More stories

  • in

    A synthesis and future research directions for tropical mountain ecosystem restoration

    1.Dimitrov, D., Nogués-Bravo, D. & Scharff, N. Why do tropical mountains support exceptionally high biodiversity? The eastern arc mountains and the drivers of saintpaulia diversity. PLoS One 7, e48908 (2012).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    2.Spehn, E. & Körner, C. A Global Assessment of Mountain Biodiversity and its Function, vol. 23, 393–400 (2005).3.Merckx, V. S. F. T. et al. Evolution of endemism on a young tropical mountain. Nature 524, 347–350 (2015).CAS 
    PubMed 
    ADS 

    Google Scholar 
    4.Mengist, W., Soromessa, T. & Legese, G. Ecosystem services research in mountainous regions: A systematic literature review on current knowledge and research gaps. Sci. Total Environ. 702, 134581 (2020).CAS 
    PubMed 
    ADS 

    Google Scholar 
    5.Gleeson, E. H. et al. Mountains of our future earth: defining priorities for mountain research: A synthesis from the 2015 Perth III conference. Mt. Res. Dev. 36, 537–548 (2016).
    Google Scholar 
    6.Jacob, M. et al. Land use and cover dynamics since 1964 in the Afro-Alpine vegetation belt: Lib Amba Mountain in North Ethiopia. Land Degrad. Dev. 27, 641–653 (2016).
    Google Scholar 
    7.Dhakal, B. et al. Impacts of cardamom cultivation on montane forest ecosystems in Sri Lanka. For. Ecol. Manag. 274, 151–160 (2012).
    Google Scholar 
    8.Thijs, K. W. et al. Contrasting cloud forest restoration potential between plantations of different exotic tree species. Restor. Ecol. 22, 472–479 (2014).
    Google Scholar 
    9.Long, M. S. et al. Impact of nonnative feral pig removal on soil structure and nutrient availability in Hawaiian tropical montane wet forests. Biol. Invasions 19, 749–763 (2017).
    Google Scholar 
    10.Elgar, A. T., Freebody, K., Pohlman, C. L., Shoo, L. P. & Catterall, C. P. Overcoming barriers to seedling regeneration during forest restoration on tropical pasture land and the potential value of woody weeds. Front. Plant Sci. 5, 200 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    11.Rojas-Botero, S., Solorza-Bejarano, J., Kollmann, J. & Teixeira, L. H. Nucleation increases understory species and functional diversity in early tropical forest restoration. Ecol. Eng. 158, 106031 (2020).
    Google Scholar 
    12.Hooper, E., Legendre, P. & Condit, R. Barriers to forest regeneration of deforested and abandoned land in Panama. J. Appl. Ecol. 42, 1165–1174 (2005).
    Google Scholar 
    13.Krishnaswamy, J., John, R. & Joseph, S. Consistent response of vegetation dynamics to recent climate change in tropical mountain regions. Glob. Change Biol. 20, 203–215 (2013).ADS 

    Google Scholar 
    14.Soh, M. C. K. et al. Impacts of habitat degradation on tropical montane biodiversity and ecosystem services: A systematic map for identifying future research priorities. Front. For. Glob. Change 2, 1–18 (2019).
    Google Scholar 
    15.Tovar, C., Arnillas, C. A., Cuesta, F. & Buytaert, W. Diverging responses of tropical andean biomes under future climate conditions. PLoS One 8, e63634 (2013).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    16.Helmer, E. H. et al. Neotropical cloud forests and páramo to contract and dry from declines in cloud immersion and frost. PLoS One 14, e0213155 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    17.Hall, J., Burgess, N. D., Lovett, J., Mbilinyi, B. & Gereau, R. E. Conservation implications of deforestation across an elevational gradient in the Eastern Arc Mountains, Tanzania. Biol. Conserv. 142, 2510–2521 (2009).
    Google Scholar 
    18.Christmann, T. & Oliveras, I. Nature of alpine ecosystems in tropical mountains of South America. in Encyclopedia of the World’s Biomes 1–10 (Elsevier Inc., 2020). https://doi.org/10.1016/B978-0-12-409548-9.12481-919.Dixon, A. P., Faber-Langendoen, D., Josse, C., Morrison, J. & Loucks, C. J. Distribution mapping of world grassland types. J. Biogeogr. 41, 2003–2019 (2014).
    Google Scholar 
    20.Young, K. R. & León, B. Tree-line changes along the Andes: Implications of spatial patterns and dynamics. Philos. Trans. R. Soc. B Biol. Sci. 362, 263–272 (2007).
    Google Scholar 
    21.Harsch, M. A. & Bader, M. Y. Treeline form—A potential key to understanding treeline dynamics. Glob. Ecol. Biogeogr. 20, 582–596 (2011).
    Google Scholar 
    22.Bruijnzeel, L. A., Mulligan, M. & Scatena, F. N. Hydrometeorology of tropical montane cloud forests: Emerging patterns. Hydrol. Process. 25, 465–498 (2011).ADS 

    Google Scholar 
    23.Kessler, M. & Kluge, J. Diversity and endemism in tropical montane forests—From patterns to processes. Tropical Mountain Forest: Patterns and Processes in a Biodiversity Hotspot, vol. 2 (2010).24.Aide, T. M. & Grau, H. R. Globalization, migration, and Latin American ecosystems. Science 305, 1915–1917 (2004).PubMed 

    Google Scholar 
    25.Bender, O. Abandoned altitudes? Decrease and expansion of grassland in the Hinterland of Popayán, Southern Colombian Andes. J. Mt. Sci. 12, 123–133 (2015).
    Google Scholar 
    26.Zhang, B., Mo, S., Tan, T., Xiao, F. & Wu, H. Urbanization and De-urbanization in mountain regions of China. Mt. Res. Dev. 24, 206–209 (2004).
    Google Scholar 
    27.Di Sacco, A. et al. Ten golden rules for reforestation to optimize carbon sequestration, biodiversity recovery and livelihood benefits. Glob. Change Biol. https://doi.org/10.1111/gcb.15498 (2021).Article 

    Google Scholar 
    28.International Union for Conservation of Nature. The Bonn Challenge | Bonchallenge. Iucn (2020).29.Society for Ecological Restoration. The SER primer on ecological restoration. Sci. Policy Work. Gr. 2002, 9 (2002).
    Google Scholar 
    30.Holl, K. D. Primer of Ecological Restoration (Island Press, 2020). https://doi.org/10.1007/s13412-020-00621-w.Book 

    Google Scholar 
    31.Chazdon, R. REVIEW: Restoring tropical forests: A practical guide. Ecol. Restor. 33, 118–119 (2015).
    Google Scholar 
    32.Chazdon, R. L. Tropical forest recovery: Legacies of human impact and natural disturbances. Perspect. Plant Ecol. Evol. Syst. 6, 51–71 (2003).
    Google Scholar 
    33.Ghazoul, J. & Chazdon, R. Degradation and recovery in changing forest landscapes: A multiscale conceptual framework. Annu. Rev. Environ. Resour. 42, 161–188 (2017).
    Google Scholar 
    34.Meli, P. et al. A global review of past land use, climate, and active vs passive restoration effects on forest recovery. PLoS One 12, 1–17 (2017).
    Google Scholar 
    35.Holl, K. D. Restoration of tropical forests. Restor. Ecol. New Front. https://doi.org/10.1002/9781118223130.ch9 (2012).Article 

    Google Scholar 
    36.Meli, P. Tropical forest restoration. Twenty years of academic research. Interciencia 28, 581 (2003).
    Google Scholar 
    37.Venkatesh, B., Lakshman, N. & Purandara, B. K. Hydrological impacts of afforestation—A review of research in India. J. For. Res. 25, 37–42 (2014).
    Google Scholar 
    38.Aide, T. M., Ruiz-Jaen, M. C. & Grau, H. R. What is the state of tropical montane cloud forest restoration? Tropical Montane Cloud Forests: science for conservation and management. For. Ecol. Manag. https://doi.org/10.1017/CBO9780511778384.010 (2011).Article 

    Google Scholar 
    39.Guariguata, M. R. Restoring tropical montane forests. Forest Restoration in Landscapes: Beyond Planting Trees (2005). https://doi.org/10.1007/0-387-29112-1_4340.Mengist, W., Soromessa, T. & Legese, G. Method for conducting systematic literature review and meta-analysis for environmental science research. MethodsX 7, 100777 (2020).PubMed 

    Google Scholar 
    41.Arasumani, M. et al. Not seeing the grass for the trees: Timber plantations and agriculture shrink tropical montane grassland by two-thirds over four decades in the Palani Hills, a Western Ghats Sky Island. PLoS One 13, e0190003 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Raman, T. R. S., Mudappa, D. & Kapoor, V. Restoring rainforest fragments: survival of mixed-native species seedlings under contrasting site conditions in the Western Ghats, India. Restor. Ecol. 17, 137–147 (2009).
    Google Scholar 
    43.Körner, C. et al. A global inventory of mountains for bio-geographical applications. Alp. Bot. 127, 1–15 (2017).
    Google Scholar 
    44.Lewin-Koh, N. J. et al. maptools: Tools for reading and handling spatial objects. R package version 0.8-10. http://CRAN.R-project.org/package=maptools (2011).45.R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2019). https://www.R-project.org/46.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016). ISBN 978-3-319-24277-4. https://ggplot2.tidyverse.org47.Srinivasan, M. P., Bhatia, S. & Shenoy, K. Vegetation-environment relationships in a South Asian tropical montane grassland ecosystem: Restoration implications. Trop. Ecol. 56, 201–217 (2015).
    Google Scholar 
    48.Le Stradic, S., Buisson, E. & Fernandes, G. W. Restoration of Neotropical grasslands degraded by quarrying using hay transfer. Appl. Veg. Sci. 17, 482–492 (2014).
    Google Scholar 
    49.De De Vasconcelos, M. F. O que são campos rupestres e campos de altitude nos topos de montanha do Leste do Brasil?. Rev. Bras. Bot. 34, 241–246 (2011).
    Google Scholar 
    50.Seddon, N. et al. Getting the message right on nature-based solutions to climate change. Glob. Change Biol. https://doi.org/10.1111/gcb.15513 (2021).Article 

    Google Scholar 
    51.Home | Trillion Trees (2020).52.Abadín, J. et al. Successional dynamics of soil characteristics in a long fallow agricultural system of the high tropical Andes. Soil Biol. Biochem. 34, 1739–1748 (2002).
    Google Scholar 
    53.Abreu, Z., Llambí, L. D. & Sarmiento, L. Sensitivity of soil restoration indicators during páramo succession in the high tropical andes: Chronosequence and permanent plot approaches. Restor. Ecol. 17, 619–627 (2009).
    Google Scholar 
    54.Bueno, A. & Llambí, L. D. Facilitation and edge effects influence vegetation regeneration in old-fields at the tropical Andean forest line. Appl. Veg. Sci. 18, 613–623 (2015).
    Google Scholar 
    55.Sarmiento, L., Llambí, L. D., Escalona, A. & Marquez, N. Vegetation patterns, regeneration rates and divergence in an old-field succession of the high tropical Andes. Plant Ecol. 166, 63–74 (2003).
    Google Scholar 
    56.Sarmiento, L., Smith, J. K., Márquez, N., Escalona, A. & Erazo, M. C. Constraints for the restoration of tropical alpine vegetation on degraded slopes of the Venezuelan Andes. Plant Ecol. Divers. 8, 277–291 (2015).
    Google Scholar 
    57.Sarmiento, L. & Bottner, P. Carbon and nitrogen dynamics in two soils with different fallow times in the high tropical Andes: Indications for fertility restoration. Appl. Soil Ecol. 19, 79–89 (2002).
    Google Scholar 
    58.Sarmiento, L., Abadín, J., González-Prieto, S. & Carballas, T. Assessing and modeling the role of the native legume Lupinus meridanus in fertility restoration in a heterogeneous mountain environment of the tropical Andes. Agric. Ecosyst. Environ. 159, 29–39 (2012).
    Google Scholar 
    59.Hilário, R. R., Castro, S. A. B., Ker, F. T. O. & Fernandes, G. Unexpected effects of pigeon-peas (Cajanus cajan) in the restoration of rupestrian fields [Efeito Inesperado do Feijão-Guandu (Cajanus cajan) na Restauração de Campos Rupestres]. Planta Daninha 29, 717–723 (2011).
    Google Scholar 
    60.Le Stradic, S., Buisson, E., Negreiros, D., Campagne, P. & Wilson Fernandes, G. The role of native woody species in the restoration of Campos Rupestres in quarries. Appl. Veg. Sci. 17, 109–120 (2014).
    Google Scholar 
    61.Arasumani, M., Bunyan, M. & Robin, V. V. Opportunities and challenges in using remote sensing for invasive tree species management, and in the identification of restoration sites in tropical montane grasslands. J. Environ. Manag. 280, 111759 (2020).
    Google Scholar 
    62.Sarmiento, F. O. Arrested succession in pastures hinders regeneration of Tropandean forests and shreds mountain landscapes. Environ. Conserv. 24, 14–23 (1997).
    Google Scholar 
    63.Wesche, K. et al. Recruitment of trees at tropical alpine treelines: Erica in Africa versus Polylepis in South America. Plant Ecol. Divers. 1, 35–46 (2008).
    Google Scholar 
    64.Middendorp, R. S., Pérez, A. J., Molina, A., Lambin, E. F. & Pérez Castañeda, A. J. The potential to restore native woody plant richness and composition in a reforesting landscape: A modeling approach in the Ecuadorian Andes. Landsc. Ecol. 31, 1581–1599 (2016).
    Google Scholar 
    65.De Guevara, I.H.-L., Rojas-Soto, O. R., López-Barrera, F., Puebla-Olivares, F. & Díaz-Castelazo, C. Seed dispersal by birds in a cloud forest landscape in central Veracruz, Mexico: Its role in passive restoration. Rev. Chil. Hist. Nat. 85, 89–100 (2012).
    Google Scholar 
    66.Lira-Noriega, A., Guevara, S., Laborde, J. & Sanchez-Rios, G. Floristic composition in pastures of Los Tuxtlas, Veracruz, Mexico. ACTA Bot. Mex. 80, 59–87 (2007).
    Google Scholar 
    67.Muniz-Castro, M. A., Williams-Linera, G. & Benayas, J. M. R. Distance effect from cloud forest fragments on plant community structure in abandoned pastures in Veracruz, Mexico. J. Trop. Ecol. 22, 431–440 (2006).
    Google Scholar 
    68.Räger, N., Williams-Linera, G. & Huth, A. Modeling the dynamics of tropical montane cloud forest in central Veracruz, Mexico. in Tropical Montane Cloud Forests: Science for Conservation and Management 584–594 (2011). https://doi.org/10.1017/CBO9780511778384.06369.Violi, H. A. et al. Disturbance changes arbuscular mycorrhizal fungal phenology and soil glomalin concentrations but not fungal spore composition in montane rainforests in Veracruz and Chiapas, Mexico. For. Ecol. Manag. 254, 276–290 (2008).
    Google Scholar 
    70.Williams-Linera, G., Alvarez-Aquino, C. & Pedraza, R. A. Forest restoration in the tropical montane cloud forest belt of central veracruz, Mexico. Tropical Montane Cloud Forests: Science for Conservation and Management (2011). https://doi.org/10.1017/CBO9780511778384.06771.Cole, R. J., Litton, C. M., Koontz, M. J. & Loh, R. K. Vegetation recovery 16 years after feral pig removal from a wet Hawaiian forest. Biotropica 44, 463–471 (2012).
    Google Scholar 
    72.Cole, R. J. & Litton, C. M. Vegetation response to removal of non-native feral pigs from Hawaiian tropical montane wet forest. Biol. Invasions 16, 125–140 (2014).
    Google Scholar 
    73.Gould, R. K., Mooney, H., Nelson, L., Shallenberger, R. & Daily, G. C. Restoring native forest understory: The influence of ferns and light in a Hawaiian experiment. Sustainability 5, 1317–1339 (2013).
    Google Scholar 
    74.Hart, P. J. Tree growth and age in an ancient Hawaiian wet forest: Vegetation dynamics at two spatial scales. J. Trop. Ecol. 26, 1–11 (2010).
    Google Scholar 
    75.Ibanez, T. & Hart, P. J. Spatial patterns of tree recruitment in a montane Hawaiian wet forest after cattle removal and pig population control. Appl. Veg. Sci. 23, 197–209 (2020).
    Google Scholar 
    76.Pinto, J. R., Davis, A. S., Leary, J. J. K. & Aghai, M. M. Stocktype and grass suppression accelerate the restoration trajectory of Acacia koa in Hawaiian montane ecosystems. New For. 46, 855–867 (2015).
    Google Scholar 
    77.Hylander, K. & Nemomissa, S. Complementary roles of home gardens and exotic tree plantations as alternative habitats for plants of the Ethiopian montane rainforest [Roles complementarios de jardines doḿesticos y plantaciones de ’arboles ex́oticos como h́abitats alternativos para plan. Conserv. Biol. 23, 400–409 (2009).PubMed 

    Google Scholar 
    78.Roose, E. & Ndayizigiye, F. Agroforestry, water and soil fertility management to fight erosion in tropical mountains of Rwanda. Soil Technol. 11, 109–119 (1997).
    Google Scholar 
    79.Uhlig, S. K. Tropical mountain ecology in Ethiopia as a basis for conservation, management and restoration. Trop. For. Transit. https://doi.org/10.1007/978-3-0348-7256-0_8 (1992).Article 

