More stories

  • in

    A functional vulnerability framework for biodiversity conservation

    Intergovernmental Panel on Climate Change (IPCC). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., et al. (2021).Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).PubMed 
    Article 
    CAS 

    Google Scholar 
    Maire, E. et al. Micronutrient supply from global marine fisheries under climate change and overfishing. Curr. Biol. 31, 4132–4138.e3 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Vicedo-Cabrera, A. M. et al. The burden of heat-related mortality attributable to recent human-induced climate change. Nat. Clim. Change 11, 492–500 (2021).ADS 
    CAS 
    Article 

    Google Scholar 
    Hoegh-Guldberg, O. et al. The human imperative of stabilizing global climate change at 1.5 °C. Science 365, eaaw6974 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Turner, B. L. et al. A framework for vulnerability analysis in sustainability science. Proc. Natl Acad. Sci. USA 100, 8074–8079 (2003).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jarić, I., Lennox, R. J., Kalinkat, G., Cvijanović, G. & Radinger, J. Susceptibility of European freshwater fish to climate change: Species profiling based on life‐history and environmental characteristics. Glob. Change Biol. 25, 448–458 (2018).ADS 
    Article 

    Google Scholar 
    Comte, L. & Olden, J. D. Climatic vulnerability of the world’s freshwater and marine fishes. Nat. Clim. Change 7, 718–722 (2017).ADS 
    Article 

    Google Scholar 
    Song, H. et al. Thresholds of temperature change for mass extinctions. Nat. Commun. 12, 4694 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Watson, A. J. Certainty and uncertainty in climate change predictions: what use are climate models? Environ. Resour. Econ. 39, 37–44 (2008).Article 

    Google Scholar 
    Field, C. B. et al. Summary for policymakers. Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 1–32 (2014).Shiogama, H. et al. Predicting future uncertainty constraints on global warming projections. Sci. Rep. 6, 18903 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Li, S. et al. The Pacific Decadal Oscillation is less predictable under greenhouse warming. Nat. Clim. Change 10, 30–34 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    Halpern, B. S., Selkoe, K. A., Micheli, F. & Kappel, C. V. Evaluating and ranking the vulnerability of global marine ecosystems to anthropogenic threats. Conserv. Biol. 21, 1301–1315 (2007).PubMed 
    Article 

    Google Scholar 
    Mbaru, E. K., Graham, N. A. J., McClanahan, T. R. & Cinner, J. E. Functional traits illuminate the selective impacts of different fishing gears on coral reefs. J. Appl. Ecol. 57, 241–252 (2020).Article 

    Google Scholar 
    Francalanci, S., Paris, E. & Solari, L. On the vulnerability of woody riparian vegetation during flood events. Environ. Fluid Mech. 20, 635–661 (2020).Article 

    Google Scholar 
    Pellegrini, A. F. A. et al. Convergence of bark investment according to fire and climate structures ecosystem vulnerability to future change. Ecol. Lett. 20, 307–316 (2017).PubMed 
    Article 

    Google Scholar 
    Jørgensen, L. L., Planque, B., Thangstad, T. H. & Certain, G. Vulnerability of megabenthic species to trawling in the Barents Sea. ICES J. Mar. Sci. 73, i84–i97 (2016).Article 

    Google Scholar 
    Certain, G., Jørgensen, L. L., Christel, I., Planque, B. & Bretagnolle, V. Mapping the vulnerability of animal community to pressure in marine systems: disentangling pressure types and integrating their impact from the individual to the community level. ICES J. Mar. Sci. 72, 1470–1482 (2015).Article 

    Google Scholar 
    Albouy, C. et al. Global vulnerability of marine mammals to global warming. Sci. Rep. 10, 548 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Staudt, A. et al. The added complications of climate change: understanding and managing biodiversity and ecosystems. Front. Ecol. Env. 11, 494–501 (2013).Article 

    Google Scholar 
    Korpinen, S. & Andersen, J. H. A global review of cumulative pressure and impact assessments in marine environments. Front. Mar. Sci. 3, 153 (2016).Article 

    Google Scholar 
    O’Neill, B. C. et al. Achievements and needs for the climate change scenario framework. Nat. Clim. Change 10, 1074–1084 (2020).ADS 
    Article 

    Google Scholar 
    Stoddard, J. L., Larsen, D. P., Hawkins, C. P., Johnson, R. K. & Norris, R. H. Setting expectations for the ecological condition of streams: the concept of reference condition. Ecol. Appl. 16, 1267–1276 (2006).PubMed 
    Article 

    Google Scholar 
    Soranno, P. A. et al. Quantifying regional reference conditions for freshwater ecosystem management: a comparison of approaches and future research needs. Lake Reserv. Manag. 27, 138–148 (2011).Article 

    Google Scholar 
    Cinner, J. E. et al. Meeting fisheries, ecosystem function, and biodiversity goals in a human-dominated world. Science 368, 307–311 (2020).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    D’agata, S. et al. Marine reserves lag behind wilderness in the conservation of key functional roles. Nat. Commun. 7, 12000 (2016).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Beyer, H. L., Venter, O., Grantham, H. S. & Watson, J. E. M. Substantial losses in ecoregion intactness highlight urgency of globally coordinated action. Cons. Lett. 13, 1–9 (2020).Article 

    Google Scholar 
    Williams, B. A. et al. Global rarity of intact coastal regions. Cons. Biol. c13874, 1–12 (2022).Kültz, D. Defining biological stress and stress responses based on principles of physics. J. Exp. Zool. A: Ecol. Integr. Physiol. 333, 350–358 (2020).Article 

    Google Scholar 
    Tinker, J., Lowe, J., Pardaens, A., Holt, J. & Barciela, R. Uncertainty in climate projections for the 21st century northwest European shelf seas. Prog. Oceanogr. 148, 56–73 (2016).ADS 
    Article 

    Google Scholar 
    Xu, L. et al. Potential precipitation predictability decreases under future warming. Geophys. Res Lett. 47, e2020GL090798 (2020).ADS 

    Google Scholar 
    Cadotte, M. W. Functional traits explain ecosystem function through opposing mechanisms. Ecol. Lett. 20, 989–996 (2017).PubMed 
    Article 

    Google Scholar 
    Bruelheide, H. et al. Global trait–environment relationships of plant communities. Nat. Ecol. Evol. 2, 1906–1917 (2018).PubMed 
    Article 

    Google Scholar 
    Trindade-Santos, I., Moyes, F. & Magurran, A. E. Global change in the functional diversity of marine fisheries exploitation over the past 65 years. Proc. R. Soc. B. 287, 20200889 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    McLean, M. et al. Trait structure and redundancy determine sensitivity to disturbance in marine fish communities. Glob. Change Biol. 25, 3424–3437 (2019).ADS 
    Article 

    Google Scholar 
    Walker, B. H. Biodiversity and ecological redundancy. Conserv. Biol. 6, 18–23 (1992).Article 

    Google Scholar 
    McWilliam, M. et al. Biogeographical disparity in the functional diversity and redundancy of corals. Proc. Nat. Acad. Sci. USA 115, 3084–3089 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Murgier, J. et al. Rebound in functional distinctiveness following warming and reduced fishing in the North Sea. Proc. R. Soc. B. 288, 20201600 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lavergne, S., Thuiller, W., Molina, J. & Debussche, M. Environmental and human factors influencing rare plant local occurrence, extinction and persistence: a 115-year study in the Mediterranean region: environmental factors influencing the distribution of rare plants. J. Biogeogr. 32, 799–811 (2005).Article 

    Google Scholar 
    Stewart, P. S. et al. Global impacts of climate change on avian functional diversity. Ecol. Lett. 25, 673–685 (2022).PubMed 
    Article 

    Google Scholar 
    Violle, C. et al. Functional rarity: the ecology of outliers. Trends Ecol. Evol. 32, 356–367 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mouillot, D. et al. Functional over-redundancy and high functional vulnerability in global fish faunas on tropical reefs. Proc. Nat. Acad. Sci. USA 111, 13757–13762 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Waldock, C. et al. A quantitative review of abundance-based species distribution models. Ecography 2022, e05694 (2022).Article 

    Google Scholar 
    Global Biodiversity Information Facility. available at: https://www.gbif.org/Ocean Biodiversity Information System. available at: https://obis.org/Edgar, G. J. & Stuart-Smith, R. D. Systematic global assessment of reef fish communities by the Reef Life Survey program. Sci. Data 1, 140007 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Edgar, G. J. et al. Reef life survey: establishing the ecological basis for conservation of shallow marine life. Biol. Conserv. 252, 108855 (2020).Article 

    Google Scholar 
    Cinner, J. E. et al. Gravity of human impacts mediates coral reef conservation gains. Proc. Natl Acad. Sci. USA 115, E6116–E6125 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kulbicki, M. et al. Global biogeography of reef fishes: a hierarchical quantitative delineation of regions. PLoS ONE 8, e81847 (2013).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    United Nations Framework Convention on Climate Change. Paris Agreement. United Nations (2015).Thorson, J. T., Munch, S. B., Cope, J. M. & Gao, J. Predicting life history parameters for all fishes worldwide. Ecol. Appl. 27, 2262–2276 (2017).PubMed 
    Article 

    Google Scholar 
    Peterson, G. et al. Uncertainty, climate change, and adaptive management. Conserv. Ecol. 1, art4 (1997).
    Google Scholar 
    Dewulf, A. & Biesbroek, R. Nine lives of uncertainty in decision-making: strategies for dealing with uncertainty in environmental governance. Policy Soc. 37, 441–458 (2018).Article 

    Google Scholar 
    Parravicini, V. et al. Global mismatch between species richness and vulnerability of reef fish assemblages. Ecol. Lett. 17, 1101–1110 (2014).PubMed 
    Article 

    Google Scholar 
    Bartomeus, I. & Godoy, O. Biotic controls of plant coexistence. J. Ecol. 106, 1767–1772 (2018).Article 

    Google Scholar 
    Beissinger, S. R. & Riddell, E. A. Why are species’ traits weak predictors of range shifts? Annu. Rev. Ecol. Evol. Syst. 52, 47–66 (2021).Article 

    Google Scholar 
    Halpern, B. S. et al. Spatial and temporal changes in cumulative human impacts on the world’s ocean. Nat. Commun. 6, 7615 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    ICES (2021). Working Group for the Assessment of Demersal Stocks in the North Sea and Skagerrak (WGNSSK). ICES Scientific Reports. Report. https://doi.org/10.17895/ices.pub.8211.Frid, C. L. J., Harwood, K. G., Hall, S. J. & Hall, J. A. Long-term changes in the benthic communities on North Sea fishing grounds. ICES J. Mar. Sci. 57, 1303–1309 (2000).Article 

    Google Scholar 
    Montero‐Serra, I., Edwards, M. & Genner, M. J. Warming shelf seas drive the sub tropicalization of European pelagic fish communities. Glob. Change Biol. 21, 144–153 (2014).ADS 
    Article 

    Google Scholar 
    Guillen, J. et al. A review of the European union landing obligation focusing on its implications for fisheries and the environment. Sustainability 10, 900 (2018).Article 

    Google Scholar 
    Mouillot, D. et al. The dimensionality and structure of species trait spaces. Ecol. Lett. 24, 1988–2009 (2021).PubMed 
    Article 

    Google Scholar 
    Valencia, E. et al. Synchrony matters more than species richness in plant community stability at a global scale. Proc. Nat. Acad. Sci. USA 117, 24345–24351 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Doak, D. F. et al. The statistical inevitability of stability-diversity relationships in community ecology. Am. Nat. 151, 264–276 (1998).CAS 
    PubMed 
    Article 

    Google Scholar 
    Halpern, B. S. et al. A global map of human impact on marine ecosystems. Science 319, 948–952 (2008).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Avila, I. C., Kaschner, K. & Dormann, C. F. Current global risks to marine mammals: taking stock of the threats. Biol. Cons. 221, 44–58 (2018).Article 

    Google Scholar 
    Petchey, O. L. Functional diversity: back to basics and looking forward. Ecol Lett. 9, 741–758 (2006).Lefcheck, J. S., Bastazini, V. A. G. & Griffin, J. N. Choosing and using multiple traits in functional diversity research. Environ. Conserv. 42, 104–107 (2015).Article 

    Google Scholar 
    Zhu, L. et al. Trait choice profoundly affected the ecological conclusions drawn from functional diversity measures. Sci. Rep. 7, 3643 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Carmona, C. P., Guerrero, I., Morales, M. B., Oñate, J. J. & Peco, B. Assessing vulnerability of functional diversity to species loss: a case study in Mediterranean agricultural systems. Funct. Ecol. 31, 427–435 (2017).Article 

    Google Scholar 
    Tobias, J. A. et al. AVONET: morphological, ecological and geographical data for all birds. Ecol. Lett. 25, 581–597 (2022).PubMed 
    Article 

    Google Scholar 
    de Bello, F., Carmona, C. P., Leps, J., Szava-Kovats, R. & Pärtel, M. Functional diversity through the mean trait dissimilarity: resolving shortcomings with existing paradigms and algorithms. Oecologia 180, 933–940 (2016).ADS 
    PubMed 
    Article 

    Google Scholar 
    Boyer, A. G. & Jetz, W. Extinctions and the loss of ecological function in island bird communities. Glob. Ecol. Biogeogr. 23, 679–688 (2014).Article 

    Google Scholar 
    Stuart-Smith, R. D. et al. Integrating abundance and functional traits reveals new global hotspots of fish diversity. Nature 501, 539–542 (2013).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    D’agata, S. et al. Human-mediated loss of phylogenetic and functional diversity in coral reef fishes. Curr. Biol. 24, 555–560 (2014).PubMed 
    Article 
    CAS 

    Google Scholar 
    United Nations General Assembly. Transforming our world: The 2030 Agenda for Sustainable Development, 21 October 2015, A/RES/70/1. United Nations. https://www.refworld.org/docid/57b6e3e44.html (2015).Grorud-Colvert, K. et al. The MPA Guide: a framework to achieve global goals for the ocean. Science 373, eabf0861 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mouillot, D. et al. Rare species support vulnerable functions in high-diversity ecosystems. PLoS Biol. 11, e1001569 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Maire, E., Grenouillet, G., Brosse, S. & Villéger, S. How many dimensions are needed to accurately assess functional diversity? A pragmatic approach for assessing the quality of trait spaces: assessing trait space quality. Glob. Ecol. Biogeogr. 24, 728–740 (2015).Article 

    Google Scholar 
    Villéger, S., Mason, N. W. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).PubMed 
    Article 

    Google Scholar 
    Grenié, M., Denelle, P., Tucker, C. M., Munoz, F. & Violle, C. funrar: an R package to characterize functional rarity. Divers. Distrib. 23, 1365–1371 (2017).Article 

    Google Scholar 
    Borcard, D., Gillet, F. & Legendre, P. Numerical Ecology with R (2011).Villéger, S., Brosse, S., Mouchet, M., Mouillot, D. & Vanni, M. J. Functional ecology of fish: current approaches and future challenges. Aquat. Sci. 79, 783–801 (2017).Article 

    Google Scholar 
    Beukhof, E., Dencker, T. S., Palomares, M. L. D. & Maureaud, A. A trait collection of marine fish species from North Atlantic and Northeast Pacific continental shelf seas. PANGAEA, https://doi.org/10.1594/PANGAEA.900866 (2019).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).ADS 
    Article 

    Google Scholar 
    Phillips, S. J. et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).PubMed 
    Article 

    Google Scholar 
    Righetti, D., Vogt, M., Gruber, N., Psomas, A. & Zimmermann, N. E. Global pattern of phytoplankton diversity driven by temperature and environmental variability. Sci. Adv. 5, 1–11 (2019).Article 

    Google Scholar 
    Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    Oliver, E. C. J. et al. Projected marine heatwaves in the 21st century and the potential for ecological impact. Front. Mar. Sci. 6, 734 (2019).Article 

    Google Scholar 
    Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).PubMed 
    Article 

    Google Scholar 
    Stekhoven, D. J. & Bürhlmann, P. MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics 28, 112–118 (2012).CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    The shrunk genetic diversity of coral populations in North-Central Patagonia calls for management and conservation plans for marine resources

    Försterra, G. et al. Animal forests in the Chilean fiord region: Discoveries and perspectives in shallow and deep waters. In Marine Animal Forests. Orejas Saco del Valle (eds Rossi, S. et al.) 1–35 (Springer, 2016). https://doi.org/10.1007/978-3-319-17001-5_3-1.Chapter 

    Google Scholar 
    Castilla, J. C. et al. (eds) Conservación en la Patagonia Chilena: Evaluación del conocimiento, oportunidades y desafíos (Ediciones Universidad Católica, 2021).
    Google Scholar 
    Iriarte, J. L. et al. Oceanographic Processes in Chilean Fjords of Patagonia: From small to large-scale studies. Prog. Oceanogr. 129, 1–7. https://doi.org/10.1016/j.pocean.2014.10.004 (2014).ADS 
    Article 

    Google Scholar 
    Iriarte, J. L. Natural and human influences on marine processes in Patagonian Subantarctic coastal waters. Front. Mar. Sci. 5, 360. https://doi.org/10.3389/fmars.2018.00360 (2018).Article 

    Google Scholar 
    Strub, P. T. et al. Ocean circulation along the southern Chile transition region (38°–46°S): Mean, seasonal and interannual variability, with a focus on 2014–2016. Prog. Oceanogr. 172, 159–198. https://doi.org/10.1016/j.pocean.2019.01.004 (2019).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Häussermann, V. et al. Macrobentos de fondos duros de la Patagonia chilena: Énfasis en la conservación de bosques sublitorales de invertebrados y algas. In Conservación en la Patagonia Chilena: Evaluación del conocimiento, oportunidades y desafíos (eds Castilla, J. C. et al.) (Ediciones Universidad Católica, 2021).
    Google Scholar 
    Kol, P. H. Los Riesgos de la Expansión Salmonera en la Patagonia Chilena. Estado de la Salmonicultura Intensiva en la Región de Magallanes (AIDA, 2018).Iversen, A. et al. Production cost and competitiveness in major salmon farming countries 2003–2018. Aquaculture 522, 735089. https://doi.org/10.1016/j.aquaculture.2020.735089 (2020).Article 

    Google Scholar 
    Cárdenas-Retamal, R. et al. Impact assessment of salmon farming on income distribution in remote coastal areas: The Chilean case. Food Policy 101, 102078. https://doi.org/10.1016/j.foodpol.2021.102078 (2021).Article 

    Google Scholar 
    Chavez, C. et al. Main issues and challenges for sustainable development of salmon farming in Chile: A socio-economic perspective. Rev. Aquac. 11, 403–421. https://doi.org/10.1111/raq.12338 (2019).Article 

    Google Scholar 
    Quiñones, R. A. et al. Environmental issues in Chilean salmon farming: A review. Rev. Aquac. 11, 375–402. https://doi.org/10.1111/raq.12337 (2019).Article 

    Google Scholar 
    Mardones, J. I. et al. Disentangling the environmental processes responsible for the world’s largest farmed fish-killing harmful algal bloom: Chile, 2016. Sci. Total Environ. 76, 1–19. https://doi.org/10.1016/j.scitotenv.2020.144383 (2021).CAS 
    Article 

    Google Scholar 
    Navedo, J. G. et al. Upraising a silent pollution: Antibiotic resistance at coastal environments and transference to long-distance migratory shorebirds. Sci. Total Environ. 777, 1–7. https://doi.org/10.1016/j.scitotenv.2021.146004 (2021).CAS 
    Article 

