 1.7 ± 0.3 μs (42%), τ = 13 ± 1.8 μs (58%), II: τ = 118 ± 2 μs, III: τ = 1.6 ± 0.1 ms, IV: τ = 23.5 ± 1.0 ms, V: τ = 98.4 ± 6.4 ms (56%), τ = 384 ± 18 ms (44%). d Genomic context of SacRNC335. Neighboring genes with above-threshold KEGG annotations are indicated in gray with the highest-scoring HMM model. Genes without KEGG annotations are indicated in white.Full size imageProton transport assays for the SacRs and Gammaproteobacteria proteins expressed in Escherichia coli showed marked decrease of external pH upon light illumination (Fig. 1b and Fig. S2), indicating that these proteins are light-driven outward H+ pumps. The pH decrease was almost eliminated after adding the protonophore carbonyl cyanide m-chlorophenyl hydrazone (CCCP), which dissipates the H+ gradient, confirming that it was indeed formed upon illumination (Fig. 1b and Fig. S2). We also characterized the absorption spectra and the photocycle of the SacRs, showing that the three rhodopsins have an absorption peak around 550 nm (Fig. S3). The photocycle of the SacRs, determined by measuring the transient absorption change after nanosecond laser pulse illumination (Fig. 1c and Fig. S4), displays a blue-shifted M intermediate that represents the deprotonated state of the retinal chromophore. This has been observed for other H+ pumping rhodopsins [7, 8] and indicates that the proton bound to retinal is translocated during pumping.Given that SacRs function as outward proton pumps, we searched Saccharibacteria genomes for the F1Fo ATP synthase that would be required to harness the generated proton motive force for ATP synthesis. HMM searches showed that all genomes encoded the complete ATP synthase gene cluster and, furthermore, had c subunits with motifs consistent with H+ binding, instead of Na+ binding (Table S1 and Fig. S5). Together, our experimental and genomic analyses strongly suggest that some Saccharibacteria utilize rhodopsins for auxiliary energy generation in addition to their core fermentative capacities [6].Retinal is the rhodopsin chromophore that enables function of the complex upon illumination [9]. We found no evidence for the presence of β-carotene 15,15’-dioxygenase (blh), which produces all-trans-retinal (ATR) from β-carotene, in Saccharibacteria genomes encoding rhodopsin. This absence was likely not due to genome incompleteness, as genomic bins were generally of high quality (79–98% completeness, Table S1) and rhodopsin genomic loci were well-sampled. Additionally, no conserved hypothetical proteins were present in these regions, where blh is often found [10] (Fig. 1d, Fig. S6 and Table S2). As SacRs do contain the conserved lysine for retinal binding [4], we instead hypothesized that Saccharibacteria may uptake retinal from the environment, as has been previously observed for other microorganisms encoding rhodopsin but also lacking blh [11, 12].We tested the ability of SacR proteins to bind ATR from an external source by performing a retinal reconstitution assay. In contrast to the proton transport assays, where rhodopsin was expressed in the presence of ATR, here ATR was dissociated from the purified complex and the visible absorbance of rhodopsin was measured upon re-addition of ATR [13]. Both Gloeobacter rhodopsin (GR), a typical Type-1 outward H+ pump, and SacRs showed an increase in absorption in the visible region with time after the addition of ATR (Fig. 2a and Fig. S7). For all SacRs, the binding of ATR by their apoprotein was saturated within 30 sec after retinal addition (Fig. 2b), indicating that SacR is able to be efficiently functionalized using externally derived ATR. The observed reconstitution rate is substantially faster than that of GR (  > 20 min) and comparable to that of heliorhodopsin, which is used by other microorganisms also lacking a retinal synthetic pathway and rapidly binds ATR through a small opening in the apoprotein [12]. In the structure of SacRNC335 modeled by Alphafold2 [14, 15], a similar hole is visible in the protein moiety constructing the retinal binding pocket (Fig. S8). Hence, SacRs may also bind retinal through this hole in a similar manner to TaHeR (heliorhodopsin).Fig. 2: Binding of retinal by Saccharibacteria rhodopsins and context for biosynthesis.a UV-visible absorption spectra showing the regeneration of retinal binding to SacRNC335 and GR in 20 mM HEPES–NaOH, pH 7.0, 100 mM NaCl and 0.05% n-dodecyl-β-D-maltoside (DDM). In SacRNC335, a peak around 470 nm was transiently observed in the spectrum 30 s after the addition of ATR, suggesting that an intermediate species appears during the retinal incorporation process that involves formation of the Schiff base linkage. b Time evolution of visible absorption representing retinal binding to apo-protein. Numbers in parentheses in the legend indicate the absorption maxima of each rhodopsin. c Genetic potential for β-carotene 15,15’-dioxygenase (blh) production in freshwater lake metagenomes where SacRs are found. Fractions indicate the number of blh-encoding scaffolds taxonomically affiliated with the Actinobacteria in each sample. d Conceptual diagram illustrating potential retinal exchange between Saccharibacteria and host cells. ATR all-trans-retinal, GR Gloeobacter rhodopsin, AM Alinen Mustajärvi, Ki Kiruna, rhod. rhodopsin.Full size imageSaccharibacteria with rhodopsin must obtain retinal from other organisms. To evaluate possible sources of ATR, we investigated the genetic potential for retinal biosynthesis in 15 subarctic and boreal lakes [16] where Saccharibacteria with rhodopsin were present (Fig. S9). Blh-encoding scaffolds were found in 14 of the 15 metagenomes profiled (~93%) and, in nearly all cases, these scaffolds derived from Actinobacteria (Fig. 2c and Table S3). This is intriguing because Actinobacteria are known to be hosts of Saccharibacteria in the human microbiome [17, 18] and potentially more generally [4, 19]. BLAST searches against genome bins from the same samples indicated that these Actinobacteria were members of the order Nanopelagicales (Table S3) and often encode a rhodopsin (phylogenetically distinct from SacRs) in close genomic proximity to blh genes (Table S4). HMM searches revealed that these genomes also harbor homologs of the crtI, crtE, crtB, and crtY genes necessary for β-carotene production [20]. More