    Google Scholar 
    80.Carilla, J. & Grau, H. R. 150 years of tree establishment, land use and climate change in Montane grasslands, Northwest Argentina. Biotropica 42, 49–58 (2010).
    Google Scholar 
    81.Camelo, O. J., Urrego, L. E. & Orrego, S. A. Environmental and socioeconomic drivers of woody vegetation recovery in a human-modified landscape in the Rio Grande basin (Colombian Andes). Restor. Ecol. 25, 912–921 (2017).
    Google Scholar 
    82.Wilson, S. J., Coomes, O. T. & Dallaire, C. O. The `ecosystem service scarcity path’ to forest recovery: A local forest transition in the Ecuadorian Andes. Reg. Environ. Change 19, 2437–2451 (2019).
    Google Scholar 
    83.Middendorp, R. S., Pérez, A. J., Molina, A. & Lambin, E. F. The potential to restore native woody plant richness and composition in a reforesting landscape: A modeling approach in the Ecuadorian Andes. Landsc. Ecol. 31, 1581–1599 (2016).
    Google Scholar 
    84.Bingli, L., Weide, Z. & Rongyuan, Z. The rebirth of tropical rainforest – ecological restoration planning for Sanda Mountain of Xishuangbanna, China. Landsc. Archit. Front. 8, 108–125 (2020).
    Google Scholar 
    85.Byers, A. C. Alpine habitat conservation and restoration in tropical and sub-tropical high mountains. Routledge Handb. Ecol. Environ. Restor. https://doi.org/10.4324/9781315685977 (2017).Article 

    Google Scholar 
    86.Guariguata, M. R. Restoring tropical montane forests. in Forest Restoration in Landscapes: Beyond Planting Trees 298–302 (2005). https://doi.org/10.1007/0-387-29112-1_4387.González-Espinosa, M. et al. Restoration of forest ecosystems in fragmented landscapes of temperate and montane tropical Latin America. in Biodiversity Loss and Conservation in Fragmented Forest Landscapes: The Forests of Montane Mexico and Temperate South America 335–369 (2007).88.Newmark, W. D., Jenkins, C. N., Pimm, S. L., McNeally, P. B. & Halley, J. M. Targeted habitat restoration can reduce extinction rates in fragmented forests. Proc. Natl. Acad. Sci. U.S.A. 114, 9635–9640 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    89.Holl, K. D. Research directions in tropical forest restoration. Ann. Mo. Bot. Gard. 102, 237–250 (2017).
    Google Scholar 
    90.Roose, E., Ndayizigiye, F. & Sekayange, L. Agroforestry and land husbandry in Rwanda. How to restore the acid soils productivity in tropical mountains densely populated? [L’agroforesterie et la GCES au Rwanda. Comment restaurer la productivite des terres acides dans une region tropicale de montagn. Cah. ORSTOM Ser. Pedol. 28, 327–349 (1993).
    Google Scholar 
    91.Diego Leon, J., Isabel Gonzalez, M. & Fernando Gallardo, J. Biogeochemical cycles in natural forest and conifer plantations in the high mountains of Colombia. Rev. Biol. Trop. 59, 1883–1894 (2011).
    Google Scholar 
    92.Chazdon, R. L. et al. Erratum: Fostering natural forest regeneration on former agricultural land through economic and policy interventions. Environ. Res. Lett. 15, 043002. https://doi.org/10.1088/1748-9326/ab79e6 (2020).Article 
    ADS 

    Google Scholar 
    93.Miranda-Castro, L. & Padrón, S. From the mountains to the sea: Restoring shaded coffee plantations to protect tropical coastal ecosystems. in Proceedings of MTS/IEEE OCEANS, 2005 vol. 2005 662–669 (2005).94.Hakim, L. & Miyakawa, H. Integrating ecosystem restoration and development of recreation sites in degraded tropical mountain areas in East Java, Indonesia. AIP Conf. Proc. 2019 (2018).95.Gilroy, J. J. et al. Cheap carbon and biodiversity co-benefits from forest regeneration in a hotspot of endemism. Nat. Clim. Change 4, 503–507 (2014).ADS 

    Google Scholar 
    96.Räger, N., Williams-Linera, G. & Huth, A. Modeling the dynamics of tropical montane cloud forest in central Veracruz, Mexico. Tropical Montane Cloud Forests: Science for Conservation and Management (2011). https://doi.org/10.1017/CBO9780511778384.06397.Chen, T.-S., Lin, C.-Y., Ho, S.-H., Lin, C.-Y. & Yang, Y.-L. Evaluation of priority order for the landslide treatment using biodiversity index in a watershed. J. Chin. Soil Water Conserv. 45, 119–127 (2014).
    Google Scholar 
    98.Liu, H., Yi, Y., Blagodatsky, S. & Cadisch, G. Impact of forest cover and conservation agriculture on sediment export: A case study in a montane reserve, south-western China. Sci. Total Environ. 702, 134802 (2020).CAS 
    PubMed 
    ADS 

    Google Scholar 
    99.Crespo, P. et al. Land use change impacts on the hydrology of wet Andean paramo ecosystems. in Status and Perspectives of Hydrology in Small Basins (Proceedings of the Workshop held at Goslar-Hahnenklee, Germany, 30 March–2 April 2009) (IAHS, 2010). doi:https://doi.org/10.13140/2.1.5137.6320100.Muñoz-Villers, L. E. & McDonnell, J. J. Land use change effects on runoff generation in a humid tropical montane cloud forest region. Hydrol. Earth Syst. Sci. 17, 3543–3560 (2013).ADS 

    Google Scholar 
    101.Calle, Z., Henao-Gallego, N., Giraldo, C. & Armbrecht, I. A comparison of vegetation and ground-dwelling ants in abandoned and restored gullies and landslide surfaces in the Western Colombian Andes. Restor. Ecol. 21, 729–735 (2013).
    Google Scholar 
    102.Posada, J. M., Mitche, T. & Cavelier, J. Cattle and weedy shrubs as restoration tools of tropical montane rainforest. Restor. Ecol. 8, 370–379 (2000).
    Google Scholar 
    103.Lemenih, M. & Teketay, D. Changes in soil seed bank composition and density following deforestation and subsequent cultivation of a tropical dry Afromontane forest in Ethiopia. Trop. Ecol. 47, 1–12 (2006).
    Google Scholar 
    104.Galindo, V., Calle, Z., Chará, J. & Armbrecht, I. Facilitation by pioneer shrubs for the ecological restoration of riparian forests in the Central Andes of Colombia. Restor. Ecol. 25, 731–737 (2017).
    Google Scholar 
    105.Slocum, M. G., Aide, T. M., Zimmerman, J. K. & Navarro, L. A strategy for restoration of montane forest in anthropogenic fern thickets in the Dominican Republic. Restor. Ecol. 14, 526–536 (2006).
    Google Scholar 
    106.Rurangwa, M. L., Matthews, T. J., Niyigaba, P., Tobias, J. A. & Whittaker, R. J. Assessing tropical forest restoration after fire using birds as indicators: An afrotropical case study. For. Ecol. Manag. 10, 118765. https://doi.org/10.1016/j.foreco.2020.118765 (2020).Article 

    Google Scholar 
    107.Gunaratne, A. M. T. A., Gunatilleke, C. V. S., Gunatilleke, I. A. U. N., Madawala Weerasinghe, H. M. S. P. & Burslem, D. F. R. P. Barriers to tree seedling emergence on human-induced grasslands in Sri Lanka. J. Appl. Ecol. 47, 157–165 (2010).
    Google Scholar 
    108.Le Stradic, S., Fernandes, G. W. & Buisson, E. No recovery of campo rupestre grasslands after gravel extraction: implications for conservation and restoration. Restor. Ecol. 26, S151–S159 (2018).
    Google Scholar 
    109.Sanchez-De Leon, Y., Zou, X., Borges, S. & Ruan, H. Recovery of native earthworms in abandoned tropical pastures. Conserv. Biol. 17, 999–1006 (2003).
    Google Scholar 
    110.Wilms, J. & Kappelle, M. Frugivorous birds, habitat preference and seed dispersal in a fragmented Costa Rican montane oak forest landscape. in Ecology and conservation of neotropical montane oak forests 309–324 (Springer, 2006).111.Shoo, L. P., Storlie, C., Vanderwal, J., Little, J. & Williams, S. E. Targeted protection and restoration to conserve tropical biodiversity in a warming world. Glob. Change Biol. 17, 186–193 (2011).ADS 

    Google Scholar 
    112.Edwards, D. P., Massam, M. R., Haugaasen, T. & Gilroy, J. J. Tropical secondary forest regeneration conserves high levels of avian phylogenetic diversity. Biol. Conserv. 209, 432–439 (2017).
    Google Scholar 
    113.Gutierrez-Chacon, C., Valderrama-A, C. & Klein, A.-M. Biological corridors as important habitat structures for maintaining bees in a tropical fragmented landscape. J. Insect Conserv. 24, 187–197 (2020).
    Google Scholar 
    114.Kattan, G. H., Correa, D., Escobar, F. & Medina, C. Leaf-litter arthropods in restored forests in the Colombian Andes: A comparison between secondary forest and tree plantations. Restor. Ecol. 14, 95–102 (2006).
    Google Scholar 
    115.Davies, R. W., Edwards, D. P. & Edwards, F. A. Secondary tropical forests recover dung beetle functional diversity and trait composition. Anim. Conserv. 23, 617–627 (2020).
    Google Scholar 
    116.Marian, F. et al. Conversion of Andean montane forests into plantations: Effects on soil characteristics, microorganisms, and microarthropods. Biotropica https://doi.org/10.1111/btp.12813 (2020).Article 

    Google Scholar 
    117.Brancalion, P. H. S. & Holl, K. D. Functional composition trajectory: A resolution to the debate between Suganuma, Durigan, and Reid. Restor. Ecol. 24, 1–3 (2016).
    Google Scholar 
    118.Matos, I. S., Eller, C. B., Oliveras, I., Mantuano, D. & Rosado, B. H. P. Three eco-physiological strategies of response to drought maintain the form and function of a tropical montane grassland. J. Ecol. https://doi.org/10.1111/1365-2745.13481 (2020).Article 

    Google Scholar 
    119.Eller, C. B., Lima, A. L. & Oliveira, R. S. Cloud forest trees with higher foliar water uptake capacity and anisohydric behavior are more vulnerable to drought and climate change. New Phytol. 211, 489–501 (2016).CAS 
    PubMed 

    Google Scholar 
    120.Barnes, A. D. & Chapman, H. M. Dispersal traits determine passive restoration trajectory of a Nigerian montane forest. Acta Oecol. 56, 32–40 (2014).ADS 

    Google Scholar 
    121.Dimson, M. & Gillespie, T. W. Trends in active restoration of tropical dry forest: Methods, metrics, and outcomes. For. Ecol. Manage. 467, 118150 (2020).
    Google Scholar 
    122.Gann, G. D. et al. International principles and standards for the practice of ecological restoration. Second edition. Restor. Ecol. 27, S1–S46 (2019).
    Google Scholar 
    123.Wilson, S. J. & Rhemtulla, J. M. Acceleration and novelty: Community restoration speeds recovery and transforms species composition in Andean cloud forest. Ecol. Appl. 26, 203–218 (2016).PubMed 

    Google Scholar 
    124.Muñiz-Castro, M. A. et al. Distance effect from cloud forest fragments on plant community structure in abandoned pastures in Veracruz, Mexico. J. Trop. Ecol. 22, 431–440 (2006).
    Google Scholar 
    125.Van Do, T., Osawa, A. & Thang, N. T. Recovery process of a mountain forest after shifting cultivation in Northwestern Vietnam. For. Ecol. Manag. 259, 1650–1659 (2010).
    Google Scholar 
    126.Joshua Atondo-Bueno, E., Bonilla-Moheno, M. & Lopez-Barrera, F. Cost-efficiency analysis of seedling introduction vs. direct seeding of Oreomunnea mexicana for secondary forest enrichment. For. Ecol. Manag. 409, 399–406 (2018).
    Google Scholar 
    127.Trujillo-Miranda, A. L., Toledo-Aceves, T., Lopez-Barrera, F. & Guenter, S. Tree diversity and timber productivity in planted forests: Pinus patula versus mixed cloud forest species. New For. https://doi.org/10.1007/s11056-020-09787-1 (2020).Article 

    Google Scholar 
    128.Gallegos, S. C., Hensen, I., Saavedra, F. & Schleuning, M. Bracken fern facilitates tree seedling recruitment in tropical fire-degraded habitats. For. Ecol. Manag. 337, 135–143 (2015).
    Google Scholar 
    129.Peláez-Silva, J. A., León-Peláez, J. D. & Lema-Tapias, A. Conifer tree plantations for land rehabilitation: An ecological-functional evaluation. Restor. Ecol. 27, 607–615 (2019).
    Google Scholar 
    130.Ortega-Pieck, A., López-Barrera, F., Ramírez-Marcial, N. & García-Franco, J. G. Early seedling establishment of two tropical montane cloud forest tree species: The role of native and exotic grasses. For. Ecol. Manag. 261, 1336–1343 (2011).
    Google Scholar 
    131.Muniz-Castro, M.-A. et al. Restoring montane cloud forest: Establishment of three Fagaceae species in the old fields of central Veracruz, Mexico. Restor. Ecol. 23, 26–33 (2015).
    Google Scholar 
    132.Zhang, Z. H., Hu, G., Zhu, J. D. & Ni, J. Stand structure, woody species richness and composition of subtropical karst forests in Maolan, south-west China. J. Trop. For. Sci. 24, 498–506 (2012).
    Google Scholar 
    133.Garcia-De La Cruz, Y., Lopez-Barrera, F. & MariaRamos-Prado, J. Germination and seedling emergence of four endangered oak species. Madera y Bosques 22, 77–87 (2016).
    Google Scholar 
    134.Bare, M. C. & Ashton, M. S. Growth of native tree species planted in montane reforestation projects in the Colombian and Ecuadorian Andes differs among site and species. New For. 47, 333–355 (2016).
    Google Scholar 
    135.Borja, P., Molina, A., Govers, G. & Vanacker, V. Check dams and afforestation reducing sediment mobilization in active gully systems in the Andean mountains. CATENA 165, 42–53 (2018).
    Google Scholar 
    136.Gomez-Ruiz, P. A., Saenz-Romero, C. & Lindig-Cisneros, R. Early performance of two tropical dry forest species after assisted migration to pine-oak forests at different altitudes: strategic response to climate change. J. For. Res. 31, 1215–1223 (2020).
    Google Scholar 
    137.Toledo-Aceves, T. & Del-Val, E. Do plant-herbivore interactions persist in assisted migration plantings? Restor. Ecol. 29, (2020).138.Urgiles, N. et al. Application of mycorrhizal roots improves growth of tropical tree seedlings in the nursery: A step towards reforestation with native species in the Andes of Ecuador. New For. 38, 229–239 (2009).
    Google Scholar 
    139.Braasch, M., Garcia-Barrios, L., Ramirez-Marcial, N., Huber-Sannwald, E. & Cortina-Villar, S. Can cattle grazing substitute fire for maintaining appreciated pine savannas at the frontier of a montane forest biosphere-reserve?. Agric. Ecosyst. Environ. 250, 59–71 (2017).
    Google Scholar 
    140.Hernandez-Ladron De Guevara, I., Rojas-Soto, O. R., Lopez-Barrera, F., Puebla-Olivares, F. & Diaz-Castelazo, C. Seed dispersal by birds in a cloud forest landscape in central Veracruz, Mexico: Its role in passive restoration. Rev. Chil. Hist. Nat. 85, 89–100 (2012).
    Google Scholar 
    141.Holl, K. D., Loik, M. E., Lin, E. H. V. & Samuels, I. A. Tropical montane forest restoration in Costa Rica: Overcoming barriers to dispersal and establishment. Restor. Ecol. 8, 339–349 (2000).
    Google Scholar 
    142.Derroire, G., Coe, R. & Healey, J. R. Isolated trees as nuclei of regeneration in tropical pastures: Testing the importance of niche-based and landscape factors. J. Veg. Sci. 27, 679–691 (2016).
    Google Scholar 
    143.Rhoades, C. C., Eckert, G. E. & Coleman, D. C. Effect of pasture trees on soil nitrogen and organic matter: Implications for tropical montane forest restoration. Restor. Ecol. 6, 262–270 (1998).
    Google Scholar 
    144.Sheldon, K. S. & Nadkarni, N. M. The use of pasture trees by birds in a tropical montane landscape in Monteverde, Costa Rica. J. Trop. Ecol. 29, 459–462 (2013).
    Google Scholar 
    145.Sprenkle-Hyppolite, S. D., Latimer, A. M., Young, T. P. & Rice, K. J. Landscape factors and restoration practices associated with initial reforestation success in Haiti. Ecol. Restor. 34, 306–316 (2016).
    Google Scholar 
    146.Pang, C.-C., Ma, X.K.-K., Hung, T.T.-H. & Hau, B.C.-H. Early ecological succession on landslide trails, Hong Kong, China. Ecoscience 25, 153–161 (2018).
    Google Scholar 
    147.Scowcroft, P. G. & Jeffrey, J. Potential significance of frost, topographic relief, and Acacia koa stands to restoration of mesic Hawaiian forests on abandoned rangeland. For. Ecol. Manag. 114, 447–458 (1999).
    Google Scholar 
    148.Zahawi, R. A. Establishment and growth of living fence species: An overlooked tool for the restoration of degraded areas in the tropics. Restor. Ecol. 13, 92–102 (2005).
    Google Scholar 
    149.Dhakal, B., Pinard, M. A., Gunatilleke, I. A. U. N., Gunatilleke, C. V. S. & Burslem, D. F. R. P. Strategies for restoring tree seedling recruitment in high conservation value tropical montane forests underplanted with cardamom. Appl. Veg. Sci. 18, 121–133 (2015).
    Google Scholar 
    150.Wilson, S. J. & Coomes, O. T. ‘Crisis restoration’ in post-frontier tropical environments: Replanting cloud forests in the Ecuadorian Andes. J. Rural Stud. 67, 152–165 (2019).
    Google Scholar 
    151.Pethiyagoda, R. S. & Manamendra-Arachchi, K. Endangered anurans in a novel forest in the highlands of Sri Lanka. Wildl. Res. 39, 641–648 (2012).
    Google Scholar 
    152.Del Castillo, R. F. & Blanco-Macías, A. Secondary succession under a slash-and-burn regime in a tropical montane cloud forest: soil and vegetation characteristics. Biodivers. loss Conserv. Fragm. For. landscapes. For. Mont. Mex. Temp. South Am. CABI, Wallingford, Oxfordshire, UK 158–180 (2007).153.Bautista-Cruz, A., Del Castillo, R. F., Etchevers-Barra, J. D., Gutiérrez-Castorena, M. D. C. & Baez, A. Selection and interpretation of soil quality indicators for forest recovery after clearing of a tropical montane cloud forest in Mexico. For. Ecol. Manag. 277, 74–80 (2012).
    Google Scholar 
    154.Sarmiento, L., Llambí, L. D., Escalona, A. & Marquez, N. Vegetation patterns, regeneration rates and divergence in an old-field succession of the high tropical Andes. Plant Ecol. 166, 145–156 (2003).
    Google Scholar 
    155.Raman, T. R. S. Effects of habitat structure and adjacent habitats on birds in tropical rainforest fragments and shaded plantations in the Western Ghats, India. Biodivers. Conserv. 15, 1577–1607 (2006).
    Google Scholar 
    156.Gunaratne, A. M. T. A., Gunatilleke, C. V. S., Gunatilleke, I. A. U. N., Madawala, H. M. S. P. & Burslem, D. F. R. P. Overcoming ecological barriers to tropical lower montane forest succession on anthropogenic grasslands: Synthesis and future prospects. For. Ecol. Manag. 329, 340–350 (2014).
    Google Scholar 
    157.Mendoza-Vega, J., Ku-Quej, V. M., Messing, I. & Pérez-Jiménez, J. C. Effects of native tree planting on soil recovery in tropical montane cloud forests. For. Sci. 66, 700–711 (2020).
    Google Scholar 
    158.Calle, A. & Holl, K. D. Riparian forest recovery following a decade of cattle exclusion in the Colombian Andes. For. Ecol. Manag. 452, 117563 (2019).
    Google Scholar 
    159.Holl, K. D. Factors limiting tropical rain forest regeneration in abandoned pasture: Seed rain, seed germination, microclimate, and soil. Biotropica 31, 229–242 (1999).
    Google Scholar 
    160.Mullah, C. J. A., Klanderud, K., Totland, O. & Kigomo, B. Recovery of plant species richness and composition in an abandoned forest settlement area in Kenya. Restor. Ecol. 52, 77–87 (2011).
    Google Scholar 
    161.Liu, X., Lu, Y., Yang, Z. & Zhou, Y. Regeneration and development of native plant species in restored mountain forests, Hainan Island, China. Mt. Res. Dev. 34, 396–404 (2014).CAS 