    Google Scholar 
    SUBPESCA. Listado de concesiones de acuicultura d salmónidos por agrupación de concesiones en las regiones X, XI y XII (Julio 2021). https://www.subpesca.cl/portal/619/w3-article-103129.html (2021).Gorny, M. et al. Las comunidades marinas bentónicas de la Reserva Nacional Katalalixar (Chile). Oceanografía, 29–44 (2020).Friedlander, A. M. et al. Marine communities of the newly created Kawésqar National Reserve, Chile: From glaciers to the Pacific Ocean. PLoS One 16(4), e0249413. https://doi.org/10.1371/journal.pone.0249413 (2021).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mardones, J. I. et al. Toxic dinoflagellate blooms of Alexandrium catenella in Chilean fjords: A resilient winner from climate change. ICES J. Mar. Sci. 74(4), 988–995. https://doi.org/10.1093/icesjms/fsw164 (2016).Article 

    Google Scholar 
    Alvarez-Garreton, C. et al. The CAMELS-CL dataset: Catchment attributes and meteorology for large sample studies—Chile dataset. Hydrol. Earth Syst. Sci. 22, 5817–5846. https://doi.org/10.5194/hess-22-5817-2018 (2018).ADS 
    Article 

    Google Scholar 
    Novak, B. J. et al. Transforming ocean conservation: Applying the genetic rescue toolkit. Genes 11, 209. https://doi.org/10.3390/genes11020209 (2020).CAS 
    Article 
    PubMed Central 

    Google Scholar 
    Outeiro, L. et al. Using ecosystem services mapping for marine spatial planning in southern Chile under scenario assessment. Ecosyst. Serv. 16, 341–353. https://doi.org/10.1016/j.ecoser.2015.03.004 (2015).Article 

    Google Scholar 
    Anbleyth-Evans, J. et al. Toward marine democracy in Chile: Examining aquaculture ecological impacts through common property local ecological knowledge. Mar. Policy 113, 103690. https://doi.org/10.1016/j.marpol.2019.103690 (2019).Article 

    Google Scholar 
    Kershaw, F. et al. Geospatial genetics: Integrating genetics into marine protection and spatial planning. Aquat. Conserv. Mar Freshw. Ecosyst. https://doi.org/10.1002/aqc.3622 (2021).Article 

    Google Scholar 
    Jenkins, T. L. & Stevens, J. R. Assessing connectivity between MPAs: Selecting taxa and translating genetic data to inform policy. Mar. Policy 94, 165–173. https://doi.org/10.1016/j.marpol.2018.04.022 (2018).Article 

    Google Scholar 
    Paredes, J. et al. Population genetic structure at the northern edge of the distribution of Alexandrium catenella in the Patagonian fjords and its expansion along the open Pacific Ocean coast. Front. Mar. Sci. 5, 532. https://doi.org/10.3389/fmars.2018.00532 (2019).Article 

    Google Scholar 
    Canales-Aguirre, C. B. C. et al. Population genetic structure of Patagonian toothfish (Dissostichus eleginoides) in the Southeast Pacific and Southwest Atlantic Ocean. PeerJ 6, e4173. https://doi.org/10.7717/peerj.4173 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Canales-Aguirre, C. B. C. et al. High genetic diversity and low-population differentiation in the Patagonian sprat (Sprattus fuegensis) based on mitochondrial DNA. Mitochondrial DNA Part A 29(8), 1148–1155. https://doi.org/10.1080/24701394.2018.1424841 (2018).CAS 
    Article 

    Google Scholar 
    Pérez-Alvarez, M. et al. Historical dimensions of population structure in a continuously distributed marine species: The case of the endemic Chilean dolphin. Sci. Rep. 6, 35507. https://doi.org/10.1038/srep35507 (2016).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pérez-Alvarez, J. M. et al. Phylogeography and demographic inference of the endangered sei whale, with implications for conservation. Aquat. Conserv. Mar. Freshw. Ecosyst. https://doi.org/10.1002/aqc.3717 (2021).Article 

    Google Scholar 
    Addamo, A. M. et al. Global-scale genetic structure of a cosmopolitan cold-water coral species. Aquat. Conserv. Mar. Freshw. Ecosyst. 31(1), 1–14. https://doi.org/10.1002/aqc.3421 (2021).Article 

    Google Scholar 
    Addamo, A. M. et al. Genetic conservation management of marine resources and ecosystems of Patagonian Fjords. Front. Mar. Sci. 8, 612195. https://doi.org/10.3389/fmars.2021.612195 (2021).Article 

    Google Scholar 
    Addamo, A. M. et al. Development of microsatellite markers in the deep-sea cup coral Desmophyllum dianthus and cross-species amplifications in the Scleractinia Order. J. Hered. 106(3), 322–330. https://doi.org/10.1093/jhered/esv010 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Miller, K. J. & Gunasekera, R. M. A comparison of genetic connectivity in two deep sea corals to examine whether seamounts are isolated islands or stepping stones for dispersal. Sci. Rep. 7, 1–14. https://doi.org/10.1038/srep46103 (2017).CAS 
    Article 

    Google Scholar 
    Holloley, C. E. & Geerts, P. G. Multiplex Manager 1.0: A cross-platform computer program that plans and optimizes multiplex PCR. Biotechniques 46, 511–517. https://doi.org/10.2144/000113156 (2009).Article 

    Google Scholar 
    Brookfield, J. F. Y. A simple new method for estimating null allele frequency from heterozygote deficiency. Mol. Ecol. 5, 453–455. https://doi.org/10.1046/j.1365-294X.1996.00098.x (1996).CAS 
    Article 
    PubMed 

    Google Scholar 
    Van Oosterhout, C. et al. Micro-Checker: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538. https://doi.org/10.1111/j.1471-8286.2004.00684.x (2004).CAS 
    Article 

    Google Scholar 
    Chapuis, M.-P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24(3), 621–631 (2007).CAS 
    Article 

    Google Scholar 
    Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—An update. Bioinformatics 28, 2537–2539. https://doi.org/10.1093/bioinformatics/bts460 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rousset, F. Genepop’007: A complete re-implementation of the Genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106. https://doi.org/10.1111/j.1471-8286.2007.01931.x (2008).Article 
    PubMed 

    Google Scholar 
    Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).MathSciNet 
    MATH 

    Google Scholar 
    Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567. https://doi.org/10.1111/j.1755-0998.2010.02847.x (2010).Article 
    PubMed 

    Google Scholar 
    Falush, D. et al. Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies. Genetics 164, 1567–1587. https://doi.org/10.1111/j.1471-8286.2007.01758.x (2003).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Earl, D. A. & vonHoldt, B. M. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361. https://doi.org/10.1007/s12686-011-9548-7 (2012).Article 

    Google Scholar 
    Li, Y. L. & Liu, J. X. StructureSelector: A web based software to select and visualize the optimal number of clusters using multiple methods. Mol. Ecol. Resour. 18, 176–177. https://doi.org/10.1111/1755-0998.12719 (2018).Article 
    PubMed 

    Google Scholar 
    Kopelman, N. M. et al. CLUMPAK: A program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191. https://doi.org/10.1111/1755-0998.12387 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pritchard, J. K. et al. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
    Article 

    Google Scholar 
    Evanno, G. et al. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    Puechmaille, S. J. The program structure does not reliably recover the correct population structure when sampling is uneven: Subsampling and new estimators alleviate the problem. Mol. Ecol. Resour. 16, 608–627. https://doi.org/10.1111/1755-0998 (2016).Article 
    PubMed 

    Google Scholar 
    Piry, S. et al. GeneClass2: A software for genetic assignment and first-generation migrant detection. J. Hered. 95, 536–539. https://doi.org/10.1093/jhered/esh074 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    Cornuet, J. M. & Luikart, G. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144, 2001–2014 (1997).Article 

    Google Scholar 
    Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4(43), 1686. https://doi.org/10.21105/joss.01686 (2019).ADS 
    Article 

    Google Scholar 
    Wickham, H. et al. dplyr: A grammar of data manipulation. https://dplyr.tidyverse.org, https://github.com/tidyverse/dplyr (2022).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2022). https://ggplot2.tidyverse.org. ISBN 978-3-319-24277-4.Addamo, A. M. et al. Microsatellites of Desmophyllum dianthus—Comau Fjord. Front. Mar. Sci. https://doi.org/10.3389/fmars.2021.612195. Zenodo. https://doi.org/10.5281/zenodo.4435966 (2021).Tecklin, D. Sensing the limits of fixed marine property rights in changing coastal ecosystems: Salmon aquaculture concessions, crises, and governance challenges in Southern Chile. J. Int. Wildl. Law Policy 19(4), 284–300. https://doi.org/10.1080/13880292.2016.1248647 (2016).Article 

    Google Scholar 
    Buschmann, A. H. et al. Salmon aquaculture and coastal ecosystem health in Chile: Analysis of regulations, environmental impacts and bioremediation systems. Ocean Coast. Manag. 52, 243–249. https://doi.org/10.1016/j.ocecoaman.2009.03.002 (2009).Article 

    Google Scholar 
    Pantoja, S. et al. Oceanography of the Chilean Patagonia. Cont. Shelf Res. 31, 149–153. https://doi.org/10.1016/j.csr.2010.10.013 (2011).ADS 
    Article 

    Google Scholar 
    Molina, V. & Fernández, C. Bacterioplankton response to nitrogen and dissolved organic matter produced from salmon mucus. Microbiol. Open 9(12), e1132. https://doi.org/10.1002/mbo3.1132 (2020).CAS 
    Article 

    Google Scholar 
    Försterra, G. & Häussermann, V. First report on large scleractinian (Cnidaria: Anthozoa) accumulations in cold-temperate shallow water of south Chilean fjords. Zool. Verh. 345, 117–128 (2003).
    Google Scholar 
    Brown, S. M. et al. Limited population structure, genetic drift and bottlenecks characterise an endangered bird species in a dynamic, fire-prone ecosystem. PLoS One 8(4), e59732. https://doi.org/10.1371/journal.pone.0059732 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Takahashi, Y. et al. Lack of genetic variation prevents adaptation at the geographic range margin in a damselfly. Mol. Ecol. 25, 4450–4460. https://doi.org/10.1111/mec.13782 (2016).Article 
    PubMed 

    Google Scholar 
    Thiel, M. et al. The Humboldt Current system of Northern and Central Chile. Oceanographic processes, ecological interactions and socioeconomic feedback. Oceanogr. Mar. Biol. Annu. Rev. 45, 195–344 (2007).
    Google Scholar 
    Giesecke, R. et al. General Hydrography of the Beagle Channel, a Subantarctic Interoceanic Passage at the Southern Tip of South America. Front. Mar. Sci. Coast. Ocean Process. 8, 621822. https://doi.org/10.3389/fmars.2021.621822 (2021).Article 

    Google Scholar 
    Chaigneau, A. Surface circulation and fronts of the South Pacific Ocean, east of 120°. Geophys. Res. Lett. 32, L08605. https://doi.org/10.1029/2004GL022070 (2005).ADS 
    Article 

    Google Scholar 
    Aiken, C. M. A reanalysis of the Chilean ocean circulation: Preliminary results for the region between 20°S to 40°S. Lat. Am. J. Aquat. Res. 45(1), 193–198. https://doi.org/10.3856/vol45-issue1-fulltext-19 (2017).Article 

    Google Scholar 
    González, H. E. et al. Primary production and plankton dynamics in the Reloncaví Fjord and the Interior Sea of Chiloé, Northern Patagonia, Chile. Mar. Ecol. Prog. Ser. 402, 13–30. https://doi.org/10.3354/meps08360 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    González, H. E. et al. Seasonal plankton variability in Chilean Patagonia fjords: Carbon flow through the pelagic food web of Aysen Fjord and plankton dynamics in the Moraleda Channel basin. Cont. Shelf Res. 31, 225–243. https://doi.org/10.1016/j.csr.2010.08.010 (2011).ADS 
    Article 

    Google Scholar 
    Feehan, K. A. et al. Highly seasonal reproduction in deep-water emergent Desmophyllum dianthus (Scleractinia: Caryophylliidae) from the Northern Patagonian Fjords. Mar. Biol. 166(4), 52. https://doi.org/10.1007/s00227-019-3495-3 (2019).Article 

    Google Scholar 
    Försterra, G. et al. Mass die off of the cold-water coral Desmophyllum dianthus in the Chilean Patagonian Fjord Region. Bull. Mar. Sci. 90(3), 895–899 (2014).Article 

    Google Scholar 
    Mora-Soto, A. et al. A song of wind and ice: Increased frequency of marine cold-spells in southwestern Patagonia and their possible effects on giant kelp forests. J. Geophys. Res. Oceans 127, e2021JC017801. https://doi.org/10.1029/2021JC017801 (2022).ADS 
    Article 

    Google Scholar 
    Brown, J. H. On the relationship between abundance and distribution of species. Am. Nat. 124, 255–279 (1984).Article 

    Google Scholar 
    Verberk, W. Explaining general patterns in species abundance and distributions. Nat. Sci. Educ. 3(10), 38 (2011).
    Google Scholar 
    Devenish, C. et al. Extreme and complex variation in range-wide abundances across a threatened Neotropical bird community. Divers. Distrib. 23, 910–921. https://doi.org/10.1111/ddi.12577 (2017).Article 

    Google Scholar 
    Iriarte, J. L. et al. Influence of seasonal freshwater streamflow regimes on phytoplankton blooms in a Patagonian fjord. N. Z. J. Mar. Freshw. Res. 51(2), 304–315. https://doi.org/10.1080/00288330.2016.1220955 (2016).CAS 
    Article 

    Google Scholar 
    Silva, N. et al. Características oceanográficas físicas y químicas de canales australes chilenos entre Puerto Montt y Laguna San Rafael (Crucero Cimar-Fiordo 1). Cienc. Tecnol. Mar. 20, 23–106 (1997).
    Google Scholar 
    Iriarte, J. L. et al. Low spring primary production and microplankton carbon biomass in Sub-Antarctic Patagonian channels and fjords (50–53°S). Arct. Antarct. Alp. Res. 50(1), e1525186. https://doi.org/10.1080/15230430.2018.1525186 (2018).Article 

    Google Scholar 
    Höfer, J. et al. All you can eat: The functional response of the cold-water coral Desmophyllum dianthus feeding on krill and copepods. PeerJ 6, e5872. https://doi.org/10.7717/peerj.5872 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Montero, P. et al. A winter dinoflagellate bloom drives high rates of primary production in a Patagonian fjord ecosystem. Estuar. Coast. Shelf Sci. 199, 105e116. https://doi.org/10.1016/j.ecss.2017.09.027 (2017).CAS 
    Article 

    Google Scholar 
    Quiroga, E. et al. Seasonal benthic patterns in a glacial Patagonian fjord: The role of suspended sediment and terrestrial organic matter. Mar. Ecol. Prog. Ser. 561, 31–50. https://doi.org/10.3354/meps11903 (2016).ADS 
    Article 

    Google Scholar 
    Escribano, R. et al. Seasonal and inter-annual variation of mesozooplankton in the coastal upwelling zone off central-southern Chile. Prog. Oceanogr. 75, 470–485. https://doi.org/10.1016/j.pocean.2007.08.027 (2007).ADS 
    Article 

    Google Scholar 
    Gori, A. et al. Physiological response of the cold-water coral Desmophyllum dianthus to thermal stress and ocean acidification. PeerJ 4, e1606. https://doi.org/10.7717/peerj.1606 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Martínez-Dios, A. et al. Effects of low pH and feeding on calcification rates of the cold-water coral Desmophyllum dianthus. PeerJ 8, e8236. https://doi.org/10.7717/peerj.8236 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    López-Márquez, V. et al. Asexual reproduction in bad times? The case of Cladocora caespitosa in the eastern Mediterranean Sea. Coral Reefs 40, 663–677. https://doi.org/10.1007/s00338-020-02040-3 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Silva, N. & Calvete, C. Características oceanográficas físicas y químicas de canales australes chilenos entre el Golfo de Penas y el Estrecho de Magallanes (Crucero Cimar-Fiordo 2). Cienc. Tecnol. Mar. 20, 23–88 (2002).
    Google Scholar 
    Häussermann, V. et al. Species that fly at a higher game: Patterns of deep–water emergence along the Chilean coast, including a global review of the phenomenon. Front. Mar. Sci. 8, 688316. https://doi.org/10.3389/fmars.2021.688316 (2021).Article 

    Google Scholar 
    Fillinger, L. & Richter, C. Vertical and horizontal distribution of Desmophyllum dianthus in Comau Fjord, Chile: A cold-water coral thriving at low pH. PeerJ 1, e194. https://doi.org/10.7717/peerj.194 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Addamo, A. M. et al. Biodiversity and distribution of corals in Chile. Mar. Biodivers. 52, 33. https://doi.org/10.1007/s12526-022-01271-7 (2022).Article 

    Google Scholar 
    Figuerola, B. et al. A review and meta-analysis of potential impacts of ocean acidification on marine calcifiers from the Southern Ocean. Front. Mar. Sci. 8, 584445. https://doi.org/10.3389/fmars.2021.584445 (2021).Article 

    Google Scholar 
    SGS SIGA. 4.15 Pobreza multidimensional y pobreza por ingresos de la Region de los Lagos. Agosto 2018. Subsecreteria de Desarollo Regional y Administrativo, Gobierno de Chile (2018).FAO. The state of world fisheries and aquaculture. http://www.fao.org/3/a-i720e.pdf (2014).Niklitschek, E. J. et al. Southward expansion of the Chilean salmon industry in the Patagonian Fjords: Main environmental challenges. Rev. Aquac. 4, 1–24. https://doi.org/10.1111/raq.1201 (2013).Article 

    Google Scholar 
    Soto, M. V. et al. Natural hazards and exposure of strategic connectivity in extreme territories. Comau Fjord, North Patagonia, Chile. Rev. Geogr. Norte Grande 73, 57–75 (2019).Article 

    Google Scholar 
    Montes, R. M. et al. Quantifying harmful algal bloom thresholds for farmed salmon in southern Chile. Harmful Algae 77, 55–65. https://doi.org/10.1016/j.hal.2018.05.004 (2018).Article 
    PubMed 

    Google Scholar 
    Lembeye, G. Harmful algal blooms in the austral Chilean channels and fjords. In Progress in the Oceanographic Knowledge of Chilean Interior Waters, from Puerto Montt to Cape Horn (eds Silva, N. & Palma, S.) 99–103 (Comité Oceanográfico, 2008).
    Google Scholar 
    Häussermann, V. et al. Largest baleen whale mass mortality during strong El Niño event is likely related to harmful toxic algal bloom. PeerJ 5, e3123. https://doi.org/10.7717/peerj.3123 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Google IncGoogle Earth. Retrieved from https://www.google.com/earth/versions/#download-pro (2009). More

  • in

    Asymmetrical dose responses shape the evolutionary trade-off between antifungal resistance and nutrient use

    Fisher, M. C. et al. Threats posed by the fungal kingdom to humans, wildlife, and agriculture. MBio 11, e00449–20 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Fisher, M. C., Hawkins, N. J., Sanglard, D. & Gurr, S. J. Worldwide emergence of resistance to antifungal drugs challenges human health and food security. Science 360, 739–742 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Nash, A. et al. MARDy: Mycology Antifungal Resistance Database. Bioinformatics 34, 3233–3234 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ksiezopolska, E. et al. Narrow mutational signatures drive acquisition of multidrug resistance in the fungal pathogen Candida glabrata. Curr. Biol. 4, 5314–5326.e10 (2021).Article 
    CAS 

