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    Sustainable palm fruit harvesting as a pathway to conserve Amazon peatland forests

    Dargie, G. C. et al. Age, extent and carbon storage of the central Congo Basin peatland complex. Nature 542, 86–90 (2017).CAS 
    Article 

    Google Scholar 
    Horn, C. M., Vargas Paredes, V. H., Gilmore, M. P. & Endress, B. A. Spatio-temporal patterns of Mauritia flexuosa fruit extraction in the Peruvian Amazon: implications for conservation and sustainability. Appl. Geogr. 97, 98–108 (2018).Article 

    Google Scholar 
    Virapongse, A., Endress, B. A., Gilmore, M. P., Horn, C. & Romulo, C. Ecology, livelihoods, and management of the Mauritia flexuosa palm in South America. Glob. Ecol. Conserv. 10, 70–92 (2017).Article 

    Google Scholar 
    van der Hoek, Y., Solas, S. Á. & Peñuela, M. C. The palm Mauritia flexuosa, a keystone plant resource on multiple fronts. Biodivers. Conserv. 28, 539–551 (2019).Article 

    Google Scholar 
    Roucoux, K. H. et al. Threats to intact tropical peatlands and opportunities for their conservation. Conserv. Biol. 31, 1283–1292 (2017).CAS 
    Article 

    Google Scholar 
    Dargie, G. C. et al. Congo Basin peatlands: threats and conservation priorities. Mitig. Adapt. Strateg. Glob. Change 24, 669–686 (2019).Article 

    Google Scholar 
    Pandey, A. K., Tripathi, Y. C. & Kumar, A. Non timber forest products (NTFPs) for sustained livelihood: challenges and strategies. Res. J. For. 10, 1–7 (2016).CAS 

    Google Scholar 
    Kor, L., Homewood, K., Dawson, T. P. & Diazgranados, M. Sustainability of wild plant use in the Andean Community of South America. Ambio 50, 1681–1697 (2021).Draper, F. C. et al. The distribution and amount of carbon in the largest peatland complex in Amazonia. Environ. Res. Lett. 9, 124017 (2014).Article 

    Google Scholar 
    Freitas, L. Impacto del aprovechamiento en la estructura, producción y valor de uso del aguaje en la Amazonía peruana. Recur. Naturales y Ambient. 67, 35–45 (2012).
    Google Scholar 
    Aprovechamiento de los Residuos de Mauritia flexuosa (ITP-CITE, 2018).Falen, L. Y. & Honorio Coronado, E. N. Assessment of the techniques use to harvest buriti fruits (Mauritia flexuosa L.f.) in the district of Jenaro Herrera, Loreto, Peru. Folia Amazónica 27, 131–150 (2018).Article 

    Google Scholar 
    Draper, F. C. et al. Peatland forests are the least diverse tree communities documented in Amazonia, but contribute to high regional beta-diversity. Ecography 41, 1256–1269 (2018).Article 

    Google Scholar 
    Bejarano, P. & Piana, R. Plan de Manejo de los Aguajales Aledaños al Caño Parinari (WWF-AIF/DK – Reserva Nacional Pacaya Samiria, 2002).Manzi, M. & Coomes, O. T. Managing Amazonian palms for community use: a case of aguaje palm (Mauritia flexuosa) in Peru. For. Ecol. Manage. 257, 510–517 (2009).Article 

    Google Scholar 
    Baker, T. R. et al. How can ecologists help realise the potential of payments for carbon in tropical forest countries? J. Appl. Ecol. 47, 1159–1165 (2010).Article 

    Google Scholar 
    Padoch, C. Marketing of non-timber forest products in Western Amazonia: general observations and research priorities. Adv. Econ. Bot. 9, 43–50 (1192).
    Google Scholar 
    Delgado, C., Couturierb, G. & Mejía, K. Mauritia flexuosa (Arecaceae: Calamoideae), an Amazonian palm with cultivation purposes in Peru. Fruits 62, 157–169 (2007).Article 

    Google Scholar 
    Living Planet Index 2020—Bending the Curve of Biodiversity Loss (WWF, 2020).Gentry, A. H. & Vasquez, R. Where have all the ceibas gone? A case history of mismanagement of a tropical forest resource. For. Ecol. Manage. 23, 73–76 (1988).Article 

    Google Scholar 
    Pauly, D. Anecdotes and the shifting baseline syndrome of fisheries. Trends Ecol. Evol. 10, 430 (1995).CAS 
    Article 

    Google Scholar 
    Soga, M. & Gaston, K. J. Shifting baseline syndrome: causes, consequences, and implications. Front. Ecol. 16, 222–230 (2018).Article 

    Google Scholar 
    Nic Lughadha, E. et al. Extinction risk and threats to plants and fungi. Plants People Planet 2, 389–408 (2020).Article 

    Google Scholar 
    Ter Steege, H. et al. Estimating the global conservation status of more than 15,000 Amazonian tree species. Sci. Adv. 1, e1500936 (2015).Article 

    Google Scholar 
    Khan, F. & de Granville, J. J. Palms in Forest Ecosystems of Amazonia (Springer-Verlag, 1992).Freitas, L., Zárate, Z., Bardales, R. & Del Castillo, D. Efecto de la densidad de siembra en el desarrollo vegetativo del aguaje (Mauritia flexuosa L.f.) en plantaciones forestales. Rev. Peru. de. Biol. 26, 227–234 (2019).Article 

    Google Scholar 
    Benítez-López, A. et al. The impact of hunting on tropical mammal and bird populations. Science 356, 180–183 (2017).Article 

    Google Scholar 
    Endress, B. A., Gilmore, M. P., Vargas, V. H. & Horn, C. Data on spatio-temporal patterns of wild fruit harvest from the economically important palm Mauritia flexuosa in the Peruvian Amazon. Data Brief 20, 132–139 (2018).Article 

    Google Scholar 
    Ahrends, A. et al. Predictable waves of sequential forest degradation and biodiversity loss spreading from an African city. Proc. Natl Acad. Sci. USA 107, 14556–14561 (2010).Article 

    Google Scholar 
    Hardin, G. The tragedy of the commons. Science 162, 1243–1248 (1968).CAS 
    Article 

    Google Scholar 
    Ostrom, E. in The New Palgrave Dictionary of Economics Online (eds Durlauf, N.S. & Blume, L.E.) (Palgrave Macmillan, 2008); https://hdl.handle.net/10535/5887Dietz, T., Ostrom, E. & Stern, P. C. The struggle to govern the commons. Science 302, 1907–1912 (2003).CAS 
    Article 

    Google Scholar 
    Isaza, C., Bernal, R., Galeano, G. & Martorell, C. Demography of Euterpe precatoria and Mauritia flexuosa in the Amazon: application of integral projection models for their harvest. Biotropica 49, 653–664 (2017).Article 

    Google Scholar 
    Chuquinbalqui, C. M. et al. Diagnóstico socioeconómico de la población organizada para el manejo de recursos naturales en las cuencas Yanayacu Pucate y Pacaya en la Reserva Nacional Pacaya Samiria (Reserva Nacional Pacaya Samiria – SERNANP, 2014).Koh, L. & Wilcove, D. Cashing in palm oil for conservation. Nature 448, 993–994 (2007).CAS 
    Article 

    Google Scholar 
    Murdiyarso, D., Suryadiputra, I. N. & Wahyunto. Tropical peatlands management and climate change: a case study in Sumatra, Indonesia. In Proc. 12th International Peat Congress on Wise Use of Peatlands Vol. 1 (ed. Paivanen, J.) 698–706 (International Peat Society, 2004).Freitas, M. A. B. et al. Intensification of açaí palm management largely impoverishes tree assemblages in the Amazon estuarine forest. Biol. Conserv. 261, 109251 (2021).Article 

    Google Scholar 
    Plan Operativo de Castaña Región Madre de Dios (MINCETUR, 2007).La Industria de la Madera en el Perú. Identificación de las Barreras y Oportunidades para el Comercio Interno de Productos Responsables de Madera, Provenientes de Fuentes Sostenibles y Legales en las MIPYMES del Perú (FAO, 2018).Transferencias por Tipo de Canon, Regalías, y Otros (Congreso Perú, 2019).Peters, C. M., Gentry, A. H. & Mendelsohn, R. O. Valuation of an Amazonian rainforest. Nature 339, 655–656 (1989).Article 

    Google Scholar 
    Sheil, D. & Wunder, S. The value of tropical forest to local communities: complications, caveats, and cautions. Conserv. Ecol. 6, 9 (2002).Belcher, B. & Schreckenberg, K. Commercialisation of non-timber forest products: a reality check. Dev. Policy Rev. 25, 355–377 (2007).Article 

    Google Scholar 
    López, M. et al. What Do We Know about Peruvian Peatlands? (CIFOR, 2020).Gilmore, M. P., Endress, B. A. & Horn, C. M. The socio-cultural importance of Mauritia flexuosa palm swamps (aguajales) and implications for multi-use management in two Maijuna communities of the Peruvian Amazon. J. Ethnobiol. Ethnomed. 9, 29 (2013).Article 

    Google Scholar 
    Tagle Casapia, X. et al. Identifying and quantifying the abundance of economically important palms in tropical moist forest using UAV imagery. Remote Sens 12, 9 (2020).Article 

    Google Scholar 
    Bruenig, E. F. Conservation and Management of Tropical Rainforests: An integrated Approach to Sustainability 2nd edn (CABI, 2016).de Mello, N. G., Gulinckb, H., Van den Broeckc, P. & Parra, P. Social-ecological sustainability of non-timber forest products: a review and theoretical considerations for future research. For. Policy Econ. 112, 102109 (2020).Article 

    Google Scholar 
    van Lent, J. Land-Use Change and Greenhouse Gas Emissions in the Tropics: Forest Degradation on Peat Soils. PhD thesis, Wageningen Univ. Res. (2020).Baker, T. R. et al. in Peru: Deforestation in Times of Climate Change (ed. Chirif, A.) 155–174 (IWGIA, Servindi, ONAMIAP & COHARYIMA, 2019).Bhomia, R. K. et al. Impacts of Mauritia flexuosa degradation on the carbon stocks of freshwater peatlands in the Pastaza-Marañón river basin of the Peruvian Amazon. Mitig. Adapt Strateg. Glob. Change 24, 645–668 (2019).Article 

    Google Scholar 
    Marengo, J. in Geoecología y Desarrollo Amazónico: Estudio Integrado en la Zona de Iquitos Biológica – Geographica – Geológica (eds Kalliola, R. & Flores, S.) 35–57 (Univ. Turku Press, 1998).Koolen, H. H. F., Da Silva, F. M. A., Da Silva, V. S. V., Paz, W. H. P. & Bataglion, G. A. in Exotic Fruits (eds Rodrigues, S. et al.) 61–67 (Elsevier, 2018).Malleux, O. J. Inventarios Forestales en Bosques Tropicales (Universidad Nacional Agraria La Molina, 1982).Del Castillo, D., Otárola, E. & Freitas, L. Aguaje, La Maravillosa Palmera de la Vida (Instituto de Investigaciones de la Amazonía Peruana, 2006).Khorsand Rosa, M., Barbosa, R. & Koptur, S. Which factors explain reproductive output of Mauritia flexuosa (Arecaceae) in forest and savanna habitats of northern Amazonia? Int. J. Plant Sci. 175, 307–318 (2014).Article 