    Google Scholar 
    162.Gunaratne, A. M. T. A., Gunatilleke, C. V. S., Gunatilleke, I. A. U. N., Weerasinghe, H. M. S. P. M. & Burslem, D. F. R. P. Release from root competition promotes tree seedling survival and growth following transplantation into human-induced grasslands in Sri Lanka. For. Ecol. Manag. 262, 229–236 (2011).163.Cole, R. J., Holl, K. D., Keene, C. L. & Zahawi, R. A. Direct seeding of late-successional trees to restore tropical montane forest. For. Ecol. Manag. 261, 1590–1597 (2011).
    Google Scholar 
    164.Alvarez-Aquino, C., Williams-Linera, G. & Newton, A. C. Experimental native tree seedling establishment for the restoration of a Mexican cloud forest. Restor. Ecol. 12, 412–418 (2004).
    Google Scholar 
    165.Joshi, A. A., Ratnam, J. & Sankaran, M. Frost maintains forests and grasslands as alternate states in a montane tropical forest–grassland mosaic; But alien tree invasion and warming can disrupt this balance. J. Ecol. https://doi.org/10.1111/1365-2745.13239 (2019).Article 

    Google Scholar 
    166.Singh, K. P., Mandal, T. N. & Tripathi, S. K. Patterns of restoration of soil physicochemical properties and microbial biomass in different landslide sites in the Sal forest ecosystem of Nepal Himalaya. Ecol. Eng. 17, 385–401 (2001).
    Google Scholar 
    167.Wilcke, W. et al. Soil properties on a chronosequence of landslides in montane rain forest, Ecuador. CATENA 53, 79–95 (2003).
    Google Scholar 
    168.Diaz-Garcia, J. M., Pineda, E., Lopez-Barrera, F. & Moreno, C. E. Amphibian species and functional diversity as indicators of restoration success in tropical montane forest. Biodivers. Conserv. 26, 2569–2589 (2017).
    Google Scholar 
    169.Doust, S. J., Erskine, P. D. & Lamb, D. Direct seeding to restore rainforest species: Microsite effects on the early establishment and growth of rainforest tree seedlings on degraded land in the wet tropics of Australia. For. Ecol. Manag. 234, 333–343 (2006).
    Google Scholar 
    170.Howorth, R. T. & Pendry, C. A. Post-cultivation secondary succession in a Venezuelan lower montane rain forest. Biodivers. Conserv. 15, 693–715 (2006).
    Google Scholar 
    171.Gomes, L. G. L., Oostra, V., Nijman, V., Cleef, A. M. & Kappelle, M. Tolerance of frugivorous birds to habitat disturbance in a tropical cloud forest. Biol. Conserv. 141, 860–871 (2008).
    Google Scholar 
    172.Cole, R. J., Holl, K. D. & Zahawi, R. A. Seed rain under tree islands planted to restore degraded lands in a tropical agricultural landscape. Ecol. Appl. 20, 1255–1269 (2010).CAS 
    PubMed 

    Google Scholar 
    173.Pérez-García, O. & del Castillo, R. F. Shifts in swidden agriculture alter the diversity of young fallows: Is the regeneration of cloud forest at stake in southern Mexico?. Agric. Ecosyst. Environ. 248, 162–174 (2017).
    Google Scholar 
    174.Gallegos, S. C. et al. Factors limiting montane forest regeneration in bracken-dominated habitats in the tropics. For. Ecol. Manag. 381, 168–176 (2016).
    Google Scholar 
    175.Riviere, J.-N. et al. Role of tree ferns in flowering plant settlement in the tropical montane rainforests of La Reunion (Mascarene Archipelago, Indian Ocean). Rev. D Ecol. TERRE LA VIE 63, 199–207 (2008).
    Google Scholar 
    176.Mohandass, D., Chhabra, T., Singh Pannu, R. & Beng, K. C. Recruitment of saplings in active tea plantations of the Nilgiri mountains: Implications for restoration ecology. Trop. Ecol. 57, 101–118 (2016).CAS 

    Google Scholar 
    177.Wassie, A., Bongers, F., Sterck, F. J. & Teketay, D. Church forests—relics of dry afromontane forests of Northern Ethiopia: opportunities and challenges for conservation and restauration. Degrad. For. East. Africa Manag. Restor. 123–133 (2010).178.Townsend, P. A. & Masters, K. L. Lattice-work corridors for climate change: A conceptual framework for biodiversity conservation and social-ecological resilience in a tropical elevational gradient. Ecol. Soc. https://doi.org/10.5751/ES-07324-200201 (2015).Article 

    Google Scholar 
    179.Nogués-Bravo, D., Araújo, M. B., Errea, M. P. & Martínez-Rica, J. P. Exposure of global mountain systems to climate warming during the 21st Century. Glob. Environ. Change 17, 420–428 (2007).
    Google Scholar 
    180.Pepin, N. et al. Elevation-dependent warming in mountain regions of the world. Nat. Clim. Change 5, 424–430 (2015).ADS 

    Google Scholar 
    181.Fadrique, B. et al. Widespread but heterogeneous responses of Andean forests to climate change. Nature 564, 207–212 (2018).CAS 
    PubMed 
    ADS 

    Google Scholar 
    182.Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92 (2019).CAS 
    PubMed 
    ADS 

    Google Scholar 
    183.Feeley, K. J. & Rehm, E. M. Downward shift of montane grasslands exemplifies the dual threat of human disturbances to cloud forest biodiversity. Proc. Natl. Acad. Sci. 112, E6084–E6084 (2015).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    184.Gómez-Ruiz, P. A., Sáenz-Romero, C. & Lindig-Cisneros, R. Early performance of two tropical dry forest species after assisted migration to pine–oak forests at different altitudes: strategic response to climate change. J. For. Res. 31, 1215–1223 (2020).
    Google Scholar 
    185.Joppa, L. N. & Pfaff, A. High and far: Biases in the location of protected areas. PLoS One 4, 1–6 (2009).
    Google Scholar 
    186.von Holle, B., Yelenik, S. & Gornish, E. S. Restoration at the landscape scale as a means of mitigation and adaptation to climate change. Curr. Landsc. Ecol. Rep. 5, 85–97 (2020).
    Google Scholar 
    187.Fischer, J., Riechers, M., Loos, J., Martin-Lopez, B. & Temperton, V. M. Making the UN decade on ecosystem restoration a social-ecological endeavour. Trends Ecol. Evol. xx, 1–9 (2020).
    Google Scholar 
    188.Monitoring Task Force. Briefing note on the Task Force on Monitoring for the UN Decade on Ecosystem Restoration 2021–2030 (2020).189.Elliott, S. The potential for automating assisted natural regeneration of tropical forest ecosystems. Biotropica 48, 825–833 (2016).
    Google Scholar  More

  • in

    Ecological dependencies make remote reef fish communities most vulnerable to coral loss

    Fish distributionWe rasterized a detailed reef distribution vector map35 at 5 × 5 latitude/longitude degrees (by considering as reef area each cell in the raster intersecting a polygon in the original shapefile). We collected all the occurrences of fish species intersecting the rasterized reef area from both the Ocean Biogeographic Information System36 and the Global Biodiversity Information Facility37. We used taxonomic and biogeographical (i.e., latitudinal/longitudinal extremes for a given species) information from FishBase38 to exclude potential incorrect occurrences (i.e., all the records falling outside the known species ranges). We also restricted the list to all the species for which FishBase provided relevant ecological information (as these were needed to evaluate prey-predator species interactions and identify indirect links between fish species and coral, see below). The filtered list comprises 9143 fish species.For these species, we used occurrence data to generate species ranges. For this, we used the α-hull procedure39, but instead of pre-selecting an α parameter and using it for all species, we developed a procedure to obtain conservative species ranges while including most of the known occurrences. First, we selected a very small α (0.001), to obtain a hull including most of the occurrences. Then, we progressively incremented α in small amounts (0.005) by computing, for each increment, the ratio between the relative reduction in the resulting hull area (in respect to the previous hull), and the relative reduction of occurrences included in the hull (in respect to the total number of available occurrences for the target species). We stopped increasing α when the ratio became 0.97.The random forest predictor was used to assess the probability of trophic interaction between a large list of potential interactions generated by combining all fish species from our reef fish occurrence dataset known to rely mainly or exclusively on fish for their survival (i.e. “true piscivores”, FishBase trophic level  > 3.5), with all the fish in the dataset. The full list included 31,768,450 potential interactions, that we reduced to 6,721,450 interactions by keeping only the interacting pairs identified by the random forest classifier with a probability ≥0.9.(3) If the ecological dependency between two species is actually manifested then the two species must obviously co-occur at some locations, and vice-versa, co-occurrence is a necessary pre-requisite for an ecological dependency. Following this logic, we took a final, additional step to further filter and improve the fish → fish interaction list. In particular, we quantified the tendency for species to co-occur in the same locality as one potential proxy layer for species interactions, complementary to our other approaches. There are various factors that can affect the co-occurrence of two species. In a simplification, this can emerge from stochasticity, shared environmental requirements, shared evolutionary history, and ecological dependencies. We attempted to disentangle the effect of the last factor from the first three.For each target species pair, we computed overlap in distribution as the raw number of reef localities where both target species were found. Then, we compared this number with the null expectation obtained by randomizing the distribution of species occurrences across reef localities. We designed a null model accounting for randomness, species niche and biogeographical history, and hence randomizing the occurrence of species only within areas where they could have possibly occurred according to environmental conditions and biogeographical factors (e.g., in the absence of hard or soft barriers). To implement the null model, we first excluded from the list of potential localities all the areas outside the biogeographical regions where the target species had been recorded, with regions identified according to Spalding et al.49. Then, within the remaining areas, we identified all the reef localities with climate envelopes favourable to target species survival. For this, we identified the min and max of major environmental drivers (mean annual surface temperature, salinity, pH) where the target species occurred, and then we identified all the localities with conditions not exceeding these limits. We generated, for each pairwise species comparison, one thousand randomized sets of species occurrences by rearranging randomly species occurrence within all suitable localities. We quantified co-occurrence between the species pair in each random scenario. Finally, we compared the observed co-occurrence with the random co-occurrences, computing a p-value as the fraction of null models with co-occurrence identical or higher than the observed one. We kept only the pairs with a p-value  More

  • in

    Using a climate attribution statistic to inform judgments about changing fisheries sustainability

    1.Silvy, Y., Guilyardi, E., Sallee, J.-B. & Durack, P. J. Human-induced changes to the global ocean water masses and their time of emergence. Nat. Clim. Change 10, 1030–1036 (2020).ADS 
    CAS 

    Google Scholar 
    2.Laufkötter, C., Zscheischler, J. & Frölicher, T. L. High-impact marine heatwaves attributable to human-induced global warming. Science 369, 1621–1625 (2020).ADS 

    Google Scholar 
    3.Henson, S. A. et al. Rapid emergence of climate change in environmental drivers of marine ecosystems. Nat. Commun. 8, 14682 (2017).ADS 
    PubMed Central 
    PubMed 

    Google Scholar 
    4.Grothmann, T. & Patt, A. Adaptive capacity and human cognition: The process of individual adaptation to climate change. Glob. Environ. Change 15, 199–213 (2005).
    Google Scholar 
    5.Adger, W. N. Vulnerability. Glob. Environ. Change 16, 268–281 (2006).
    Google Scholar 
    6.Cinner, J. E. et al. Building adaptive capacity to climate change in tropical coastal communities. Nat. Clim. Change 8, 117–123 (2018).ADS 

    Google Scholar 
    7.van Putten, I. E. et al. Empirical evidence for different cognitive effects in explaining the attribution of marine range shifts to climate change. ICES J. Mar. Sci. 73, 1306–1318 (2016).
    Google Scholar 
    8.Salinger, J. et al. Decadal-scale forecasting of climate drivers for marine applications. in Advances in Marine Biology (ed. Curry, BE) vol. 74, 1–68 (2016).9.Williams, J. W. & Jackson, S. T. Novel climates, no-analog communities, and ecological surprises. Front. Ecol. Environ. 5, 475–482 (2007).
    Google Scholar 
    10.Pershing, A. J. et al. Challenges to natural and human communities from surprising ocean temperatures. Proc. Natl. Acad. Sci. U. S. A. 116, 18378–18383 (2019).CAS 
    PubMed Central 
    PubMed 

    Google Scholar 
    11.Overland, J. E. et al. Climate controls on marine ecosystems and fish populations. J. Mar. Syst. 79, 305–315 (2010).
    Google Scholar 
    12.Merryfield, W. J. et al. Current and emerging developments in subseasonal to decadal prediction. Bull. Am. Meteorol. Soc. 101, E869–E896 (2020).
    Google Scholar 
    13.Deser, C. et al. Insights from Earth system model initial-condition large ensembles and future prospects. Nat. Clim. Change 10, 277–286 (2020).ADS 

    Google Scholar 
    14.Palmer, T. N. & Stevens, B. The scientific challenge of understanding and estimating climate change. Proc. Natl. Acad. Sci. U. S. A. 116, 24390–24395 (2019).ADS 
    CAS 
    PubMed Central 
    PubMed 

    Google Scholar 
    15.Parmesan, C. et al. Beyond climate change attribution in conservation and ecological research. Ecol. Lett. 16, 58–71 (2013).
    Google Scholar 
    16.Myers, R. A. When do environment-recruitment correlations work?. Rev. Fish Biol. Fish. 8, 285–305 (1998).
    Google Scholar 
    17.Litzow, M. A. et al. Non-stationary climate–salmon relationships in the Gulf of Alaska. Proc. R. Soc. B Biol. Sci. 285, 20181855 (2018).
    Google Scholar 
    18.Deyle, E. R. et al. Predicting climate effects on Pacific sardine. Proc. Natl. Acad. Sci. U. S. A. 110, 6430–6435 (2013).ADS 
    CAS 
    PubMed Central 
    PubMed 

    Google Scholar 
    19.Planque, B. Projecting the future state of marine ecosystems, ‘la grande illusion’?. ICES J. Mar. Sci. 73, 204–208 (2016).MathSciNet 