    Google Scholar 
    Bryce Taylor, M. et al. yEvo: Experimental evolution in high school classrooms selects for novel mutations and epistatic interactions that impact clotrimazole resistance in S. cerevisiae. Preprint at bioRxiv https://doi.org/10.1101/2021.05.02.442375 (2021).Andersson, D. I. & Hughes, D. Antibiotic resistance and its cost: is it possible to reverse resistance? Nat. Rev. Microbiol. 8, 260–271 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gerstein, A. C., Lo, D. S. & Otto, S. P. Parallel genetic changes and nonparallel gene-environment interactions characterize the evolution of drug resistance in yeast. Genetics 192, 241–252 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yang, F. et al. The fitness costs and benefits of trisomy of each Candida albicans chromosome. Genetics 218, iyab056 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kanafani, Z. A. & Perfect, J. R. Antimicrobial resistance: resistance to antifungal agents: mechanisms and clinical impact. Clin. Infect. Dis. 46, 120–128 (2008).PubMed 
    Article 

    Google Scholar 
    Iyer, K. R., Revie, N. M., Fu, C., Robbins, N. & Cowen, L. E. Treatment strategies for cryptococcal infection: challenges, advances and future outlook. Nat. Rev. Microbiol. 19, 454–466 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Longley, D. B., Harkin, D. P. & Johnston, P. G. 5-fluorouracil: mechanisms of action and clinical strategies. Nat. Rev. Cancer 3, 330–338 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Erbs, P., Exinger, F. & Jund, R. Characterization of the Saccharomyces cerevisiae FCY1 gene encoding cytosine deaminase and its homologue FCA1 of Candida albicans. Curr. Genet. 31, 1–6 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wrenbeck, E. E., Azouz, L. R. & Whitehead, T. A. Single-mutation fitness landscapes for an enzyme on multiple substrates reveal specificity is globally encoded. Nat. Commun. 8, 15695 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Chen, J. Z., Fowler, D. M. & Tokuriki, N. Comprehensive exploration of the translocation, stability and substrate recognition requirements in VIM-2 lactamase. eLife 9, e56707 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Li, A., Acevedo-Rocha, C. G. & Reetz, M. T. Boosting the efficiency of site-saturation mutagenesis for a difficult-to-randomize gene by a two-step PCR strategy. Appl. Microbiol. Biotechnol. 102, 6095–6103 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Biot-Pelletier, D. & Martin, V. J. J. Seamless site-directed mutagenesis of the Saccharomyces cerevisiae genome using CRISPR-Cas9. J. Biol. Eng. 10, 6 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Dionne, U. et al. Protein context shapes the specificity of SH3 domain-mediated interactions in vivo. Nat. Commun. 12, 1597 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Eddy, A. A. Expulsion of uracil and thymine from the yeast Saccharomyces cerevisiae: contrasting responses to changes in the proton electrochemical gradient. Microbiology 143, 219–229 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kurtz, J. E., Exinger, F., Erbs, P. & Jund, R. New insights into the pyrimidine salvage pathway of Saccharomyces cerevisiae: requirement of six genes for cytidine metabolism. Curr. Genet. 36, 130–136 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    Fujimura, H. Growth inhibition of Saccharomyces cerevisiae by the immunosuppressant leflunomide is due to the inhibition of uracil uptake via Fur4p. Mol. Gen. Genet. 260, 102–107 (1998).CAS 
    PubMed 
    Article 

    Google Scholar 
    Després, P. C., Dubé, A. K., Nielly-Thibault, L., Yachie, N. & Landry, C. R. Double selection enhances the efficiency of Target-AID and Cas9-based genome editing in yeast. G3 8, 3163–3171 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Wang, J. et al. Role of glutamate 64 in the activation of the prodrug 5-fluorocytosine by yeast cytosine deaminase. Biochemistry 51, 475–486 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ivankov, D. N., Finkelstein, A. V. & Kondrashov, F. A. A structural perspective of compensatory evolution. Curr. Opin. Struct. Biol. 26, 104–112 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mayrose, I., Graur, D., Ben-Tal, N. & Pupko, T. Comparison of site-specific rate-inference methods for protein sequences: empirical Bayesian methods are superior. Mol. Biol. Evol. 21, 1781–1791 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    Tarassov, K. An in vivo map of the yeast protein interactome. Science 320, 1465–1470 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Freschi, L., Torres-Quiroz, F., Dubé, A. K. & Landry, C. R. qPCA: a scalable assay to measure the perturbation of protein–protein interactions in living cells. Mol. Biosyst. 9, 36–43 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Chang, A. et al. BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res. 49, D498–D508 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mirdita, M. et al. ColabFold – Making protein folding accessible to all. Preprint at bioRxiv https://doi.org/10.1101/2021.08.15.456425 (2022).Pokusaeva, V. O. et al. An experimental assay of the interactions of amino acids from orthologous sequences shaping a complex fitness landscape. PLoS Genet. 15, e1008079 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Oliver, J. D. et al. F901318 represents a novel class of antifungal drug that inhibits dihydroorotate dehydrogenase. Proc. Natl Acad. Sci. USA 113, 12809–12814 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hoenigl, M. et al. The antifungal pipeline: fosmanogepix, ibrexafungerp, olorofim, opelconazole, and rezafungin. Drugs https://doi.org/10.1007/s40265-021-01611-0 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Verweij, P. E., Te Dorsthorst, D. T. A., Janssen, W. H. P., Meis, J. F. G. M. & Mouton, J. W. In vitro activities at pH 5.0 and pH 7.0 and in vivo efficacy of flucytosine against Aspergillus fumigatus. Antimicrob. Agents Chemother. 52, 4483–4485 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gsaller, F. et al. Mechanistic basis of pH-dependent 5-flucytosine resistance in Aspergillus fumigatus. Antimicrob. Agents Chemother. https://doi.org/10.1128/AAC.02593-17 (2018).Garland, T. Jr. Trade-offs. Curr. Biol. 24, R60–R61 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Chang, Y. C. et al. Moderate levels of 5-fluorocytosine cause the emergence of high frequency resistance in cryptococci. Nat. Commun. 12, 3418 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Billmyre, R. B., Applen Clancey, S., Li, L. X., Doering, T. L. & Heitman, J. 5-fluorocytosine resistance is associated with hypermutation and alterations in capsule biosynthesis in Cryptococcus. Nat. Commun. 11, 127 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Brachmann, C. B. et al. Designer deletion strains derived from Saccharomyces cerevisiae S288C: a useful set of strains and plasmids for PCR-mediated gene disruption and other applications. Yeast 14, 115–132 (1998).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gietz, R. D. & Schiestl, R. H. High-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method. Nat. Protoc. 2, 31–34 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Janke, C. et al. A versatile toolbox for PCR-based tagging of yeast genes: new fluorescent proteins, more markers and promoter substitution cassettes. Yeast 21, 947–962 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    Goldstein, A. L. & McCusker, J. H. Three new dominant drug resistance cassettes for gene disruption in Saccharomyces cerevisiae. Yeast 15, 1541–1553 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    DeLuna, A., Springer, M., Kirschner, M. W. & Kishony, R. Need-based up-regulation of protein levels in response to deletion of their duplicate genes. PLoS Biol. 8, e1000347 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Casadaban, M. J. & Cohen, S. N. Analysis of gene control signals by DNA fusion and cloning in Escherichia coli. J. Mol. Biol. 138, 179–207 (1980).CAS 
    PubMed 
    Article 

    Google Scholar 
    Yachie, N. et al. Pooled-matrix protein interaction screens using barcode fusion genetics. Mol. Syst. Biol. 12, 863 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Andrews, S. FastQC: A quality control analysis tool for high throughput sequencing data (Babraham Bioinformatics, 2016); https://www.bioinformatics.babraham.ac.uk/projects/fastqc/Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).Article 

    Google Scholar 
    Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Reback, J. et al. pandas-dev/pandas: Pandas 1.3.4. Zenodo https://doi.org/10.5281/zenodo.5574486 (2021).Waskom, M. seaborn: statistical data visualization. J. Open Source Softw. 6, 3021 (2021).Article 

    Google Scholar 
    Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Masella, A. P., Bartram, A. K., Truszkowski, J. M., Brown, D. G. & Neufeld, J. D. PANDAseq: paired-end assembler for illumina sequences. BMC Bioinform. https://doi.org/10.1186/1471-2105-13-31 (2012).Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rice, P., Longden, L. & Bleasby, A. EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. https://doi.org/10.1016/S0168-9525(00)02024-2 (2000).Article 
    PubMed 

    Google Scholar 
    Ryan, O. W., Poddar, S. & Cate, J. H. D. Crispr–cas9 genome engineering in Saccharomyces cerevisiae cells. Cold Spring Harb. Protoc. https://doi.org/10.1101/pdb.prot086827 (2016).Amberg, D. C., Burke, D. J. & Strathern, J. N. Methods in Yeast Genetics: A Cold Spring Harbor Laboratory Course Manual (CSHL Press, 2005).Ireton, G. C., Black, M. E. & Stoddard, B. L. The 1.14 A crystal structure of yeast cytosine deaminase: evolution of nucleotide salvage enzymes and implications for genetic chemotherapy. Structure 11, 961–972 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Schymkowitz, J. et al. The FoldX web server: an online force field. Nucleic Acids Res. 33, W382–W388 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Marchant, A. et al. The role of structural pleiotropy and regulatory evolution in the retention of heteromers of paralogs. eLife 8, e46754 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Usmanova, D. R. et al. Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. Bioinformatics 34, 3653–3658 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Howe, K. L. et al. Ensembl 2021. Nucleic Acids Res. 49, D884–D891 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Chorostecki, U., Molina, M., Pryszcz, L. P. & Gabaldón, T. MetaPhOrs 2.0: integrative, phylogeny-based inference of orthology and paralogy across the tree of life. Nucleic Acids Res. 48, W553–W557 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Byrne, K. P. & Wolfe, K. H. The Yeast Gene Order Browser: combining curated homology and syntenic context reveals gene fate in polyploid species. Genome Res. 15, 1456–1461 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Edgar, R. C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinform. 5, 113 (2004).Article 
    CAS 

    Google Scholar 
    Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lõoke, M., Kristjuhan, K. & Kristjuhan, A. Extraction of genomic DNA from yeasts for PCR-based applications. Biotechniques 50, 325–328 (2011).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Schlecht, U., Miranda, M., Suresh, S., Davis, R. W. & St Onge, R. P. Multiplex assay for condition-dependent changes in protein-protein interactions. Proc. Natl Acad. Sci. USA 109, 9213–9218 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Diss, G. & Lehner, B. The genetic landscape of a physical interaction. eLife 7, e32472 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    Ecological and human health risk assessment of heavy metal(loid)s in agricultural soil in hotbed chives hometown of Tangchang, Southwest China

    Soil physical–chemical properties and HMs concentrationsThe soil physical–chemical properties and HMs concentrations are summarized in Table 1. The soil mean pH value was 6.17 and ranged from 4.16 to 9.04 in different sites. The samples sites of level for pH ≤ 6.0(acidic soil), 6.0  7.5(alkaline soil) were 51.5%, 36.4% and 12.1%, respectively. The average content of TN, TP and TK were 1.33 g kg−1, 1.16 g kg−1 and 23.6 g kg−1, and ranged from 0.7 g kg−1 to 2.4 g kg−1, 0.22 g kg−1 to 20.8 g kg−1 and 10.3 g k g−1 to 28 g kg−1, respectively.The mean concentration of Cd, Hg, As, Pb, Cr, Cu, Ni and Zn were 0.221, 0.155, 9.76, 32.2, 91.9, 35.2, 37.1 and 108.8 mg kg−1. Except Cd, the average concentration of Hg, As, Pb, Cr, Cu, Ni and Zn exceeded 93.8%, 7.1%, 6.3%, 17.8%, 25.3%, 10.7%, and 32.4% the soil background values for Chengdu, respectively, which indicates that HMs are enriched to a certain extent in soil. The CV of the HMs in the agricultural soils increased in the order Ni(10.1%), Cr(10.9%), Pb(15.4%), As(21.4%), Cd(31.1%), Zn(57.8%), Hg(58.1%) and Cu(59.9%). The exceptionally high variability of Cu, Hg and Zn indicates that these metals differed greatly with respect to different sites, and the existence of abnormally high values is the main reason that the CV was high. It further indicates that Cu, Hg and Zn may be affected by external interference factors. The mean concentration of all HMs in soil were below the risk screening values for soil contamination (GB 15618-2018) (MEEC, 2018), however, the results showed that in 76, 1, 1, 6 and 2 of sample sites the level of Cd, Pb, Cr, Cu and Zn exceeded the risk screening values.Assessment of heavy metal(loid)s pollutionIndex of geo-accumulationThe index of geo-accumulation of HMs in the soil in the study area are shown in Fig. 2. In a descending order of magnitude of Igeo mean value, the eight elements were as follows: Hg(0.18)  > Zn(-0.22)  > Cu(-0.30)  > Cr(-0.36)  > Ni(-0.45)  > Pb(-0.51)  > As(-0.52)  > Cd(-0.82), indicating that the soil HMs Zn, Cu, Cr, Ni, Pb, As and Cd in the study area were generally in a no contamination according to the defined classes, while Hg was in uncontaminated to moderately contaminated.Figure 2Indexes of geo-accumulation, pollution indexes and potential ecological risk indexes of HMs in study aera. Circles at the top and bottom of box plots correspond to the maximum and minimum values, respectively. The square in the box plot is the average value. Horizontal lines at the top, middle, and bottom of the box plot correspond to 75% percentile, median, and 25% percentile, respectively.Full size imageThe Igeo values for Zn, Cu, Cr, Ni, Pb, As and Cd in more than 90% of samples were less than zero, only a few outliers in the soil were classified as moderately contaminated or worse. Which meaning point pollution at those sample sites. However, the Igeo values for Hg in 47.34%, 40.61%,10.66% and 1.40% of samples were belonged to  As  > Pb  > Ni  > Cu  > Hg  > Zn  > Cd. Cr, As and Pb were the largest contributors for both adults and children, accounting for 47.33% and 42.37%, 32.68% and 39.64%, 15.95% and 13.26%, respectively. Indicating that attention should be pain to the Cr, As and Pb elements due to their noncarcinogenic risk. Which was basically consistent with the research results of Bo et al.1 and Bao et al.32. Overall, The HMs of Cr, As and Pb were the main non-carcinogenic factors in soil in the study area, and the risk control of these elements should be strengthened.Carcinogenic risk assessmentThe CR of the HMs are shown in Table 5. Three exposure pathways were considered for Cd and Hg in our study. But As and Pb were considered carcinogenic by ingestion and inhalation, and Cr was considered carcinogenic through inhalation. The TCR mean values were 3.68 × 10−5 for adults and 6.27 × 10−5 for children, obviously were in the range 1 × 10−4 from 1 × 10−6, suggesting the TCR caused by HMs in the study area was acceptable on the whole, but it still exceeds the soil treatment threshold value 10−6. The CR average values through the three exposure pathways were CRing 3.65 × 10−5, CRinh 2.53 × 10−7 and CRderm 1.07 × 10−7 for adults, CRing 6.25 × 10−5, CRinh 1.12 × 10−7 and CRderm 4.50 × 10−8 for adults. Clearly the CRing was much larger than CRinh and CRderm for both adults and children, which indicates that oral ingestion is the major exposure pathway for CR. Which was consistent with the research results of Song et al.31 and Bo et al.1. For single HM, the CR value of Pb and Hg for adults were 2.72 × 10−5 and 8.6 × 10−6, and Pb, Hg and Cd for children were 4.63 × 10−5, 1.48 × 10−5 and 1.36 × 10−6, respectively, within acceptable criterion. Overall, the longterm health effects for adults and children are not serious at current single HM level.In this study, the total contents of HMs in the soils were used to assess health risk, the bioavailability of HMs were not considered, which may have caused the assessment results to be higher than the actual local situation1,31,42. In addition, because the parameters of health risk evaluation for children were set to be more sensitive than those for adults, the non-carcinogenic risk and carcinogenic risk for children were higher than those for adults2,43,44. However, those risks were at acceptable or negligible levels. Therefore, the study area is suitable for safe and clean production of hotbed chives. More

  • in

    Observing and modeling long-term persistence of P. noctiluca in coupled complementary marine systems (Southern Tyrrhenian Sea and Messina Strait)

    Lucas, C. H. et al. Gelatinous zooplankton biomass in the global oceans: Geographic variation and environmental drivers. Glob. Ecol. Biogeogr. 23, 701–714. https://doi.org/10.1111/Geb.12169 (2014).Article 

    Google Scholar 
    Condon, R. H. et al. Recurrent jellyfish blooms are a consequence of global oscillations. Proc. Natl. Acad. Sci. USA 110, 1000–1005. https://doi.org/10.1073/pnas.1210920110 (2013).ADS 
    Article 
    PubMed 

    Google Scholar 
    Graham, W. M. et al. Linking human well-being and jellyfish: Ecosystem services, impacts, and societal responses. Front. Ecol. Environ. 12, 515–523. https://doi.org/10.1890/130298 (2014).Article 

    Google Scholar 
    Lucas, C. H., Gelcich, S. & Uye, S. I. Living with jellyfish: Management and adaptation strategies. In Jellyfish Blooms (eds Pitt, K. A. & Lucas, C. H.) 129–150 (Springer, 2014).Chapter 

    Google Scholar 
    De Donno, A. et al. Impact of stinging jellyfish proliferations along south Italian coasts: Human health hazards, treatment and social costs. Int. J. Environ. Res. Public Health 11, 2488–2503 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bosch-Belmar, M. et al. Consequences of stinging plankton blooms on finfish mariculture in the Mediterranean Sea. Front. Mar. Sci. 4, 240. https://doi.org/10.3389/fmars.2017.0024 (2017).Article 

    Google Scholar 
    Mayer, A. G. Medusae of the World: The Hydromedusae 132–498 (Carnegie institution of Washington, 1910).Book 

    Google Scholar 
    Kramp, P. L. Synopsis of the medusae of the world. J. Mar. Biol. Assoc. UK 40, 1–469 (1961).
    Google Scholar 
    Canepa, A. et al. Pelagia noctiluca in the Mediterranean Sea. In Jellyfish Blooms (eds Pitt, K. A. & Lucas, C. H.) 237–266 (Springer, 2014).Chapter 

    Google Scholar 
    Marambio, M. et al. Unfolding jellyfish bloom dynamics along the Mediterranean basin by transnational citizen science initiatives. Diversity 13, 274. https://doi.org/10.3390/d13060274 (2021).Article 

    Google Scholar 
    Mamish, S., Durgham, H. & Ikhtiyar, S. The first Pelagia noctiluca outbreak off the Syrian coast (the eastern Mediterranean Sea), five years after its first appearance. SSRG Int. J. Agric. Environ. Sci. 6, 72–75 (2019).
    Google Scholar 
    Daly Yahia, M. N. et al. Are outbreaks of Pelagia noctiluca (Forsskäl, 1775) more frequent in the Mediterranean Basin?. ICES Coop. Res. Rep. 300, 8–14 (2010).
    Google Scholar 
    Aissi, M., Touzri, C., Gueroun, S. K. M., Kefi-Daly Yahia, O. & Daly Yahia, M. N. Persistent occurrence and life cycle of Pelagia noctiluca in the channel of Bizerte (Northern Tunisia). Ecol. Environ. Conserv. 20, 1453–1460 (2014).
    Google Scholar 
    Kogovsĕk, T., Bogunović, B. & Malej, A. Recurrence of bloom forming scyphomedusae: Wavelet analysis of a 200-year time series. Hydrobiologia 645, 81–96 (2010).Article 
    CAS 