    Google Scholar 
    Quinteros, Y., Roca, F. & Quinteros, V. in XIV. Morichales y cananguchales y otros palmares inundables de Suramérica. Parte II: Colombia, Venezuela, Brasil, Perú, Bolivia, Paraguay, Uruguay y Argentina Vol. XIV Serie recursos hidrobiológicos y pesqueros continentales de Colombia (eds Lasso, C. A. et al.) 265–282 (Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, 2016).Hergoualc’h, K., Gutiérrez-Vélez, V. H., Menton, M. & Verchot, L. V. Characterizing degradation of palm swamp peatlands from space and on the ground: an exploratory study in the Peruvian Amazon. For. Ecol. Manage. 393, 63–73 (2017).Article 

    Google Scholar 
    Honorio Coronado, E. N. et al. Intensive field sampling increases the known extent of carbon-rich Amazonian peatland pole forests. Environ. Res. Lett. 16, 074048 (2021).Article 

    Google Scholar 
    de Jong, J. The Impact of Indigenous and Local Communities in the Peruvian Amazon: Integrating Forest Inventory and Remote Sensing. MSc thesis, Wageningen Univ. Res. (2019).Alvarado, L. Estudio del Potencial de las Embarcaciones Solares en la Amazonía. Caso de Estudio Río Napo. MA thesis, Universidad Politécnica Madrid (2017).ArcGIS Desktop v.10.4 (ESRI, 2015).Directorio Nacional de Centrol Poblados – Censos Nacionales 2017- XII de Poblacion, VII de vivienda y III de Comunidades indigenas (Instituto Nacional de Estadítica e Informática, 2018).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).R Core Team. R: A Language and Environment for Statistical Computing. R version 3.5.3 (R Foundation for Statistical Computing, 2019).Taylor, J. R. An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements 2nd edn (University Science Books, 1997).Consumer Price Index (Peru) (World Bank Group, 2020); https://data.worldbank.org/indicator/FP.CPI.TOTL?locations=PE More

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    Black Kites on a flyway between Western Siberia and the Indian Subcontinent

    Ferguson-Lees, J., Christie, D. A. Raptors of the World. Helm Identification Guides (Christopher Helm, London, 2001).
    Google Scholar 
    BirdLife International 2021 Species factsheet: Milvus migrans. Downloaded from http://www.birdlife.org on 10 May 2021.Sergio, F., Pedrini, P. & Marchesi, L. Adaptive selection of foraging and nesting habitat by black kites Milvus migrans and its implications for conservation: a multi-scale approach. Biol. Conserv. 112, 351–362 (2003).
    Google Scholar 
    Tanferna, A., López-Jiménez, L., Blas, J., Hiraldo, F. & Sergio, F. Habitat selection by Black kite breeders and floaters: implications for conservation management of raptor floaters. Biol. Conserv. 160, 1–9 (2013).
    Google Scholar 
    Cortés-Avizanda, A. et al. Spatial heterogeneity in resource distribution promotes facultative sociality in two Trans-Saharan migratory birds. PLoS ONE 6, e21016 (2011).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Panuccio, M., Agostini, N., Mellone. U. & Bogliani, G. Circannual variation in movement patterns of the Black Kite (Milvus migrans migrans): A review. Ethol. Ecol. Evol. 26, 1–18 (2013).Dickinson, E. C. & Remsen, J. V. The Howard and Moore Complete Checklist of the Birds of the World, 4th (Aves Press, 2013).
    Google Scholar 
    Clements, J. F. et al. The eBird/Clements Checklist of Birds of the World: v2019. (2019).Orta, J., Marks, J. S., Garcia, E. & Kirwan, G. M. Black Kite (Milvus migrans). In Birds of the World (eds. Billerman, S.M., Keeney, B.K., Rodewald, P.G. & Schulenberg T.S.) 168–172 (Cornell Lab of Ornithology, 2020).Gill, F., Donsker, D. & Rasmussen, P. IOC World Bird List – version 11.1 (worldbirdnames.org., 2021).Dementiev, G. P., Gladkov, N. A., Ptushenko, E. S., Spangenberg, E. P. & Sudilovskaya, A. M. Birds of the Soviet Union, Vol. 1 (Sovetskaya Nauka, Moscow, in Russian, 1951).
    Google Scholar 
    Stepanyan, L. S. Conspectus of the Ornithological Fauna of the USSR (Nauka, Moscow, in Russian, 1990).Karyakin, I. Problem of identification of Eurasian subspecies of the Black Kite and records of the Pariah Kite in Southern Siberia, Russia. Raptors Conserv. 34, 49–67 (2017).
    Google Scholar 
    Skyrpan, M. & Literák, I. A kite Milvus migrans migrans/lineatus in Ukraine. Biologia 74, 1669–1673 (2019).
    Google Scholar 
    Panter, C. T. et al. Kites (Milvus spp.) wintering on Crete. Eur. Zool. J. 87, 591–596 (2020).
    Google Scholar 
    Skyrpan, M. et al. Kites Milvus migrans lineatus (Milvus migrans migrans/lineatus) are spreading west across Europe. J. Ornithol. 162, 317–323 (2021).
    Google Scholar 
    Onrubia Baticón A. Patrones espacio-temporales de la migración de aves planeadoras en el Estrecho de Gibraltar (Spatial and temporal patterns of soaring birds migration through the straits of Gibraltar). Doctoral thesis (Universidad de León, 2015).Literák, I. et al. Weather-influenced water-crossing behaviour of black kites Milvus migrans during migration. Biologia 76, 1267–1273 (2021).
    Google Scholar 
    Ovčiariková, S. et al. Natal dispersal in Black Kites Milvus migrans migrans in Europe. J. Ornithol. 161, 935–951 (2020).
    Google Scholar 
    Sklyarenko, S., Gavrilov, E. & Gavrilov, A. Migratory flyways of raptors and owls in Kazakhstan according to ringing data. Vogelwarte 41, 263–268 (2002).
    Google Scholar 
    Probst, R. & Pavličev, M. Migration in the Novosibirsk region and the Kuznetsky Alatau, Russia. Sandgrouse 28, 114–118 (2006).
    Google Scholar 
    Harris, T. Migration Hotspots. The World’s Best Bird Migration Sites. (Bloomsbury, London, New Delhi, New York, Sydney, 2013).Hirano, T. & Ueda, M. Black Kite Milvus migrans in Japanese. Bird Res. News 810, 1–6 (2011).
    Google Scholar 
    Choudhuri, A. Migration of Black-eared or Large Indian Kite Milvus migrans lineatus Gray from Mongolia to North-Eastern India. J. Bombay Nat. Hist. Soc. 102, 229–230 (2005).
    Google Scholar 
    Davaasuren, B. Khurkh Bird Ringing Station Annual Report 2018. (Wildlife Science Conservation Center of Mongolia, Ulaanbaatar, 2019).Kumar, N. et al. GPS-telemetry unveils the regular high-elevation crossing of the Himalayan by a migratory raptor: Implications for definition of a “Central Asian Flyway”. Sci. Rep. 10, 15988 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Juhant, M. A. & Bildstein, K. L. Raptor migration across and around the Himalayas. In Bird Migration Across the Himalayas (eds. Prins, H. H. T. & Namgail, T.) 98–116 (Cambridge University Press, Cambridge, 2017).
    Google Scholar 
    Rotics, S. et al. The challenges of the first migration: Movement and behaviour of juvenile vs. adult white storks with insights regarding juvenile mortality. J. Anim. Ecol. 85, 938–947 (2016).PubMed 

    Google Scholar 
    Vidal-Mateo, J. et al. Wind effects on the migration routes of trans-Saharan soaring raptors: Geographical, seasonal and interspecific variation. Curr. Zool. 62, 89–97 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Safi, K. et al. Flying with the wind: Scale dependency of speed and direction measurements in modelling wind support in avian flight. Mov. Ecol. 1, 4 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Green, M., Alerstam, T., Clausen, P., Drent, R. & Ebbinge, B. S. Dark-bellied Brent Geese Branta bernicla bernicla, as recorded by satellite telemetry, do not minimize flight distance during spring migration. Ibis 144, 106–121 (2002).
    Google Scholar 
    Malmiga, G., Nilsson, C., Bäckman, J. & Alerstam, T. Interspecific comparison of the flight performance between sparrowhawks and common buzzards migrating at the Falsterbo peninsula: a radar study. Curr. Zool. 605, 670–679 (2014).
    Google Scholar 
    Vansteelant, W. M. G. et al. Regional and seasonal flight speeds of soaring migrants and the role of weather conditions at hourly and daily scales. J. Avian Biol. 46, 25–39 (2015).
    Google Scholar 
    Dodge, S., Bohrer, G. & Weinzierl, R. MoveBank track annotation project: linking animal movement data with the environment to discover the impact of environmental change in animal migration. In Workshop on GIScience in the Big Data Age in Conjunction with the Seventh International Conference on Geographic Information Science 2012 GIScience (eds. Janowicz, K., Kessler, C., Kauppinen, T. & Kolas, D.) 35–41 (Columbus, OH, 2012).Scott, G. R. et al. How bar-headed geese fly over the Himalayas. Physiology 30, 107–115 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Andreyenkova, N. G., Andreyenkov, O. V., Karyakin, I. V. & Zhimulev, I. F. New haplotypes of the mitochondrial gene cytB in the nesting population of the Siberian Black Kite Milvus migrans lineatus Gray, 1831 in the territory of the Republic of Tyva. Dokl. Biochem. Biophys. 482, 242–244 (2018).CAS 
    PubMed 

    Google Scholar 
    Mellone, U. et al. Interspecific comparison of the performance of soaring migrants in relation to morphology, meteorological conditions and migration strategies. PLoS ONE 7, e39833 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kemp, M. U., Emiel van Loon, E., Shamoun-Baranes, J. & Bouten, W. RNCEP: global weather and climate data at your fingertips. Methods Ecol. Evol. 3, 65–70 (2012).
    Google Scholar 
    Team, R.C. R: A Language and Environment for Statistical Computing. R 739 (Foundation for Statistical Computing [Internet], Vienna, Austria, 2018). https://www.R-project.org/Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
    Google Scholar 
    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).MATH 