    Google Scholar 
    20.Schindler, D. E. & Hilborn, R. Prediction, precaution, and policy under global change. Science 347, 953–954 (2015).ADS 
    CAS 

    Google Scholar 
    21.Maguire, K. C., Nieto-Lugilde, D., Fitzpatrick, M. C., Williams, J. W. & Blois, J. L. Modeling species and community responses to past, present, and future episodes of climatic and ecological change. Annu. Rev. Ecol. Evol. Syst. 46, 343–368 (2015).
    Google Scholar 
    22.Glaser, S. M. et al. Complex dynamics may limit prediction in marine fisheries. Fish Fish. 15, 616–633 (2014).
    Google Scholar 
    23.Pershing, A. J. et al. Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery. Science 350, 809–812 (2015).ADS 
    CAS 

    Google Scholar 
    24.Palmer, M. C., Deroba, J. J., Legault, C. M. & Brooks, E. N. Comment on “Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery”. Science 352, 423 (2016).ADS 
    CAS 

    Google Scholar 
    25.Swain, D. P., Benoit, H. P., Cox, S. P. & Cadigan, N. G. Comment on “Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery”. Science 352, 423 (2016).ADS 
    CAS 

    Google Scholar 
    26.Pershing, A. J. et al. Response to comments on “Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery”. Science 352, 423 (2016).CAS 

    Google Scholar 
    27.Stott, P. A., Stone, D. A. & Allen, M. R. Human contribution to the European heatwave of 2003. Nature 432, 610–614 (2004).ADS 
    CAS 

    Google Scholar 
    28.Stott, P. A. et al. Attribution of extreme weather and climate-related events. Wiley Interdiscip. Rev. Clim. Change 7, 23–41 (2016).
    Google Scholar 
    29.Walsh, J. E. et al. The high latitude heat wave of 2016 and its impacts on Alaska. Bull. Am. Meteorol. Soc. 99, S39–S43 (2018).
    Google Scholar 
    30.Schwalm, C. R., Glendon, S. & Duffy, P. B. RCP85 tracks cumulative CO2 emissions. Proc. Natl. Acad. Sci. U. S. A. 117, 19656–19657 (2020).ADS 
    CAS 
    PubMed Central 
    PubMed 

    Google Scholar 
    31.Dorn, M. W. et al. Assessment of the walleye pollock stock in the Gulf of Alaska. https://www.fisheries.noaa.gov/resource/data/2020-assessment-walleye-pollock-stock-gulf-alaska (2020).32.Barbeaux, S. J. et al. Assessment of the Pacific cod stock in the Gulf of Alaska. https://www.fisheries.noaa.gov/resource/data/2020-assessment-pacific-cod-stock-gulf-alaska (2020).33.Litzow, M. A. et al. Evaluating ecosystem change as Gulf of Alaska temperature exceeds the limits of preindustrial variability. Prog. Oceanogr. 186, 102393 (2020).
    Google Scholar 
    34.Caley, M. J. et al. Recruitment and the local dynamics of open marine populations. Annu. Rev. Ecol. Syst. 27, 477–500 (1996).
    Google Scholar 
    35.Barbeaux, S. J., Holsman, K. & Zador, S. Marine heatwave stress test of ecosystem-based fisheries management in the Gulf of Alaska Pacific cod fishery. Front. Mar. Sci. 7, 703 (2020).
    Google Scholar 
    36.Piatt, J. F. et al. Extreme mortality and reproductive failure of common murres resulting from the northeast Pacific marine heatwave of 2014–2016. PLoS ONE 15, e0226087 (2020).CAS 
    PubMed Central 
    PubMed 

    Google Scholar 
    37.Harley, C. D. G. et al. The impacts of climate change in coastal marine systems. Ecol. Lett. 9, 228–241 (2006).ADS 

    Google Scholar 
    38.Hsieh, C.-H. et al. Fishing elevates variability in the abundance of exploited species. Nature 443, 859–862 (2006).ADS 
    CAS 

    Google Scholar 
    39.Laurel, B. J. & Rogers, L. A. Loss of spawning habitat and prerecruits of Pacific cod during a Gulf of Alaska heatwave. Can. J. Fish. Aquat. Sci. 77, 644–650 (2020).
    Google Scholar 
    40.Koenker, B. L., Laurel, B. J., Copeman, L. A. & Ciannelli, L. Effects of temperature and food availability on the survival and growth of larval Arctic cod (Boreogadus saida) and walleye pollock (Gadus chalcogrammus). ICES J. Mar. Sci. 75, 2386–2402 (2018).
    Google Scholar 
    41.Rogers, L. A., Wilson, M. T., Duffy-Anderson, J. T., Kimmel, D. G. & Lamb, J. F. Pollock and “the Blob”: Impacts of a marine heatwave on walleye pollock early life stages. Fish. Oceanogr. 30, 142–158 (2021).
    Google Scholar 
    42.Filbee-Dexter, K. et al. Quantifying ecological and social drivers of ecological surprise. J. Appl. Ecol. 55, 2135–2146 (2018).
    Google Scholar 
    43.Allen, M. Liability for climate change. Nature 421, 891–892 (2003).ADS 
    CAS 

    Google Scholar 
    44.Lloyd, E. A. & Oreskes, N. Climate change attribution: When is it appropriate to accept new methods?. Earths Future 6, 311–325 (2018).ADS 

    Google Scholar 
    45.Kirchmeier-Young, M. C., Gillett, N. P., Zwiers, F. W., Cannon, A. J. & Anslow, F. S. Attribution of the influence of human-induced climate change on an extreme fire season. Earths Future 7, 2–10 (2019).ADS 

    Google Scholar 
    46.Frame, D. J. et al. Climate change attribution and the economic costs of extreme weather events: A study on damages from extreme rainfall and drought. Clim. Change 162, 781–797 (2020).ADS 

    Google Scholar 
    47.Frame, D. J., Wehner, M. F., Noy, I. & Rosier, S. M. The economic costs of Hurricane Harvey attributable to climate change. Clim. Change 160, 271–281 (2020).ADS 

    Google Scholar 
    48.Winkler, A. J. et al. Slowdown of the greening trend in natural vegetation with further rise in atmospheric CO2. Biogeosciences 18, 4985–5010 (2021).ADS 

    Google Scholar 
    49.Shepherd, T. G. Storyline approach to the construction of regional climate change information. Proc. R. Soc. A Math. Phys. Eng. Sci. 475, 20190013 (2019).ADS 

    Google Scholar 
    50.Litzow, M. A. et al. Quantifying a novel climate through changes in PDO-climate and PDO-salmon relationships. Geophys. Res. Lett. 47, 2020GL087972 (2020).ADS 

    Google Scholar 
    51.Laurel, B. J. et al. Regional warming exacerbates match/mismatch vulnerability for cod larvae in Alaska. Prog. Oceanogr. 193, 102555 (2021).
    Google Scholar 
    52.Bailey, K. M. Shifting control of recruitment of walleye pollock Theragra chalcogramma after a major climatic and ecosystem change. Mar. Ecol. Prog. Ser. 198, 215–224 (2000).ADS 

    Google Scholar 
    53.Jutfelt, F. Metabolic adaptation to warm water in fish. Funct. Ecol. 34, 1138–1141 (2020).
    Google Scholar 
    54.Walsh, J. E. et al. Downscaling of climate model output for Alaskan stakeholders. Environ. Model. Softw. 110, 38–51 (2018).
    Google Scholar 
    55.Lott, F. C. & Stott, P. A. Evaluating simulated fraction of attributable risk using climate observations. J. Clim. 29, 4565–4575 (2016).ADS 

    Google Scholar 
    56.Freeland, H. & Ross, T. `The Blob’—or, how unusual were ocean temperatures in the Northeast Pacific during 2014–2018?. Deep-Sea Res. I: Oceanogr. Res. Pap. 150, 103061 (2019).
    Google Scholar 
    57.Knutti, R. & Sedlacek, J. Robustness and uncertainties in the new CMIP5 climate model projections. Nat. Clim. Change 3, 369–373 (2013).ADS 

    Google Scholar 
    58.Adamson, M. W. & Hilker, F. M. Resource-harvester cycles caused by delayed knowledge of the harvested population state can be dampened by harvester forecasting. Theor. Ecol. 13, 425–434 (2020).
    Google Scholar 
    59.Dorn, M. W. & Zador, S. G. A risk table to address concerns external to stock assessments when developing fisheries harvest recommendations. Ecosyst. Heal. Sustain. 6, 2 (2020).
    Google Scholar 
    60.Rudnick, D. L. & Davis, R. E. Red noise and regime shifts. Deep-Sea Res. I: Oceanogr Res. Pap. 50, 691–699 (2003).ADS 

    Google Scholar 
    61.Lauffenburger, N., Williams, K. & Jones, D. Results of the acoustic-trawl surveys of walleye pollock (Gadus chalcogrammus) in the Gulf of Alaska, March 2019. https://repository.library.noaa.gov/view/noaa/23711/ (2019).62.Stone, D. A., Rosier, S. M. & Frame, D. J. The question of life, the universe and event attribution. Nat. Clim. Change 11, 276–278 (2021).ADS 

    Google Scholar 
    63.Zuur, A. F., Tuck, I. D. & Bailey, N. Dynamic factor analysis to estimate common trends in fisheries time series. Can. J. Fish. Aquat. Sci. 60, 542–552 (2003).
    Google Scholar 
    64.Holmes, E. E., Ward, E. J. & Wills, K. MARSS: Multivariate autoregressive state-space models for analyzing time-series data. R J. 4, 11–19 (2012).
    Google Scholar 
    65.Yau, K. K. W., Wang, K. & Lee, A. H. Zero-inflated negative binomial mixed regression modeling of over-dispersed count data with extra zeros. Biom. J. 45, 437–452 (2003).MathSciNet 
    MATH 

    Google Scholar 
    66.Zuur, A. F., Ieno, N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).MATH 

    Google Scholar 
    67.Wood, S. N. Thin plate regression splines. J. R. Stat. Soc. Series B Stat. Methodol. 65, 95–114 (2003).MathSciNet 
    MATH 

    Google Scholar 
    68.Carpenter, B. et al. Stan: A probabilistic programming language. J. Stat. Softw. 76, 1–29 (2017).
    Google Scholar 
    69.R Core Team. R: A language and environment for statistical computing. v4.0.2. http://www.r-project.org/ (2020).70.Buerkner, P.-C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).
    Google Scholar 
    71.Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. & Gelman, A. Visualization in Bayesian workflow. J. R. Stat. Soc. Series Stat. Soc. 182, 389–402 (2019).MathSciNet 

    Google Scholar  More

  • in

    Multi-centennial phase-locking between reproduction of a South American conifer and large-scale drivers of climate

    1.Kelly, D. The evolutionary ecology of mast seeding. Trends Ecol. Evol. 9, 465–470 (1994).CAS 
    PubMed 

    Google Scholar 
    2.Janzen, D. H. Seed predation by animals. Annu. Rev. Ecol. Syst. 2, 465–492 (1971).
    Google Scholar 
    3.Silvertown, J. W. The evolutionary ecology of mast seeding in trees. Biol. J. Linn. Soc. 14, 235–250 (1980).
    Google Scholar 
    4.Koenig, W. D. Global patterns of environmental synchrony and the Moran effect. Ecography 25, 283–288 (2002).
    Google Scholar 
    5.Moran, P. A. P. The statistical analysis of the Canadian Lynx cycle. Aust. J. Zool. 1, 291–298 (1953).
    Google Scholar 
    6.Ranta, E., Veijo, K. & Lindströom, J. Spatially autocorrelated disturbances and patterns in population synchrony. Proc. R. Soc. Lond. B 266, 1851–1856 (1999).
    Google Scholar 
    7.Liebhold, A., Koenig, W. D. & Bjørnstad, O. N. Spatial synchrony in population dynamics. Annu. Rev. Ecol. Evol. Syst. 35, 467–490 (2004).
    Google Scholar 
    8.Sanguinetti, J. Producción y Predación de Semillas, Efectos de Corto y Largo Plazo Sobre el Reclutamiento de Plántulas. Caso de Estudio: Araucaria araucana (Universidad Nacional del Comahue, 2008).9.Schauber, E. M. et al. Masting by eighteen New Zealand plant species: the role of temperature as a synchronizing cue. Ecology 83, 1214–1225 (2002).
    Google Scholar 
    10.Fletcher, M.-S. Mast seeding and the El Niño-Southern Oscillation: a long-term relationship? Plant Ecol. 216, 527–533 (2015).
    Google Scholar 
    11.Koenig, W. D. & Knops, J. M. H. Scale of mast-seeding and tree-ring growth. Nature 396, 225 (1998).CAS 

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

    Google Scholar 
    13.Hadad, M. A., Roig, F. A., Arco Molina, J. G. & Hacket-Pain, A. Growth of male and female Araucaria araucana trees respond differently to regional mast events, creating sex-specific patterns in their tree-ring chronologies. Ecol. Indic. 122, 107245 (2021).
    Google Scholar 
    14.Thompson, D. W. J., Wallace, J. M. & Hegerl, G. C. Annular modes in the extratropical circulation. Part II: trends. J. Clim. 13, 1018–1036 (2000).
    Google Scholar 
    15.Holz, A., Kitzberger, T., Paritsis, J. & Veblen, T. T. Ecological and climatic controls of modern wildfire activity patterns across southwestern South America. Ecosphere 3, 1–25 (2012).
    Google Scholar 
    16.Mundo, I. A., Kitzberger, T., Roig Juñent, F. A., Villalba, R. & Barrera, M. D. Fire history in the Araucaria araucana forests of Argentina: human and climate influences. Int. J. Wildland Fire 22, 194–206 (2013).
    Google Scholar 
    17.Mundo, I. A., Roig Juñent, F. A., Villalba, R., Kitzberger, T. & Barrera, M. D. Araucaria araucana tree-ring chronologies in Argentina: spatial growth variations and climate influences. Trees-Struct. Funct. 26, 443–458 (2012).
    Google Scholar 
    18.Veblen, T. T., Kitzberger, T., Villalba, R. & Donnegan, J. Fire history in Northern Patagonia: the roles of humans and climatic variation. Ecol. Monogr. 69, 47–67 (1999).
    Google Scholar 
    19.Sanguinetti, J. & Kitzberger, T. Patterns and mechanisms of masting in the large-seeded southern hemisphere conifer Araucaria araucana. Austral Ecol. 33, 78–87 (2008).
    Google Scholar 
    20.Wigley, T. M. L., Briffa, K. & Jones, P. D. On the average value of correlated time series, with applications in dendroclimatology and hydrometeorology. J. Clim. Appl. Meteorol. 23, 201–213 (1984).
    Google Scholar 
    21.Swetnam, T. W. & Lynch, A. M. Multicentury, regional-scale patterns of Western Spruce budworm outbreaks. Ecol. Monogr. 63, 399–424 (1993).
    Google Scholar 
    22.Kitzberger, T., Veblen, T. T. & Villalba, R. Tectonic influences on tree growth in northern Patagonia, Argentina: the roles of substrate stability and climatic variation. Can. J. Res. 25, 1684–1696 (1995).
    Google Scholar 
    23.Mundo, I. A. et al. Austrocedrus chilensis growth decline in relation to drought events in northern Patagonia, Argentina. Trees Struct. Funct. 24, 561–570 (2010).
    Google Scholar 
    24.Rozas, V. et al. Climatic cues for secondary growth and cone production are sex-dependent in the long-lived dioecious conifer Araucaria araucana. Agric. Meteorol. 274, 132–143 (2019).
    Google Scholar 
    25.Pearse, I. S., Koenig, W. D. & Kelly, D. Mechanisms of mast seeding: resources, weather, cues, and selection. New Phytol. 212, 546–562 (2016).CAS 
    PubMed 

    Google Scholar 
    26.Sanguinetti, J. & Kitzberger, T. Factors controlling seed predation by rodents and non-native Sus scrofa in Araucaria araucana forests: potential effects on seedling establishment. Biol. Invasions 12, 689–706 (2010).
    Google Scholar 
    27.Sanguinetti, J. & Kitzberger, T. Efectos de la producción de semillas y de la heterogeneidad vegetal sobre la supervivencia de semillas y el patrón espacio-temporal de establecimiento de plántulas en Araucaria araucana. Rev. Chil. Hist. Nat. 82, 319–335 (2009).
    Google Scholar 
    28.Kelly, D. et al. Of mast and mean: differential-temperature cue makes mast seeding insensitive to climate change. Ecol. Lett. 16, 90–98 (2013).PubMed 

    Google Scholar 
    29.Ostfeld, R. S. & Keesing, F. Pulsed resources and community dynamics of consumers in terrestrial ecosystems. Trends Ecol. Evol. 15, 232–237 (2000).CAS 
    PubMed 

    Google Scholar 
    30.Holmgren, M., Scheffer, M., Ezcurra, E., Gutiérrez, J. R. & Mohren, G. M. J. El Niño effects on the dynamics of terrestrial ecosystems. Trends Ecol. Evol. 16, 89–94 (2001).CAS 
    PubMed 