    Google Scholar 
    Pestoric, B. et al. Scyphomedusae and ctenophora of the eastern adriatic: Historical overview and new data. Diversity 13, 186. https://doi.org/10.3390/d13050186 (2021).CAS 
    Article 

    Google Scholar 
    UNEP (United Nations Environmental Programme). Workshop on Jellyfish Blooms in the Mediterranean, Athens (1984).UNEP (United Nations Environmental Programme). Jellyfish blooms in the Mediterranean Sea. Proceedings of II Workshop on Jellyfish in the Mediterranean Sea, Athens (1991).Goy, J., Morand, P. & Etienne, M. Long term fluctuations of Pelagia noctiluca (Cnidaria, Scyphomedusa) in the western Mediterranean. Sea Prediction by climatic variables. Deep-Sea Res. A 36, 269–279 (1989).ADS 
    Article 

    Google Scholar 
    Bernard, P., Berline, L. & Gorsky, G. Long term (1981–2008) monitoring of the jellyfish Pelagia noctiluca (Cnidaria, Scyphozoa) on the French Mediterranean Coasts. J. Oceanogr. Res. Data 4, 1–10 (2011).
    Google Scholar 
    Brotz, L., Cheung, W. W. L., Kleisner, K., Pakhomov, E. & Pauly, D. Increasing jellyfish population: Trends in large marine ecosystems. Hydrobiologia 690, 3–20 (2012).Article 

    Google Scholar 
    Rosa, S., Pansera, M., Granata, A. & Guglielmo, L. Interannual variability, growth, reproduction and feeding of Pelagia noctiluca (Cnidaria: Scyphozoa) in the Straits of Messina (Central Mediterranean Sea): Linkages with temperature and diet. J. Mar. Syst. 111–112, 97–107 (2013).Article 

    Google Scholar 
    Aoutien, M., Bekkali, R., Nachit, D., Luan, K. & Mrhraoui, M. Predicting jellyfish strandings in the Moroccan North-West Mediterranean coastline. Eur. Sci. J. 15, 72–84. https://doi.org/10.19044/esj.2019.v15n2p72 (2019).Article 

    Google Scholar 
    Lynam, C. P., Hay, S. J. & Brierley, A. S. Interannual variability in abundance of North Sea jellyfish and links to the North Atlantic Oscillation. Limnol. Oceanogr. 49, 637–643 (2004).ADS 
    Article 

    Google Scholar 
    Lynam, C. P. et al. Have jellyfish in the Irish Sea benefited from climate change and overfishing?. Glob. Change Biol. 17, 767–782 (2011).ADS 
    Article 

    Google Scholar 
    Brodeur, R. D. et al. Rise and fall of jellyfish in the eastern Bering Sea in relation to climate regime shifts. Prog. Oceanogr. 77, 103–111 (2008).ADS 
    Article 

    Google Scholar 
    Molinero, J. C. et al. Climate control on the longterm anomalous changes of zooplankton communities in the Northwestern Mediterranean. Glob. Change Biol. 14, 11–26 (2008).ADS 
    Article 

    Google Scholar 
    Licandro, P. et al. A blooming jellyfish in the northeast Atlantic and Mediterranean. Biol. Let. 6, 688–691 (2010).CAS 
    Article 

    Google Scholar 
    Ferraris, M. et al. Distribution of Pelagia noctiluca (Cnidaria, Scyphozoa) in the Ligurian Sea (NW Mediterranean Sea). J. Plankton Res. 34, 874–885 (2012).Article 

    Google Scholar 
    Malačič, V., Petelin, B. & Malej, A. Advection of the jellyfish Pelagia noctiluca (Scyphozoa) studied by the Lagrangian tracking of water mass in the climatic circulation of the Adriatic Sea. Geophys. Res. Abstr. 9, 02802 (2007).
    Google Scholar 
    Rubio, P. & Muñoz, J. M. Predicción estival del riesgo de blooms de Pelagia noctiluca (litoral central catalán). In Situaciones de riesgo climático en España (ed. Novau, J. C.) 281–287 (Instituto Pirenaico de Ecología, 1997).
    Google Scholar 
    Berline, L., Zakardjian, B., Molcard, A., Ourmieres, Y. & Guihou, K. Modeling jellyfish Pelagia noctiluca transport and stranding in the Ligurian Sea. Mar. Pollut. Bull. 70, 90–99 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Olds, A. D. et al. Quantifying the conservation value of seascape connectivity: A global synthesis. Glob. Ecol. Biogeogr. 25, 3–15 (2016).Article 

    Google Scholar 
    Vodopivec, M., Peliz, A. J. & Malej, A. Offshore marine constructions as propagators of moon jellyfish dispersal. Environ. Res. Lett. 12, 084003 (2017).ADS 
    Article 

    Google Scholar 
    Chen, J. Z., Huang, S. L. & Han, Y. S. Impact of long-term habitat loss on the Japanese eel Anguilla japonica. Estuar. Coast. Shelf Sci. 151, 361–369 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    Fernandez-Arcaya, U. et al. Ecological role of submarine canyons and need for canyon conservation: A review. Front. Mar. Sci. 4, 5. https://doi.org/10.3389/fmars.2017.00005 (2017).Article 

    Google Scholar 
    Würtz, M. Towards a Mediterranean canyon inventory. Workshop (EBSAs), 7 to 11 April 2014, Málaga, Spain, 1–4 (2014).Sacchetti, F. Il ritorno di MeteoMedusa. Focus (Madison) 237, 92–94 (2012).
    Google Scholar 
    Benedetti-Cecchi, L. et al. Deterministic factors overwhelm stochastic environmental fluctuations as drivers of jellyfish outbreaks. PLoS ONE 10, e0141060. https://doi.org/10.1371/journal.pone.0141060 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Malej, A. & Malej, M. Population dynamics of the jellyfish Pelagia noctiluca (Forsskäl, 1775). In Proceedings of the 25th EMBS, Marine Eutrophication and Population Dynamics (ed. Colombo, G.A.) 215–219 (Olsen & Olsen, 1992).Rottini-Sandrini, L., Avian, M., Axiak, V. & Malej, A. The breeding period of Pelagia noctiluca (Scyphozoa, Semaeostomeae) in the Adriatic and central Mediterranean Sea. Nova Thalass. 6, 65–75 (1983).
    Google Scholar 
    Milisenda, G. et al. Reproductive and bloom patterns of Pelagia noctiluca in the Strait of Messina, Italy. Estuar. Coast. Shelf Sci. 201, 29–39. https://doi.org/10.1016/j.ecss.2016.01.002 (2018).ADS 
    Article 

    Google Scholar 
    Magazzù, G. et al. Picoplankton: Contribution to phytoplankton production in the Strait of Messina. Mar. Ecol. 8, 21–31 (1987).ADS 
    Article 

    Google Scholar 
    Guglielmo, L., Crescenti, N., Costanzo, G. & Zagami, G. Zooplankton and micronekton communities in the Straits of Messina. In The Straits of Messina ecosystem, present knowledge for an ecohydrodynamical approach. Proceedings of Symposium held in Messina, 4–6 April 1991, Messina (eds. Guglielmo, L., Manganaro, A. & De Domenico, E.) 247–270 (Dipartimento di Biologia Animale ed Ecologia, 1995).Guglielmo, L. et al. The Strait of Messina: A key area for Pelagia noctiluca (Cnidaria, Scyphozoa). In Jellyfish: Ecology, Distribution Patterns and Human Interactions (ed. Mariottini, G. L.) 71–90 (Nova Science Publishers Inc., 2017).
    Google Scholar 
    Astraldi, M. & Gasparini, G. P. The seasonal characteristics of the circulation in the Tyrrhenian Sea. In: Seasonal and Interannual Variability of the Western Mediterranean Sea, Coast. Estuar. Studies, Vol. 46, 115–134 (American Geophysical Union, 1994).Krivosheya, V. G. Water circulation and structure in the Tyrrhenian Sea. Oceanology 23, 166–171 (1983).
    Google Scholar 
    Millot, C. Circulation in the Western Mediterranean Sea. J. Mar. Syst. 20, 423–442. https://doi.org/10.1016/S0924-7963(98)00078-5 (1999).Article 

    Google Scholar 
    Vetrano, A., Napolitano, E., Iacono, R., Schroeder, K. & Gasparini, G. P. Tyrrhenian Sea circulation and water mass fluxes in spring 2004: Observations and model results. J. Geophys. Res. 115, C06023 (2010).ADS 

    Google Scholar 
    Iacono, R., Napolitano, E., Marullo, S., Artale, V. & Vetrano, A. Seasonal variability of the Tyrrhenian Sea surface geostrophic circulation as assessed by altimeter data. J. Phys. Oceanogr. 43, 1710–1732. https://doi.org/10.1175/JPO-D-12-0112.1 (2013).ADS 
    Article 

    Google Scholar 
    Boero, F. et al. CoCoNet: Towards coast to coast networks of Marine Protected Areas (from the shore to the high and deep sea), coupled with sea-based wind energy potential. Sci. Res. Inf. Technol. 6(Suppl.), 1–95 (2016).
    Google Scholar 
    Rio, M. H. et al. A mean dynamic topography of the Mediterranean Sea computed from altimetric data, in-situ measurements and a general circulation model. J. Mar. Syst. 65, 484–508 (2007).Article 

    Google Scholar 
    Cucco, A. et al. Hydrodynamic modelling of coastal seas: The role of tidal dynamics in the Messina Strait, Western Mediterranean Sea. Nat. Hazards Earth Syst. Sci. 16, 1553–1569 (2016).ADS 
    Article 

    Google Scholar 
    Hopkins, T. S., Salusti, E. & Settimi, D. Tidal forcing of the water mass interface in the Straits of Messina. J. Geophys. Res. 89, 2013–2024 (1984).ADS 
    Article 

    Google Scholar 
    Bignami, F. & Salusti, E. Tidal currents and transient phenomena in the Strait of Messina: A review. In: The Physical Oceanography of Sea Straits (ed. Pratt, L. J.) 95–124 (Kluwer Academic, 1990).Azzaro, F., Decembrini, F., Raffa, F. & Crisafi, E. Seasonal variability of phytoplankton fluorescence in relation to the Straits of Messina (Sicily) tidal upwelling. Ocean Sci. Discuss. 4, 415–440 (2007).ADS 

    Google Scholar 
    De Domenico, E., Cortese, G. & Pulicanò, G. Chemical characteristics of the waters in the Straits of Messina. In The Straits of Messina ecosystem, present knowledge for an ecohydrodynamical approach. Proceedings of Symposium held in Messina, 4–6 April 1991, Messina (eds. Guglielmo, L., Manganaro, A., & De Domenico, E.) 155–167 (Dipartimento di Biologia Animale ed Ecologia Marina, 1995).Guglielmo, L. Distribuzione di Chetognati nell’area idrografica dello Stretto di Messina. Pubbl. Staz. Zool. Napoli 40, 34–72 (1976).
    Google Scholar 
    Sitran, R., Bergamasco, A., Decembrini, F. & Guglielmo, L. Temporal succession of tintinnids in the northern Ionian Sea, Central Mediterranean. J. Plankton Res. 29, 495–508 (2007).Article 

    Google Scholar 
    AA.VV. Final Scientific Report of the Project Cluster 10—SAM “Realizzazione ed attivazione di una rete integrata di piattaforme costiere e mezzo mobile attrezzati per Sistemi Avanzati di Monitoraggio delle acque (SAM)”, funded by the Italian Ministry of University and Scientifical and Technological Research (MURST), Internal Data File, Istituto Sperimentale Talassografico, National Research Council, Messina, Italy (2005).Sitran, R. Caratterizzazione dei popolamenti microzooplanctonici nell’area idrografica dello Stretto di Messina, University of Messina, Ph.D. Thesis XVII cycle (2006) (in Italian).Bergamasco, A. et al. A laboratory for the observation of a highly-energetic coastal marine system: The Straits of Messina. In Volume DTA/06–2011, “Marine Research at CNR” 2185–2202 (Department of Earth and Environment of National Research Council, 2011).Doyle, T. K. et al. Widespread occurrence of the jellyfish Pelagia noctiluca in Irish coastal and shelf waters. J. Plankton Res. 30, 963–968 (2008).Article 

    Google Scholar 
    Guglielmo, L. Spiaggiamenti di eufausiacei lungo la costa messinese dello Stretto dal dicembre 1968 al dicembre 1969. Boll. Pesca Piscic. Idrobiol. 24, 71–77 (1969).
    Google Scholar 
    Guglielmo, L., Costanzo, G. & Berdar, A. Ulteriore contributo alla conoscenza dei crostacei spiaggiati lungo il litorale messinese dello Stretto. Atti Soc. Pelorit. 19, 129–156 (1973).
    Google Scholar 
    Scotto Di Carlo, B., Costanzo, G., Fresi, E., Guglielmo, L. & Ianora, A. Feeding ecology and stranding mechanisms in two lanternfishes, Hygophum benoiti and Myctophum punctatum. Mar. Ecol. Prog. Ser 9, 13–24 (1982).ADS 
    Article 

    Google Scholar 
    Battaglia, P., Ammendolia, G., Cavallaro, M., Consoli, P. & Esposito, V. Influence of lunar phases, winds and seasonality on the stranding of mesopelagic fish in the Strait of Messina (Central Mediterranean Sea). Mar. Ecol. 38, e12459. https://doi.org/10.1111/maec.12459 (2017).Article 

    Google Scholar 
    Umgiesser, G., Canu, D. M., Cucco, A. & Solidoro, C. A finite element model for the Venice Lagoon. Development, set up, calibration and validation. J. Mar. Syst. 51, 123–145 (2004).Article 

    Google Scholar 
    Ferrarin, C., Bergamasco, A., Umgiesser, G. & Cucco, A. Hydrodynamics and spatial zonation of the Capo Peloro coastal system (Sicily) through 3-D numerical modeling. J. Mar. Syst. 117, 96–107 (2013).Article 

    Google Scholar 
    Umgiesser, G., Ferrarin, C., Cucco, A., De Pascalis, F. & Bellafiore, D. Comparative hydrodynamics of 10 Mediterranean lagoons by means of numerical modeling. J. Geophys. Res. Oceans 119, 2212–2226 (2014).ADS 
    Article 

    Google Scholar 
    Cucco, A., Quattrocchi, G., Satta, A., Antognarelli, F. & De Biasio, F. Predictability of wind-induced sea surface transport in coastal areas. J. Geophys. Res. Oceans 121, 5847–5871. https://doi.org/10.1002/2016JC011643 (2016).ADS 
    Article 

    Google Scholar 
    Cucco, A., Quattrocchi, G. & Zecchetto, S. The role of temporal resolution in modeling the wind induced sea surface transport in coastal seas. J. Mar. Syst. 193, 46–58. https://doi.org/10.1016/j.jmarsys.2019.01.004 (2019).Article 

    Google Scholar 
    Quattrocchi, G. et al. An operational numerical system for oil stranding risk assessment in a high-density vessel traffic area. Front. Mar. Sci. 8, 585396. https://doi.org/10.3389/fmars.2021.585396 (2021).Article 

    Google Scholar 
    Cucco, A. et al. A high-resolution real-time forecasting system for predicting the fate of oil spills in the Strait of Bonifacio (western Mediterranean Sea). Mar. Pollut. Bull. 64, 1186–1200 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Cucco, A. & Umgiesser, G. The Trapping Index: How to integrate the Eulerian and the Lagrangian approach for the computation of the transport time scales of semi-enclosed basins. Mar. Pollut. Bull. 98, 210–220 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Quattrocchi, G. et al. Optimal design of a Lagrangian observing system for hydrodynamic surveys. J. Oper. Oceanogr. 9(suppl.), s77–s88. https://doi.org/10.1080/1755876X.2015.1114805 (2016).Article 

    Google Scholar 
    Quattrocchi, G. et al. Hydrodynamic controls on connectivity of the high commercial value shrimp Parapenaeus longirostris (Lucas, 1846) in the Mediterranean Sea. Sci. Rep. 9, 16935. https://doi.org/10.1038/s41598-019-53245-8 (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pastor-Prieto, M. et al. Spatial heterogeneity of Pelagia noctiluca ephyrae linked to water masses in the Western Mediterranean. PLoS ONE 16, e0249756. https://doi.org/10.1371/journal.pone.0249756 (2021).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Haeckel, E. Das system der medusen. Monographie der Medusen 499–510 (Gustav Fischer Verlag, 1880).
    Google Scholar 
    Avian, M. Temperature influence on in vitro reproduction and development of Pelagia noctiluca (Forsskäl, 1775). Boll. Zool. 53, 385–391 (1986).Article 

    Google Scholar 
    Fossette, S. et al. Current-oriented swimming by jellyfish and its role in bloom maintenance. Curr. Biol. 25, 342–347. https://doi.org/10.1016/j.cub.2014.11.050 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Pinardi, N. et al. Mediterranean Sea large-scale low-frequency ocean variability and water mass formation rates from 1987 to 2007: A retrospective analysis. Prog. Oceanogr. 132, 318–332 (2015).ADS 
    Article 

    Google Scholar 
    Demirov, E. & Pinardi, N. Simulation of the Mediterranean Sea circulation from 1979 to 1993: Part I. The interannual variability. J. Mar. Syst. 33–34, 23–50 (2002).Article 

    Google Scholar 
    Menna, M. et al. New insights of the Sicily channel and Southern Tyrrhenian sea variability. Water 11, 1355 (2019).Article 

    Google Scholar 
    Avian, M. & Rottini Sandrini, L. Oocyte development in four species of scyphomedusa in the northern Adriatic Sea. Hydrobiologia 216/217, 189–195 (1991).Article 

    Google Scholar 
    Malej, A. Behaviour and trophic ecology of the jellyfish Pelagia noctiluca (Forsskäl, 1775). J. Exp. Mar. Biol. Ecol. 126, 259–270 (1989).Article 

    Google Scholar 
    Lo Bianco, S. Notizie biologiche riguardanti specialmente il periodo di maturità sessuale degli animali del golfo di Napoli. Mitt. Zool. Stn. Neapel 19, 513–761 (1909).
    Google Scholar 
    Purcell, J. E., Malej, A. & Benović, A. Potential links of jellyfish to eutrophication and fisheries. In Coastal and Estuarine Studies, Ecosystem at the Land-Sea Margin Drainage Basin to Coastal Sea (eds Malone, T. C. et al.) 241–263 (American Geophysical Union, 1999).Chapter 