    Google Scholar 
    Andreyenkova, N. G. et al. Phylogeography and demographic history of the Black Kite Milvus migrans, raptor widespread in Eurasia, Australia and Africa. J. Avian Biol. 52, e02822 (2021).
    Google Scholar 
    Lindholm, A. & Forsten, A. Black Kites Milvus migrans in Russian Altai. Caluta 2, 1–6 (2011).
    Google Scholar 
    Vansteelant, W.M.G. An ontogenetic perspective on migration learning and critical life-history traits in raptors. In Abstracts of British Ornithologists’ Union 2019 Annual Conference Tracking Migration: Drivers, Challenges and Consequences of Seasonal Movements. 45–46. (University of Warwick, UK, 2019).Dixon, A., Rahman, L., Sokolov, A. & Sokolov, V. Peregrine Falcons crossing the „Roof of the World”. In Bird Migration Across the Himalayas, Wetland Functioning Amidst Mountains and Glaciers (eds. Prins, H.T. & Namgail, T.) 128–141 (Cambridge University Press, Cambridge, 2017).Parr, N. et al. High altitude flights by ruddy shelduck Tadorna ferruginea during trans-Himalayan migrations. J. Avian Biol. 48, 1310–1315 (2017).
    Google Scholar 
    Hawkes, L. A. et al. The paradox of extreme high-altitude migration in bar-headed geese Anser indicus. Proc. R. Soc. B 280, 1–8 (2013).
    Google Scholar 
    Bishop, C. M. et al. The roller coaster flight strategy of bar-headed geese conserves energy during Himalayan migrations. Science 347, 250–254 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Agostini, N., Pannucio, M. & Pasquaretta, C. Morphology, flight performace, and water crossing tendencies of Afro-Palearctic raptors during migration. Curr. Zool. 61, 951–958 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Altshuler, D. & Dudley, R. The physiology and biomechanics of avian flight at high altitude. Integr. Comp. Biol. 46, 62–71 (2006).PubMed 

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

    Google Scholar 
    Ohlmann, K. The wind system in the Himalayas: From a Bird’s-Eye View. In Bird Migration Across the Himalayas, Wetland Functioning Amidst Mountains and Glaciers (eds. Prins, H.T. & Namgail, T.), 9–28 (Cambridge University Press, Cambridge, 2017).Heise, R. Birds, gliders and uplift systems over the Himalayas. In Bird Migration Across the Himalayas, Wetland Functioning Amidst Mountains and Glaciers (eds. Prins, H.T. & Namgail, T.), 229–40 (Cambridge University Press, Cambridge, 2017).Harel, R. et al. Decision-making by a soaring bird: time, energy and risk considerations at different spatiotemporal scales. Philos. T. R. Soc. B 371, 20150397 (2016).
    Google Scholar 
    Vansteelant, W. M. G., Shamoun-Baranes, J., McLaren, J., van Diermen, J. & Bouten, W. Soaring across continents: Decision-making of a soaring migrant under changing atmospheric conditions along an entire flyway. J. Avian Biol. 48, 887–896 (2017).
    Google Scholar 
    Nilsson, C., Klaassen, R. H. G. & Alerstam, T. Differences in speed and duration of bird migration between spring and autumn. Am. Nat. 181, 837–845 (2013).PubMed 

    Google Scholar 
    Kokko, H. Competition for early arrival in migratory birds. J. Anim. Ecol. 68, 940–150 (1999).
    Google Scholar 
    Moore, F.R., Smith, R.J. & Sandberg, R. Stopover ecology of intercontinental migrants: en route problems and consequences for reproductive performance. In Birds of Two Worlds: the Ecology and Evolution of Migration (eds. Greenberg, R. & Marra, P.P.), 251–261 (Johns Hopkins University Press, Baltimore, 2005).McNamara, J. M., Welham, R. K. & Houston, A. I. The timing of migration within the context of an annual routine. J. Avian Biol. 29, 416–423 (1998).
    Google Scholar 
    Köppen, U. et al. Seasonal migrations of four individual bar-headed geese Anser indicus from Kyrgyzstan followed by satellite telemetry. J. Ornithol. 151, 703–712 (2010).
    Google Scholar 
    Kölzsch, A. et al. Towards a new understanding of migration timing: slower spring than autumn migration in geese reflects different decision rules for stopover use and departure. Oikos 125, 1496–1507 (2016).
    Google Scholar 
    Butler, R. W., Williams, T. D., Warnock, N. & Bishop, M. A. Wind assistance: a requirement for migration of shorebirds? Auk 114, 456–466 (1997).
    Google Scholar 
    Santos, C. D., Silva, J. P., Muñoz, A. R., Onrubia, A. & Wikelski, M. The gateway to Africa: What determines sea crossing performance of a migratory soaring birds at the Strait of Gibraltar. J. Anim. Ecol. 89, 1317–1328 (2020).PubMed 

    Google Scholar 
    Kumerloeve, H. V. Überwintern des Schwarzmilans im vorderen Orient. Falke 14, 274–227 (1967).
    Google Scholar 
    Baumgart, W., Kasparek, M. & Stephan, B. Die Vögel Syrien: eine Übersicht (Max Kasparek Verlag, 1995).
    Google Scholar 
    Tsvelykh, A. N. & Panyushkin, V. E. Wintering of the Black Kite Milvus migrans in Ukraine. Vestn. Zool. 36, 81–83 (2002).
    Google Scholar 
    Sarà, M. The colonisation of Sicily by the Black Kite Milvus migrans. J. Raptor Res. 37, 167–172 (2003).
    Google Scholar 
    Domashevskii, S. V. First record of the Black Kite in winter in the northern part of Ukraine. Berkut 18, 212–213 (2009).
    Google Scholar 
    Ciach, M. & Kruszyk, R. Foraging of White Storks Ciconia ciconia on rubbish dumps on nonbreeding grounds. Waterbirds 33, 101–104 (2010).
    Google Scholar 
    Biricik, M. & Karakaş. R. Black Kites Milvus migrans winter in Southeastern Anatolia, Turkey. J. Raptor Res. 45, 370–373 (2011).Literák, I., Horal, D., Alivizatos, H. & Matušík, H. Common wintering of black kites Milvus migrans migrans in Greece, and new data on their wintering elsewhere in Europe. Slovak Raptor J. 11, 91–102 (2017).
    Google Scholar 
    Shirihai, H., Yosef, R., Alon, D., Kirwan, G.M. & Spaar, R. Raptor Migration in Israel and the Middle East (International Birdwatching Centre Eilat IBRCE, IOC, Israel, 2000).Forsman, D. Identification of Black-eared Kite. Bird. World 16, 156–216 (2003).
    Google Scholar 
    Abuladze, A. Birds of Prey of Georgia, Materials towards a Fauna of Georgia, Issue VI (Ilia State University, Tbilisi, 2013).
    Google Scholar 
    Brooke, R. K. The migratory Black Kite Milvus migrans migrans Aves: Accipitridae of the Palearctic in southern Africa. Durb. Mus. Novit. 10, 53–66 (1974).
    Google Scholar 
    Forsman, D. Flight Identifcation of Raptors of Europe (North Africa and the Middle East (Christopher Helm, 2016).
    Google Scholar 
    Percie du Sert, N. et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. PLoS Biol. 18, e3000410 (2020). More

  • in

    Population density, bottom-up and top-down control as an interactive triplet to trigger dispersal

    Nathan, R. The challenges of studying dispersal. Trends. Ecol. Evol. 16, 481–483. https://doi.org/10.1016/S0169-5347(01)02272-8 (2001).CAS 
    Article 

    Google Scholar 
    Bonte, D. et al. Costs of dispersal. Biol. Rev. Camb. Philos. Soc. 87, 290–312. https://doi.org/10.1111/j.1469-185X.2011.00201.x (2012).Article 
    PubMed 

    Google Scholar 
    Matthysen, E. Multicausality of dispersal: A review. In Dispersal Ecology and Evolution (eds Clobert, J. et al.) 3–18 (Oxford University Press, 2012).Chapter 

    Google Scholar 
    Clobert, J., Le Galliard, J.-F., Cote, J., Meylan, S. & Massot, M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–209. https://doi.org/10.1111/j.1461-0248.2008.01267.x (2009).Article 
    PubMed 

    Google Scholar 
    Poethke, H. J. & Hovestadt, T. Evolution of density- and patch-size-dependent dispersal rates. Proc. R. Soc. Lond. 269, 637–645. https://doi.org/10.1098/rspb.2001.1936 (2002).Article 

    Google Scholar 
    Benton, T. G. & Bowler, D. E. Dispersal in invertebrates: Influences on individual decisions. Ecol. Evol. 1, 41–49 (2012).
    Google Scholar 
    Legrand, D. et al. Ranking the ecological causes of dispersal in a butterfly. Ecography 38, 822–831. https://doi.org/10.1111/ecog.01283 (2015).Article 

    Google Scholar 
    Travis, J. M. J., Murrell, D. J. & Dytham, C. The evolution of density–dependent dispersal. Proc. R. Soc. Lond. B 266, 1837–1842. https://doi.org/10.1098/rspb.1999.0854 (1999).Article 

    Google Scholar 
    Matthysen, E. Density-dependent dispersal in birds and mammals. Ecography 28, 403–416. https://doi.org/10.1111/j.0906-7590.2005.04073.x (2005).Article 

    Google Scholar 
    de Meester, N., Derycke, S., Rigaux, A. & Moens, T. Active dispersal is differentially affected by inter- and intraspecific competition in closely related nematode species. Oikos 124, 561–570. https://doi.org/10.1111/oik.01779 (2015).Article 

    Google Scholar 
    Bowler, D. E. & Benton, T. G. Causes and consequences of animal dispersal strategies: Relating individual behaviour to spatial dynamics. Biol. Rev. 80, 205–225. https://doi.org/10.1017/S1464793104006645 (2005).Article 
    PubMed 

    Google Scholar 
    Bengtsson, G., Hedlund, K. & Rundgren, S. Food- and density-dependent dispersal: Evidence from a soil collembolan. J. Anim. Ecol. 63, 513. https://doi.org/10.2307/5218 (1994).Article 

    Google Scholar 
    Fellous, S., Duncan, A., Coulon, A. & Kaltz, O. Quorum sensing and density-dependent dispersal in an aquatic model system. PLoS ONE 7, e48436. https://doi.org/10.1371/journal.pone.0048436 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Aguillon, S. M. & Duckworth, R. A. Kin aggression and resource availability influence phenotype-dependent dispersal in a passerine bird. Behav. Ecol. Sociobiol. 69, 625–633. https://doi.org/10.1007/s00265-015-1873-5 (2015).Article 

    Google Scholar 
    Byers, J. E. Effects of body size and resource availability on dispersal in a native and a non-native estuarine snail. J. Exp. Mar. Biol. Ecol. 248, 133–150. https://doi.org/10.1016/S0022-0981(00)00163-5 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    de Meester, N., Derycke, S. & Moens, T. Differences in time until dispersal between cryptic species of a marine nematode species complex. PLoS ONE 7, e42674. https://doi.org/10.1371/journal.pone.0042674 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sepulveda, A. J. & Marczak, L. B. Active dispersal of an aquatic invader determined by resource and flow conditions. Biol. Invasions 14, 1201–1209. https://doi.org/10.1007/s10530-011-0149-x (2012).Article 

    Google Scholar 
    Lobbia, P. A. & Mougabure-Cueto, G. Active dispersal in Triatoma infestans (Klug, 1834) (Hemiptera Reduviidae: Triatominae): Effects of nutritional status, the presence of a food source and the toxicological phenotype. Acta Trop. 204, 105345. https://doi.org/10.1016/j.actatropica.2020.105345 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Barbraud, C., Johnson, A. R. & Bertault, G. Phenotypic correlates of post-fledging dispersal in a population of greater flamingos: The importance of body condition. J. Anim. Ecol. 72, 246–257. https://doi.org/10.1046/j.1365-2656.2003.00695.x (2003).Article 