REN21. Renewables 2018 global status report. (REN21 Secretariat, 2018).Schuster, E., Bulling, L. & Koppel, J. Consolidating the state of knowledge: A synoptical review of wind energy’s wildlife effects. Environ. Manag. 56, 300–331 (2015).Article 

Google Scholar 
Thaxter, C. B. et al. Bird and bat species’ global vulnerability to collision mortality at wind farms revealed through a trait-based assessment. Proc. R. Soc. Lond. B Biol. Sci. 284, 20170829 (2017).
Google Scholar 
Marques, A. T. et al. Understanding bird collisions at wind farms: An updated review on the causes and possible mitigation strategies. Biol. Conserv. 179, 40–52 (2014).Article 

Google Scholar 
Katzner, T. E. et al. Topography drives migratory flight altitude of golden eagles: Implications for on-shore wind energy development. J. Appl. Ecol. 49, 1178–1186 (2012).Article 

Google Scholar 
Watson, R. T. et al. Raptor interactions with wind energy: Case studies from around the world. J. Raptor Res. 52, 1–18 (2018).Article 

Google Scholar 
May, R. F. A unifying framework for the underlying mechanisms of avian avoidance of wind turbines. Biol. Conserv. 190, 179–187 (2015).Article 

Google Scholar 
Cabrera-Cruz, S. A. & Villegas-Patraca, R. Response of migrating raptors to an increasing number of wind farms. J. Appl. Ecol. 53, 1667–1675 (2016).Article 

Google Scholar 
Hull, C. L. & Muir, S. C. Behavior and turbine avoidance rates of eagles at two wind farms in Tasmania, Australia. Wildl. Soc. Bull. 37, 49–58 (2013).Article 

Google Scholar 
Marques, A. T. et al. Wind turbines cause functional habitat loss for migratory soaring birds. J. Anim. Ecol. 89, 93–103 (2020).Article 