    Google Scholar 
    Spezie, G. C., Sansone, E., Budillon, G. & Gallarato, A. Caratterizzazione idrodinamica del sistema Eolie e dei bacini limitrofi di Cefalù e Gioia. Campagna oceanografica 1994. Caratterizzazione ambientale marina del sistema Eolie e dei bacini limitrofi di Cefalù e Gioia (EUCUMM94). In Data Rep., (eds. Faranda, F. M. & Povero, P.) 1–82 (1995).Spezie, G. C. et al. Rilievi idrodinamici nel sistema Eolie e nei bacini limitrofi di Cefalù e Gioia. Campagna oceanografiche 1995. Caratterizzazione ambientale marina del sistema Eolie e dei bacini limitrofi di Cefalù e Gioia (EUCUMM95). In Data Rep. (eds. Faranda, F. M. & Povero, P.) 1–98 (1996).Carrada, G. C., Ribera D’Alcalà, M. & Saggiomo, V. The pelagic system of the Southern Tyrrhenian Sea. Some comments and working hypotheses. In Proceedings IX Proceedings XII Italian Association of Oceanography and Limnology Congress 151–166 (1992).Povero, P., Misic, C., Acconero, A. & Fabiano M. Distribuzione e caratterizzazione biochimica della sostanza organica particellata nelle acque del Tirreno Sud Orientale. In Acts 12 Congress of the Italian Association of Oceanology and Limnology 227–237 (1998).Brancato, G., Minutoli, R., Granata, A., Sidoti, O. & Guglielmo L. Diversity and vertical migration of euphausiids across the Straits of Messina area. In: Mediterranean Ecosystem: Structures and Processes (eds. Faranda, F. M., Guglielmo, L. & Spezie, G.) 131–141 (Springer, 2001).Sitran, R., Bergamasco, A., Decembrini, F. & Guglielmo, L. Microzooplankton (tintinnid ciliates) diversity: Coastal community structure and driving mechanisms in the Southern Tyrrhenian Sea (Western Mediterranean). J. Plankton Res. 31, 153–170 (2009).Article 

    Google Scholar 
    Fonda Umani, S., Monti, M., Minutoli, R. & Guglielmo, L. Recent advances in the Mediterranean researches on zooplankton: from spatial–temporal patterns of distribution to processes oriented studies. Adv. Oceanogr. Limnol. 1, 295–356 (2010).Article 

    Google Scholar 
    Giordano, D. et al. Summer larval fish assemblages in the Southern Tyrrhenian Sea (Western Mediterranean Sea). Mar. Ecol. 36, 104–117. https://doi.org/10.1111/maec.12123 (2015).ADS 
    Article 

    Google Scholar 
    Fonda Umani, S., Milani, L. & Martecchini, E. Distribuzione dei popolamenti microzooplanctonici durante la campagna oceanografica Eolie 1994. Caratterizzazione ambientale marina del sistema Eolie e dei bacini limitrofi di Cefalù e Gioia (EUCUMM95). In Data Rep. (eds. Faranda, F. M. & Povero, P.) 199–222 (1995).Carrada, G. C., Mangoni, O. & Sgrosso, S. Distribuzione spaziale di clorofilla a e di feopigmenti in diverse frazioni dimensionali del fitoplancton. Caratterizzazione ambientale marina del sistema Eolie e dei bacini limitrofi di Cefalù e Gioia (EUCUMM95). In Data Rep. (eds. Faranda, F. M. & Povero, P.) 197–216 (1996).Guglielmo, L. et al. Distribuzione verticale e migrazione giornaliera dello zooplancton e del micronecton nel Tirreno meridionale (Isole Eolie). Caratterizzazione ambientale marina del sistema Eolie e dei bacini limitrofi di Cefalù e Gioia (EUCUMM95). In Data Rep. (eds. Faranda, F. M. & Povero, P.) 217–246 (1996).Innamorati, M., Lazzara, L., Massi, L., Biondi, N. & Nuccio, C. Fitoplancton, luce e produzione primaria nella’Arcipelago delle Isole Eolie, in estate. Caratterizzazione ambientale marina del sistema Eolie e dei bacini limitrofi di Cefalù e Gioia (EUCUMM95). In Data Rep. (eds. Faranda, F. M. & Povero, P.) 161–196 (1996).Zunini Sertorio, T., Licandro, P., Giallain, M. & Bernat, P. Distribuzione verticale della biomassa zooplanctonica su una stazione delle Isole Eolie (Luglio 1995). Caratterizzazione ambientale marina del sistema Eolie e dei bacini limitrofi di Cefalù e Gioia (EUCUMM95). In Data Rep. (eds. Faranda, F. M. & Povero, P.) 247–254 (1996).Sabates, A. et al. Pathways for Pelagia noctiluca jellyfish intrusions onto the Catalan shelf and their interactions with early life fish stages. J. Mar. Syst. 187, 52–61 (2018).Article 

    Google Scholar 
    Mosetti, F. Currents in the Straits of Messina. In The Straits of Messina ecosystem (eds Guglielmo, L. et al.) 13–29 (University of Messina, Department of Marine Biology and Ecology, 1995).
    Google Scholar 
    Zavodnik, D. Spatial aggregations of the swarming jellyfish Pelagia noctiluca (Scyphozoa). Mar. Biol. 94, 265–269 (1987).Article 

    Google Scholar 
    El Rahi, J., Weeber, M. P. & El Serafy, G. Modelling the effect of behavior on the distribution of the jellyfish Mauve stinger (Pelagia noctiluca) in the Balearic Sea using an individual-based model. Ecol. Model. 433, 109230 (2020).Article 

    Google Scholar 
    Axiak, V. & Civili, F. S. Jellyfish blooms in the Mediterranean: causes, mechanisms, impact on man and the environment. A programme review. In: UNEP: Jellyfish blooms in the Mediterranean. Proceedings of the II Workshop on Jellyfish in the Mediterranean Sea. MAP Tech. Rep. Ser. Vol. 47, 1–10 (UNEP, 1991).Keesing, J. K. et al. Role of winds and tides in timing of beach strandings, occurrence, and significance of swarms of the jellyfish Crambione mastigophora Mass 1903 (Scyphozoa: Rhizostomeae: Catostylidae) in north-western Australia. Hydrobiologia 768, 19–36. https://doi.org/10.1007/s10750-015-2525-5 (2016).CAS 
    Article 

    Google Scholar 
    Aglieri, G. et al. First evidence of inbreeding, relatedness and chaotic genetic patchiness in the holoplanktonic jellyfish Pelagia noctiluca (Scyphozoa, Cnidaria). PLoS ONE 9, e99647. https://doi.org/10.1371/journal.pone.0099647 (2014).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Alpers, W., Brandt, P. & Rubino, A. Internal waves generated in the Strait of Gibraltar and Messina: Observations from space. In Remote Sensing of the European Seas (eds. Barale, V. & Gade, M.) 319–330 (Springer, 2008). https://doi.org/10.1007/978-1-4020-6772.Droghei, R. et al. The role of Internal Solitary Waves on deep-water sedimentary processes: The case of up-slope migrating sediment waves off the Messina Strait. Sci. Rep. 6, 36376. https://doi.org/10.1038/srep36376 (2016).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    La Forgia, G. et al. Sediment resuspension and bedform generation induced by internal solitary waves. Geophys. Res. Abs. Vol. 21, EGU2019-9121, EGU General Assembly (2019).Lohmann, H. Die Stromunger in der Strasse von Messina und die verteilung des planktons in derselben. Int. Rev. Ges. Hydrobiol. 2, 505–556 (1909).Article 

    Google Scholar 
    Magazzù, G. & Andreoli, C. Trasferimenti fitoplanctonici attraverso lo Stretto di Messina in relazione alle condizioni idrologiche. Boll. Pesca Piscic. Idrobiol. 26, 125–193 (1971).
    Google Scholar 
    Palanques, A. et al. General patterns of circulation, sediment fluxes and ecology of the Palamòs (La Fonera) submarine canyon, northwestern Mediterranean. Progr. Oceanogr. 66, 89–119 (2005).ADS 
    Article 

    Google Scholar 
    Granata, A. et al. Vertical distribution and diel migration of zooplankton and micronekton in Polcevera submarine canyon of the Ligurian mesopelagic zone (NW Mediterranean Sea). Progr. Oceanogr. 183, 102298. https://doi.org/10.1016/j.pocean (2020).Article 

    Google Scholar 
    Zagami, G. et al. Spring copepod vertical zonation pattern and diel migration in the open Ligurian Sea (north-western Mediterranean). Progr. Oceanogr. 183, 102297. https://doi.org/10.1016/j.pocean (2020).Article 

    Google Scholar 
    Danovaro, R. & Boero, F. Italian seas. In: World Seas: An Environmental Evaluation. Vol. I Europe, The Americas and West Africa. (ed. Sheppard, C.) 283–306 (Elsevier Ltd., 2019). https://doi.org/10.1016/B978-0-12-805068-2.00044-9Lo Iacono, C., Sulli, A. & Agate, M. Submarine canyons of north-western Sicily (Southern Tyrrhenian Sea): Variability in morphology, sedimentary processes and evolution on a tectonically active margin. Deep-Sea Res. 104, 93–105 (2014).
    Google Scholar  More

  • in

    Global patterns of vascular plant alpha diversity

    Linder, H. P. Plant diversity and endemism in sub‐Saharan tropical Africa. J. Biogeogr. 28, 169–182 (2001).Article 

    Google Scholar 
    Kier, G. et al. Global patterns of plant diversity and floristic knowledge. J. Biogeogr. 32, 1107–1116 (2005).Article 

    Google Scholar 
    Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Nat. Acad. Sci. 104, 5925–5930 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Brummitt, N., Araújo, A. C. & Harris, T. Areas of plant diversity—What do we know? Plants, People, Planet 3, 33–44 (2020).Article 

    Google Scholar 
    Gentry, A. H. Changes in plant community diversity and floristic composition on environmental and geographical gradients. Ann. Mo. Bot. Gard. 75, 1–34 (1988).Article 

    Google Scholar 
    Slik, J. F. et al. An estimate of the number of tropical tree species. Proc. Natl Acad. Sci. 112, 7472–7477 (2015).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Parmentier, I. et al. The odd man out? Might climate explain the lower tree α‐diversity of African rain forests relative to Amazonian rain forests? J. Ecol. 95, 1058–1071 (2007).Article 

    Google Scholar 
    Weigand, A. et al. Global fern and lycophyte richness explained: How regional and local factors shape plot richness. J. Biogeogr. 47, 59–71 (2020).Article 

    Google Scholar 
    Keil, P. & Chase, J. M. Global patterns and drivers of tree diversity integrated across a continuum of spatial grains. Nat. Ecol. Evol. 3, 390–399 (2019).PubMed 
    Article 

    Google Scholar 
    Lenoir, J. et al. Cross-scale analysis of the region effect on vascular plant species diversity in southern and northern European mountain ranges. PLoS ONE 5, e15734 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Chase, J. M. et al. Species richness change across spatial scales. Oikos 128, 1079–1091 (2019).Article 

    Google Scholar 
    Bruelheide, H., Jiménez-Alfaro, B., Jandt, U. & Sabatini, F. M. Deriving site-specific species pools from large databases. Ecography 43, 1215–1228 (2020).Article 

    Google Scholar 
    Dengler, J. et al. Species–area relationships in continuous vegetation: Evidence from Palaearctic grasslands. J. Biogeogr. 47, 72–86 (2020).Article 

    Google Scholar 
    Whittaker, R. J. & Fernández-Palacios, J. M. Island Biogeography: Ecology, Evolution, And Conservation (Oxford University Press, 2007).Bruelheide, H. et al. sPlot —a new tool for global vegetation analyses. J. Veg. Sci. 30, 161–186 (2019).Article 

    Google Scholar 
    Sabatini, F. M. et al. sPlotOpen—an environmentally balanced, open-access, global dataset of vegetation plots. Glob. Ecol. Biogeogr. 30, 1740–1764 (2021).Article 

    Google Scholar 
    Ricklefs, R. E. Community diversity—relative roles of local and regional processes. Science 235, 167–171 (1987).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Crawley, M. J. & Harral, J. E. Scale dependence in plant biodiversity. Science 291, 864–868 (2001).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Antonelli, A. et al. An engine for global plant diversity: highest evolutionary turnover and emigration in the American tropics. Front. Genet. 6, 130 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jiménez-Alfaro, B. et al. History and environment shape species pools and community diversity in European beech forests. Nat. Ecol. Evol. 2, 483–490 (2018).PubMed 
    Article 

    Google Scholar 
    Sabatini, F. M., Jiménez-Alfaro, B., Burrascano, S. & Blasi, C. Drivers of herb-layer species diversity in two unmanaged temperate forests in northern Spain. Community Ecol. 15, 147–157 (2014).Article 

    Google Scholar 
    Bruelheide, H. et al. Global trait–environment relationships of plant communities. Nat. Ecol. Evol. 2, 1906–1917 (2018).PubMed 
    Article 

    Google Scholar 
    Pärtel, M., Bennett, J. A. & Zobel, M. Macroecology of biodiversity: disentangling local and regional effects. N. Phytol. 211, 404–410 (2016).Article 

    Google Scholar 
    Field, R. et al. Spatial species‐richness gradients across scales: a meta‐analysis. J. Biogeogr. 36, 132–147 (2009).Article 

    Google Scholar 
    Biurrun, I. et al. Benchmarking plant diversity of Palaearctic grasslands and other open habitats. J. Veg. Sci. 32, e13050 (2021).Article 

    Google Scholar 
    Da, S. S. et al. Plant biodiversity patterns along a climatic gradient and across protected areas in West Africa. Afr. J. Ecol. 56, 641–652 (2018).Article 

    Google Scholar 
    Gerstner, K., Dormann, C. F., Václavík, T., Kreft, H. & Seppelt, R. Accounting for geographical variation in species–area relationships improves the prediction of plant species richness at the global scale. J. Biogeogr. 41, 261–273 (2014).Article 

    Google Scholar 
    Myers, J. A. et al. Beta-diversity in temperate and tropical forests reflects dissimilar mechanisms of community assembly. Ecol. Lett. 16, 151–157 (2013).PubMed 
    Article 

    Google Scholar 
    Muñoz Mazón, M. et al. Mechanisms of community assembly explaining beta-diversity patterns across biogeographic regions. J. Veg. Sci. 32, e13032 (2021).Article 

    Google Scholar 
    Sabatini, F. M., Jiménez-Alfaro, B., Burrascano, S., Lora, A. & Chytrý, M. Beta-diversity of central European forests decreases along an elevational gradient due to the variation in local community assembly processes. Ecography 41, 1038–1048 (2018).Article 

    Google Scholar 
    Večeřa, M. et al. Alpha diversity of vascular plants in European forests. J. Biogeogr. 46, 1919–1935 (2019).Article 

    Google Scholar 
    Wüest, R. O. et al. Macroecology in the age of Big Data—Where to go from here? J. Biogeogr. 47, 1–12 (2019).Article 

    Google Scholar 
    Valavi, R., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. blockCV: an r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods Ecol. Evol. 10, 225–232 (2019).Article 

    Google Scholar 
    Ploton, P. et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 11, 4540 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Belitz, K. & Stackelberg, P. Evaluation of six methods for correcting bias in estimates from ensemble tree machine learning regression models. Environ. Model. Softw. 139, 105006 (2021).Article 

    Google Scholar 
    Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853 (2000).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Barthlott, W., Mutke, J., Rafiqpoor, D., Kier, G. & Kreft, H. Global centers of vascular plant diversity. Nova Acta Leopoldina NF 92, 61–83 (2005).
    Google Scholar 
    Testolin, R. et al. Global patterns and drivers of alpine plant species richness. Glob. Ecol. Biogeogr. 30, 1218–1231 (2021).Article 

    Google Scholar 
    Wilson, J. B., Peet, R. K., Dengler, J. & Pärtel, M. Plant species richness: the world records. J. Veg. Sci. 23, 796–802 (2012).Article 

    Google Scholar 
    Chytrý, M. et al. The most species-rich plant communities in the Czech Republic and Slovakia (with new world records). Preslia 87, 217–278 (2015).
    Google Scholar 
    Whitmore, T. C., Peralta, R. & Brown, K. Total species count in a Costa Rican tropical rain forest. J. Trop. Ecol. 1, 375–378 (1985).Article 

    Google Scholar 
    Chytrý, M. et al. High species richness in hemiboreal forests of the northern Russian Altai, southern Siberia. J. Veg. Sci. 23, 605–616 (2012).Article 

    Google Scholar 
    Duivenvoorden, J. Vascular plant species counts in the rain forests of the middle Caquetá area, Colombian Amazonia. Biodivers. Conserv. 3, 685–715 (1994).Article 

    Google Scholar 
    Balslev, H., Valencia, R., Paz y Miño, G., Christensen, H. & Nielsen, I. in Forest Biodiversity in North, Central and South America and the Carribean: Research and Monitoring. Man and the Biosphere Series (eds. Dallmeier, F. & Comiskey, J. A.) (Unesco and The Parthenon Publishing Group, 1998).Mendieta‐Leiva, G. et al. EpIG‐DB: a database of vascular epiphyte assemblages in the Neotropics. J. Veg. Sci. 31, 518–528 (2020).Article 

    Google Scholar 
    Spicer, M. E., Mellor, H. & Carson, W. P. Seeing beyond the trees: a comparison of tropical and temperate plant growth forms and their vertical distribution. Ecology 101, e02974 (2020).PubMed 
    Article 

    Google Scholar 
    Royo, A. A. & Carson, W. P. The herb community of a tropical forest in central Panama: dynamics and impact of mammalian herbivores. Oecologia 145, 66–75 (2005).ADS 
    PubMed 
    Article 

    Google Scholar 
    Sosef, M. S. M. et al. Exploring the floristic diversity of tropical Africa. BMC Biol. 15, 15 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dwomoh, F. K. & Wimberly, M. C. Fire regimes and forest resilience: alternative vegetation states in the West African tropics. Landsc. Ecol. 32, 1849–1865 (2017).Article 

    Google Scholar 
    Condit, R. et al. Beta-diversity in tropical forest trees. Science 295, 666–669 (2002).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Cao, K. et al. Species packing and the latitudinal gradient in beta-diversity. Proc. R. Soc. B 288, 20203045 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhong, Y. et al. Arbuscular mycorrhizal trees influence the latitudinal beta-diversity gradient of tree communities in forests worldwide. Nat. Commun. 12, 3137 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Graco-Roza, C. et al. Distance decay 2.0—a global synthesis of taxonomic and functional turnover in ecological communities. Glob. Ecol. Biogeogr. 31, 1399–1421 (2022).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Johnson, D. J., Condit, R., Hubbell, S. P. & Comita, L. S. Abiotic niche partitioning and negative density dependence drive tree seedling survival in a tropical forest. Proc. R. Soc. B 284, 20172210 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Stevens, G. C. The latitudinal gradient in geographical range: how so many species coexist in the tropics. Am. Naturalist 133, 240–256 (1989).Article 

    Google Scholar 
    Andermann, T., Antonelli, A., Barrett, R. L. & Silvestro, D. Estimating alpha, beta, and gamma diversity through deep learning. Front Plant Sci. 13, 839407 (2022).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cardoso, D. et al. Amazon plant diversity revealed by a taxonomically verified species list. Proc. Nat. Acad. Sci. 114, 10695–10700 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cayuela, L. et al. Species distribution modeling in the tropics: problems, potentialities, and the role of biological data for effective species conservation. Trop. Conserv. Sci. 2, 319–352 (2009).Article 

    Google Scholar 
    Lenoir, J. et al. Local temperatures inferred from plant communities suggest strong spatial buffering of climate warming across Northern Europe. Glob. Change Biol. 19, 1470–1481 (2013).ADS 
    Article 

    Google Scholar 
    Ellis, E. C., Antill, E. C. & Kreft, H. All is not loss: plant biodiversity in the Anthropocene. PLoS ONE 7, e30535 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kattge, J. et al. TRY plant trait database-enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).ADS 
    Article 