    Google Scholar 
    Bonte, D. & de La Peña, E. Evolution of body condition-dependent dispersal in metapopulations. J. Evol. Biol. 22, 1242–1251. https://doi.org/10.1111/j.1420-9101.2009.01737.x (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    Moran, N. P., Sánchez-Tójar, A., Schielzeth, H. & Reinhold, K. Poor nutritional condition promotes high-risk behaviours: A systematic review and meta-analysis. Biol. Rev. Camb. Philos. Soc. 96, 269–288. https://doi.org/10.1111/brv.12655 (2021).Article 
    PubMed 

    Google Scholar 
    Altermatt, F. & Fronhofer, E. A. Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities. Freshw. Biol. 63, 22–32. https://doi.org/10.1111/fwb.12951 (2018).Article 

    Google Scholar 
    McCauley, S. J. & Rowe, L. Notonecta exhibit threat-sensitive, predator-induced dispersal. Biol. Lett. 6, 449–452. https://doi.org/10.1098/rsbl.2009.1082 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Baines, C. B., McCauley, S. J. & Rowe, L. Dispersal depends on body condition and predation risk in the semi-aquatic insect, Notonecta undulata. Ecol. Evol. 5, 2307–2316. https://doi.org/10.1002/ece3.1508 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hammill, E., Fitzjohn, R. G. & Srivastava, D. S. Conspecific density modulates the effect of predation on dispersal rates. Oecologia 178, 1149–1158. https://doi.org/10.1007/s00442-015-3303-9 (2015).ADS 
    Article 
    PubMed 

    Google Scholar 
    Fronhofer, E. A. et al. Bottom-up and top-down control of dispersal across major organismal groups. Nat. Ecol. Evol. 2, 1859–1863. https://doi.org/10.1038/s41559-018-0686-0 (2018).Article 
    PubMed 

    Google Scholar 
    Delm, M. Vigilance for predators: Detection and dilution effects. Behav. Ecol. Sociobiol. https://doi.org/10.1007/BF00171099 (1990).Article 

    Google Scholar 
    Matthysen, E. Multicausality of dispersal: A review. Ecol. Evol. 1, 3–18 (2012).
    Google Scholar 
    Bowler, D. E. & Benton, T. G. Variation in dispersal mortality and dispersal propensity among individuals: The effects of age, sex and resource availability. J. Anim. Ecol. 78, 1234–1241. https://doi.org/10.1111/j.1365-2656.2009.01580.x (2009).Article 
    PubMed 

    Google Scholar 
    Giere, O. Meiobenthology. The microscopic motile fauna of aquatic sediments 2nd edn. (Springer, 2009).
    Google Scholar 
    Ptatscheck, C. & Traunspurger, W. The ability to get everywhere: Dispersal modes of free-living, aquatic nematodes. Hydrobiologia 22, 71. https://doi.org/10.1007/s10750-020-04373-0 (2020).Article 

    Google Scholar 
    Ptatscheck, C. & Gansfort, B. Dispersal of free-living nematodes. In Ecology of Freshwater Nematodes (ed. Traunspurger, W.) 151–184 (CABI, 2021).Chapter 

    Google Scholar 
    Traunspurger, W., Bergtold, M., Ettemeyer, A. & Goedkoop, W. Effects of copepods and chironomids on the abundance and vertical distribution of nematodes in a freshwater sediment. J. Freshw. Ecol. 21, 81–90. https://doi.org/10.1080/02705060.2006.9664100 (2006).Article 

    Google Scholar 
    Bargmann, C. I. Chemosensation in C. elegans. WormBook 1, 1–29. https://doi.org/10.1895/wormbook.1.123.1 (2006).Article 

    Google Scholar 
    Chasnov, J. R. & Chow, K. L. Why are there males in the hermaphroditic species Caenorhabditis elegans?. Genetics 160, 983–994 (2002).CAS 
    Article 

    Google Scholar 
    Ramot, D., Johnson, B. E., Berry, T. L., Carnell, L. & Goodman, M. B. The Parallel Worm Tracker: A platform for measuring average speed and drug-induced paralysis in nematodes. PLoS ONE 3, e2208. https://doi.org/10.1371/journal.pone.0002208 (2008).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Muschiol, D. & Traunspurger, W. Life cycle and calculation of the intrinsic rate of natural increase of two bacterivorous nematodes, Panagrolaimus sp. and Poikilolaimus sp. from chemoautotrophic Movile Cave, Romania. Nematology 9, 271–284. https://doi.org/10.1163/156854107780739117 (2007).Article 

    Google Scholar 
    Beier, S., Bolley, M. & Traunspurger, W. Predator-prey interactions between Dugesia gonocephala and free-living nematodes. Freshw. Biol. 49, 77–86. https://doi.org/10.1046/j.1365-2426.2003.01168.x (2004).Article 

    Google Scholar 
    Powers, E. M. & Sayre, R. M. A predacious soil turbellarian that feeds on free-living and plant-parasitic nematodes. Nematology 12, 619–629. https://doi.org/10.1163/187529266X00482 (1966).Article 

    Google Scholar 
    Kreuzinger-Janik, B., Kruscha, S., Majdi, N. & Traunspurger, W. Flatworms like it round: Nematode consumption by Planaria torva (Müller 1774) and Polycelis tenuis (Ijima 1884). Hydrobiologia 819, 231–242. https://doi.org/10.1007/s10750-018-3642-8 (2018).Article 

    Google Scholar 
    Burnham, K. P. & Anderson, D. R. Practical use of the information-theoretic approach. In Model Selection and Inference (eds Burnham, K. P. & Anderson, D. R.) 75–117 (Springer, 1998).Chapter 

    Google Scholar 
    McCulloch, C. E., Searle, S. R. & Neuhaus, J. M. Generalized, Linear, and Mixed Models (Wiley, 2008).MATH 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2021). https://www.R-project.org/.Mazerolle, M. J. AICcmodavg: Model Selection and Multimodel Inference Based on (Q)AIC(c) (2020).Bonte, D., de Roissart, A., Wybouw, N. & van Leeuwen, T. Fitness maximization by dispersal: Evidence from an invasion experiment. Ecology 95, 3104–3111. https://doi.org/10.1890/13-2269.1 (2014).Article 

    Google Scholar 
    You, Y., Kim, J., Raizen, D. M. & Avery, L. Insulin, cGMP, and TGF-beta signals regulate food intake and quiescence in C. elegans: a model for satiety. Cell Metab. 7, 249–257. https://doi.org/10.1016/j.cmet.2008.01.005 (2008).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shtonda, B. B. & Avery, L. Dietary choice behavior in Caenorhabditis elegans. J. Exp. Biol. 209, 89–102. https://doi.org/10.1242/jeb.01955 (2006).Article 
    PubMed 

    Google Scholar 
    Mathieu, J. et al. Habitat quality, conspecific density, and habitat pre-use affect the dispersal behaviour of two earthworm species, Aporrectodea icterica and Dendrobaena veneta, in a mesocosm experiment. Soil Biol. Biochem. 42, 203–209. https://doi.org/10.1016/j.soilbio.2009.10.018 (2010).CAS 
    Article 

    Google Scholar 
    Oro, D., Cam, E., Pradel, R. & Martínez-Abraín, A. Influence of food availability on demography and local population dynamics in a long-lived seabird. Proc. R. Soc. Lond. B 271, 387–396. https://doi.org/10.1098/rspb.2003.2609 (2004).Article 

    Google Scholar 
    Harvey, S. C. Non-dauer larval dispersal in Caenorhabditis elegans. J. Exp. Zool. B Mol. Dev. Evol. 312B, 224–230. https://doi.org/10.1002/jez.b.21287 (2009).Article 
    PubMed 

    Google Scholar 
    Wilden, B., Majdi, N., Kuhlicke, U., Neu, T. R. & Traunspurger, W. Flatworm mucus as the base of a food web. BMC Ecol. 19, 15. https://doi.org/10.1186/s12898-019-0231-2 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gloria-Soria, A. & Azevedo, R. B. R. npr-1 Regulates foraging and dispersal strategies in Caenorhabditis elegans. Curr. Biol. 18, 1694–1699. https://doi.org/10.1016/j.cub.2008.09.043 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    Harrison, R. G. Dispersal Polymorphisms in Insects. Annu. Rev. Ecol. Syst. 11, 95–118. https://doi.org/10.1146/annurev.es.11.110180.000523 (1980).Article 

    Google Scholar 
    Denno, R. F. & Peterson, M. A. Density-dependent dispersal and its consequences for population dynamics. Popul Dyn 1, 113–130 (2021).
    Google Scholar 
    Srinivasan, J. et al. A modular library of small molecule signals regulates social behaviors in Caenorhabditis elegans. PLoS Biol. 10, e1001237. https://doi.org/10.1371/journal.pbio.1001237 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bretscher, A. J. et al. Temperature, oxygen, and salt-sensing neurons in C. elegans are carbon dioxide sensors that control avoidance behavior. Neuron 69, 1099–1113. https://doi.org/10.1016/j.neuron.2011.02.023 (2011).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Freckman, D. W., Duncan, D. A. & Larson, J. R. Nematode density and biomass in an annual grassland ecosystem. J. Range Manag. 32, 418. https://doi.org/10.2307/3898550 (1979).Article 

    Google Scholar 
    Cote, J. et al. Evolution of dispersal strategies and dispersal syndromes in fragmented landscapes. Ecography 40, 56–73. https://doi.org/10.1111/ecog.02538 (2017).Article 

    Google Scholar  More

  • in

    A hierarchical inventory of the world’s mountains for global comparative mountain science