    Google Scholar 
    31.Swetnam, T. W. & Betancourt, J. L. Mesoscale disturbance and ecological response to decadal climatic variability in the American southwest. J. Clim. 11, 3128–3147 (1998).
    Google Scholar 
    32.Kitzberger, T., Swetnam, T. W. & Veblen, T. T. Inter-hemispheric synchrony of forest fires and the El Niño-Southern Oscillation. Glob. Ecol. Biogeogr. 10, 315–326 (2001).
    Google Scholar 
    33.Marshall, G. J. Trends in the Southern Annular Mode from observations and reanalyses. J. Clim. 16, 4134–4143 (2003).
    Google Scholar 
    34.Silvestri, G. E. & Vera, C. S. Antarctic Oscillation signal on precipitation anomalies over southeastern South America. Geophys. Res. Lett. 30, 2115 (2003).
    Google Scholar 
    35.Cai, W. et al. Climate impacts of the El Niño–Southern Oscillation on South America. Nat. Rev. Earth Environ. 1, 215–231 (2020).
    Google Scholar 
    36.Piovesan, G. & Adams, J. M. Masting behaviour in beech: linking reproduction and climatic variation. Can. J. Bot. 79, 1039–1047 (2001).
    Google Scholar 
    37.Drobyshev, I., Niklasson, M., Mazerolle, M. J. & Bergeron, Y. Reconstruction of a 253-year long mast record of European beech reveals its association with large scale temperature variability and no long-term trend in mast frequencies. Agric. Meteorol. 192–193, 9–17 (2014).
    Google Scholar 
    38.Fernández-Martínez, M., Vicca, S., Janssens, I. A., Espelta, J. M. & Peñuelas, J. The North Atlantic Oscillation synchronises fruit production in western European forests. Ecography 40, 864–874 (2017).
    Google Scholar 
    39.Ascoli, D. et al. Two centuries of masting data for European beech and Norway spruce across the European continent. Ecology 98, 1473 (2017).PubMed 

    Google Scholar 
    40.Thompson, D. W. J. et al. Signatures of the Antarctic ozone hole in Southern Hemisphere surface climate change. Nat. Geosci. 4, 741–749 (2011).CAS 

    Google Scholar 
    41.Jacques-Coper, M., Brönnimann, S., Martius, O., Vera, C. & Cerne, B. Summer heat waves in southeastern Patagonia: an analysis of the intraseasonal timescale. Int. J. Climatol. 36, 1359–1374 (2016).
    Google Scholar 
    42.Estudio de la Variabilidad Climáticas en Chile para el Siglo XXI (CONAMA, 2006).43.Garreaud, R. D. et al. The 2010–2015 megadrought in central Chile: impacts on regional hydroclimate and vegetation. Hydrol. Earth Syst. Sci. 21, 6307–6327 (2017).
    Google Scholar 
    44.Tortorelli, L. A. La explotación racional de los bosques de Araucaria de Neuquén. Su importancia económica. Servir (separata) VI, 1–74 (1942).45.Veblen, T. T., Burns, B. R., Kitzberger, T., Lara, A. & Villalba, R. in Ecology of the Southern Conifers (eds Enright, N. J. & Hill, R. S) 120–155 (Melbourne Univ. Press, 1995).46.Lara, A. et al. Mapeo de la Ecoregión de los Bosques Valdivianos de Argentina y Chile, en escala 1:500.000 (Fundación Vida Silvestre Aregentina, 1999).47.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).48.Cook, E. R., Briffa, K., Shiyatov, S. & Mazepa, V. in Methods of Dendrochronology—Applications in the Environmental Sciences (eds Cook, E. & Kairiukstis, L. A.) 104–132 (Kluwer Academic Publishers, 1990).49.Yamaguchi, D. K. A simple method for cross-dating increment cores from living trees. Can. J. Res. 21, 414–416 (1991).
    Google Scholar 
    50.Holmes, R. L. Computer-assisted quality control in tree-ring dating and measurement. Tree-Ring Bull. 43, 69–78 (1983).
    Google Scholar 
    51.Visser, H. Note on the relation between ring widths and basal area increments. Forest Sci. 41, 297–304 (1995).52.Pedersen, B. S. The role of stress in the mortality of midwestern oaks as indicated by growth prior to death. Ecology 79, 79–93 (1998).
    Google Scholar 
    53.Cook, E. R. A Time Series Analysis Approach to Tree Ring Standardization (University of Arizona, School of Renewable Natural Resources, 1985).54.Melvin, T. M., Briffa, K. R., Nicolussi, K. & Grabner, M. Time-varying-response smoothing. Dendrochronologia 25, 65–69 (2007).
    Google Scholar 
    55.Biondi, F. Comparing tree-ring chronologies and repeated timber inventories as forest monitoring tools. Ecol. Appl. 9, 216–227 (1999).
    Google Scholar 
    56.Battipaglia, G. et al. Long tree-ring chronologies provide evidence of recent tree growth decrease in a Central African tropical forest. PLoS ONE 10, e0120962 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    57.Blasing, T. J., Solomon, A. M. & Duvick, D. N. Response function revisited. Tree-Ring Bull. 44, 1–15 (1984).
    Google Scholar 
    58.Sanguinetti, J. Producción de semillas de Araucaria araucana (Molina) K. Koch durante 15 años en diferentes poblaciones del Parque Nacional Lanín (Neuquén-Argentina). Ecol. Austral 24, 265–275 (2014).
    Google Scholar 
    59.Ficha de Valorización de Resultados. Proyecto Producción, Técnicas de Poscosecha y Desarrollo de Productos a partir del Piñón (FIA, 2011).60.Delignette-Muller, M. L. & Dutang, C. fitdistrplus: an R package for fitting distributions. J. Stat. Softw. 64, 1–34 (2015).
    Google Scholar 
    61.Villalba, R. et al. Unusual Southern Hemisphere tree growth patterns induced by changes in the Southern Annular Mode. Nat. Geosci. 5, 793–798 (2012).CAS 

    Google Scholar 
    62.Grissino-Mayer, H. D. Tree-ring Reconstructions of Climate and Fire at El Malpais National Monument, New Mexico (Univ. of Arizona, 1995).63.Torrence, C. & Compo, G. P. A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc. 79, 61–78 (1998).
    Google Scholar 
    64.Grinsted, A., Moore, J. C. & Jevrejeva, S. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process. Geophys. 11, 561–566 (2004).
    Google Scholar 
    65.Gouhier, T., Grinsted, A. & Simko, V. Biwavelet: Conduct Univariate and Bivariate Wavelet Analyses. R package version 0.20.19 https://github.com/tgouhier/biwavelet (2019).66.Mundo, I. A. Historia de incendios en bosques de Araucaria araucana (Molina) K. Koch de Argentina a través de un análisis dendroecológico (Universidad Nacional de La Plata, 2011).67.Emile-Geay, J., Cobb, K. M., Mann, M. E. & Wittenberg, A. T. Estimating Central Equatorial Pacific SST variability over the past millennium. Part I: methodology and validation. J. Clim. 26, 2302–2328 (2013).
    Google Scholar 
    68.Mundo, I. A. et al. Multi-century tree-ring based reconstruction of the Neuquén River streamflow, northern Patagonia, Argentina. Clim. Past 8, 815–829 (2012).
    Google Scholar  More

  • in

    Strategic Forest Reserves can protect biodiversity in the western United States and mitigate climate change

    1.Ripple, W. J. et al. World Scientists’ Warning of a Climate Emergency 2021. BioScience. https://doi.org/10.1093/biosci/biab079 (2021).2.Liu, P. R. & Raftery, A. E. Country-based rate of emissions reductions should increase by 80% beyond nationally determined contributions to meet the 2 C target. Commun. Earth Environ. 2, 1–10 (2021).
    Google Scholar 
    3.IPBES. (eds Brondizio, E. S., Settele, J., Díaz, S. & Ngo, H. T.) 56 (IPBES, 2019).4.CBD Secretariat. The Strategic Plan for Biodiversity 2011-2020 and the Aichi Biodiversity Targets Vol. Document UNEP/CBD/COP/DEC/X/2 (Secretariat of the Convention on Biological Diversity, 2010).5.Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).CAS 

    Google Scholar 
    6.United State of America. The United States of America Nationally Determined Contribution- Reducing Greenhouse Gases in the United States: A 2030 Emissions Target. 24 (Submitted to the UNFCCC Secretariat under the Paris Agreement; https://www4.unfccc.int/sites/ndcstaging/PublishedDocuments/United%20States%20of%20America%20First/United%20States%20NDC%20April%2021%202021%20Final.pdf, 2021).7.Nelson, M. D. et al. Defining the United States land base: a technical document supporting the USDA Forest Service 2020 RPA assessment. Gen. Tech. Rep. NRS-191. 191, 1–70 (2020).
    Google Scholar 
    8.Pörtner, H. O. & et al. IPBES-IPCC co-sponsored workshop report on biodiversity and climate change. (IPBES and IPCC, https://doi.org/10.5281/zenodo.4782538, 2021).9.Elsen, P. R., Monahan, W. B., Dougherty, E. R. & Merenlender, A. M. Keeping pace with climate change in global terrestrial protected areas. Sci. Adv. 6, eaay0814 (2020).
    Google Scholar 
    10.Dinerstein, E. et al. A “Global Safety Net” to reverse biodiversity loss and stabilize Earth’s climate. Sci. Adv. 6, eabb2824 (2020).
    Google Scholar 
    11.Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545 (2017).
    Google Scholar 
    12.Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. 114, 11645–11650 (2017).CAS 

    Google Scholar 
    13.Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).
    Google Scholar 
    14.Sexton, J. O. et al. Conservation policy and the measurement of forests. Nat. Clim. Chang. 6, 192–196 (2016).
    Google Scholar 
    15.Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Natl Acad. Sci. 104, 5925–5930 (2007).CAS 

    Google Scholar 
    16.Houghton, R. A., Hall, F. & Goetz, S. J. Importance of biomass in the global carbon cycle. J. Geophys. Res. 114, G00E03 (2009).
    Google Scholar 
    17.Mackey, B. et al. Understanding the importance of primary tropical forest protection as a mitigation strategy. Mitig. Adapt. Strateg. Glob. Chang. 25, 763–787 (2020).
    Google Scholar 
    18.Buotte, P. C., Law, B. E., Ripple, W. J. & Berner, L. T. Carbon sequestration and biodiversity co‐benefits of preserving forests in the western United States. Ecol. Appl.30, e02039 (2020).
    Google Scholar 
    19.Ruefenacht, B. et al. Conterminous US and Alaska forest type mapping using forest inventory and analysis data. Photogramm. Eng. Remote Sensing 74, 1379–1388 (2008).
    Google Scholar 
    20.USGS GAP. Protected Areas Database of the United States (PAD-US) 2.1: U.S. Geological Survey data release, https://doi.org/10.5066/P92QM3NT (2020).21.USGS. Gap Analysis Project Species Habitat Maps CONUS_2001. U.S. Geological Survey, https://doi.org/10.5066/F7V122T2 (2018).22.Wilson, B. T., Lister, A. J., Riemann, R. I. & Griffith, D. M. Live tree species basal area of the contiguous United States (2000-2009). (USDA Forest Service, Rocky Mountain Research Station, 2013).23.Wilson, B. T., Woodall, C. & Griffith, D. Imputing forest carbon stock estimates from inventory plots to a nationally continuous coverage. Carbon Balance Management 8, 1–15 (2013).
    Google Scholar 
    24.Oleson, K. et al. Technical Descriptioin of Version 4.5 of the Community Land Model (CLM) (National Center for Atmospheric Research, 2013).25.Buotte, P. C. et al. Near‐future forest vulnerability to drought and fire varies across the western United States. Glob. Chang. Biol. 25, 290–303 (2019).
    Google Scholar 
    26.Noss, R. F. et al. Bolder thinking for conservation. Conserv. Biol. 26, 1–4 (2012).
    Google Scholar 
    27.Allen, C. D. & Breshears, D. D. Drought-induced shift of a forest–woodland ecotone: rapid landscape response to climate variation. Proc. Natl Acad. Sci. 95, 14839–14842 (1998).CAS 

    Google Scholar 
    28.Watson, J. E. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).
    Google Scholar 
    29.Lecina‐Diaz, J. et al. The positive carbon stocks–biodiversity relationship in forests: co‐occurrence and drivers across five subclimates. Ecol. Appl. 28, 1481–1493 (2018).
    Google Scholar 
    30.Di Marco, M., Ferrier, S., Harwood, T. D., Hoskins, A. J. & Watson, J. E. Wilderness areas halve the extinction risk of terrestrial biodiversity. Nature 573, 582–585 (2019).
    Google Scholar 
    31.Glaser, C., Romaniello, C. & Moskowitz, K. Costs and consequences: the real price of livestock grazing on America’s public lands. Tucson, AZ: Center for Biological Diversity (2015).32.Flather, C. H. Species endangerment patterns in the United States. Vol. 241 (US Department of Agriculture, Forest Service, Rocky Mountain Forest and …, 1994).33.Beschta, R. L. et al. Adapting to climate change on western public lands: addressing the ecological effects of domestic, wild, and feral ungulates. Environ. Manag. 51, 474–491 (2013).
    Google Scholar 
    34.Betts, M. G., Gutiérrez Illán, J., Yang, Z., Shirley, S. M. & Thomas, C. D. Synergistic effects of climate and land-cover change on long-term bird population trends of the western USA: a test of modeled predictions. Front. Ecol. Evol. 7, https://doi.org/10.3389/fevo.2019.00186 (2019).35.Berner, L. T., Law, B. E., Meddens, A. J. & Hicke, J. A. Tree mortality from fires, bark beetles, and timber harvest during a hot and dry decade in the western United States (2003–2012). Environ. Res. Lett. 12, 065005 (2017).
    Google Scholar 
    36.Law, B. E. et al. Land use strategies to mitigate climate change in carbon dense temperate forests. Proc. Natl Acad. Sci. 115, 3663 (2018).CAS 

    Google Scholar 
    37.Ouren, D. S. et al. Environmental effects of off-highway vehicles on Bureau of land management lands: a literature synthesis, annotated bibliographies, extensive bibliographies, and internet resources. US Geol. Survey Open-File Rep. 1353, 225 (2007).
    Google Scholar 
    38.Talty, M. J., Mott Lacroix, K., Aplet, G. H. & Belote, R. T. Conservation value of national forest roadless areas. Conserv. Sci. Pract. 2, e288 (2020).
    Google Scholar 
    39.Belote, R. T. & Wilson, M. B. Delineating greater ecosystems around protected areas to guide conservation. Conserv. Sci. Pract. 2, e196 (2020).
    Google Scholar 
    40.DellaSala, D. A., Karr, J. R. & Olson, D. M. Roadless areas and clean water. J. Soil Water Conserv. 66, 78–84 (2011).
    Google Scholar 
    41.McLaren, D. P., Tyfield, D. P., Willis, R., Szerszynski, B. & Markusson, N. O. Beyond “net-zero”: a case for separate targets for emissions reduction and negative emissions. Front. Clim. 1, 4 (2019).
    Google Scholar 
    42.Mildrexler, D. J., Berner, L. T., Law, B. E., Birdsey, R. A. & Moomaw, W. R. Large Trees Dominate Carbon Storage in Forests East of the Cascade Crest in the United States Pacific Northwest. Front. For. Glob. Chang. 3, https://doi.org/10.3389/ffgc.2020.594274 (2020).43.Hudiburg, T. W., Luyssaert, S., Thornton, P. E. & Law, B. E. Interactive effects of environmental change and management strategies on regional forest carbon emissions. Environ. Sci. Tech. 47, 13132–13140 (2013).CAS 

    Google Scholar 
    44.Noss, R. F. & Daly, K. M. In Connectivity Conservation (eds K. Crooks & M. Sanjayan) 587–619 (Cambridge Univ. Press, 2010).45.Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).
    Google Scholar 
    46.Omernik, J. M. Perspectives on the nature and definition of ecological regions. Environ. Manag. 34, S27–S38 (2004).
    Google Scholar 
    47.Hudiburg, T. et al. Carbon dynamics of Oregon and Northern California forests and potential land-based carbon storage. Ecol. Appl. 19, 163–180 (2009).
    Google Scholar 
    48.Leu, M., Hanser, S. E. & Knick, S. T. The human footprint in the west: a large‐scale analysis of anthropogenic impacts. Ecol. Appl. 18, 1119–1139 (2008).
    Google Scholar 
    49.Haight, J. & Hammill, E. Protected areas as potential refugia for biodiversity under climatic change. Biol. Conserv. 241, 108258 (2020).
    Google Scholar 
    50.Dobrowski, S. Z. A climatic basis for microrefugia: the influence of terrain on climate. Glob. Chang. Biol. 17, 1022–1035 (2011).
    Google Scholar 
    51.Jantz, P., Goetz, S. & Laporte, N. Carbon stock corridors to mitigate climate change and promote biodiversity in the tropics. Nat. Clim. Chang. 4, 138–142 (2014).CAS 

    Google Scholar 
    52.McMenamin, S. K., Hadly, E. A. & Wright, C. K. Climatic change and wetland desiccation cause amphibian decline in Yellowstone National Park. Proc. Natl Acad. Sci. 105, 16988–16993 (2008).CAS 

    Google Scholar 
    53.Scott, J. M. et al. Recovery of imperiled species under the Endangered Species Act: the need for a new approach. Front. Ecol. Environ. 3, 383–389 (2005).
    Google Scholar 
    54.Miller, S. L. et al. Recent population decline of the Marbled Murrelet in the Pacific Northwest. Condor 114, 771–781 (2012).
    Google Scholar 
    55.Noon, B. R. & McKelvey, K. S. Management of the spotted owl: a case history in conservation biology. Annu. Rev. Ecol. System. 27, 135–162 (1996).
    Google Scholar 
    56.Ripple, W. J. et al. Ruminants, climate change and climate policy. Nat. Clim. Chang. 4, 2–5 (2014).CAS 