    Google Scholar 
    Dengler, J. et al. The Global Index of Vegetation-Plot Databases (GIVD): a new resource for vegetation science. J. Veg. Sci. 22, 582–597 (2011).Article 

    Google Scholar 
    Lopez‐Gonzalez, G., Lewis, S. L., Burkitt, M. & Phillips, O. L. ForestPlots.net: a web application and research tool to manage and analyse tropical forest plot data. J. Veg. Sci. 22, 610–613 (2011).Article 

    Google Scholar 
    Chytrý, M. Database of Masaryk University Vegetation Research in Siberia. Biodiver. Ecol. 4, 290 (2012).Article 

    Google Scholar 
    Schmidt, M. et al. The West African Vegetation Database. Biodiv. Ecol. 4, 105–110 (2012).Article 

    Google Scholar 
    Muche, G., Schmiedel, U. & Jürgens, N. BIOTA Southern Africa Biodiversity Observatories Vegetation Database. Biodiver. Ecol. 4, 111–123 (2012).Article 

    Google Scholar 
    Revermann, R. et al. Vegetation database of the Okavango Basin. Phytocoenologia 46, 103–104 (2016).Article 

    Google Scholar 
    N’Guessan, A. E. et al. Drivers of biomass recovery in a secondary forested landscape of West Africa. Ecol. Manag. 433, 325–331 (2019).Article 

    Google Scholar 
    Müller, J. Zur Vegetationsökologie der Savannenlandschaften im Sahel Burkina Fasos (Frankfurt-Main Universität, 2003).Kearsley, E. et al. Conventional tree height–diameter relationships significantly overestimate aboveground carbon stocks in the Central Congo Basin. Nat. Commun. 4, 2269 (2013).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    Djomo Nana, E. et al. Relationship between Survival Rate of Avian Artificial Nests and Forest Vegetation Structure along a Tropical Altitudinal Gradient on Mount Cameroon. Biotropica 47, 758–764 (2015).Article 

    Google Scholar 
    Wana, D. & Beierkuhnlein, C. Responses of plant functional types to environmental gradients in the south‐west Ethiopian highlands. J. Trop. Ecol. 27, 289–304 (2011).Article 

    Google Scholar 
    Finckh, M. Vegetation Database of Southern Morocco. Biodiver. Ecol. 4, 297 (2012).Article 

    Google Scholar 
    Strohbach, B. & Kangombe, F. National Phytosociological Database of Namibia. Biodiver. Ecol. 4, 298–298 (2012).Article 

    Google Scholar 
    Samimi, C. Das Weidepotential im Gutu‐Distrikt (Zimbabwe)—Möglichkeiten und Grenzen der Modellierung unter Verwendung von Landsat TM‐5. Vol. 19 (2003).Černý, T. et al. Classification of Korean forests: patterns along geographic and environmental gradients. Appl. Veg. Sci. 18, 5–22 (2015).Article 

    Google Scholar 
    Nowak, A. et al. Vegetation of Middle Asia: the project state of the art after ten years of survey and future perspectives. Phytocoenologia 47, 395–400 (2017).Article 

    Google Scholar 
    Liu, H., Cui, H., Pott, R. & Speier, M. Vegetation of the woodland‐steppe ecotone in southeastern Inner Mongolia, China. J. Veg. Sci. 11, 525–532 (2000).Article 

    Google Scholar 
    Wang, Y. et al. Combined effects of livestock grazing and abiotic environment on vegetation and soils of grasslands across Tibet. Appl. Veg. Sci. 20, 327–339 (2017).Article 

    Google Scholar 
    Bruelheide, H. et al. Community assembly during secondary forest succession in a Chinese subtropical forest. Ecol. Monogr. 81, 25–41 (2011).Article 

    Google Scholar 
    Cheng, X.-L. et al. Taxonomic and phylogenetic diversity of vascular plants at Ma’anling volcano urban park in tropical Haikou, China: Reponses to soil properties. PLoS ONE 13, e0198517 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Hatim, M. Vegetation Database of Sinai in Egypt. Biodiver. Ecol. 4, 303 (2012).Article 

    Google Scholar 
    Drescher, J. et al. Ecological and socio-economic functions across tropical land use systems after rainforest conversion. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 371, 20150275 (2016).Article 

    Google Scholar 
    Dolezal, J., Dvorsky, M. & Kopecky, M. Vegetation dynamics at the upper elevational limit of vascular plants in Himalaya. Sci. Rep. 6, 24881 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Borchardt, P. & Schickhoff, U. Vegetation Database of South‐Western Kyrgyzstan—the walnut‐wildfruit forests and alpine pastures. Biodiver. Ecol. 4, 309 (2012).Article 

    Google Scholar 
    Wagner, V. Eurosiberian meadows at their southern edge: patterns and phytogeography in the NW Tien Shan. J. Veg. Sci. 20, 199–208 (2009).Article 

    Google Scholar 
    von Wehrden, H., Wesche, K. & Miehe, G. Plant communities of the southern Mongolian Gobi. Phytocoenologia 39, 331–376 (2009).Article 

    Google Scholar 
    Chepinoga, V. V. Wetland Vegetation Database of Baikal Siberia (WETBS). Biodiver. Ecol. 4, 311 (2012).Article 

    Google Scholar 
    Korolyuk, A. et al. Database of Siberian Vegetation (DSV). Biodiver. Ecol. 4, 312–312 (2012).Article 

    Google Scholar 
    El-Sheikh, M. A. et al. SaudiVeg ecoinformatics: aims, current status and perspectives. Saudi J. Biol. Sci. 24, 389–398 (2017).PubMed 
    Article 

    Google Scholar 
    Vanselow, K. A. Eastern Pamirs—a vegetation‐plot database for the high mountain pastures of the Pamir Plateau (Tajikistan). Phytocoenologia 46, 105 (2016).Article 

    Google Scholar 
    De Sanctis, M. & Attorre, F. Socotra Vegetation Database. Biodiver. Ecol. 4, 315 (2012).Article 

    Google Scholar 
    Chabbi, A. & Loescher, H. W. Terrestrial Ecosystem Research Infrastructures: Challenges and Opportunities (CRC Press, 2017).Ibanez, T. et al. Structural and floristic diversity of mixed rainforest in New Caledonia: New data from the New Caledonian Plant Inventory and Permanent Plot Network (NC‐PIPPN). Appl. Veg. Sci. 17, 386–397 (2014).Wiser, S. K., Bellingham, P. J. & Burrows, L. E. Managing biodiversity information: development of New Zealand’s National Vegetation Survey databank. N. Z. J. Ecol. 25, 1–17 (2001).
    Google Scholar 
    Whitfeld, T. J. S. et al. Species richness, forest structure, and functional diversity during succession in the New Guinea lowlands. Biotropica 46, 538–548 (2014).Article 

    Google Scholar 
    Dengler, J. & Rūsiņa, S. Database dry grasslands in the Nordic and Baltic Region. Biodiver. Ecol. 4, 319–320 (2012).Article 

    Google Scholar 
    Biurrun, I., García-Mijangos, I., Campos, J. A., Herrera, M. & Loidi, J. Vegetation-plot database of the University of the Basque Country (BIOVEG). Biodiver. Ecol. 4, 328 (2012).Article 

    Google Scholar 
    Vassilev, K., Stevanović, Z. D., Cušterevska, R., Bergmeier, E. & Apostolova, I. Balkan Dry Grasslands Database. Biodiver. Ecol. 4, 330–330 (2012).Article 

    Google Scholar 
    Marcenò, C. & Jiménez‐Alfaro, B. The Mediterranean Ammophiletea Database: a comprehensive dataset of coastal dune vegetation. Phytocoenologia 47, 95–105 (2017).
    Google Scholar 
    Vassilev, K. et al. Balkan Vegetation Database: historical background, current status and future perspectives. Phytocoenologia 46, 89–95 (2016).Article 

    Google Scholar 
    Landucci, F. et al. WetVegEurope: a database of aquatic and wetland vegetation of Europe. Phytocoenologia 45, 187–194 (2015).Article 

    Google Scholar 
    Peterka, T., Jiroušek, M., Hájek, M. & Jiménez‐Alfaro, B. European Mire Vegetation Database: a gap‐oriented database for European fens and bogs. Phytocoenologia 45, 291–297 (2015).Article 

    Google Scholar 
    De Sanctis, M., Fanelli, G., Mullaj, A. & Attorre, F. Vegetation database of Albania. Phytocoenologia 47, 107–108 (2017).Article 

    Google Scholar 
    Willner, W., Berg, C. & Heiselmayer, P. Austrian Vegetation Database. Biodiver. Ecol. 4, 333 (2012).Article 

    Google Scholar 
    Apostolova, I., Sopotlieva, D., Pedashenko, H., Velev, N. & Vasilev, K. Bulgarian Vegetation Database: historic background, current status and future prospects. Biodiver. Ecol. 4, 141–148 (2012).Article 

    Google Scholar 
    Wohlgemuth, T. Swiss Forest Vegetation Database. Biodiver. Ecol. 4, 340 (2012).Article 

    Google Scholar 
    Chytrý, M. & Rafajová, M. Czech National Phytosociological Database: basic statistics of the available vegetation‐plot data. Preslia 75, 1–15 (2003).
    Google Scholar 
    Jansen, F., Dengler, J. & Berg, C. VegMV—the vegetation database of Mecklenburg‐Vorpommern. Biodiver. Ecol. 4, 149–160 (2012).Article 

    Google Scholar 
    Ewald, J., May, R. & Kleikamp, M. VegetWeb—the national online‐repository of vegetation plots from Germany. Biodiver. Ecol. 4, 173–175 (2012).Article 

    Google Scholar 
    Jandt, U. & Bruelheide, H. German vegetation reference database (GVRD). Biodiver. Ecol. 4, 355–355 (2012).Article 

    Google Scholar 
    Garbolino, E., De Ruffray, P., Brisse, H. & Grandjouan, G. The phytosociological database SOPHY as the basis of plant socio-ecology and phytoclimatology in France. Biodiver. Ecol. 4, 177–184 (2012).Article 

    Google Scholar 
    Dimopoulos, P. & Tsiripidis, I. Hellenic Natura 2000 Vegetation Database (HelNAtVeg). Biodiver. Ecol. 4, 388 (2012).Article 

    Google Scholar 
    Fotiadis, G., Tsiripidis, I., Bergmeier, E. & Dimopoulos, P. Hellenic Woodland Database. Biodiver. Ecol. 4, 389 (2012).Article 

    Google Scholar 
    Stančić, Z. Phytosociological Database of Non‐Forest Vegetation in Croatia. Biodiver. Ecol. 4, 391 (2012).Article 

    Google Scholar 
    Lájer, K. et al. Hungarian Phytosociological database (COENODATREF): sampling methodology, nomenclature and its actual stage. Ann. Botanica Nuova Ser. 7, 197–201 (2008).
    Google Scholar 
    Landucci, F. et al. VegItaly: The Italian collaborative project for a national vegetation database. Plant Biosyst. 146, 756–763 (2012).Article 

    Google Scholar 
    Casella, L., Bianco, P. M., Angelini, P. & Morroni, E. Italian National Vegetation Database (BVN/ISPRA). Biodiver. Ecol. 4, 404 (2012).Article 

    Google Scholar 
    Agrillo, E. et al. Nationwide Vegetation Plot Database—Sapienza University of Rome: state of the art, basic figures and future perspectives. Phytocoenologia 47, 221–229 (2017).Article 

    Google Scholar 
    Rūsiņa, S. Semi‐natural Grassland Vegetation Database of Latvia. Biodiver. Ecol. 4, 409 (2012).Article 

    Google Scholar 
    Schaminée, J. H. J. et al. Schatten voor de natuur. Achtergronden, inventaris en toepassingen van de Landelijke Vegetatie Databank (KNNV Uitgeverij, 2006).Kącki, Z. & Śliwiński, M. The Polish Vegetation Database: structure, resources and development. Acta Soc. Bot. Pol. 81, 75–79 (2012).Article 

    Google Scholar 
    Indreica, A., Turtureanu, P. D., Szabó, A. & Irimia, I. Romanian Forest Database: a phytosociological archive of woody vegetation. Phytocoenologia 47, 389–393 (2017).Article 

    Google Scholar 
    Vassilev, K. et al. The Romanian Grassland Database (RGD): historical background, current status and future perspectives. Phytocoenologia 48, 91–100 (2018).Article 

    Google Scholar 
    Aćić, S., Petrović, M., Dajić Stevanović, Z. & Šilc, U. Vegetation database Grassland vegetation in Serbia. Biodiver. Ecol. 4, 418 (2012).Article 

    Google Scholar 
    Golub, V. et al. Lower Volga Valley Phytosociological Database. Biodiver. Ecol. 4, 419 (2012).Article 

    Google Scholar 
    Lysenko, T., Kalmykova, O. & Mitroshenkova, A. Vegetation Database of the Volga and the Ural Rivers Basins. Biodiver. Ecol. 4, 420–421 (2012).Article 

    Google Scholar 
    Prokhorov, V., Rogova, T. & Kozhevnikova, M. Vegetation database of Tatarstan. Phytocoenologia 47, 309–313 (2017).Article 

    Google Scholar 
    Šilc, U. Vegetation Database of Slovenia. Biodiver. Ecol. 4, 428 (2012).Article 

    Google Scholar 
    Šibík, J. Slovak Vegetation Database. Biodiver. Ecol. 4, 429 (2012).Article 

    Google Scholar 
    Kuzemko, A. Ukrainian Grasslands Database. Biodiver. Ecol. 4, 430 (2012).Article 

    Google Scholar 
    Cayuela, L. et al. The Tree Biodiversity Network (BIOTREE-NET): prospects for biodiversity research and conservation in the Neotropics. Biodiver. Ecol. 4, 211–224 (2012).Article 

    Google Scholar 
    Wagner, V., Spribille, T., Abrahamczyk, S. & Bergmeier, E. Timberline meadows along a 1000 km transect in NW North America: species diversity and community patterns. Appl. Veg. Sci. 17, 129–141 (2014).Article 

    Google Scholar 
    Aubin, I., Gachet, S., Messier, C. & Bouchard, A. How resilient are northern hardwood forests to human disturbance? An evaluation using a plant functional group approach. Ecoscience 14, 259–271 (2007).Article 

    Google Scholar 
    Sieg, B., Drees, B. & Daniëls, F. J. A. Vegetation and altitudinal zonation in continental West Greenland. Medd. om. Gr.ønland Biosci. 57, 1–93 (2006).
    Google Scholar 
    Peet, R. K., Lee, M. T., Jennings, M. D. & Faber-Langendoen, D. VegBank—a permanent, open-access archive for vegetation-plot data. Biodiv. Ecol. 4, 233–241 (2012).Article 

    Google Scholar 
    Peet, R. K. et al. Vegetation‐plot database of the Carolina Vegetation Survey. Biodiver. Ecol. 4, 243–253 (2012).Article 

    Google Scholar 
    Walker, D. A. et al. The Alaska Arctic Vegetation Archive (AVA‐AK). Phytocoenologia 46, 221–229 (2016).Peyre, G. et al. VegPáramo, a flora and vegetation database for the Andean páramo. Phytocoenologia 45, 195–201 (2015).Article 

    Google Scholar 
    Vibrans, A. C., Sevgnani, L., Lingner, D. V., Gasper, A. L. & Sabbagh, S. The Floristic and Forest Inventory of Santa Catarina State (IFFSC): methodological and operational aspects. Pesqui. Florest. Brasileira 30, 291–302 (2010).Article 

    Google Scholar 
    Pauchard, A., Fuentes, N., Jiménez, A., Bustamante, R. & Marticorena, A. In Plant Invasions in Protected Areas (eds Foxcroft, L., Pyšek, P., Richardson, D., Genovesi, P.) (Springer, 2013).González-Caro, S., Umaña, M. N., Álvarez, E., Stevenson, P. R. & Swenson, N. G. Phylogenetic alpha and beta diversity in tropical tree assemblages along regional-scale environmental gradients in northwest South America. J. Plant Ecol. 7, 145–153 (2014).Article 

    Google Scholar 
    Bresciano, D., Altesor, A. & Rodríguez, C. The growth form of dominant grasses regulates the invasibility of Uruguayan grasslands. Ecosphere 5, 1–12 (2014).Aiba, S.-i & Kitayama, K. Structure, composition and species diversity in an altitude-substrate matrix of rain forest tree communities on Mount Kinabalu, Borneo. Plant Ecol. 140, 139–157 (1999).Article 

    Google Scholar 
    Armstrong, A. H., Shugart, H. H. & Fatoyinbo, T. E. Characterization of community composition and forest structure in a Madagascar lowland rainforest. Tropical Conserv. Sci. 4, 428–444 (2011).Article 

    Google Scholar 
    Ayyappan, N. & Parthasarathy, N. Biodiversity inventory of trees in a large-scale permanent plot of tropical evergreen forest at Varagalaiar, Anamalais, Western Ghats, India. Biodivers. Conserv 8, 1533–1554 (1999).Article 

    Google Scholar 
    Balslev, H., Valencia, R., Paz y Miño, G., Christensen, H. & Nielsen, I. In Forest biodiversity in North, Central and South America, and the Caribbean: research and monitoring (eds. Dallmeier, F. & Comiskey, J. A.) 585–594 (1998).Bordenave, B. G., Granville, J.-J. D. & Hoff, M. Measurement of species richness of vascular plants in a neotropical rain forest in French Guiana. (1998).Boyle, T. J. B. & Boontawee, B. CIFOR’s Research Programme on Conservation of Tropical Forest Genetic Resources, 395 (Center for International Forestry Research CIFOR, 1995).Bunyavejchewin, S., Baker, P. J., LaFrankie, J. V. & Ashton, P. S. Stand structure of a seasonal dry evergreen forest at Huai Kha Khaeng Wildlife Sanctuary, western Thailand. Nat. Hist. Bull. Siam Soc. 49, 89–106 (2001).
    Google Scholar 
    Cadotte, M. W., Franck, R., Reza, L. & Lovett-Doust, J. Tree and shrub diversity and abundance in fragmented littoral forest of southeastern Madagascar. Biodivers. Conserv. 11, 1417–1436 (2002).Article 

    Google Scholar 
    Cano Ortiz, A. et al. Phytosociological study, diversity and conservation status of the cloud forest in the Dominican Republic. Plants (Basel, Switzerland) 9, 741 (2020).Chisholm, R. A. et al. Scale-dependent relationships between tree species richness and ecosystem function in forests. J. Ecol. 101, 1214–1224 (2013).Article 

    Google Scholar 
    Chu, C. et al. Direct and indirect effects of climate on richness drive the latitudinal diversity gradient in forest trees. Ecol. Lett. 22, 245–255 (2019).ADS 
    PubMed 

    Google Scholar 
    Condit, R. S. et al. Tropical Tree a—Diversity: Results From a Worldwide Network of Large Plots (CABI, 2005).D’Amico, C. & Gautier, L. Inventory of a 1-ha lowland rainforest plot in Manongarivo, (NW Madagascar). Candollea 55, 319–340 (2000).
    Google Scholar 
    Davidar, P., Mohandass, D. & Vijayan, L. Floristic inventory of woody plants in a tropical montane (shola) forest in the Palni hills of the Western Ghats, India. Trop. Ecol. 12, 42–58 (2007).
    Google Scholar 
    Davies, S. J. & Becker, P. Floristic composition and stand structure of mixed dipterocarp and heath forests in Brunei Darussalam. J. Trop. Sci. 8, 542–569 (1996).
    Google Scholar 
    Duivenvoorden, J. F. Vascular plant species counts in the rain forests of the middle Caquet area. Colomb. Amazon. Biodivers. Conserv. 3, 685–715 (1994).Article 