    The generation of this map of the world’s mountains consisted of five steps (Fig. 1): (i) the identification and hierarchisation of named mountain ranges and the recording of range-specific information; (ii) the manual digitization of the ranges’ general shape; (iii) the definition of mountainous terrain (and the inventory’s outer borders) using a DEM-based algorithm; (iv) the automatic refinement of the digitized and named ranges’ inner borders; and (v) the preparation of the final layers. The resulting products consist of a refined mountain definition (GMBA Definition v2.0), two versions of the inventory (GMBA Inventory v2.0_standard & GMBA Inventory v2.0_broad), and a set of tools to work with the inventories.Step i: Identification and hierarchisation of mountain rangesIn a first step, we identified mountain ranges worldwide. To do so we adopted the mountain ranges identified in the GMBA Inventory v1.410,14 and searched existing resources in any languages for other named ranges not yet included. The ranges added could either be adjacent to, included in (child range or subrange) or including (parent range or mountain system) mountain ranges of the GMBA Inventory v1.4. The resources used for our searches included world atlases (e.g. The Times Comprehensive Atlas of the World19, Knaurs grosser Weltatlas20, Pergamon World Atlas21); topographic maps (e.g. http://legacy.lib.utexas.edu/maps/imw/, http://legacy.lib.utexas.edu/maps/onc/, https://maps.lib.utexas.edu/maps/tpc/, www.topomap.co.nz, https://norgeskart.no, www.ign.es/iberpix/visor/); encyclopaedias (www.wikipedia.org; www.britannica.com); online gazetteers and reference sites (e.g. www.wikidata.org, www.geonames.org (GeoNames), www.mindat.org); mountain classification systems (e.g. the International Standardized Mountain Subdivision of the Alps or SOIUSA for the Alps22, Alpenvereinseinteilung der Ostalpen23, Classification of the Himalaya24, www.peakbagger.com/rangindx.aspx (PEMRACS), www.carpathian-research-network.eu/ogulist, http://www.sopsr.sk/symfony-bioregio/lkpcarporog, www.dinarskogorje.com, https://bivouac.com/, https://climbnz.org.nz/); and national or regional landscape, geomorphological, or physiographic maps and publications4,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42. The full list of the consulted sources and references is available on GitHub at https://www.github.com/GMBA-biodiversity/Inventory (GMBA Mountain Inventory v2.0 References.pdf).All identified mountain ranges were recorded in a Microsoft Access relational database (“Mountain database”, see below) and given a name, a unique 5-digit identifier (GMBA_V2_ID), and the corresponding Wikidata unique resource identifier (URI), when available. This URI gives access to a range’s name as well as to its Wikipedia page URL in all available languages and lists other identifiers for given mountain ranges in a variety of other repositories such as GeoNames or PEMRACS. The primary mountain range names were based on the resources used for range identification and were preferably recorded in English. Names used nationally, locally, as well as/or by indigenous people and local communities were extracted from Wikidata and recorded in a separate attribute field.In the process of cataloguing, we attributed a parent range to each of the mapped mountain ranges. Information about parent ranges is included in PEMRACS, often also in Wikidata as a property that can be extracted though a SPARQL query, in the corresponding Wikipedia pages description, and in regional hierarchical mountain classifications that exist for the European Alps (SOIUSA), the Carpathians, and the Dinaric Alps. When no such information was available, we relied on other sources of information that we found either using a general web search (leading to specific papers, reports, or web pages on mountain ranges) or by consulting (online) topographical maps and atlases at different scales. The information about parent ranges was used to construct a hierarchy of up to 10 levels using a recursive SQL query (see Step v). The result of this step was a relational database with a hierarchy of mountain systems and (sub-) ranges (Fig. 1, “Mountain database”).Step ii: Digitization of the mountain rangesIn a second step, we digitized all identified ‘childless’ mountain ranges (i.e. smallest mapping units, called ‘Basic’ as opposed to ‘Aggregated’ in the database) in one vector GIS layer. To do so, we used the Google Maps Terrain layers (Google, n.d.) as background and the WHYMAP named rivers layer42 as spatial reference since descriptions of mountain range areal extension is often given with reference to major rivers. The digitization, which was done in QGIS43 using the WGS 84 / Pseudo-Mercator (EPSG 3857) coordinate reference system, consisted in the drawing of shapes (polygons) that roughly followed the core area of each mountain range. In general, the approximate shape and extent of the mountain ranges we digitized could be distinguished based on the terrain structure as represented by the shaded relief background that corresponded to the placement and orientation of the range’s name label on a topographical map, atlas or other resource. As the exact placement and orientation of mountain range labels in each specific source can be influenced by cartographic considerations (e.g. avoiding overlaps with other features), the final approximation of the mountain range was obtained by consulting a variety of sources for each mountain range. Occasionally, the mountain terrain’s geomorphological characteristics strongly hampered the accuracy of our visual identification of mountain subranges within larger systems. This was particularly the case in old, eroded massifs such as the Brazilian Highlands or the highlands of Madagascar, where individual mountain ranges are not separated by deep well-defined valleys and have a very complex topography. In these cases, we referred to available topographical descriptions of range extent and to the river layer (see above). Other complex regions included Borneo and the Angolan Highlands, whereas subranges in mountain systems such as the European Alps, the Himalayas, and the North American Cordillera were comparatively easy to map. Moreover, the density of currently available mountain toponymical information varied quite strongly between regions. Accordingly, regional variation in the size of the smallest mountain range map units can be considerable. The result of this step was a (manually) digitized vector layer of named mountain ranges shapes (Fig. 1, “Manual mountain shapes”).Step iii: Definition of mountainous terrainIn a third step, we defined mountainous terrain (GMBA Definition v2.0). To distinguish mountainous from non-mountainous terrain, we developed a simple algorithm which we implemented in ArcMap 10.7.144. This algorithm is based on ruggedness (defined as highest minus lowest elevation in meter) within eight circular neighbourhood analysis windows (NAWs) of different sizes (from 1 pixel (≈ 250 m) to 20 (≈ 5 km) around each point, Fig. 2c) combined with empirically derived thresholds for each NAW (Fig. 2). The decision to use multiple NAW sizes was made because calculating ruggedness based on only a small or a large NAW comes at the risk of identifying the many local irregularities typically occurring in flat or rolling terrain as mountainous or of including extensive flat ‘skirts’ through the smoothing and generalization of large NAWs3. Accordingly, our approach ensures that any point in the landscape classified as mountainous showed some level of ruggedness not only at one but across scales. This also resulted in a smooth and homogeneous delineation of mountainous terrain, very suitable for our mapping purpose.Fig. 2Elevation range thresholds for the eight neighbourhood analysis windows (NAW) and their contribution to calculations of the GMBA Definition v2.0. (a) distribution of elevation range values (ruggedness) for NAWs (numbered I to VIII) in mountain regions as defined by the geometric intersection of K1, K2 and K3. (b): plot of the minimum elevation range versus the area of the NAW (n = 920). (c) NAWs and their corresponding threshold values. (d) percent overlap between GMBA Definition v2.0 (intersection of eight NAW-threshold pairs) and area defined by each individual NAW-threshold pair. (e) percent eliminated by each NAW-threshold pair (I to VIII) from the mountain area defined by the other 7 NAW-threshold combinations. Highlighted bars in the two graphs represent the combination of three NAW-threshold pairs that results in the highest overlap with the GMBA Definition v2.0.Full size imageWe used the median value of the 7.5 arc second GMTED2010 DEM45 as our source map. To reduce the latitudinal distortion of the raster, and thus the shape and area of the NAWs, we divided the global DEM into three raster layers corresponding to three latitudinal zones (84° N to 30° N, 30° N to 30° S and 30° S to 56° S) excluding ice-covered Antarctica and projected the two high latitude zones to Lambert Azimuthal Equal Area and the equatorial zone to WGS 1984 Cylindrical Equal Area. We used these reprojected DEM layers to produce eight ruggedness layers, each using one of the eight NAWs.To determine the threshold values of our algorithm, we selected 1000 random points within the area defined by the geometric intersection (Fig. 1b) of the three commonly applied mountain definitions, i.e. the definitions by UNEP-WCMC46, GMBA15, and USGS3. These layers (referred to as K1, K2, and K3, respectively by Sayre and co-authors12) were obtained from the Global Mountain Explorer47. We eliminated 80 clearly misclassified points (i.e., points that fell within lakes, oceans, or clearly flat areas according to the shaded relief map we used as a background) and used the remaining 920 to sample the eight ruggedness layers. For each of the 8 layers, we retained the lowest of the 920 ruggedness values as the threshold for the layer’s specific NAW (Fig. 2c). The eight threshold values were then used to reclassify each of the eight layers by attributing the value 1 to all cells with a ruggedness value higher than or equal to the corresponding threshold and the value 0 to all other cells. Finally, we performed a geometric intersection (see Fig. 1b) of the eight reclassified layers to derive the new mountain definition.After these calculations, we reprojected the three raster layers to WGS84 and combined them through mosaic to new raster. To eliminate isolated cells and jagged borders, we then generalized the resulting raster map by passing a majority filter (3 × 3 pixels, majority threshold) three times. This layer corresponds to the GMBA Definition v2.0.The resulting mountain definition (GMBA Definition v2.0) distinguishes itself from previous ones because of the empirically derived thresholds method used to develop it and the use of eight NAWs. In line with the previous GMBA definition, it relies entirely on the ruggedness values within NAWs. The GMBA Definition v2.0 was used to determine the outer delineation of this inventory’s mountainous terrain. As expected, it includes neither the wide ‘skirts’ of flat or undulating land around mountain ranges nor the topographical irregularities that are both typically included when other approaches are applied. It also successfully excludes extensive areas of rolling non-mountainous terrain such as the 52,000 km2 Badain Jaran Desert sand dunes in China. However, this mountain definition is conservative and only includes the highest, most rugged cores of low mountain systems, as for example in the Central Uplands of Germany, and therefore excludes some lower hill areas still considered by some as mountains.As a further step towards generalization, we considered that small ( More

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    Reduced bacterial mortality and enhanced viral productivity during sinking in the ocean

    Volk T, Hoffert MI. Ocean carbon pumps: Analysis of relative strengths and efficiencies in ocean-driven atmospheric CO2 changes. In: Sundquist ET, Broecker WS. (eds). The carbon cycle and atmospheric CO2: Natural variations archean to present. American Geophysical Union, Geophysical Monograph, Washington, DC: 1985. p. 32:99–110.Scharek R, Tupas LM, Karl DM. Diatom fluxes to the deep sea in the oligotrophic North Pacific gyre at Station ALOHA. Mar Ecol-Prog Ser. 1999;182:55–67.
    Google Scholar 
    Simon M, Grossart H, Schweitzer B, Ploug H. Microbial ecology of organic aggregates in aquatic ecosystems. Aquat Micro Ecol. 2002;28:175–211.
    Google Scholar 
    Siegenthaler U, Sarmiento JL. Atmospheric carbon dioxide and the ocean. Nature. 1993;365:119–25.CAS 

    Google Scholar 
    Ducklow H, Steinberg DK. Upper ocean carbon export and the biological pump. Oceanography. 2001;14:50–58.
    Google Scholar 
    Jiao N, Herndl GJ, Hansell DA, Benner R, Kattner G, Wilhelm SW, et al. Microbial production of recalcitrant dissolved organic matter: long-term carbon storage in the global ocean. Nat Rev Microbiol. 2010;8:593–9.CAS 
    PubMed 

    Google Scholar 
    DeLong EF, Franks DG, Alldredge AL. Phylogenetic diversity of aggregate-attached vs. free-living marine bacterial assemblages. Limnol Oceanogr. 1993;38:924–34.
    Google Scholar 
    Allen AE, Allen LZ, McCrow JP. Lineage specific gene family enrichment at the microscale in marine systems. Curr Opin Microbiol. 2013;16:605–17.CAS 
    PubMed 

    Google Scholar 
    D’Ambrosio L, Ziervogel K, MacGregor B, Teske A, Arnosti C. Composition and enzymatic function of particle-associated and free-living bacteria: a coastal/offshore comparison. ISME J. 2014;8:2167–79.PubMed 
    PubMed Central 

    Google Scholar 
    Martin JH, Knauer GA, Karl DM, Broenkow WW. VERTEX: carbon cycling in the northeast Pacific. Deep-Sea Res Part I-Oceanogr Res Pap. 1987;34:267–85.CAS 

    Google Scholar 
    Buesseler KO. The decoupling of production and particulate export in the surface ocean. Glob Biogeochem Cycle. 1998;12:297–310.CAS 