    Google Scholar 
    57.King, T. W. et al. Will Lynx lose their edge? Canada Lynx occupancy in Washington. J. Wildl. Manag. 84, 705–725 (2020).
    Google Scholar 
    58.Cayan, D. R. et al. Future dryness in the southwest US and the hydrology of the early 21st century drought. Proc. Natl Acad. Sci. 107, 21271–21276 (2010).CAS 

    Google Scholar 
    59.Rhoades, A. M., Ullrich, P. A. & Zarzycki, C. M. Projecting 21st century snowpack trends in western USA mountains using variable-resolution CESM. Clim. Dyn. 50, 261–288 (2018).
    Google Scholar 
    60.Williams, A. P. et al. Large contribution from anthropogenic warming to an emerging North American megadrought. Science 368, 314 (2020).CAS 

    Google Scholar 
    61.Mote, P. W., Hamlet, A. F., Clark, M. P. & Lettenmaier, D. P. Declining mountain snowpack in western north America. Bull. Am. Meteorol. Soc. 86, 39–49 (2005).
    Google Scholar 
    62.Cook, B. et al. Twenty‐first century drought projections in the CMIP6 forcing scenarios. Earth’s Futur. 8, e2019EF001461 (2020).
    Google Scholar 
    63.Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).
    Google Scholar 
    64.Johnson, Z. C., Leibowitz, S. G. & Hill, R. A. Revising the index of watershed integrity national maps. Sci. Total Environ. 651, 2615–2630 (2019).CAS 

    Google Scholar 
    65.Anderegg, W. R. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, eaaz7005 (2020).CAS 

    Google Scholar 
    66.Buotte, P., Levis, S. & Law, B. E. NACP forest carbon stocks, fluxes, and productivity estimates, Western USA, 1979-2099. ORNL Distributed Active Archive Center, https://doi.org/10.3334/ORNLDAAC/1662 (2019).67.Williams, A. P. et al. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Chang. 3, 292–297 (2012).
    Google Scholar 
    68.McDowell, N. G. et al. Multi-scale predictions of massive conifer mortality due to chronic temperature rise. Nat. Clim. Chang. 6, 295–300 (2015).
    Google Scholar 
    69.Williams, A. P. et al. Correlations between components of the water balance and burned area reveal new insights for predicting forest fire area in the southwest United States. Int. J. Wildland Fire 24, 14–26 (2014).
    Google Scholar 
    70.Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. 113, 11770–11775 (2016).CAS 

    Google Scholar 
    71.Dennison, P. E., Brewer, S. C., Arnold, J. D. & Moritz, M. A. Large wildfire trends in the western United States, 1984–2011. Geophys. Res. Lett. 41, 2928–2933 (2014).
    Google Scholar 
    72.Balch, J. K. et al. Human-started wildfires expand the fire niche across the United States. Proc. Natl Acad. Sci. 114, 2946–2951 (2017).CAS 

    Google Scholar 
    73.Schoennagel, T. et al. Adapt to more wildfire in western North American forests as climate changes. Proc. Natl Acad. Sci. 114, 4582–4590 (2017).CAS 

    Google Scholar 
    74.Law, B. E. & Waring, R. H. Carbon implications of current and future effects of drought, fire and management on Pacific Northwest forests. For. Ecol. Management 355, 4–14 (2015).
    Google Scholar 
    75.Donato, D. C., Campbell, J. L. & Franklin, J. F. Multiple successional pathways and precocity in forest development: can some forests be born complex? J. Veg. Sci. 23, 576–584 (2012).
    Google Scholar 
    76.Campbell, J. L., Harmon, M. E. & Mitchell, S. R. Can fuel‐reduction treatments really increase forest carbon storage in the western US by reducing future fire emissions? Front. Ecol. Environ. 10, 83–90 (2012).
    Google Scholar 
    77.Harris, N. et al. Attribution of net carbon change by disturbance type across forest lands of the conterminous United States. Carbon Balanc. Management 11, 24 (2016).CAS 

    Google Scholar 
    78.Ghimire, B. et al. Large carbon release legacy from bark beetle outbreaks across Western United States. Glob. Chang. Biol. 21, 3087–3101 (2015).
    Google Scholar 
    79.Mitchell, S. R., Harmon, M. E. & O’connell, K. E. Forest fuel reduction alters fire severity and long‐term carbon storage in three Pacific Northwest ecosystems. Ecol. Appl. 19, 643–655 (2009).
    Google Scholar 
    80.Rhodes, J. J. & Baker, W. L. Fire probability, fuel treatment effectiveness and ecological tradeoffs in western US public forests. Open For. Sci. J. 1, 1–7 (2008).
    Google Scholar 
    81.Law, B. E. & Harmon, M. E. Forest sector carbon management, measurement and verification, and discussion of policy related to climate change. Carbon Management 2, 73–84 (2011).
    Google Scholar 
    82.Hudiburg, T. W., Law, B. E., Wirth, C. & Luyssaert, S. Regional carbon dioxide implications of forest bioenergy production. Nat. Clim. Chang. 1, 419–423 (2011).CAS 

    Google Scholar 
    83.Bonan, G. B. & Doney, S. C. Climate, ecosystems, and planetary futures: the challenge to predict life in Earth system models. Science 359, eaam8328 (2018).
    Google Scholar 
    84.Law, B. E. Regional analysis of drought and heat impacts on forests: current and future science directions. Glob. Chang. Biol. 20, 3595–3599 (2014).
    Google Scholar 
    85.Spawn, S. A., Sullivan, C. C., Lark, T. J. & Gibbs, H. K. Harmonized global maps of above and belowground biomass carbon density in the year 2010. Sci. Data 7, 1–22 (2020).
    Google Scholar 
    86.Kullberg, P. & Moilanen, A. How do recent spatial biodiversity analyses support the convention on biological diversity in the expansion of the global conservation area network? Natureza Conservacao 12, 3–10 (2014).
    Google Scholar 
    87.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).88.Hijmans, R. J. raster: Geographic Analysis and Modeling. R package version 3.0-12. http://CRAN.R-project.org/package=raster (2019).89.Bivand, R., Keitt, T. & Rowlingson, B. rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. R package version 1.4-8. https://CRAN.R-project.org/package=rgdal (2019).90.O’Brien, J. gdalUtilities: Wrappers for ‘GDAL’ Utilities Executables. R package version 1. https://CRAN.R-project.org/package=gdalUtilities (2019).91.Dawle, M. & Srinivasan, A. data.table: Extension of ‘data.frame’. R package version 1.12.8. https://CRAN.R-project.org/package=data.table. (2019).92.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlang New York, 2016).93.Hurrell, J. W. et al. The community earth system model: a framework for collaborative research. Bull. Am. Meteorol. Soc. 94, 1339–1360 (2013).
    Google Scholar 
    94.Conservation Biology Institute. Protected Areas Database of the United States, CBI Edition Version 2. http://consbio.org/products/projects/pad-us-cbi-edition (2012).95.USDA Forest Service. Forests to Faucets 2.0 [spatial data set]. Retrieved from https://usfs-public.app.box.com/v/Forests2Faucets[Sept 21, 2021] (2019). More

  • in

    Potato leafroll virus reduces Buchnera aphidocola titer and alters vector transcriptome responses

    1.Remaudiere, G., & Remaudiere, M. Catalogue of the World’s Aphididae: Homoptera Aphidoidea. 473–1275. (Institut National de la Recherche Agronomique (INRA), 1997).2.Fereres, A., Irwin, M.E., & Kamppeier, G.E. Aphid movement: Process and consequences. in (van Emden H.F.R.H. ed.) Aphids as Crop Pests. 2nd edn. 196–200. (CABI, 2017).3.Ng, J. C. K. & Perry, K. L. Transmission of plant viruses by aphid vectors. Mol. Plant Pathol. 5(5), 505–511. https://doi.org/10.1111/j.1364-3703.2004.00240.x (2004).Article 
    PubMed 

    Google Scholar 
    4.Whitfield, A. E., Falk, B. W. & Rotenberg, D. Insect vector-mediated transmission of plant viruses. Virology 479–480, 278–289. https://doi.org/10.1016/j.virol.2015.03.026 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    5.Elena, S. F., Bernet, G. P. & Carrasco, J. L. The games plant viruses play. Curr. Opin. Virol. 8, 62–67. https://doi.org/10.1016/j.coviro.2014.07.003 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    6.Casteel, C.L., & Falk, B.W. Plant virus-vector interactions: More than just for virus transmission. in (Wang, A., & Zhou, X. eds.) Current Research Topics in Plant Virology. 2016. 217–240. https://doi.org/10.1007/978-3-319-32919-2_9 (2016).7.Eigenbrode, S. D., Bosque-Pérez, N. A. & Davis, T. S. Insect-borne plant pathogens and their vectors: Ecology, evolution, and complex interactions. Annu. Rev. Entomol. 63, 169–191. https://doi.org/10.1146/annurev-ento-020117-043119 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    8.Blanc, S. & Michalakis, Y. Manipulation of hosts and vectors by plant viruses and impact of the environment. Curr. Opin. Insect Sci. 16, 36–43. https://doi.org/10.1016/j.cois.2016.05.007 (2016).Article 
    PubMed 

    Google Scholar 
    9.Ingwell, L. L., Eigenbrode, S. D. & Bosque-Pérez, N. A. Plant viruses alter insect behavior to enhance their spread. Sci. Rep. 2(1), 578. https://doi.org/10.1038/srep00578 (2012).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    10.Stafford, C. A., Yang, L. H., Mcmunn, M. S. & Ullman, D. E. Virus infection alters the predatory behavior of an omnivorous vector. Oikos 123, 1384–1390. https://doi.org/10.1111/oik.01148 (2014).Article 

    Google Scholar 
    11.Wang, Q. et al. Rice dwarf virus infection alters green rice leafhopper host preference and feeding behavior. PLoS ONE 13(9), 1–16. https://doi.org/10.1371/journal.pone.0203364 (2018).CAS 
    Article 

    Google Scholar 
    12.Stafford, C. A., Walker, G. P. & Ullman, D. E. Infection with a plant virus modifies vector feeding behavior. Proc. Natl. Acad. Sci. 108(23), 9350–9355. https://doi.org/10.1073/pnas.1100773108 (2011).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    13.Zhang, Y. C., Cao, W. J., Zhong, L. R., Godfray, H. C. J. & Liu, X. D. Host plant determines the population size of an obligate symbiont (Buchnera aphidicola) in aphids. Appl. Environ. Microbiol. 82(8), 2336–2346. https://doi.org/10.1128/AEM.04131-15 (2016).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    14.Hansen, A. K. & Moran, N. A. Aphid genome expression reveals host-symbiont cooperation in the production of amino acids. Proc. Natl. Acad. Sci. 108(7), 2849–2854. https://doi.org/10.1073/pnas.1013465108 (2011).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Nakabachi, A. et al. Transcriptome analysis of the aphid bacteriocyte, the symbiotic host cell that harbors an endocellular mutualistic bacterium, Buchnera. Proc. Natl. Acad. Sci. 102(15), 5477–5482. https://doi.org/10.1073/pnas.1013465108 (2005).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Wernegreen, J. J. Strategies of genomic integration within insect-bacterial mutualisms. Biol Bull. 223(1), 112–122. https://doi.org/10.1086/BBLv223n1p112 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    17.Zhang, Y. et al. Genetic structure of the bacterial endosymbiont, Buchnera aphidicola, from its host aphid, Schlechtendalia chinensis, and evolutionary implications. Curr. Microbiol. 75(3), 309–315. https://doi.org/10.1007/s00284-017-1381-0 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    18.Zhang, F. et al. Bacterial symbionts, Buchnera, and starvation on wing dimorphism in English grain aphid, Sitobion avenae (F) (Homoptera: Aphididae). Front. Physiol. 6, 155. https://doi.org/10.3389/fphys.2015.00155 (2015).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    19.Machado-Assefh, C. R., Lopez-Isasmendi, G., Tjallingii, W. F., Jander, G. & Alvarez, A. E. Disrupting Buchnera aphidicola, the endosymbiotic bacteria of Myzus persicae, delays host plant acceptance. Arthropod. Plant Interact. 9(5), 529–541. https://doi.org/10.1007/s11829-015-9394-8 (2015).Article 

    Google Scholar 
    20.Douglas, A. E. Nutritional interactions in insect-microbial symbioses: Aphids and their symbiotic bacteria Buchnera. Annu. Rev. Entomol. 43(1), 17–37. https://doi.org/10.1146/annurev.ento.43.1.17 (1998).CAS 
    Article 
    PubMed 

    Google Scholar 
    21.Tamas, I. et al. 50 million years of genomic stasis in endosymbiotic bacteria. Science 296(5577), 2376–2379. https://doi.org/10.1126/science.1071278 (2002).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    22.Van Ham, R. C. H. J. et al. Reductive genome evolution in Buchnera aphidicola. Proc. Natl. Acad. Sci. 100(2), 581–586. https://doi.org/10.1073/pnas.0235981100 (2003).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Bouvaine, S., Boonham, N. & Douglas, A. E. Interactions between a Luteovirus and the GroEL chaperonin protein of the symbiotic bacterium Buchnera aphidicola of aphids. J. Gen. Virol. 92(6), 1467–1474. https://doi.org/10.1099/vir.0.029355-0 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    24.Rana, V. S., Singh, S. T., Priya, N. G., Kumar, J. & Rajagopal, R. Arsenophonus GroEL interacts with CLCuV and is localized in midgut and salivary gland of whitefly B. tabaci. PLoS ONE 7(8), e42168. https://doi.org/10.1371/journal.pone.0042168 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    25.Kliot, A. & Ghanim, M. The role of bacterial chaperones in the circulative transmission of plant viruses by insect vectors. Viruses 5(6), 1516–1535. https://doi.org/10.3390/v5061516 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    26.Filichkin, S. A., Brumfield, S., Filichkin, T. P. & Young, M. J. In vitro interactions of the aphid endosymbiotic SymL chaperonin with Barley yellow dwarf virus. J. Virol. 71(1), 569–577. https://doi.org/10.1128/JVI.71.1.569-577.1997 (1997).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    27.van den Heuvel, J. F., Verbeek, M. & van der Wilk, F. Endosymbiotic bacteria associated with circulative transmission of Potato leafroll virus by Myzus persicae. J. Gen. Virol. 75(Pt 10), 2559–2565. https://doi.org/10.1099/0022-1317-75-10-2559 (1994).Article 
    PubMed 

    Google Scholar 
    28.Gray, S. M. & Gildow, F. E. Luteovirus-aphid interactions. Annu. Rev. Phytopathol. 41(1), 539–566. https://doi.org/10.1146/annurev.phyto.41.012203.105815 (2003).CAS 
    Article 
    PubMed 

    Google Scholar 
    29.Li, C., Cox-Foster, D., Gray, S. M. & Gildow, F. Vector specificity of Barley yellow dwarf virus (BYDV) transmission: Identification of potential cellular receptors binding BYDV-MAV in the aphid, Sitobion avenae. Virology 286(1), 125–133. https://doi.org/10.1006/viro.2001.0929 (2001).CAS 
    Article 
    PubMed 

    Google Scholar 
    30.Dombrovsky, A., Gollop, N., Chen, S., Chejanovsky, N. & Raccah, B. In vitro association between the helper component-proteinase of Zucchini yellow mosaic virus and cuticle proteins of Myzus persicae. J. Gen. Virol. 88(5), 1602–1610. https://doi.org/10.1099/vir.0.82769-0 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    31.van den Heuvel, J. F. et al. The N-terminal region of the luteovirus readthrough domain determines virus binding to Buchnera GroEL and is essential for virus persistence in the aphid. J. Virol. 71(10), 7258–7265. https://doi.org/10.1128/JVI.71.10.7258-7265.1997 (1997).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Morin, S. et al. A GroEL homologue from endosymbiotic bacteria of the whitefly Bemisia tabaci is implicated in the circulative transmission of Tomato yellow leaf curl virus. Virology 256(1), 75–84. https://doi.org/10.1006/viro.1999.9631 (1999).CAS 
    Article 
    PubMed 

    Google Scholar 
    33.Chaudhary, R., Atamian, H. S., Shen, Z., Briggs, S. P. & Kaloshian, I. GroEL from the endosymbiont Buchnera aphidicola betrays the aphid by triggering plant defense. Proc. Natl. Acad. Sci. 111(24), 8919–8924. https://doi.org/10.1073/pnas.1407687111 (2014).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.Vandermoten, S. et al. Comparative analyses of salivary proteins from three aphid species. Insect Mol. Biol. 23(1), 67–77. https://doi.org/10.1111/imb.12061 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    35.Gray, S. M., Cilia, M. & Ghanim, M. Circulative, “nonpropagative” virus transmission: An orchestra of virus-, insect-, and plant-derived instruments. Adv. Virus Res. 2014, 89. https://doi.org/10.1016/B978-0-12-800172-1.00004-5 (2014).Article 

    Google Scholar 
    36.Eigenbrode, S. D., Ding, H., Shiel, P. & Berger, P. H. Volatiles from potato plants infected with Potato leafroll virus attract and arrest the virus vector, Myzus persicae (Homoptera: Aphididae). Proc. Biol. Sci. 269(1490), 455–460. https://doi.org/10.1098/rspb.2001.1909 (2002).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Rajabaskar, D., Wu, Y., Bosque-Pérez, N. A. & Eigenbrode, S. D. Dynamics of Myzus persicae arrestment by volatiles from Potato leafroll virus-infected potato plants during disease progression. Entomol. Exp. Appl. 148(2), 2. https://doi.org/10.1111/eea.12087 (2013).Article 