Google Scholar 
Pearce-Higgins, J. W., Stephen, L., Langston, R. H. W., Bainbridge, I. P. & Bullman, R. The distribution of breeding birds around upland wind farms. J. Appl. Ecol. 46, 1323–1331 (2009).Article 

Google Scholar 
Schaub, T., Klaassen, R. H. G., Bouten, W., Schlaich, A. E. & Koks, B. J. Collision risk of Montagu’s Harriers Circus pygargus with wind turbines derived from high-resolution GPS tracking. Ibis 162, 520–534 (2020).Article 

Google Scholar 
Santos, C. D., Ferraz, R., Muñoz, A.-R., Onrubia, A. & Wikelski, M. Black kites of different age and sex show similar avoidance responses to wind turbines during migration. R. Soc. Open Sci. 8, 201933 (2021).Article 

Google Scholar 
Santos, C. D., Ferraz, R., Muñoz, A.-R., Onrubia, A. & Wikelski, M. Data from: Black kites of different age and sex show similar avoidance responses to wind turbines during migration. Movebank Data Repository https://doi.org/10.5441/001/1.23n2m412 (2021).Article 

Google Scholar 
Khosravifard, S. et al. Identifying birds’ collision risk with wind turbines using a multidimensional utilization distribution method. Wildl. Soc. Bull. 44, 191–199 (2020).Article 

Google Scholar 
Hoover, S. L. & Morrison, M. L. Behavior of red-tailed hawks in a wind turbine development. J. Wildl. Manag. 69, 150–159 (2005).Article 

Google Scholar 
Miller, R. A. et al. Local and regional weather patterns influencing post-breeding migration counts of soaring birds at the Strait of Gibraltar Spain. Ibis 158, 106–115 (2016).Article 

Google Scholar 
Santos, C. D., Silva, J. P., Muñoz, A.-R., Onrubia, A. & Wikelski, M. The gateway to Africa: What determines sea crossing performance of a migratory soaring bird at the Strait of Gibraltar?. J. Anim. Ecol. 89, 1317–1328 (2020).Article 

Google Scholar 
Santos, C. D. et al. Match between soaring modes of black kites and the fine-scale distribution of updrafts. Sci. Rep. 7, 6421 (2017).Article 

Google Scholar 
Porté-Agel, F., Bastankhah, M. & Shamsoddin, S. Wind-turbine and wind-farm flows: A review. Bound. Layer Meteorol. 174, 1–59 (2020).Article 

Google Scholar 
Wood, S. & Scheipl, F. gamm4: Generalized Additive Mixed Models using “mgcv” and “lme4” (R package version 0.2-5, 2017).Bates, D., Maechler, M., Bolker, B. & Walker, S. lme4: Linear mixed-effects models using Eigen and S4 (R package version 1.1-19, 2016).Bjornstad, O. N. ncf: Spatial Covariance Functions (R package version 1.2-6, 2018).R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2016).Bartoń, K. MuMIn: Multi-model inference (R package version 1.43.15, 2019).Bellebaum, J., Korner-Nievergelt, F., Dürr, T. & Mammen, U. Wind turbine fatalities approach a level of concern in a raptor population. J. Nat. Conserv. 21, 394–400 (2013).Article 

Google Scholar 
Heuck, C. et al. Sex- but not age-biased wind turbine collision mortality in the White-tailed Eagle Haliaeetus albicilla. J. Ornithol. 161, 753–757 (2020).Article 

Google Scholar 
Hunt, W. G. et al. Quantifying the demographic cost of human-related mortality to a raptor population. PLoS One 12, e0172232 (2017).Article 

Google Scholar 
Martín, B., Perez-Bacalu, C., Onrubia, A., De Lucas, M. & Ferrer, M. Impact of wind farms on soaring bird populations at a migratory bottleneck. Eur. J. Wildl. Res. 64, 33 (2018).Article 

Google Scholar 
Everaert, J. Collision risk and micro-avoidance rates of birds with wind turbines in Flanders. Bird Study 61, 220–230 (2014).Article 

Google Scholar 
Pearce-Higgins, J. W., Stephen, L., Douse, A. & Langston, R. H. W. Greater impacts of wind farms on bird populations during construction than subsequent operation: Results of a multi-site and multi-species analysis. J. Appl. Ecol. 49, 386–394 (2012).Article 