    Google Scholar 
    Schlitzer R. Applying the adjoint method for biogeochemical modeling: export of particulate organic matter in the world ocean. In: Kasibhata P, editor. Inverse Methods in Global biogeochemical Cycles. Washington, DC: American Geophysical Union; 2000. p. 114:107–24.Steinberg DK, Van Mooy BAS, Buesseler KO, Boyd PW, Kobari T, Karl DM. Bacterial vs. zooplankton control of sinking particle flux in the ocean’s twilight zone. Limnol Oceanogr. 2008;53:1327–38.
    Google Scholar 
    Cho BC, Azam F. Major role of bacteria in biogeochemical fluxes in the ocean’s interior. Nature. 1988;332:441–3.CAS 

    Google Scholar 
    Herndl GJ, Reinthaler T. Microbial control of the dark end of the biological pump. Nat Geosci. 2013;6:718–24.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bergh Ø, Borsheim KY, Bratbak G, Heldal M. High abundance of viruses found in aquatic environments. Nature. 1989;340:467–8.CAS 
    PubMed 

    Google Scholar 
    Suttle CA. Viruses in the sea. Nature. 2005;437:356–61.CAS 
    PubMed 

    Google Scholar 
    Zhang R, Wei W, Cai L. The fate and biogeochemical cycling of viral elements. Nat Rev Microbiol. 2014;12:850–1.CAS 
    PubMed 

    Google Scholar 
    Middelboe M, Lyck PG. Regeneration of dissolved organic matter by viral lysis in marine microbial communities. Aquat Micro Ecol. 2002;27:187–94.
    Google Scholar 
    Weinbauer MG, Brettar I, Hofle MG. Lysogeny and virus-induced mortality of bacterioplankton in surface, deep, and anoxic marine waters. Limnol Oceanogr. 2003;48:1457–65.
    Google Scholar 
    Fuhrman JA. Marine viruses and their biogeochemical and ecological effects. Nature. 1999;399:541–8.CAS 
    PubMed 

    Google Scholar 
    Jover LF, Effler TC, Buchan A, Wilhelm SW, Weitz JS. The elemental composition of virus particles: implications for marine biogeochemical cycles. Nat Rev Microbiol. 2014;12:519–28.CAS 
    PubMed 

    Google Scholar 
    Bongiorni L, Magagnini M, Armeni M, Noble R, Danovaro R. Viral production, decay rates, and life strategies along a trophic gradient in the North Adriatic Sea. Appl Environ Microbiol. 2005;71:6644–50.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Weinbauer MG, Bettarel Y, Cattaneo R, Luef B, Maier C, Motegi C, et al. Viral ecology of organic and inorganic particles in aquatic systems: avenues for further research. Aquat Micro Ecol. 2009;57:321–41.CAS 

    Google Scholar 
    Tian Y, Cai L, Xu Y, Luo T, Zhao Z, Wang Q, et al. Stability and infectivity of allochthonous viruses in deep sea: A long-term high pressure simulation experiment. Deep-Sea Res Part I-Oceanogr Res Pap. 2020;161:103302.
    Google Scholar 
    Lara E, Vaqué D, Sà EL, Boras JA, Gomes A, Borrull E, et al. Unveiling the role and life strategies of viruses from the surface to the dark ocean. Sci Adv. 2017;3:e1602565.PubMed 
    PubMed Central 

    Google Scholar 
    Zhang R, Li Y, Yan W, Wang Y, Cai L, Luo T, et al. Viral control of biomass and diversity of bacterioplankton in the deep sea. Commun Biol. 2020;3:256.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Woźniak SB, Stramski D, Stramska M, Reynolds RA, Wright VM, Miksic EY, et al. Optical variability of seawater in relation to particle concentration, composition, and size distribution in the nearshore marine environment at Imperial Beach, California. J Geophys Res. 2010;115:C08027.
    Google Scholar 
    White AE, Letelier RM, Whitmire AL, Barone B, Bidigare RR, Church MJ, et al. Phenology of particle size distributions and primary productivity in the North Pacific subtropical gyre (Station ALOHA). J Geophys Res-Oceans. 2015;120:7381–99.PubMed 
    PubMed Central 

    Google Scholar 
    Vaulot D, Courties C, Partensky F. A simple method to preserve oceanic phytoplankton for flow cytometric analyses. Cytom Part A. 1989;10:629–35.CAS 

    Google Scholar 
    Chen X, Liu H, Weinbauer M, Chen B, Jiao N. Viral dynamics in the surface water of the western South China Sea in summer 2007. Aquat Micro Ecol. 2011;63:145–60.
    Google Scholar 
    Wei W, Zhang R, Peng L, Liang Y, Jiao N. Effects of temperature and photosynthetically active radiation on virioplankton decay in the western Pacific Ocean. Sci Rep. 2018;8:1525–34.PubMed 
    PubMed Central 

    Google Scholar 
    Marie D, Partensky F, Vaulot D, Brussaard C. Numeration of phytoplankton, bacteria and viruses in marine samples. Curr Protoc Cytom. 1999;11:1–15.
    Google Scholar 
    Marie D, Brussaard CPD, Thyrhaug R, Bratbak G, Vaulot D. Enumeration of marine viruses in culture and natural samples by flow cytometry. Appl Environ Microbiol. 1999;65:45–52.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Brussaard CP. Optimization of procedures for counting viruses by flow cytometry. Appl Environ Microbiol. 2004;70:1506–13.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wilhelm SW, Brigden SM, Suttle CA. A dilution technique for the direct measurement of viral production: a comparison in stratified and tidally mixed coastal waters. Micro Ecol. 2002;43:168–73.CAS 

    Google Scholar 
    Weinbauer MG, Rowe JM, Wilhelm SW. Determining rates of virus production in aquatic systems by the virus reduction approach. In: Wilhelm SW, Weinbauer MG, Suttle CA. (eds). Manual of Aquatic Viral Ecology. American Society of Limnology and Oceanography Inc., Waco, TX: 2010. p. 1–8.Chen X, Wei W, Wang J, Li H, Sun J, Ma R, et al. Tide driven microbial dynamics through virus-host interactions in the estuarine ecosystem. Water Res. 2019;160:118–29.CAS 
    PubMed 

    Google Scholar 
    Luef B, Luef F, Peduzzi P. Online program ‘vipcal’ for calculating lytic viral production and lysogenic cells based on a viral reduction approach. Environ Microbiol Rep. 2009;1:78–85.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Winget DM, Helton RR, Williamson KE, Bench SR, Williamson SJ. Repeating patterns of virioplankton production within an estuarine ecosystem. Proc Natl Acad Sci USA. 2011;108:11506–11.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wei W, Wang N, Cai L, Zhang C, Jiao N, Zhang R. Impacts of freshwater and seawater mixing on the production and decay of virioplankton in a subtropical estuary. Micro Ecol. 2019;78:843–54.CAS 

    Google Scholar 
    Noble RT, Fuhrman JA. Virus decay and its causes in coastal waters. Appl Environ Microbiol. 1997;63:77–83.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Suttle CA, Chen F. Mechanisms and rates of decay of marine viruses in seawater. Appl Environ Microbiol. 1992;58:3721–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rowe JM, Saxton MA, Cottrell MT, DeBruyn JM, Berg GM, Kirchman DL, et al. Constraints on viral production in the Sargasso Sea and North Atlantic. Aquat Micro Ecol. 2008;52:233–44.
    Google Scholar 
    Evans C, Pearce I, Brussaard CP. Viral-mediated lysis of microbes and carbon release in the sub-Antarctic and Polar Frontal zones of the Australian Southern Ocean. Environ Microbiol. 2009;11:2924–34.CAS 
    PubMed 

    Google Scholar 
    De Corte D, Sintes E, Winter C, Yokokawa T, Reinthaler T, Herndl GJ. Links between viral and prokaryotic communities throughout the water column in the (sub)tropical Atlantic Ocean. ISME J. 2010;4:1431–42.PubMed 

    Google Scholar 
    Li Y, Lou T, Sun J, Cai L, Liang Y, Jiao N, et al. Lytic viral infection of bacterioplankton in deep waters of the western Pacific Ocean. Biogeosciences. 2014;11:2531–42.
    Google Scholar 
    Liang Y, Zhang Y, Zhang Y, Luo T, Rivkin R, Jiao N. Distributions and relationships of virio- and picoplankton in the epi-, meso- and bathypelagic zones of the Western Pacific Ocean. FEMS Microbiol Ecol. 2017;93:fiw238.PubMed 

    Google Scholar 
    Wommack KE, Colwell RR. Virioplankton: viruses in aquatic ecosystems. Microbiol Mol Biol Rev. 2000;64:69–114.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Parikka KJ, Le Romancer M, Wauters N, Jacquet S. Deciphering the virus-to-prokaryote ratio (VPR): insights into virus-host relationships in a variety of ecosystems. Biol Rev. 2016;92:1081–1100.PubMed 

    Google Scholar 
    Parada V, Herndl GJ, Weinbauer MG. Viral burst size of heterotrophic prokaryotes in aquatic systems. J Mar Biol Assoc UK. 2006;86:613–21.
    Google Scholar 
    Yuan D. A numerical study of the South China Sea deep circulation and its relation to the Luzon Strait transport. Acta Oceano Sin. 2002;21:187–202.
    Google Scholar 
    Tian J, Yang Q, Zhao W. Enhanced diapycnal mixing in the South China Sea. J Phys Oceanogr. 2009;39:3191–203.
    Google Scholar 
    Alford MH, Lien R, Simmons H, Klymak J, Ramp S, Yang YJ, et al. Speed and evolution of nonlinear internal waves transiting the South China Sea. J Phys Oceanogr. 2010;40:1338–55.
    Google Scholar 
    Parada V, Sintes E, Van Aken HM, Weinbauer MG, Herndl GJ. Viral abundance, decay, and diversity in the meso- and bathypelagic waters of the north atlantic. Appl Environ Microbiol. 2007;73:4429–38.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    De Corte D, Sintes E, Yokokawa T, Reinthaler T, Herndl GJ. Links between viruses and prokaryotes throughout the water column along a North Atlantic latitudinal transect. ISME J. 2012;6:1566–77.PubMed 
    PubMed Central 

    Google Scholar 
    Zachary A. An ecological study of bacteriophages of Vibrio natriegens. Appl Environ Microbiol. 1978;24:321–4.CAS 

    Google Scholar 
    Motegi C, Nagata T. Enhancement of viral production by addition of nitrogen or nitrogen plus carbon in subtropical surface waters of the South Pacific. Aquat Micro Ecol. 2007;48:27.
    Google Scholar 
    Bratbak G, Egge JK, Heldal M. Viral mortality of the marine alga Emiliania huxleyi (Haptophyceae) and termination of algal blooms. Mar Ecol-Prog Ser. 1993;93:39–48.
    Google Scholar 
    Motegi C, Kaiser K, Benner R, Weinbauer MG. Effect of P-limitation on prokaryotic and viral production in surface waters of the Northwestern Mediterranean Sea. J Plankton Res. 2015;37:16–20.CAS 