    Google Scholar 
    Ek, R. C. Botanical diversity in the tropical rain forest of Guyana: Botanische diversiteit in het tropisch regenwoud van Guyana. (Met een samenvatting in het Nederlands) (Universiteit Utrecht, 1997).Galeano, G., Suárez, S. & Balslev, H. Vascular plant species count in a wet forest in the Chocó area on the Pacific coast of Colombia. Biodivers. Conserv. 7, 1563–1575 (1998).Article 

    Google Scholar 
    Garrigues, J. P. Action anthropique sur la dynamique des formations végétales au sud de l’Inde (Ghâts occidentaux, Etat du Karnataka, District de Shimoga) (University of Claude Bernard, Lyon I, 1999).Gastauer, M., Leyh, W. & Meira-Neto, J. A. A. Tree Diversity and Dynamics of the Forest of Seu Nico, Viçosa, Minas Gerais, Brazil. Biodiv. Data J. 3, e5425 (2015).Article 

    Google Scholar 
    Helmi, N., Kartawinata, K. & Samsoedin, I. An undescribed lowland natural forest at Bodogol, Gunung Gede Pangrango National Park, Cibodas Biosphere Reserve, West Java, Indonesia. Reinwardtia 13, 33–44 (2009).
    Google Scholar 
    Hernández, L., Dezzeo, N., Sanoja, E., Salazar, L. & Castellanos, H. Changes in structure and composition of evergreen forests on an altitudinal gradient in the Venezuelan Guayana Shield. Rev. de. Biol.ía Tropical 60, 11–33 (2012).
    Google Scholar 
    Ho, B. C. et al. The plant diversity in Bukit Timah Nature Reserve, Singapore. Gardens’ Bull. Singap. 71, 41–144 (2019).Article 

    Google Scholar 
    Hubbel, S. P. & Foster, R. B. In Tropical Rain Forest: Ecology and Management (eds Sutton, S. L., Whitmore, T. C. & Chadwick, S.) 25–41 (Blackwell Scientific Publications,1983).Kartawinata, K., Samsoedin, I., Heriyanto, M. & Afriastini, J. J. A tree species inventory in a one-hectare plot at the Batang Gadis National Park, North Sumatra, Indonesia. Reinwardtia 12, 145 (2013).Article 

    Google Scholar 
    Kiratiprayoon, S. Measuring and monitoring biodiversity in tropical and temperate forests. In: IUFRO Symposium, Chiang Mai (Thailand), 27 Aug-2 (CIFOR, 1994).KuoJung, C., WeiChun, C., KeiMei, C. & ChangFu, H. Vegetation dynamics of a lowland rainforest at the northern border of the paleotropics at Nanjenshan, southern Taiwan. Taiwan J. Sci. 25, 29–40 (2010).
    Google Scholar 
    Lan, G., Zhu, H. & Cao, M. Tree species diversity of a 20-ha plot in a tropical seasonal rainforest in Xishuangbanna, southwest China. J. For. Res. 17, 432–439 (2012).CAS 
    Article 

    Google Scholar 
    Lee, H. S. et al. Floristic and structural diversity of 52 hectares of mixed dipterocarp forest in Lambir Hills National Park, Sarawak, Malaysia. J. Trop. Sci. 14, 379–400 (2002).
    Google Scholar 
    Linares-Palomino, R. et al. Non-woody life-form contribution to vascular plant species richness in a tropical American forest. Plant Ecol. 201, 87–99 (2009).Article 

    Google Scholar 
    Lubini, A. & Mandango, A. Etude phytosociologique et ecologique des forets a Uapaca guineensis dans le nord-est du district forestier central (Zaire). Bull. Jard. Bot. Natl Belg. 51, 231 (1981).Article 

    Google Scholar 
    Makana, J.-R., Hart, T. & Hart, J. Forest structure and diversity of lianas and understory treelets in monodominant and mixed stands in the Ituri Forest, Democratic Republic of the Congo. Liana Article Index 20 (1998).Mansur, M. & Kartawinata, K. Phytosociology of a lower montane forest on Mt. Batulanteh, Sumbawa, Indonesia. Reinwardtia 16, 77 (2017).Article 

    Google Scholar 
    Mikoláš, M. et al. Natural disturbance impacts on trade-offs and co-benefits of forest biodiversity and carbon. Proc. R. Soc. B 288, 20211631 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mohandass, D. & Davidar, P. Floristic structure and diversity of a tropical montane evergreen forest (shola) of the Nilgiri Mountains, southern India. Trop. Ecol. 50, 219–229 (2009).
    Google Scholar 
    Monge González, M. et al. BIOVERA-Tree: tree diversity, community composition, forest structure and functional traits along gradients of forest-use intensity and elevation in Veracruz, Mexico. Biodiv. Data J. 9, e69560 (2021).Ngo, K. M., Davies, S., Nik, H., Faizu, N. & Lum, S. Resilience of a forest fragment exposed to long-term isolation in Singapore. Plant Ecol. Diver. 9, 397–407 (2016).Article 

    Google Scholar 
    Parthasarathy, N. Tree diversity and distribution in undisturbed and human-impacted sites of tropical wet evergreen forest in southern Western Ghats, India. Biodivers. Conserv. 8, 1365–1381 (1999).Article 

    Google Scholar 
    Parthasarathy, N. & Karthikeyan, R. Biodiversity and population density of woody species in a tropical evergreen forest in Courtallum reserve forest, Western Ghats, India. Trop. Ecol. 38 (1997).Pascal, J. P. Wet Evergreen Forests of the Western Ghats of India (Institut français de Pondichéry, 1988).Pascal, J. P. & Pelissier, R. Structure and floristic composition of a tropical evergreen forest in south-west India. J. Trop. Ecol. 12, 191–214 (1996).Article 

    Google Scholar 
    Phillips, O. L. et al. Efficient plot-based floristic assessment of tropical forests. J. Trop. Ecol. 19, 629–645 (2003).Article 

    Google Scholar 
    Proctor, J., Anderson, J. M., Chai, P. & Vallack, H. W. Ecological Studies in Four Contrasting Lowland Rain Forests in Gunung Mulu National Park, Sarawak: I. Forest Environment, Structure and Floristics. J. Ecol. 71, 237 (1983).Article 

    Google Scholar 
    Ramesh, B. R. et al. Forest stand structure and composition in 96 sites along environmental gradients in the central Western Ghats of India. Ecology 91, 3118 (2010).Article 

    Google Scholar 
    Razak, S. A. & Haron, N. W. Phytosociology of Aquilaria Malaccensis Lamk. and its communities from a tropical forest reserve in peninsular Malaysia. Pak. J. Bot. 47, 2143–2150 (2015).
    Google Scholar 
    Romoleroux, K. et al. Especies leñosas (dap= 1 cm) encontradas en dos hectáreas de un bosque de la Amazonía ecuatoriana. Estudios sobre diversidad y ecología de plantas, 189–215 (1997).Sarah, A. R., Nuradnilaila, H., Haron, N. W. & Azani, M. A Phytosociological Study on the Community of Palaquium gutta (Hook. f.) Baill.(Sapotaceae) at Ayer Hitam Forest Reserve, Selangor, Malaysia. Sains Malaysiana 44, 491–496 (2015).Article 

    Google Scholar 
    Schrader, J., Moeljono, S., Tambing, J., Sattler, C. & Kreft, H. A new dataset on plant occurrences on small islands, including species abundances and functional traits across different spatial scales. Biodiv. Data J. 8, e55275 (2020).Article 

    Google Scholar 
    Sheil, D., Kartawinata, K., Samsoedin, I., Priyadi, H. & Afriastini, J. J. The lowland forest tree community in Malinau, Kalimantan (Indonesian Borneo): results from a one-hectare plot. Plant Ecol. Diver. 3, 59–66 (2010).Article 

    Google Scholar 
    Sukumar, R. et al. Long-term monitoring of vegetation in a tropical deciduous forest in Mudumalai, southern India. Curr. Sci. 62, 608–616 (1992).
    Google Scholar 
    van Andel, T. R. Floristic composition and diversity of three swamp forests in northwest Guyana. Plant Ecol. 167, 293–317 (2003).Article 

    Google Scholar 
    Webb, E. L. & Fa’aumu, S. Diversity and structure of tropical rain forest of Tutuila, American Samoa: effects of site age and substrate. Plant Ecol. 144, 257–274 (1999).Article 

    Google Scholar 
    Zimmerman, J. K. et al. Responses of Tree Species to Hurricane Winds in Subtropical Wet Forest in Puerto Rico: Implications for Tropical Tree Life Histories. J. Ecol. 82, 911 (1994).Article 

    Google Scholar 
    Olson, D. M. et al. Terrestrial ecoregions of the worlds: a new map of life on Earth. Bioscience 51, 933–938 (2001).Article 

    Google Scholar 
    Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 180040 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sandel, B. et al. The influence of Late Quaternary climate-change velocity on species endemism. Science 334, 660–664 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Schultz, J. The Ecozones of the World (Springer, 2005).Körner, C. et al. A global inventory of mountains for bio-geographical applications. Alp. Bot. 127, 1–15 (2017).Article 

    Google Scholar 
    Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. Package ‘dismo’. Available online at: http://cran.r-project.org/web/packages/dismo/index.html (2011).Zhou, S. et al. Estimating stock depletion level from patterns of catch history. Fish. Fish. 18, 742–751 (2017).Article 

    Google Scholar 
    Rocchini, D. et al. Accounting for uncertainty when mapping species distributions: the need for maps of ignorance. Prog. Phys. Geogr. 35, 211–226 (2011).Article 

    Google Scholar 
    Potapov, P., Laestadius, L. & Minnemeyer, S. Global map of potential forest cover www.wri.org/forest-restoration-atlas (2011).Tuanmu, M. N. & Jetz, W. A global 1‐km consensus land‐cover product for biodiversity and ecosystem modelling. Glob. Ecol. Biogeogr. 23, 1031–1045 (2014).Article 

    Google Scholar 
    Roberts, D. R. et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913–929 (2017).Article 

    Google Scholar 
    Pebesma, E. & Heuvelink, G. Spatio-temporal interpolation using gstat. RFID J. 8, 204–218 (2016).
    Google Scholar 
    R Development Core Team. R: A language and environment for statistical computing v.3.6.1. R Foundation for Statistical Computing http://www.R-project.org/ (2019).South, A. rnaturalearth: World Map Data from Natural Earth v.0.1.0. R package https://CRAN.R-project.org/package=rnaturalearth (2017).Sabatini, F. M. et al. Global patterns of vascular plant alpha-diversity [Dataset]. iDiv Data Repository. https://doi.org/10.25829/idiv.3506-p4c0mo (2022).Sabatini, F. M. fmsabatini/GlobalLocal_PlantRichness: NatComms R3 v.3. Zenodo https://doi.org/10.5281/zenodo.6659837 (2022). More

  • in

    Long-term blast control in high eating quality rice using multilines

    The top-brand nonglutinous rice variety ‘Koshihikari’, which has a high palatability, is extremely susceptible to blast. Therefore, farmers apply fungicides over four times during the rice production season. As Koshihikari is sold by the Niigata brand, it has been traditionally viewed as having a high eating quality in Japan, and because of this, both farmers and consumers have requested that the multiline variety KO-BL be tested to determine if it is equivalent to Koshihikari before its introduction. Trials comparing Koshihikari and KO-BL were carried out in 2003 and 2004 in 594 and 622 fields covering 236 and 315 ha, respectively. These trials evaluated plant homogeneity, eating quality, and blast suppression using fewer fungicidal sprays. Following favorable results, in 2005, all Koshihikari were converted to KO-BL multiline variety covering an area of 94,000 ha. In addition, seed use and cultivation were restricted to the Niigata area to distinguish KO-BL from Koshihikari grown in other prefectures.Seed production and mixture processes are managed with precision by each prefectural official member (Fig. 1a). Original isogenic lines (ILs) were separately produced from the original stock in the original strain fields by the Niigata prefectural government. Using a precise mixture machine, the mixture of four ILs was then blended by weight in 2000 kg volumes, all multiplied by ten (giving a total volume of 20 t). Original production fields and commercial fields all used blended seeds that had been authorized by seed production farmers and commercial farmers in the 2003 and 2004 trials. Thus, it takes two years for seed production at the original strain field followed by the original production field for the preparation of commercial fields; thus, the seed mixture composition needs to be determined at least two years before introduction. Susceptible and resistant (effective) ILs were mixed at a ratio of 3:7 from 2005 to 2019 (Fig. 1b, Supplementary Table S1). Susceptible ILs, possessing Pia and Pii genes, were always mixed at a ratio of 1:2, but the composition of resistant ILs, containing Pita-2, Piz, Pib, Piz-t, and Pit genes, was changed every two to three years to avoid the breakdown of resistance6. These changes were determined by annually monitoring blast race distributions.Figure 1Representative seed production flow from original stock to commercial field and history of Koshihikari BL composition from 2005 to 2019 in Niigata Prefecture. (a) S1–S2, susceptible KO-BL; R1-R2, resistant KO-BL. Seeds obtained from original stock field at the Niigata Agricultural Research Institute. Seeds obtained from the original strain field and the original production field at both designated farmers’ fields. Commercial field (general farmers field) used for KO-BL production. Each field requires a year for seed production. (b) Pia and Pii, susceptible; Pita-2, Piz, Pib, Piz-t, and Pit, resistant. The proportion of susceptible KO-BLs and resistant KO-BLs was consistently 3:7 across years.Full size imageIn Niigata Prefecture, the predominant 5 blast races distributed from 1994 to 2004 were 001.0 (virulent to Koshihikari [Pik-s]), 003.0 (virulent to Pik-s and Pia), 005.0 (virulent to Pik-s and Pii), 007.0 (virulent to Pik-s, Pia, and Pii), and 037.1 (virulent to Pik-s, Pia, Pii, and Pik) (Fig. 2a, Supplementary Table S2). Because all the 5 races were virulent to Koshihikari, which had been widely cultivated in Niigata area during the years, there were no drastic race changes. In addition, genetic variations in blast resistance indicated that Koshihikari also harbored the Pish gene, and that the Pia, Pii, and Pik genes were also dominant in the Hokuriku region, including Niigata Prefecture21. Virulent blast races against the resistance genes Pish, Pia, Pii, Pi3, Pi5(t), Pik, Pik-s, and Pi19(t) were dominantly distributed in Niigata Prefecture22. These reports confirmed that Koshihikari had been susceptible to dominant blast races before KO-BL introduction.Figure 2Blast race change during the 1994–2019 period in Niigata Prefecture and the worst-case simulation of blast race dynamics in KO-BL during the 2005–2019 (years 1–15) period. Races and virulences are shown in Table 1. (a) A red line indicates the year (2005) when KO-BL was introduced. Races 007.0 and 037.1 became dominant after the introduction. (b) Actual races and their rates in 2004 and annual KO-BL compositions from 2005 to 2019 were set in the simulation. Parameters set in the simulation were as follows: maximum lesion number in a year, 10,000,000; weather condition, 10 (favorable); virulent mutation rate, 10–5; overwintering probability, 0.01; number of simulated years, 15; and number of simulation trials, 1000. The 1000 trial results for the lesion number increase in each race were averaged in each year and transformed into rates to show race dynamics. All simulation results are shown in Supplementary Table 6 in Supplementary information 2. The races 007.0 and 037.1 were also dominant until year 15 (correspond to 2019). Both actual and simulated race dynamics showed no outbreaks of the resistant composition of KO-BL.Full size imageIn the 2005 release year of KO-BL, the predominant blast races, 001.0 (virulent to Pik-s) and 003.0 (virulent to Pik-s and Pia), drastically decreased in distribution from 41.8% to 22.3% and 27.6% to 17.3%, respectively (Fig. 2a, Supplementary Table S2). Interestingly, races 001.0 and 003.0 rapidly decreased by 5.4% and 1.3% in 2006, respectively, even though especially Pia, which can be infected by the race 003.0, was used in the KO-BL composition. Because all ILs in the composition of KO-BL were resistant to race 001.0, and race 003.0 was only virulent to Pia, which made up 10% of the annual KO-BL composition (Table 1). In contrast, races 007.0 (virulent to Pik-s, Pia, and Pii) and 037.1 (virulent to Pik-s, Pia, Pii, and Pik) dominated from 2005 to 2019. The higher rate of race 007.0 detection was affected by 30% of the ILs composing the annual KO-BL were susceptible. The second highest rate of race 037.1 detection was affected by a number of factors: the high susceptibility of a minor cultivar that had Pii and Pik, the mosaic configuration of fields typical in Niigata, and the air-borne spread of race 037.1. To maintain consensus on KO-BL cultivation based on total blast suppression in Niigata, rarely detected races virulent to resistant ILs in commercial fields are strictly supervised by the prefectural government to avoid unnecessary confusion in Niigata residents.Table 1 Susceptible or resistant reaction of Koshihikari and KO-BL against blast races.Full size tableIn 2008, to mathematically support KO-BL composition changes, we developed a simulation software to estimate long-term blast race dynamics in multilines using a plant‒pathogen coevolution system23. The model calculated the persistence of resistant ILs to determine the optimal timing of changes to multiline variety compositions. To simulate race dynamics in KO-BL, we set five currently investigated races, 001.0 (virulent to Pik-s), 003.0 (virulent to Pik-s and Pia), 005.0 (virulent to Pik-s and Pii), 007.0 (virulent to Pik-s, Pia, and Pii), and 037.1 (virulent to Pik-s, Pia, Pii, and Pik), and their rates in 2004, as well as five emerging races, 043.0 (virulent to Pik-s, Pia, and Piz), 303.0 (virulent to Pik-s, Pia, and Pita-2), 003.2 (virulent to Pik-s, Pia, and Pib), 403.0 (virulent to Pik-s, Pia, and Piz-t), and 003.4 (virulent to Pik-s, Pia, and Pit) (see Fig. 2b, Supplementary Table S3) against five newly introduced respective resistant KO-BLs (see Fig. 1b, Supplementary Table S1) and the annual KO-BL compositions from 2005 to 2019. The worst case (severe epidemic) simulation result (Fig. 2b, Supplementary Tables S3 and S6) showed that race 007.0 (virulent to susceptible Pik-s, Pia and Pii) became the predominant race (77.4%), and race 037.1 (virulent to Pik-s, Pia, Pii, and Pik) remained at a low frequency (21.6%) until the fifteenth year (corresponding to 2019). In addition, super-race virulent to all KO-BLs did not emerge in this simulation. These suppression of outbreaks of newly emerged virulent races, including super-race on resistant KO-BL was apparently affected by 2–3 years of change in resistant KO-BL composition, and total suppression of blast occurrence decreasing the blast population. These results indicated that almost all the epidemics analyzed reflected actual race dynamics without affecting other minor races from other susceptible cultivars grown in Niigata, especially up to 2011. Thus, our decision support system provides an evaluation of KO-BL persistence and indicates the KO-BL composition changes needed for blast race population control in large areas. In addition, our simulation model may be useful for evaluating future KO-BL composition changes.Blast occurrence drastically decreased after 2005 (Fig. 3a, Supplementary Table S4). The average occurrence of leaf and panicle blast was 46.1% and 52.9% during the 1995–2004 period and 9.5% and 9.6% during the 2005–2019 period, respectively. This resulted in a blast suppression effect by 70% of the resistant composition in KO-BL. Current seed production fields are rarely contaminated with virulent races against resistant KO-BLs. This suggests that seed sanitation contributes to the suppression of virulent pathogen epidemics in multilines. In addition, induced resistance24,25 may have no effect on the practical use of multilines. Rice plants were found to induce a resistance response when inoculated with avirulent races of blast (those that stimulate protective responses to virulent race attacks). As the detection of several races in one area is rare and blast occurrence tends to be low, conditions that induce resistance in field situations do not occur. Fungicide applications to control blast in KO-BL and other minor cultivars decreased by approximately one-third during the 2005–2019 period compared with 2004 (Fig. 3b, Supplementary Table S5). Thus, the commercial scale use of crop diversity is clearly effective for the environmentally friendly control of airborne diseases.Figure 3Leaf and panicle blast occurrence from 1994 to 2019 and blast control area from 2004 to 2019 in Niigata Prefecture. (a) A red line indicates the year (2005) when KO-BL was introduced. (b) Gross fungicide spray area decreased by approximately one-third during the 2005–2019 period compared with 2004.Full size imageThe optimum long-term solution for pathogen population control using genetic diversity includes multilines. Blast occurrence in KO-BL introduced in Niigata, and the theoretical value of blast suppression in KO-BL tested at small scales, were reduced by approximately 10% compared to that of monoculture plots26,27,28. Thirty percent of susceptible ILs in KO-BL have the potential to improve compatible races with susceptible ILs and become predominant in large areas. This would contribute to the suppression of rapid increases in new virulent races emerging in the blast population. To maintain consensus on KO-BL cultivation based on total blast suppression in Niigata, rarely detected races virulent to resistant KO-BLs in commercial fields are strictly monitored by the prefectural government. Educating Niigata farmers ensures the long-term use of KO-BL. In fact, lower blast occurrence has been attributed to careful KO-BL cultivation and seed management.The implementation of genetically diversified homogeneous seed mixtures, rotations with resistant KO-BL, restricted KO-BL cultivation, and pathogen monitoring allowed rice quality to be maintained, diseases to be suppressed, and environmentally sound agriculture to be economically viable in Niigata. Collaboration among prefectural officers, farmers, and consumers in Niigata has resulted in safer rice production with good agricultural practices (GAPs) that meet sustainable development goals (SDGs). In addition, DNA tests differentiate KO-BL from the original Koshihikari for buyers, thereby prohibiting illegal distribution. Multiline varieties have been used in small areas in two different prefectures. For example, in Miyagi pref., Sasanishiki BL consisted of Pik, Pik-m, and Piz at ratios of 4:3:3 and 3:3:4 in 1995 and 1996, respectively. This composition was changed to Pik, Pik-m, Piz, and Piz-t at a ratio of 1:1:4:4 from 1997 to 2007 to prevent an increase in race 037.1 (virulent to the BL: Pik and Pik-m). In addition, an equal mixture of seven BLs (Pib, Pik, Pik-m, Piz, Piz-t, Pita, and Pita-2) was cultivated in 300 ha areas (maximum 4000 ha) from 2008 to 2014 without any outbreaks observed. In Toyama pref., the Koshihikari Toyama BL, which consists of resistant ILs, Pita-2, Pib, and Pik-p at a ratio of 4:4:2, was cultivated in an area of 300 ha and required a 50% reduction in chemical inputs from 2003 up to the present. Our model also calculated a greater than 50-year persistence in terms of the small area effect in both prefectural cases. This result depends on an insufficient pathogen population increase in virulent mutations against resistant ILs (data not shown). In this way, the practical use of a multiline provides control without the need for as much fungicide with or without a periodic change in IL composition. Our results demonstrate that the management of crop and pathogen coevolution can control diseases at large scales and, thereby, contribute to global food security. More