Google Scholar 
Stewart, G. B., Pullin, A. S. & Coles, C. F. Poor evidence-base for assessment of windfarm impacts on birds. Environ. Conserv. 34, 1–11 (2007).Article 

Google Scholar 
De Lucas, M., Janss, G. F. E., Whitfield, D. P. & Ferrer, M. Collision fatality of raptors in wind farms does not depend on raptor abundance. J. Appl. Ecol. 45, 1695–1703 (2008).Article 

Google Scholar 
May, R., Reitan, O., Bevanger, K., Lorentsen, S. H. & Nygard, T. Mitigating wind-turbine induced avian mortality: Sensory, aerodynamic and cognitive constraints and options. Renew. Sustain. Energy Rev. 42, 170–181 (2015).Article 

Google Scholar 
Magnusson, M. & Smedman, A. S. Air flow behind wind turbines. J. Wind Eng. Ind. Aerodyn. 80, 169–189 (1999).Article 

Google Scholar 
Walters, K., Kosciuch, K. & Jones, J. Can the effect of tall structures on birds be isolated from other aspects of development?. Wildl. Soc. Bull. 38, 250–256 (2014).Article 

Google Scholar 
Ferrer, M. et al. Weak relationship between risk assessment studies and recorded mortality in wind farms. J. Appl. Ecol. 49, 38–46 (2012).Article 

Google Scholar 
Martín, B., Onrubia, A., de la Cruz, A. & Ferrer, M. Trends of autumn counts at Iberian migration bottlenecks as a tool for monitoring continental populations of soaring birds in Europe. Biodivers. Conserv. 25, 295–309 (2016).Article 

Google Scholar 
May, R. et al. Paint it black: Efficacy of increased wind turbine rotor blade visibility to reduce avian fatalities. Ecol. Evol. 10, 8927–8935 (2020).Article 

Google Scholar  More

These authors contributed equally: György Kröel-Dulay, Andrea Mojzes.Institute of Ecology and Botany, Centre for Ecological Research, Vácrátót, HungaryGyörgy Kröel-Dulay & Andrea Mojzes‘Lendület’ Landscape and Conservation Ecology, Institute of Ecology and Botany, Centre for Ecological Research, Vácrátót, HungaryKatalin Szitár & Péter BatáryDepartment of Ecology, University of Innsbruck, Innsbruck, AustriaMichael BahnDepartment of Geosciences and Natural Resource Management, University of Copenhagen, Frederiksberg, DenmarkClaus Beier, Inger Kappel Schmidt & Klaus Steenberg LarsenNamibia University of Science and Technology, Windhoek, NamibiaMark BiltonPlants and Ecosystems (PLECO), Department of Biology, University of Antwerp, Wilrijk, BelgiumHans J. De Boeck & Sara ViccaDepartment of Forestry and Natural Resources, Purdue University, West Lafayette, IN, USAJeffrey S. DukesDepartment of Biological Sciences, Purdue University, West Lafayette, IN, USAJeffrey S. DukesCSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, SpainMarc Estiarte & Josep PeñuelasCREAF, Cerdanyola del Vallès, SpainMarc Estiarte & Josep PeñuelasGlobal Change Research Institute of the Czech Academy of Sciences, Brno, Czech RepublicPetr HolubDisturbance Ecology, Bayreuth Center of Ecology and Environmental Research, University of Bayreuth, Bayreuth, GermanyAnke JentschExperimental Plant Ecology, University of Greifswald, Greifswald, GermanyJuergen KreylingUK Centre for Ecology & Hydrology, Bangor, UKSabine ReinschSchool of Plant Sciences and Food Security, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, IsraelMarcelo SternbergPlant Ecology Group, University of Tübingen, Tübingen, GermanyKatja TielbörgerInstitute for Biodiversity and Ecosystem Dynamics (IBED), Ecosystem and Landscape Dynamics (ELD), University of Amsterdam, Amsterdam, the NetherlandsAlbert Tietema More