    Google Scholar 
    Hewson I, O’Neil JM, Fuhrman JA, Dennison WC. Virus-like particle distribution and abundance in sediments and overmaying waters along eutrophication gradients in two subtropical estuaries. Limnol Oceanogr. 2001;46:1734–46.
    Google Scholar 
    Wilson WH, Mann NH. Lysogenic and lytic viral production in marine microbial communities. Aquat Micro Ecol. 1997;13:95–100.
    Google Scholar 
    Paul JH. Prophages in marine bacteria: dangerous molecular time bombs or the key to survival in the seas? ISME J. 2008;2:579–89.CAS 
    PubMed 

    Google Scholar 
    Chibani-Chennoufi S, Bruttin A, Dillmann ML, Brussow H. Phage-host interaction: an ecological perspective. J Bacteriol. 2004;186:3677–86.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Weinbauer MG. Ecology of prokaryotic viruses. FEMS Microbiol Rev. 2004;28:127–81.CAS 
    PubMed 

    Google Scholar 
    Williamson SJ, Paul JH. Nutrient stimulation of lytic phage production in bacterial populations of the Gulf of Mexico. Aquat Micro Ecol. 2004;36:9–17.
    Google Scholar 
    Williamson SJ, Paul JH. Environmental factors that influence the transition from lysogenic to lytic existence in the ϕHSIC/Listonella pelagia marine phage–host system. Micro Ecol. 2006;52:217–25.CAS 

    Google Scholar 
    Cissoko M, Desnues A, Bouvy M, Sime-Ngando T, Verling E, Bettarel Y. Effects of freshwater and seawater mixing on virio- and bacterioplankton in a tropical estuary. Freshw Biol. 2008;53:1154–62.
    Google Scholar 
    Bettarel Y, Bouvier T, Agis M, Bouvier C, Van Chu T, Combe M, et al. Viral distribution and life strategies in the Bach Dang Estuary, Vietnam. Micro Ecol. 2011;62:143–54.
    Google Scholar 
    Shkilnyj P, Koudelka GB. Effect of salt shock on stability of λimm434 lysogens. J Bacteriol. 2007;189:3115–23.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tuomi P, Fagerbakke KM, Bratbak G, Heldal M. Nutritional enrichment of a microbial community: the effects on activity, elemental composition, community structure and virus production. FEMS Microbiol Ecol. 1995;16:23–134.
    Google Scholar 
    Dell’Anno A, Corinaldesi C, Danovaro R. Virus decomposition provides an important contribution to benthic deep-sea ecosystem functioning. Proc Natl Acad Sci USA. 2015;112:E2014–E2019.PubMed 
    PubMed Central 

    Google Scholar 
    Mojica KD, Brussaard CP. Factors affecting virus dynamics and microbial host-virus interactions in marine environments. FEMS Microbiol Ecol. 2014;89:495–515.CAS 
    PubMed 

    Google Scholar 
    Zweifel UL. Factors controlling accumulation of labile dissolved organic carbon in the Gulf of Riga. Estuar Coast Shelf Sci. 1999;48:357–70.CAS 

    Google Scholar 
    Pomeroy LR, Wiebe WJ. Temperature and substrates as interactive limiting factors for marine heterotrophic bacteria. Aquat Micro Ecol. 2001;23:187–204.
    Google Scholar 
    Ploug H, Grossart H, Azam F, Jørgensen BB. Photosynthesis, respiration, and carbon turnover in sinking marine snow from surface waters of Southern California Bight: implications for the carbon cycle in the ocean. Mar Ecol-Prog Ser. 1999;179:1–11.CAS 

    Google Scholar 
    Azam F, Malfatti F. Microbial structuring of marine ecosystems. Nature. 2007;5:782–91.CAS 

    Google Scholar 
    De Corte D, Sintes E, Yokokawa T, Lekunberri I, Herndl GJ. Large-scale distribution of microbial and viral populations in the South Atlantic Ocean. Environ Microbiol Rep. 2016;8:305–15.PubMed 
    PubMed Central 

    Google Scholar 
    Yang YH, Yokokawa T, Motegi C, Nagata T. Large-scale distribution of viruses in deep waters of the Pacific and Southern Oceans. Aquat Micro Ecol. 2014;71:193–202.
    Google Scholar 
    Labonté JM, Swan BK, Poulos B, Luo H, Koren S, Hallam SJ, et al. Single-cell genomics-based analysis of virus-host interactions in marine surface bacterioplankton. ISME J. 2015;9:2386–99.PubMed 
    PubMed Central 

    Google Scholar 
    Martinez-Hernandez F, Fornas Ò, Lluesma Gomez M, Garcia-Heredia I, Maestre-Carballa L, López-Pérez M, et al. Single-cell genomics uncover Pelagibacter as the putative host of the extremely abundant uncultured 37-F6 viral population in the ocean. ISME J. 2019;13:232–6.CAS 
    PubMed 

    Google Scholar 
    Mruwat N, Carlson MCG, Goldin S, Ribalet F, Kirzner S, Hulata Y, et al. A single-cell polony method reveals low levels of infected Prochlorococcus in oligotrophic waters despite high cyanophage abundances. ISME J. 2021;15:41–54.CAS 
    PubMed 

    Google Scholar 
    Peduzzi P, Weinbauer M. Effect of concentrating the virus-rich 2–200 nm size fraction of seawater on the formation of algal flocs (marine snow). Limnol Oceanogr. 1993;38:1562–5.
    Google Scholar 
    Uitz J, Stramski D, Baudoux A, Reynolds RA, Wright VM, Dubranna J, et al. Variations in the optical properties of a particle suspension associated with viral infection of marine bacteria. Limnol Oceanogr. 2010;55:2317–30.
    Google Scholar 
    Sullivan MB, Weitz JS, Wilhelm SW. Viral ecology comes of age. Environ Microbiol Rep. 2017;9:33–35.PubMed 

    Google Scholar 
    Laber CP, Hunter JE, Carvalho F, Collins JR, Hunter EJ, Schieler BM, et al. Coccolithovirus facilitation of carbon export in the North Atlantic. Nat Microbiol. 2018;3:537–47.CAS 
    PubMed 

    Google Scholar 
    Kranzler CF, Brzezinski MA, Cohen NR, Lampe RH, Maniscalco M, Till CP, et al. Impaired viral infection and reduced mortality of diatoms in iron-limited oceanic regions. Nat Geosci. 2021;4:231–7.
    Google Scholar 
    Hewson I, Fuhrman JA. Viriobenthos production and virioplankton sorptive scavenging by suspended sediment particles in coastal and pelagic waters. Micro Ecol. 2003;46:337–47.CAS 

    Google Scholar  More

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    MeadoWatch: a long-term community-science database of wildflower phenology in Mount Rainier National Park

    Study origin and designThe MeadoWatch project (MW) is a project run collaboratively between the University of Washington (UW) and the United States National Park Service to monitor the phenology of alpine and subalpine wildflower species across large elevational gradients in Mount Rainier National Park (Fig. 2). MW was established in 2013 with the goal of understanding long-term effects of climate change on Mount Rainier National Park wildflower communities using community-science approaches. The first MW transect was established along Reflection Lakes, Skyline, and Paradise Glacier trail system in 2013 (9–11 plots). In 2015, MW expanded to include a second transect (15–17 plots) along the Glacier Basin trail (Fig. 1a). The MW transects span around 5 km each, over a 400 m altitudinal gradient (Reflection Lakes: 1490m–1889m a.s.l.; Glacier Basin: 1460m–1831m a.s.l.)Fig. 2Alpine meadows, plot extension, and target species. (a) Species-rich alpine meadow in Mount Rainier National Park (Mount Tahoma), showing many of the target species in the foreground. (b) MW volunteer coordinator Anna Wilson at a plot, indicating the arm span that defines the plot area (personal likeness used with confirmed consent). (c) Species composition and proportion of reports per species in each of the transects; species common to both trails are highlighted with striped shadowing. Photographs: A. John (a), L. Felker (b).Full size imagePlots are located along the side of each trail, marked with a colored survey marker. The area of each plot is defined by the arm-span of volunteers when they position themselves over the plot marker looking away from the trail (Fig. 2b). While less accurate than marking the corners of plots, this approach was used to avoid establishing permanent structures in wilderness areas within the National Park. The surveyed area in each plot is, on average, 1.25 m2. Each plot is also equipped with temperature sensors (HOBO Pendant Logger, Onset Computer Corp.) buried approximately 4 cm below the ground. Sensors are placed at the start of each fall season and removed at the beginning of each summer season for data downloading. The HOBO sensors provide an estimate for the date of snow disappearance and in-situ temperature at 3 hours intervals. Once plots are covered in snow, soil temperatures remain at 0 °C and show no diurnal variation, so that daily changes in temperatures above 1 °C can be used to determine the earliest date without snow cover20. We use these approaches to provide dates of snow appearance and disappearance, snow cover duration, and minimum soil temperatures for each year and plot. Occasionally, temperature data during the snow disappearing window were lost due to sensor failure or loss of sensors (which occurs because plots are not permanently marked and/or well-meaning visitors remove sensors). This, and the lack of temperature sensors in the first year of the project, resulted in approx. 20% of cases of missing data. In those cases, we used a data imputation method to estimate the missing values based on data from nearby plots and a parallel temperature data collection with 890 total observations. These estimates were highly reliable in filling the data gaps (see Appendix C in16 for further details).Focal speciesWe originally targeted 16 native wildflower species along each transect, which were chosen based on their abundance, ease of identification, and presence in the plot. Four of those target species were present on both transects. In 2016 we replaced one species with a different one (see further information below), making for a total of 17 species monitored (Fig. 2c). The focal species are: American bistort* (Polygonum bistortoides), avalanche lily (Erythronium montanum), bracted lousewort* (Pedicularis bracteosa), broadleaf arnica (Arnica latifolia), cascade aster (Aster ledophyllus; synonym Eucephalus ledophyllus), glacier lily (Erythronium grandiflorum), Gray’s lovage (Ligusticum grayi), magenta paintbrush (Castilleja parviflora), mountain daisy (Erigenon peregrinus; synonym Erigeron glacialis), northern microseris (Microseris alpestris; synonym Nothocalais alpestris), scarlet paintbrush (Castilleja miniata), sharptooth angelica (Angelica arguta), sitka valerian* (Valeriana sitchensis), subalpine lupine* (Lupinus arcticus; synonym Lupinus latifolius var. subalpinus), tall bluebell (Mertensia paniculata), Canby’s licorice-root (Ligusticum canbyi), and western anemone (Anemone occidentalis). Asterisks denote species monitored along both trails.Due to challenges in species identification, we dropped Canby’s licorice-root (Ligusticum canbyi) as a target species in 2016. Consequently, Ligusticum canbyi has limited replication in the database (Fig. 2c). While we included the phenological records of this species for the sake of completeness, we recommend focusing on the other 16 species, which are both better represented (in terms of data coverage) and are free of any potential misidentification issues.For additional information on the species, methods, identification cues, and image resources see: http://www.meadowatch.org, https://www.youtube.com/channel/UCGBFTKxf8FIWswMDxBavpuQ, and the appendices therein16.Data collection and volunteer trainingDuring the summer months, MW volunteers and scientists collect reproductive phenology data with a frequency between 3 and 9 trail reports per week. Each report records the presence or absence of 4 phenophases for each target species present in each of the plots. The phenophases are ‘budding’, ‘flowering’, ‘ripening fruit’, and ‘releasing seed’. Phenophases were defined as follows:BuddingThe beginning growth of the flower which has not yet opened. A plant is considered budding if buds are present, but the stamen and pistils are not yet visible and available to pollinators.FloweringThe generally “showy” part of the plant that holds the reproductive parts (stamens and pistils). A plant is considered flowering when at least one flower is open, and the stamens and pistils are visible and available for pollination and reproduction.Ripening fruitThe fruit develops from the female part of the flower following successful pollination. In the target species, fruits can take many forms, from hard, fleshy capsules, juicy berries, to a feathery tuft on the end of a seed. A plant is in the ripening fruit stage when reproductive parts on at least one reproductive stalk are non-functional and the formation of the fruit part is clearly ongoing (visible), but seeds are not yet fully mature and not yet being dispersed.Releasing seedAfter the fruit ripens, seeds are released to be dispersed by gravity, wind, or animals. A plant is considered in the releasing seed stage if seeds are actively being released on at least one reproductive stalk (but there are still seeds present).A full description, including illustrations for each species’ phenophase and identification cues is available in http://www.meadowatch.org/volunteer-resources.html, as well as in Annex 1 – Supplementary Documentation. Multiple phenophases can be present simultaneously, depending on the species, and are noted independently. Additionally, volunteers are also asked to record the presence of snow (‘snow covered plot’, ‘partially covered plot’, or ‘snow-free plot’), and, since 2017, the presence of damage by herbivory (‘presence of browsed stems’) on each plot.In years not impacted by the SARS-Cov-2 pandemic MW volunteers attend an in-person 3-hour botanical and phenological training session taught by UW scientists at the beginning of each sampling season. Volunteers were also provided with detailed species-identification guides, including an extensive description of sampling methods and location of the plots. The trainings for the 2020 and 2021 seasons were held virtually via a series of online training videos. In these years, volunteers took a quiz on wildflower phenology, plant identification and data collection methods after viewing these videos and were required to ‘pass’ a certain threshold to volunteer (unlimited attempts were allowed). During these virtual trainings, volunteers were provided with digital copies of the species identification guides, with many returning volunteers using printed guides they had kept from previous years.At the end of their phenological hike, volunteers submit their data sheets either by depositing them in lockboxes located within the park, or by scanning and emailing them directly to mwatch@uw.edu. Data are then entered manually and stored in the UW repositories after being checked for consistency at the end of each sampling season.The parallel data collection by members of UW’s Hille Ris Lambers group (including PI, postdoctoral researchers, graduate students, and trained interns) acted as the following: (i) a quality-control, i.e., allowing us to compare the consistency in phenology assessments between volunteers and scientists, and (ii) a way to increase the temporal resolution and scale of the data, e.g., by reducing early season gaps and ‘weekend bias’17. This parallel expert sampling was carried out around once a week between 2013 and 2020, showing great consistency between the two groups. For detailed comparisons between volunteers and scientists’ assessments see the data validation section (as well as Appendix E in16).Processed dataIn addition to the raw phenological data, we also provide here parameters to construct the year, species, and plot-specific flowering phenology based on the timing of snow disappearance (as in16). Models describe unimodal probability distributions that were fitted with maximum likelihood models to binomial flowering data from each species, year, and plot. These curves have been used to estimate peak flowering dates and diversity and link them to reported visitor experiences16. Here, we provide the 3 parameters defining the unimodal curve of flowering probability per species i, plot j and year k: the duration of flowering (𝛿ijk), the maximum probability of flowering (𝜇ijk), and peak flowering (in DOY – ρijk); following the equations described in16 and https://github.com/ajijohn/MeadoWatch).The parameters of these probability distribution curves are ready-to-use values that can be broadly and easily used to estimate floral compositional change over past seasons due to changing environmental conditions—for example, to inform plant-pollinator interaction networks if combined with pollinator behavioral data (e.g.21). More