    Google Scholar 
    38.Patton, M. F., Bak, A., Sayre, J. M., Heck, M. L. & Casteel, C. L. A polerovirus, Potato leafroll virus, alters plant–vector interactions using three viral proteins. Plant Cell Environ. 43(2), 387–399. https://doi.org/10.1111/pce.13684 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    39.Sadowy, E., Juszczuk, M., David, C., Gronenborn, B. & Danuta Hulanicka, M. D. Mutational analysis of the proteinase function of Potato leafroll virus. J. Gen. Virol. 82(Pt 6), 1517–1527. https://doi.org/10.1099/0022-1317-82-6-1517 (2001).CAS 
    Article 
    PubMed 

    Google Scholar 
    40.DeBlasio, S. L. et al. Insights into the polerovirus– plant interactome revealed by coimmunoprecipitation and mass spectrometry. Mol. Plant-Microbe Interact. 28(4), 467–481. https://doi.org/10.1094/MPMI-11-14-0363-R (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    41.Zhong, S. et al. High-throughput illumina strand-specific RNA sequencing library preparation. Cold Spring Harb. Protoc. 2011(8), 940–949. https://doi.org/10.1101/pdb.prot5652 (2011).Article 

    Google Scholar 
    42.Anders, S. et al. Count-based differential expression analysis of RNA sequencing data using R and bioconductor. Nat. Protoc. 8(9), 1765–1786. https://doi.org/10.1038/nprot.2013.099 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    43.Morgan, M. et al. ShortRead: A bioconductor package for input, quality assessment and exploration of high-throughput sequence data. Bioinformatics 25(19), 2607–2608. https://doi.org/10.1093/bioinformatics/btp450 (2009).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. (2010).45.Gauthier, J. P., Legeai, F., Zasadzinski, A., Rispe, C. & Tagu, D. AphidBase: A database for aphid genomic resources. Bioinformatics 23(6), 783–784. https://doi.org/10.1093/bioinformatics/btl682 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    46.Kim, D. et al. TopHat2: Accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36. https://doi.org/10.1186/gb-2013-14-4-r36 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Anders, S., Pyl, P. T. & Huber, W. HTSeq-A Python framework to work with high-throughput sequencing data. Bioinformatics 31(2), 166–169. https://doi.org/10.1093/bioinformatics/btu638 (2015).CAS 
    Article 

    Google Scholar 
    48.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15(12), 550. https://doi.org/10.1186/s13059-014-0550-8 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    49.Conesa, A. et al. Blast2GO: A universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21(18), 3674–3676. https://doi.org/10.1093/bioinformatics/bti610 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    50.Patton, M. F., Arena, G. D., Salminen, J. P., Steinbauer, M. J. & Casteel, C. L. Transcriptome and defence response in Eucalyptus camaldulensis leaves to feeding by Glycaspis brimblecombei Moore (Hemiptera: Aphalaridae): A stealthy psyllid does not go unnoticed. Austral. Entomol. 57(2), 247–254. https://doi.org/10.1111/aen.12319 (2017).Article 

    Google Scholar 
    51.Casteel, C. L. et al. Disruption of ethylene responses by Turnip mosaic virus mediates suppression of plant defense against the green peach aphid vector. Plant Physiol. 169(1), 209–218. https://doi.org/10.1104/pp.15.00332 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Nikoh, N. et al. Bacterial genes in the aphid genome: Absence of functional gene transfer from Buchnera to its host. PLoS Genet. 6(2), e1000827. https://doi.org/10.1371/journal.pgen.1000827 (2010).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Hansen, A. K. & Degnan, P. H. Widespread expression of conserved small RNAs in small symbiont genomes. ISME J. 8(12), 2490–2502. https://doi.org/10.1038/ismej.2014.121 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Hogenhout, S. A., van der Wilk, F., Verbeek, M., Goldbach, R. W. & van den Heuvel, J. F. Potato leafroll virus binds to the equatorial domain of the aphid endosymbiotic GroEL homolog. J. Virol. 72(1), 358–365. https://doi.org/10.1128/JVI.72.1.358-365.1998 (1998).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    55.Camberg, J.L., Doyle, S.M., Johnston, D.M., & Wickner, S. Molecular Chaperones. in Brenner’s Encyclopedia of Genetics. 2nd Edn. 456–60. (Elsevier, 2013). https://doi.org/10.1016/B978-0-12-809633-8.06723-6.56.Segal, G. & Ron, E. Z. Regulation and organization of the groE and dnaK operons in Eubacteria. FEMS Microbiol. Lett. 138(1), 1–10. https://doi.org/10.1111/j.1574-6968.1996.tb08126.x (1996).CAS 
    Article 
    PubMed 

    Google Scholar 
    57.Zhang, L., Pelech, S. & Uitto, V. J. Bacterial GroEL-like heat shock protein 60 protects epithelial cells from stress-induced death through activation of ERK and inhibition of caspase 3. Exp. Cell Res. 292(1), 231–240. https://doi.org/10.1016/j.yexcr.2003.08.012 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    58.Shigenobu, S., Watanabe, H., Hattori, M., Sakaki, Y. & Ishikawa, H. Genome sequence of the endocellular bacterial symbiont of aphids Buchnera sp. APS Nat. 407(6800), 81–86. https://doi.org/10.1038/35024074 (2000).CAS 
    Article 

    Google Scholar 
    59.Dombrovsky, A., Sobolev, I., Chejanovsky, N. & Raccah, B. Characterization of RR-1 and RR-2 cuticular proteins from Myzus persicae. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 146(2), 256–264. https://doi.org/10.1016/j.cbpb.2006.11.013 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    60.Dombrovsky, A., Huet, H., Zhang, H., Chejanovsky, N. & Raccah, B. Comparison of newly isolated cuticular protein genes from six aphid species. Insect Biochem. Mol. Biol. 33(7), 709–715. https://doi.org/10.1016/s0965-1748(03)00065-1 (2003).CAS 
    Article 
    PubMed 

    Google Scholar 
    61.Liang, Y. & Gao, X. W. The cuticle protein gene MPCP4 of Myzus persicae (Homoptera: Aphididae) plays a critical role in cucumber mosaic virus acquisition. J. Econ. Entomol. 110(3), 848–853. https://doi.org/10.1093/jee/tox025 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    62.Silva, A. X., Jander, G., Samaniego, H., Ramsey, J. S. & Figueroa, C. C. Insecticide resistance mechanisms in the green peach aphid Myzus persicae (Hemiptera: Aphididae) I: A transcriptomic survey. PLoS ONE 7(6), e36366. https://doi.org/10.1371/journal.pone.0036366 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    63.Deshoux, M., Monsion, B. & Uzest, M. Insect cuticular proteins and their role in transmission of phytoviruses. Curr. Opin. Virol. 33, 137–143. https://doi.org/10.1016/j.coviro.2018.07.015 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    64.Gallot, A. et al. Cuticular proteins and seasonal photoperiodism in aphids. Insect Biochem. Mol. Biol. 40(3), 235–240. https://doi.org/10.1016/j.ibmb.2009.12.001 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    65.Cilia, M. et al. Genetics coupled to quantitative intact proteomics links heritable aphid and endosymbiont protein expression to circulative polerovirus transmission. J. Virol. 85(5), 2148–2166. https://doi.org/10.1128/JVI.01504-10 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    66.Wang, H., Wu, K., Liu, Y., Wu, Y. & Wang, X. Integrative proteomics to understand the transmission mechanism of Barley yellow dwarf virus-GPV by its insect vector Rhopalosiphum padi. Sci. Rep. 5, 10971. https://doi.org/10.1038/srep10971 (2015).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Seddas, P. et al. Rack-1, GAPDH3, and actin: proteins of Myzus persicae potentially involved in the transcytosis of Beet western yellows virus particles in the aphid. Virology 325(2), 399–412. https://doi.org/10.1016/j.virol.2004.05.014 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    68.Yang, Z., Zhang, F., Zhu, L. & He, G. Identification of differentially expressed genes in brown planthopper Nilaparvata lugens (Hemiptera: Delphacidae) responding to host plant resistance. Bull. Entomol. Res. 96(1), 53–59. https://doi.org/10.1079/ber2005400 (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    69.Bass, C. et al. Gene amplification and microsatellite polymorphism underlie a recent insect host shift. Proc. Natl. Acad. Sci. 110(48), 19460–19465. https://doi.org/10.1073/pnas.1314122110 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    70.Ramsey, J. S. et al. Adaptation to nicotine feeding in Myzus persicae. J. Chem. Ecol. 40(8), 869–877. https://doi.org/10.1007/s10886-014-0482-5 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    71.Casteel, C. L. & Jander, G. New synthesis: Investigating mutualisms in virus-vector interactions. J. Chem. Ecol. 39(7), 809. https://doi.org/10.1007/s10886-013-0305-0 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    72.Götz, M. et al. Implication of Bemisia tabaci HEAT SHOCK PROTEIN 70 in Begomovirus-whitefly interactions. J. Virol. 86(24), 13241–13252. https://doi.org/10.1128/JVI.00880-12 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    73.Porras, M. F. et al. Enhanced heat tolerance of viral-infected aphids leads to niche expansion and reduced interspecific competition. Nat. Commun. 11(1), 1184. https://doi.org/10.1038/s41467-020-14953-2 (2020).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    74.Syller, J. The influence of temperature on transmission of potato leaf roll virus by Myzus persicae Sulz. Potato Res. 30(1), 47–58. https://doi.org/10.1007/BF02357683 (1987).Article 

    Google Scholar 
    75.Syller, J. The effects of temperature on the susceptibility of potato plants to infection and accumulation of Potato Leafroll Virus. J. Phytopathol. 133(3), 216–224. https://doi.org/10.1111/j.1439-0434.1991.tb00156.x (1991).Article 

    Google Scholar 
    76.Chung, B. N. et al. The effects of high temperature on infection by Potato virus Y, Potato virus A, and Potato leafroll virus. Plant Pathol. J. 32(4), 321–328. https://doi.org/10.5423/PPJ.OA.12.2015.0259 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    77.Hansen, A. K. & Moran, N. A. The impact of microbial symbionts on host plant utilization by herbivorous insects. Mol. Ecol. 23(6), 1473–96 (2014).Article 

    Google Scholar 
    78.Jiang, Z. et al. Comparative analysis of genome sequences from four strains of the Buchnera aphidicola Mp endosymbion of the green peach aphid, Myzus persicae. BMC Genom. 14(1), 917. https://doi.org/10.1186/1471-2164-14-917 (2013).CAS 
    Article 

    Google Scholar 
    79.Enders, L. S. et al. Abiotic and biotic stressors causing equivalent mortality induce highly variable transcriptional responses in the soybean aphid. G3 (Bethesda) 5(2), 261–270. https://doi.org/10.1534/g3.114.015149 (2014).Article 

    Google Scholar 
    80.Wilcox, J. L., Dunbar, H. E., Wolfinger, R. D. & Moran, N. A. Consequences of reductive evolution for gene expression in an obligate endosymbiont. Mol. Microbiol. 48(6), 1491–1500. https://doi.org/10.1046/j.1365-2958.2003.03522.x (2003).CAS 
    Article 
    PubMed 

    Google Scholar 
    81.Karp, P. D. et al. The BioCyc collection of microbial genomes and metabolic pathways. Brief Bioinform. 20(4), 1085–1093. https://doi.org/10.1093/bib/bbx085 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    82.Zhang, B., Leonard, S. P., Li, Y. & Moran, N. A. Obligate bacterial endosymbionts limit thermal tolerance of insect host species. Proc. Natl. Acad. Sci. 116(49), 24712–24718. https://doi.org/10.1073/pnas.1915307116 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    83.Chong, R. A. & Moran, N. A. Intraspecific genetic variation in hosts affects regulation of obligate heritable symbionts. PNAS 113(46),13114–13119. https://doi.org/10.1073/pnas.1610749113 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    84.Pers, D. & Hansen, A. K. The boom and bust of the aphid’s essential amino acid metabolism across nymphal development. G3 (Bethesda). 11(9), jkab115. https://doi.org/10.1093/g3journal/jkab115 (2021).Article 

    Google Scholar 
    85.Dunbar, H. E., Wilson, A. C. C., Ferguson, N. R. & Moran, N. A. Aphid thermal tolerance is governed by a point mutation in bacterial symbionts. PLoS Biol. 5(5), e96. https://doi.org/10.1371/journal.pbio.0050096 (2007).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    86.Moran, N. A. & Yun, Y. Experimental replacement of an obligate insect symbiont. Proc. Natl. Acad. Sci. 112(7), 2093–2096. https://doi.org/10.1073/pnas.1420037112 (2015).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    87.Fares, M. A., Barrio, E., Sabater-Muñoz, B. & Moya, A. The evolution of the heat-shock protein GroEL from Buchnera, the primary endosymbiont of aphids, is governed by positive selection. Mol. Biol. Evol. 19(7), 1162–1170. https://doi.org/10.1093/oxfordjournals.molbev.a004174 (2002).CAS 
    Article 
    PubMed 

    Google Scholar 
    88.Kliot, A., Cilia, M., Czosnek, H., & Ghanim, M. Implication of the bacterial endosymbiont Rickettsia spp. in interactions of the whitefly Bemisia tabaci with Tomato yellow leaf curl virus. J. Virol. 88(10), 5652–5660. https://doi.org/10.1128/JVI.00071-14 (2014).89.Dheilly, N. M. et al. Who is the puppet master? Replication of a parasitic wasp-associated virus correlates with host behaviour manipulation. Proc. R. Soc. B Biol. Sci. 2015(282), 20142773 (1803).
    Google Scholar 
    90.Mohan, P. & Sinu, P. A. Does the solitary parasitoid Microplitis pennatulae use a combinatorial approach to manipulate its host?. Entomol. Exp. Appl. 168(4), 295–303 (2020).CAS 
    Article 

    Google Scholar 
    91.Smith, T. E. & Moran, N. A. Coordination of host and symbiont gene expression reveals a metabolic tug-of-war between aphids and Buchnera. Proc. Natl. Acad. Sci. 117(4), 2113–2121. https://doi.org/10.1073/pnas.1916748117 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Learning of a mimic odor combined with nectar nonsugar compounds enhances honeybee pollination of a commercial crop

    1.Klein, A. M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B 274(1608), 303–313 (2007).Article 

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

    Google Scholar 
    3.Aizen, M. A. et al. Global agricultural productivity is threatened by increasing pollinator dependence without a parallel increase in crop diversification. Glob. Change Biol. 25, 3516–3527 (2019).ADS 
    Article 

    Google Scholar 
    4.Ribbands, C. R. The scent perception of the honey bee. Proc. R. Soc. Lond. B 143(912), 367–379 (1955).ADS 
    Article 

    Google Scholar 
    5.von Frisch, K. The Dance Language and Orientation of Bees (Harvard University Press, 1967).
    Google Scholar 
    6.Reinhard, J., Srinivasan, M. V., Guez, D. & Zhang, S. W. Floral scents induce recall of navigational and visual memories in honeybees. J. Exp. Biol. 207(25), 4371–4381 (2004).Article 

    Google Scholar 
    7.Arenas, A., Fernández, V. M. & Farina, W. M. Floral odor learning within the hive affects honeybees’ foraging decisions. Naturwissenschaften 94, 218–222 (2007).ADS 
    CAS 
    Article 

    Google Scholar 
    8.Farina, W. M., Grüter, C. & Díaz, P. C. Social learning of floral odours inside the honeybee hive. Proc. R. Soc. Lond. B. 272(1575), 1923–1928 (2005).
    Google Scholar 
    9.Farina, W. M., Grüter, C., Acosta, L. & Mc Cabe, S. Honeybees learn floral odors while receiving nectar from foragers within the hive. Naturwissenschaften 94(1), 55–60 (2007).ADS 
    CAS 
    Article 

    Google Scholar 
    10.Grüter, C., Acosta, L. E. & Farina, W. M. Propagation of olfactory information within the honeybee hive. Behav. Ecol. Sociobiol. 60(5), 707–715 (2006).Article 

    Google Scholar 
    11.Arenas, A., Fernández, V. M. & Farina, W. M. Floral scents experienced within the colony affect long-term foraging preferences in honeybees. Apidologie 39, 714–722 (2008).Article 

    Google Scholar 
    12.Balbuena, M. S., Arenas, A. & Farina, W. M. Floral scents learned inside the honeybee hive have a long-lasting effect on recruitment. Anim. Behav. 84, 77–83 (2012).Article 

    Google Scholar 
    13.Farina, W. M., Arenas, A., Díaz, P. C., Susic Martin, C. & Estravis Barcala, M. C. Learning of a mimic odor within beehives improves pollination service efficiency in a commercial crop. Curr. Biol. 30, 4284–4290. https://doi.org/10.1016/j.cub.2020.08.018 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    14.Stevenson, P. C., Nicolson, S. W. & Wright, G. A. Plant secondary metabolites in nectar: Impacts on pollinators and ecological functions. Funct. Ecol. 31(1), 65–75 (2017).Article 

    Google Scholar 
    15.Baker, H. G. Non-sugar chemical constituents of nectar. Apidologie 8(4), 349–356 (1977).Article 

    Google Scholar 
    16.Chalisova, N. I. et al. Effect of encoding amino acids on associative learning of honeybee Apis mellifera. J. Evol. Biochem. Fisiol. 47(6), 607 (2011).Article 