  • in

    Eocene emergence of highly calcifying coccolithophores despite declining atmospheric CO2

    Zeebe, R. E. & Wolf-Gladrow, D. CO2 in Seawater: Equilibrium, Kinetics, Isotopes (Elsevier, 2001).Ridgwell, A. & Zeebe, R. The role of the global carbonate cycle in the regulation and evolution of the Earth system. Earth Planet. Sci. Lett. 234, 299–315 (2005).Article 

    Google Scholar 
    Moore, C. M. et al. Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701–710 (2013).Article 

    Google Scholar 
    Klausmeier, C. A., Litchman, E., Daufresne, T. & Levin, S. A. Optimal nitrogen-to-phosphorus stoichiometry of phytoplankton. Nature 429, 171–174 (2004).Article 

    Google Scholar 
    Krumhardt, K. M., Lovenduski, N. S., Iglesias-Rodriguez, M. D. & Kleypas, J. A. Coccolithophore growth and calcification in a changing ocean. Prog. Oceanogr. 159, 276–295 (2017).Article 

    Google Scholar 
    Zondervan, I. The effects of light, macronutrients, trace metals and CO2 on the production of calcium carbonate and organic carbon in coccolithophores—a review. Deep Sea Res. Part 2 54, 521–537 (2007).Article 

    Google Scholar 
    Gibbs, S. J., Sheward, R. M., Bown, P. R., Poulton, A. J. & Alvarez, S. A. Warm plankton soup and red herrings: calcareous nannoplankton cellular communities and the Palaeocene–Eocene Thermal Maximum. Phil. Trans. R. Soc. A 376, 20170075 (2018).Article 

    Google Scholar 
    Aloisi, G. Covariation of metabolic rates and cell size in coccolithophores. Biogeosciences 12, 6215–6284 (2015).Article 

    Google Scholar 
    Boudreau, B. P., Middelburg, J. J. & Luo, Y. The role of calcification in carbonate compensation. Nat. Geosci. 11, 894–900 (2018).Article 

    Google Scholar 
    Suchéras-Marx, B. & Henderiks, J. Downsizing the pelagic carbonate factory: impacts of calcareous nannoplankton evolution on carbonate burial over the past 17 million years. Glob. Planet. Change 123, 97–109 (2014).Article 

    Google Scholar 
    Beaufort, L. et al. Sensitivity of coccolithophores to carbonate chemistry and ocean acidification. Nature 476, 80–83 (2011).Article 

    Google Scholar 
    McClelland, H. L. O., Bruggeman, J., Hermoso, M. & Rickaby, R. E. M. The origin of carbon isotope vital effects in coccolith calcite. Nat. Commun. 8, 14511 (2017).Article 

    Google Scholar 
    Bolton, C. T. et al. Decrease in coccolithophore calcification and CO2 since the middle Miocene. Nat. Commun. 7, 10284 (2016).Article 

    Google Scholar 
    McClelland, H. L. O. et al. Calcification response of a key phytoplankton family to millennial-scale environmental change. Sci. Rep. 6, 34263 (2016).Article 

    Google Scholar 
    Duchamp-Alphonse, S. et al. Enhanced ocean–atmosphere carbon partitioning via the carbonate counter pump during the last deglacial. Nat. Commun. 9, 2396 (2018).Article 

    Google Scholar 
    Si, W. & Rosenthal, Y. Reduced continental weathering and marine calcification linked to late Neogene decline in atmospheric CO2. Nat. Geosci. 12, 833–838 (2019).Article 

    Google Scholar 
    Meier, K. J. S., Berger, C. & Kinkel, H. Increasing coccolith calcification during CO2 rise of the penultimate deglaciation (Termination II). Mar. Micropaleontol. 112, 1–12 (2014).Article 

    Google Scholar 
    Su, X., Liu, C. & Beaufort, L. Late Quaternary coccolith weight variations in the northern South China Sea and their environmental controls. Mar. Micropaleontol. 154, 101798 (2020).Article 

    Google Scholar 
    Berger, C., Meier, K. J. S., Kinkel, H. & Baumann, K.-H. Changes in calcification of coccoliths under stable atmospheric CO2. Biogeosciences 11, 929–944 (2014).Article 

    Google Scholar 
    Zachos, J., Dickens, G. R. & Zeebe, R. E. An early Cenozoic perspective on greenhouse warming and carbon-cycle dynamics. Nature 451, 279–283 (2008).Article 

    Google Scholar 
    Foster, G. L., Royer, D. L. & Lunt, D. J. Future climate forcing potentially without precedent in the last 420 million years. Nat. Commun. 8, 14845 (2017).Article 

    Google Scholar 
    Anagnostou, E. et al. Proxy evidence for state-dependence of climate sensitivity in the Eocene greenhouse. Nat. Commun. 11, 4436 (2020).Article 

    Google Scholar 
    Holtz, L.-M., Wolf-Gladrow, D. & Thoms, S. Stable carbon isotope signals in particulate organic and inorganic carbon of coccolithophores—a numerical model study for Emiliania huxleyi. J. Theor. Biol. 420, 117–127 (2017).Article 

    Google Scholar 
    Hermoso, M., Horner, T. J., Minoletti, F. & Rickaby, R. E. M. Constraints on the vital effect in coccolithophore and dinoflagellate calcite by oxygen isotopic modification of seawater. Geochim. Cosmochim. Acta 141, 612–627 (2014).Article 

    Google Scholar 
    Hermoso, M., Chan, I. Z. X., McClelland, H. L. O., Heureux, A. M. C. & Rickaby, R. E. M. Vanishing coccolith vital effects with alleviated carbon limitation. Biogeosciences 13, 301–312 (2016).Article 

    Google Scholar 
    Rickaby, R. E. M., Henderiks, J. & Young, J. N. Perturbing phytoplankton: response and isotopic fractionation with changing carbonate chemistry in two coccolithophore species. Clim. Past 6, 771–785 (2010).Article 

    Google Scholar 
    Ziveri, P. et al. Stable isotope ‘vital effects’ in coccolith calcite. Earth Planet. Sci. Lett. 210, 137–149 (2003).Article 

    Google Scholar 
    Bolton, C. T. & Stoll, H. M. Late Miocene threshold response of marine algae to carbon dioxide limitation. Nature 500, 558–562 (2013).Article 

    Google Scholar 
    Henderiks, J. Coccolithophore size rules—reconstructing ancient cell geometry and cellular calcite quota from fossil coccoliths. Mar. Micropaleontol. 67, 143–154 (2008).Article 

    Google Scholar 
    Sheward, R. M., Poulton, A. J., Gibbs, S. J., Daniels, C. J. & Bown, P. R. Physiology regulates the relationship between coccosphere geometry and growth phase in coccolithophores. Biogeosciences 14, 1493–1509 (2017).Article 

    Google Scholar 
    Gibbs, S. J. et al. Species-specific growth response of coccolithophores to Palaeocene–Eocene environmental change. Nat. Geosci. 6, 218–222 (2013).Article 

    Google Scholar 
    Herrmann, S. & Thierstein, H. R. Cenozoic coccolith size changes—evolutionary and/or ecological controls? Palaeogeogr. Palaeoclimatol. Palaeoecol. 333–334, 92–106 (2012).Article 

    Google Scholar 
    Young, J. R. & Ziveri, P. Calculation of coccolith volume and its use in calibration of carbonate flux estimates. Deep-Sea Research II 22, 1679–1700 (2000).Article 

    Google Scholar 
    Daniels, C. J., Sheward, R. M. & Poulton, A. J. Biogeochemical implications of comparative growth rates of Emiliania huxleyi and Coccolithus species. Biogeosciences 11, 6915–6925 (2014).Article 

    Google Scholar 
    Westerhold, T. et al. An astronomically dated record of Earth’s climate and its predictability over the last 66 million years. Science 369, 1383–1387 (2020).Article 

    Google Scholar 
    Pälike, H. et al. A Cenozoic record of the equatorial Pacific carbonate compensation depth. Nature 488, 609–614 (2012).Article 

    Google Scholar 
    Misra, S. & Froelich, P. N. Lithium isotope history of cenozoic seawater: changes in silicate weathering and reverse weathering. Science 335, 818–823 (2012).Article 

    Google Scholar 
    Ravizza, G. E. & Zachos, J. C. in Treatise on Geochemistry Vol. 6 (ed. Elderfield, H.) 551–581 (Elsevier, 2003).McArthur, J. M., Howarth, R. J. & Bailey, T. R. Strontium isotope stratigraphy: LOWESS version 3: best fit to the marine Sr‐isotope curve for 0–509 Ma and accompanying look‐up table for deriving numerical age. J. Geol. 109, 155–170 (2001).Article 

    Google Scholar 
    Pegram, W. J., Krishnaswami, S., Ravizza, G. E. & Turekian, K. K. The record of sea water 1870s/1860s variation through the Cenozoic. Earth Planet. Sci. Lett. 113, 569–576 (1992).Article 

    Google Scholar 
    Shipboard Scientific Party, 2004. Leg 208 summary. In Zachos, J. C., Kroon, D. & Blum, P., et al., Proceedings of the Ocean Drilling Program, Initial Reports, 208, 1–112: College Station, TX (Ocean Drilling Program) (2004).Brummer, G. J. A. & van Eijden, A. J. M. “Blue-ocean” paleoproductivity estimates from pelagic carbonate mass accumulation rates. Mar. Micropaleontol. 19, 99–117 (1992).Article 

    Google Scholar 
    Gafar, N. A., Eyre, B. D. & Schulz, K. G. A conceptual model for projecting coccolithophorid growth, calcification and photosynthetic carbon fixation rates in response to global ocean change. Front. Mar. Sci. 4, 433 (2018).Article 

    Google Scholar 
    Gafar, N. A. & Schulz, K. G. A three-dimensional niche comparison of Emiliania huxleyi and Gephyrocapsa oceanica: reconciling observations with projections. Biogeosciences 15, 3541–3560 (2018).Article 

    Google Scholar 
    Gafar, N. A., Eyre, B. D. & Schulz, K. G. A comparison of species specific sensitivities to changing light and carbonate chemistry in calcifying marine phytoplankton. Sci. Rep. 9, 2486 (2019).Article 

    Google Scholar 
    Zhang, Y. G. et al. Refining the alkenone–pCO2 method I: lessons from the Quaternary glacial cycles. Geochim. Cosmochim. Acta 260, 177–191 (2019).Article 

    Google Scholar 
    Freeman, K. H. & Pagani, M. in A History of Atmospheric CO2 and Its Effects on Plants, Animals, and Ecosystems Vol. 177 (eds Baldwin, I. T. et al.) 35–61 (Springer-Verlag, 2005).Pagani, M. The alkenone–CO2 proxy and ancient atmospheric carbon dioxide. Phil. Trans. R. Soc. A 360, 609–632 (2002).Article 

    Google Scholar 
    Beerling, D. J. & Royer, D. L. Convergent Cenozoic CO2 history. Nat. Geosci. 4, 418–420 (2011).Article 

    Google Scholar 
    Henehan, M. J. et al. Revisiting the Middle Eocene Climatic Optimum ‘Carbon Cycle Conundrum’ with new estimates of atmospheric pCO2 from boron isotopes. Paleoceanogr. Paleoclimatol. https://doi.org/10.1029/2019PA003713 (2020).Zachos, J., Pagani, M., Sloan, L. C., Thomas, E. & Billups, K. Trends, rhythms, and aberrations in global climate 65 Ma to present. Science 292, 686–693 (2001).Article 

    Google Scholar 
    Stap, L., Sluijs, A., Thomas, E. & Lourens, L. Patterns and magnitude of deep sea carbonate dissolution during Eocene Thermal Maximum 2 and H2, Walvis Ridge, southeastern Atlantic Ocean, Paleoceanography 24, PA1211, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2008PA001655 (2009).Sluijs, A. et al. Warm and wet conditions in the Arctic region during Eocene Thermal Maximum 2. Nat. Geosci. 2, 777–780 (2009).Article 

    Google Scholar 
    Stap, L. et al. High-resolution deep-sea carbon and oxygen isotope records of Eocene Thermal Maximum 2 and H2. Geology 38, 607–610 (2010).Article 

    Google Scholar 
    Bohaty, S. M. & Zachos, J. C. Significant Southern Ocean warming event in the late middle Eocene. Geology 31, 1017 (2003).Article 

    Google Scholar 
    van der Ploeg, R. et al. Middle Eocene greenhouse warming facilitated by diminished weathering feedback. Nat. Commun. 9, 2877 (2018).Article 

    Google Scholar 
    Bach, L. T., Riebesell, U., Gutowska, M. A., Federwisch, L. & Schulz, K. G. A unifying concept of coccolithophore sensitivity to changing carbonate chemistry embedded in an ecological framework. Prog. Oceanogr. 135, 125–138 (2015).Article 

    Google Scholar 
    Monteiro, F. M. et al. Why marine phytoplankton calcify. Sci. Adv. 2, e1501822–e1501822 (2016).Article 

    Google Scholar 
    Shipboard Scientific Party, 2004. Site 1263. In Zachos, J. C., Kroon, D., Blum, P., et al., Proceedings of the Ocean Drilling Program, Initial Reports, 208, 1–87 College Station, TX (Ocean Drilling Program) (2004).Bice, K. L., Sloan, L. C. & Barron, E. J. in Warm Climates in Earth History (eds Huber, B. T., Macleod, K. G., & Wing, S. L.) 79–129 (Cambridge Univ. Press, 2000).Handoh, I. C., Bigg, G. R. & Jones, E. J. W. Evolution of upwelling in the Atlantic Ocean basin. Palaeogeogr. Palaeoclimatol. Palaeoecol. 202, 31–58 (2003).Article 

    Google Scholar 
    Minoletti, F., Hermoso, M. & Gressier, V. Separation of sedimentary micron-sized particles for palaeoceanography and calcareous nannoplankton biogeochemistry. Nat. Protoc. 4, 14–24 (2009).Article 

    Google Scholar 
    Zhang, H., Stoll, H., Bolton, C., Jin, X. & Liu, C. A refinement of coccolith separation methods: Measuring the sinking characters of coccoliths. Biogeosciences Discussions (2018): 1–30 https://doi.org/10.5194/bg-2018-82 (2020).Hermoso, M. et al. Towards the use of the coccolith vital effects in palaeoceanography: a field investigation during the middle Miocene in the SW Pacific Ocean. Deep Sea Res. Part 1 160, 103262 (2020).Article 

    Google Scholar 
    Lauretano, V., Hilgen, F. J., Zachos, J. C. & Lourens, L. J. Astronomically tuned age model for the early Eocene carbon isotope events: a new high-resolution δ13Cbenthic record of ODP site 1263 between ~49 and ~54 Ma. Newsl. Stratigr. 49, 383–400 (2016).Article 

    Google Scholar 
    Westerhold, T., Röhl, U., Frederichs, T., Bohaty, S. M. & Zachos, J. C. Astronomical calibration of the geological timescale: closing the middle Eocene gap. Clim. Past 11, 1181–1195 (2015).Article 

    Google Scholar 
    Westerhold, T. et al. Astronomical Calibration of the Ypresian Time Scale: Implications for Seafloor Spreading Rates and the Chaotic Behaviour of the Solar System? Preprint at Clim. Past Discuss. https://doi.org/10.5194/cp-2017-15 (2017).Gatuso, J. P., Epitalon, J. M., Lavigne, H. & Orr, J. seacarb: Seawater Carbonate Chemistry (2021); https://CRAN.R-project.org/package=seacarb More