    Google Scholar 
    17.Strauss, S. Y. & Whittall, J. B. Non-pollinator agents of selection on floral traits. In Ecology and Evolution of flowers (eds. Harder, L. D. & Barret, S. C. H.) 120–138 (Oxford University Press, 2006).18.McArt, S. H., Koch, H., Irwin, R. E. & Adler, L. S. Arranging the bouquet of disease: Floral traits and the transmission of plant and animal pathogens. Ecol. Lett. 17, 624–636 (2014).Article 

    Google Scholar 
    19.Gatica Hernández, I., Palottini, F., Macri, I., Galmarini, C. R. & Farina, W. M. Appetitive behavior of the honey bee Apis mellifera in response to phenolic compounds naturally found in nectars. J. Exp. Biol. https://doi.org/10.1242/jeb.189910 (2019).Article 

    Google Scholar 
    20.Stevenson, P. C. For antagonists and mutualists: The paradox of insect toxic secondary metabolites in nectar and pollen. Phytochem. Rev. 19(3), 603–614 (2020).CAS 
    Article 

    Google Scholar 
    21.Carlesso, D., Smargiassi, S., Pasquini, E., Bertelli, G. & Baracchi, D. Nectar non-protein amino acids (NPAAs) do not change nectar palatability but enhance learning and memory in honey bees. Sci. Rep. 11(1), 1–14 (2021).Article 

    Google Scholar 
    22.Kretschmar, J. A. & Baumann, T. W. Caffeine in Citrus flowers. Phytochemistry 52(1), 19–23 (1999).CAS 
    Article 

    Google Scholar 
    23.Wright, G. A. et al. Caffeine in floral nectar enhances a pollinator’s memory of reward. Science 339(6124), 1202–1204 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    24.Couvillon, M. J. et al. Caffeinated forage tricks honeybees into increasing foraging and recruitment behaviors. Curr. Biol. 25, 2815–2818 (2015).CAS 
    Article 

    Google Scholar 
    25.Arnold, S. E. et al. Bumble bees show an induced preference for flowers when primed with caffeinated nectar and a target floral odor. Curr. Biol. 31, 1–5 (2021).Article 

    Google Scholar 
    26.Gardener, M. C. & Gillman, M. P. Analyzing variability in nectar amino acids: Composition is less variable than concentration. J. Chem. Ecol. 27(12), 2545–2558 (2001).CAS 
    Article 

    Google Scholar 
    27.Power, E. F., Stabler, D., Borland, A. M., Barnes, J. & Wright, G. A. Analysis of nectar from low-volume flowers: A comparison of collection methods for free amino acids. Methods Ecol. Evol. 9, 734–743 (2018).Article 

    Google Scholar 
    28.Terrab, A. et al. Analysis of amino acids in nectar from Silene colorata Poiret (Caryophyllaceae). Bot. J. Linn. Soc. 155, 49–56 (2007).Article 

    Google Scholar 
    29.Taha, E. K. A., Al-Kahtani, S. & Taha, R. Protein content and amino acids composition of bee-pollens from major floral sources in Al-Ahsa, eastern Saudi Arabia. Saudi J. Biol. Sci. 26, 232–237 (2019).CAS 
    Article 

    Google Scholar 
    30.Müller, U. Inhibition of nitric oxide synthase impairs a distinct form of long-term memory in the honeybee, Apis mellifera. Neuron 16, 541–549 (1996).Article 

    Google Scholar 
    31.Müller, U. The nitric oxide system in insects. Prog. Neurobiol. 51, 363–381 (1997).Article 

    Google Scholar 
    32.Lopatina, N. G., Zachepilo, T. H., Kamyshev, N. G. & Chalisova, N. I. The influence of combinations of encoded amino acids on associative learning in the honeybee Apis mellifera L. J. Evol. Biochem. Physiol. 53(2), 123–128 (2017).CAS 
    Article 

    Google Scholar 
    33.Marchi, I. L., Palottini, F. & Farina, W. M. Combined secondary compounds naturally found in nectars enhance honeybee cognition and survival. J. Exp. Biol. 224(6), jeb239616 (2021).Article 

    Google Scholar 
    34.Müller, U. Prolonged activation of cAMP-dependent protein kinase during conditioning induces long-term memory in honeybees. Neuron 27(1), 159–168 (2000).Article 

    Google Scholar 
    35.Negri, P. et al. Nitric oxide participates at the first steps of Apis mellifera cellular immune activation in response to non-self recognition. Apidologie 44(5), 575–585 (2013).CAS 
    Article 

    Google Scholar 
    36.Lu, Y. H. et al. Identification of immune regulatory genes in Apis mellifera through caffeine treatment. Insects 11(8), 516 (2020).Article 

    Google Scholar 
    37.Delaplane, K. S. & Mayer, D. F. Crop Pollination by Bees (CAB International, 2000).
    Google Scholar 
    38.Cabrera, A. L. & Willink, A. Biogeografía de América Latina (Organización de Estados Americanos, 1973).
    Google Scholar 
    39.Gabai, A. et al. Protocol for Using Pollinators in Hybrid Seed Production: An Outline for Improving Pollinator Effectiveness. (International Seed Federation, 2018).40.Torretta, J. P., Medan, D., Roig Alsina, A. H. & Montaldo, N. H. Visitantes florales diurnos del girasol (Helianthus annuus L., Asterales: Asteraceae) en la Argentina. Rev. Soc. Entomol. Argent. 69(1–2), 17–32 (2010).
    Google Scholar 
    41.Sáez, A., Sabatino, M. & Aizen, M. A. Interactive effects of large-and small-scale sources of feral honeybees for sunflower in the Argentine Pampas. PLoS One 7(1), e30968 (2012).ADS 
    Article 

    Google Scholar 
    42.Farina, W. M., Díaz, P. C. & Arenas, A. Patent AR082846B1: Una Formulación que Promueve la Polinización Dirigida de Abejas Melíferas Hacia Cultivos de Girasol (Instituto Nacional de Propiedad Intelectual, 2017).43.Noetzel, D. M. Insect Pollination Results on Sunflower 108–112 (Department of Entomology North Dakota State University Fargo USA, 1968).44.Johannsmeier, M. F. & Mostert, J. N. Crop pollination. In Beekeeping in South Africa, 3rd ed. 235–250 (ed. Johannsmeier, M. F.) (Plant Protection Research Institute handbook 14. Agricultural Research Council of South Africa, 2001).45.R Development Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing). https://www.R-project.org/ (2021).46.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9(2), 378–400 (2017).Article 

    Google Scholar 
    47.Crawley, M. J. The R Book (Wiley, 2013).MATH 

    Google Scholar 
    48.Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. R Package Version 0.2.7. https://CRAN.R-project.org/package=DHARMa (2021).49.Chambers, J. M. & Hastie, T. J. Statistical Models in S (Chapman and Hall, 1992).MATH 

    Google Scholar 
    50.Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means. R Package Version 1.4.4., https://CRAN.R-project.org/package=emmeans (2021). More

  • in

    Attachment of zebra and quagga mussel adhesive plaques to diverse substrates

    1.Hebert, P. D. N., Muncaster, B. W. & Mackie, G. L. Ecological and genetic studies on Dreissena polymorpha (Pallas): A new mollusc in the Great Lakes. Can. J. Fish. Aquat. Sci. 46, 1587–1591 (1989).
    Google Scholar 
    2.May, B. & Marsden, J. E. Genetic identification and implications of another invasive species of dreissenid mussel in the Great Lakes. Can. J. Fish. Aquat. Sci. 49, 1501–1506 (1992).
    Google Scholar 
    3.Ackerman, J. D., Cottrell, C. M., Ethier, C. R., Allen, D. G. & Spelt, J. K. Attachment strength of zebra mussels on natural, polymeric, and metallic materials. J. Environ. Eng. ASCE 122, 141–148 (1996).CAS 

    Google Scholar 
    4.Kobak, J. Attachment strength of Dreissena polymorph on artificial substrates. In The Zebra Mussel in Europe (eds van der Velde, G. et al.) 349–354 (Margraf Publishers, 2010).
    Google Scholar 
    5.Karatayev, A. Y., Burlakova, L. E. & Padilla, D. K. Zebra versus quagga mussels: A review of their spread, population dynamics, and ecosystem impacts. Hydrobiologia 746, 97–112 (2015).CAS 

    Google Scholar 
    6.Karatayev, V. A., Karatayev, A. Y., Burlakova, L. E. & Padilla, D. K. Lakewide dominance does not predict the potential for spread of dreissenids. J. Great Lakes Res. https://doi.org/10.1016/j.jglr.2013.09.007 (2013).Article 

    Google Scholar 
    7.Peyer, S. M., McCarthy, A. J. & Lee, C. E. Zebra mussels anchor byssal threads faster and tighter than quagga mussels in flow. J. Exp. Biol. https://doi.org/10.1242/jeb.028688 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.Amini, S. et al. Preventing mussel adhesion using lubricant-infused materials. Science 357, 668–673 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    9.Matsui, Y. et al. Attachment strength of Limnoperna fortunei on substrates, and their surface properties. Biofouling 17, 29–39 (2001).
    Google Scholar 
    10.Marsden, J. E. & Lansky, D. M. Substrate selection by settling zebra mussels, Dreissena polymorpha, relative to material, texture, orientation, and sunlight. Can. J. Zool. 78, 787–793 (2000).
    Google Scholar 
    11.Kobak, J. Factors influencing the attachment strength of Dreissena polymorpha (Bivalvia). Biofouling 22, 141–150 (2006).
    Google Scholar 
    12.Ackerman, J. D., Ethier, C. R., Allen, D. G. & Spelt, J. K. Investigation of zebra mussel adhesion strength using rotating disks. J. Environ. Eng. 118, 708–724 (1992).
    Google Scholar 
    13.Ackerman, J. D., Ethier, C. R., Spelt, J. K., Allen, D. G. & Cottrell, C. M. A wall jet to measure the attachment strength of zebra mussels. Can. J. Fish. Aquat. Sci. 52, 126–135 (1995).
    Google Scholar 
    14.Balogh, C., Serfőző, Z., bij de Vaate, A., Noordhuis, R. & Kobak, J. Biometry, shell resistance and attachment of zebra and quagga mussels at the beginning of their co-existence in large European lakes. J. Great Lakes Res. 45, 777–787 (2019).
    Google Scholar 
    15.Grutters, B. M. C., Verhofstad, M. J. J. M., van der Velde, G., Rajagopal, S. & Leuven, R. S. E. W. A comparative study of byssogenesis on zebra and quagga mussels: The effects of water temperature, salinity and light–dark cycle. Biofouling 28, 121–129 (2012).PubMed 

    Google Scholar 
    16.Naddafi, R. & Rudstam, L. G. Predator-induced behavioural defences in two competitive invasive species: The zebra mussel and the quagga mussel. Anim. Behav. 86, 1275–1284 (2013).
    Google Scholar 
    17.Bell, E. C. & Gosline, J. M. Mechanical design of mussel byssus: Material yield enhances attachment strength. J. Exp. Biol. 199, 1005–1017 (1996).CAS 
    PubMed 

    Google Scholar 
    18.Brazee, S. L. & Carrington, E. Interspecific comparison of the mechanical properties of mussel byssus. Biol. Bull. 211, 263–274 (2006).PubMed 
    PubMed Central 

    Google Scholar 
    19.Burkett, J. R., Wojtas, J. L., Cloud, J. L. & Wilker, J. J. A method for measuring the adhesion strength of marine mussels. J. Adhes. 85, 601–615 (2009).CAS 

    Google Scholar 
    20.Desmond, K. W., Zacchia, N. A., Waite, J. H. & Valentine, M. T. Dynamics of mussel plaque detachment. Soft Matter 11, 6832–6839 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    21.Hamada, N., Roman, V., Howell, S. & Wilker, J. Examining potential active tempering of adhesive curing by marine mussels. Biomimetics 2, 16 (2017).
    Google Scholar 
    22.Farsad, N. & Sone, E. D. Zebra mussel adhesion: Structure of the byssal adhesive apparatus in the freshwater mussel, Dreissena polymorpha. J. Struct. Biol. 177, 613–620 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    23.Stalder, A. F. et al. Low-bond axisymmetric drop shape analysis for surface tension and contact angle measurements of sessile drops. Colloids Surf. A Physicochem. Eng. Asp. 364, 72–81 (2010).CAS 

    Google Scholar 
    24.Claxton, W. T., Wilson, A. B., Mackie, G. L. & Boulding, E. G. A genetic and morphological comparison of shallow- and deep-water populations of the introduced dreissenid bivalve Dreissena bugensis. Can. J. Zool. 76, 1269–1276 (1998).
    Google Scholar 
    25.Peyer, S. M., Hermanson, J. C. & Lee, C. E. Developmental plasticity of shell morphology of quagga mussels from shallow and deep-water habitats of the Great Lakes. J. Exp. Biol. 213, 2602–2609 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    26.Sprung, M. Field and laboratory observations of Dreissena polymorpha larvae: Abundance, growth, mortality and food demands. Arch. Hydrobiol. 115, 537–561 (1989).
    Google Scholar 
    27.Nichols, S. J. Maintenance of the zebra mussel (Dreissena polymorpha) under laboratory conditions. In Zebra Mussels: Biology, Impacts, and Control (eds Nalepa, T. F. & Schloesser, D. W.) 733–747 (Lewis Publishers, 1992).
    Google Scholar 
    28.Porter, A. E. & Marsden, J. E. Adult zebra mussels (Dreissena polymorpha) avoid attachment to mesh materials. Northeast. Nat. 15, 589–594 (2008).
    Google Scholar 
    29.Kimmins, K. M., James, B. D., Nguyen, M. T., Hatton, B. D. & Sone, E. D. Oil-infused silicone prevents zebra mussel adhesion. ACS Appl. Bio Mater. https://doi.org/10.1021/acsabm.9b00832 (2019).Article 

    Google Scholar 
    30.Peyer, S. M., Hermanson, J. C. & Lee, C. E. Effects of shell morphology on mechanics of zebra and quagga mussel locomotion. J. Exp. Biol. 214, 2226–2236 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    31.Berkman, P. A., Garton, D. W., Haltuch, M. A., Kennedy, G. W. & Febo, L. R. Habitat shift in invading species: Zebra and quagga mussel population characteristics on shallow soft substrates. Biol. Invasions https://doi.org/10.1023/A:1010088925713 (2000).Article 

    Google Scholar 
    32.Skaja, A., Tordonato, D. & Merten, B. Coatings for invasive mussel control: Colorado river field study. In Biol. Manag. Invasive Quagga Zebra Mussels West. United States 451–466 (2015) https://doi.org/10.1201/b18447-37https://doi.org/10.1201/b18447-37.33.Zhao, H., Robertson, N. B., Jewhurst, S. A. & Waite, J. H. Probing the adhesive footprints of Mytilus californianus byssus. J. Biol. Chem. https://doi.org/10.1074/jbc.M510792200 (2006).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.Kimmins, K. Freshwater Mussel Adhesion: Interfacial Structures & Antifouling Surfaces (Univesity of Toronto, 2020).
    Google Scholar 
    35.Kobak, J. Behavior of juvenile and adult zebra mussels (Dreissena polymorpha). In Quagga Zebra Mussel Biol. Impacts, Control 331–344 (2013) https://doi.org/10.1201/b15437-28.36.Waite, J. H. Adhesion in byssally attached bivalves. Biol. Rev. 58, 209–231 (1983).CAS 

    Google Scholar 
    37.Lachance, A. A., Myrand, B., Tremblay, R., Koutitonsky, V. & Carrington, E. Biotic and abiotic factors influencing attachment strength of blue mussels Mytilus edulis in suspended culture. Aquat. Biol. 2, 119–129 (2008).
    Google Scholar 
    38.Lee, H., Scherer, N. F. & Messersmith, P. B. Single-molecule mechanics of mussel adhesion. Proc. Natl. Acad. Sci. 103, 12999–13003 (2006).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Rzepecki, L. M. & Waite, J. H. The byssus of the zebra mussel, Dreissena polymorpha. I: Morphology and in situ protein processing during maturation. Mol. Mar. Biol. Biotechnol. 2, 255–266 (1993).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    40.Waite, J. H. & Qin, X. Polyphosphoprotein from the adhesive pads of Mytilus edulis. Biochemistry 40, 2887–2893 (2001).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Zhao, H. & Waite, J. H. Linking adhesive and structural proteins in the attachment plaque of Mytilus californianus. J. Biol. Chem. 281, 26150–26158 (2006).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Petrone, L. et al. Mussel adhesion is dictated by time-regulated secretion and molecular conformation of mussel adhesive proteins. Nat. Commun. 6, 8737 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Waite, J. H. Mussel adhesion—Essential footwork. J. Exp. Biol. 220, 517–530 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    44.Lee, B. P., Messersmith, P. B., Israelachvili, J. N. & Waite, J. H. Mussel-inspired adhesives and coatings. Annu. Rev. Mater. Res. 41, 99–132 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Ou, X. et al. Structure and sequence features of mussel adhesive protein lead to its salt-tolerant adhesion ability. Sci. Adv. 6, eabb7620 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    46.Maier, G. P., Rapp, M. V., Waite, J. H., Israelachvili, J. N. & Butler, A. Adaptive synergy between catechol and lysine promotes wet adhesion by surface salt displacement. Science 349, 628–632 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Bilotto, P. et al. Adhesive properties of adsorbed layers of two recombinant mussel foot proteins with different levels of DOPA and tyrosine. Langmuir 35, 15481–15490 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    48.Kim, S. et al. Cation–π interaction in DOPA-deficient mussel adhesive protein mfp-1. J. Mater. Chem. B 3, 738–743 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More