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    Biological invasions facilitate zoonotic disease emergences

    Disease data sourceAll analyses were conducted at the administrative level, and the exact list of known zoonotic diseases is recorded in the GIDEON database22. GIDEON is currently the most comprehensive and frequently updated infectious disease outbreak database reporting epidemics of human infectious diseases at the global scale and has been widely used in global zoonosis studies42,43 (Last access date, November 9, 2020). The administrative designations used in our analyses were based on the Global Administrative Areas (GADM) database (www.gadm.org, downloaded on November 8, 2020), which includes very detailed boundary data for global countries and major island groups.Pattern and correlates of zoonosis events worldwideNumber of zoonosis eventsGIDEON defines human infectious disease reservoirs as any animal, plant, or substrate supporting the survival and reproduction of infectious agents and promoting transmission to potential susceptible hosts. Its host category therefore includes all human-specific, zoonotic, multihost, and environmental agents. As our main aim was to test the role of established alien animal species in the emergence of zoonotic diseases, we focused on a total of 161 diseases specified in GIDEON’s host designations and definitions as nonhuman zoonotic (n = 115) and multihost (n = 46) diseases (Supplementary Data 1) and excluded diseases with human-specific hosts that do not need animals to persist or be transmitted. The infectious agents of nonhuman zoonotic diseases complete their entire lifecycle in nonhuman hosts but may have the potential to spillover and infect human populations. Infectious agents of multihost diseases can use both human and animal hosts for their development and reproduction. We measured the number of zoonosis events for each jurisdiction according to five host taxonomic groups: mammals, birds, invertebrates, reptiles and amphibians. These zoonoses were mainly caused by bacteria, viruses, parasitic animals and fungi. We excluded zoonoses from the Algae (3 diseases) due to low sample sizes in GIDEON.Correlates of the number of zoonosis eventsClimatic variablesFollowing a previous study21, we used global environmental stratification (GEnS) as a composite bioclimatic variable generated by stratifying the Earth’s surface into zones with similar climates44. The GEnS database was constructed based on a total of 125 strata across 18 global environmental zones with a spatial resolution of 30 arc seconds (equivalent to approximately 0.86 km2 at the equator). The values in GEnS range from 1 to 18 with a higher value indicating warmer and wetter conditions.Human population densityWe used human population density as one general anthropogenic factor reflecting propagule pressure and human-assisted pathogen movements1,21,45. Human population size data and the land area of each jurisdiction were collected from World Bank Open Data from 2011 to 2020 (available at https://data.worldbank.org/indicator/SP.POP.TOTL, accessed on November 18, 2020). We then calculated the human population density using the human population size divided by the land area.Native potential host richness and biodiversity lossData on the richness of native amphibians, birds, and mammals were derived from the Biodiversity Mapping website (https://biodiversitymapping.org/wordpress/index.php/home/, accessed on August 19, 2020), which were based on studies from Jenkins et al. (2013)’s and Pimm et al. (2014)46,47. The map of reptile diversity is based on an updated database of the global spatial distribution of reptiles48. All diversity maps for each taxon were generated through the calculation of grid-based richness at a spatial resolution of 10 km × 10 km in ArcGIS46. We did not include native invertebrate richness, as global maps for most invertebrate taxa are not yet available. For the loss of native biodiversity, we followed the previous study by first extracting the list of threatened species (NT, EN and VU categories evaluated by the IUCN Red List, access on May 10th, 2021)29, and then calculated the number of threatened species for each taxon distributes in each administrative unite as a proxy of biodiversity loss.Richness of established alien zoonotic host speciesWe quantified the richness of established alien animal species from the five main taxonomic groups (mammals, birds, reptiles, amphibians and invertebrates) based on 4,522 establishment events of 795 alien animals in each of 201 jurisdictions according to various databases. Data on 262 established alien reptiles and amphibians were compiled from multiple publications, including Kraus’s compendium49 and other recent updates50. Data on 337 established alien birds after removing all migratory bird species as vagrants were collected from the Global Avian Invasions Atlas (GAVIA)51, which is a comprehensive database of the global distribution of established alien birds. Data on 119 established alien mammals were obtained from the Introduced Mammals of the World database52 and the more recent update53. Data on 77 terrestrial alien invertebrates (66 insects and 11 other groups) across 7 taxa with native and invaded range information were obtained from the Global Invasive Species Database (GISD, http://www.iucngisd.org/gisd/, accessed on July 1, 2020). We calculated the richness of both zoonotic and non-zoonotic alien host species for each order. We first conducted an intensive literature review for each established alien species of each of the four taxa to determine whether they transmit pathogens to humans (Supplementary Data 2). The identification of zoonotic or non-zoonotic host may be influenced by under-sampling in the literature. We therefore incorporated the latest synthesis of human-infecting pathogens in the ‘CLOVER’ dataset to identify zoonotic and non-zoonotic animal hosts54. The CLOVER dataset compiled GMPD255, EID256, HP323 and Shaw57 databases and is currently the most comprehensive dataset on host-pathogen associations. Based on this information, we then categorized each alien species as a ‘zoonotic host’ or ‘non-zoonotic host’. The records of the established alien species were assigned to GADM jurisdictions, and we calculated the richness of the established alien zoonotic and non-zoonotic host species for each taxonomic group within each jurisdiction. In order to increase the statistical power, we conducted subsequent modeling analyses based on four mammalian orders (i.e., Carnivora, Cetartiodactyla, Lagomorpha, and Rodentia), five avian groups (i.e., waterfowl including five orders: Anseriformes, Gruiformes, Pelecaniformes, Phoenicopteriformes and Suliformes; Columbiformes, Galliformes, Passeriformes, Psittaciformes), the order Diptera of the invertebrates, and herpetofauna as a whole, which have established alien populations in at least 50 administrative units.Climate changeWe extracted historical monthly mean temperature and precipitation data recorded between 1901 and 2009 from the University of East Anglia Climate Research Unit (CRU, https://sites.uea.ac.uk/cru/, accessed on November 30, 2020)58. This database provides historical global-scale yearly climatic data with the finest resolution of 0.5° grids. We generated the temperature and precipitation values for all grids in each jurisdiction, calculated the slope of the temperature and precipitation for the time series of the years 1901 to 2009 for each grid and generated the averages based on all grids within each jurisdiction.Anthropogenic land-use changeWe downloaded global land-use data from the Anthromes v2 Dataset (Anthropogenic Biomes version 2, accessed on October 15, 2020) in ESRI GRID format59. We used the 1900 and 2000 data to calculate the temporal changes in land use. By using the reclassify and raster function in ArcGIS, we calculated the percentage of grids in which the land-use type changed to a more anthropogenically influenced type from 1900 to 2000 for each jurisdiction, including 15 scenarios: Wildlands to Seminatural, Wildlands to Rangelands, Wildlands to Croplands, Wildlands to Villages, Wildlands to Dense Settlements, Seminatural to Rangelands, Seminatural to Croplands, Seminatural to Villages, Seminatural to Dense Settlements, Rangelands to Croplands, Rangelands to Villages, Rangelands to Dense Settlements, Croplands to Villages, Croplands to Dense Settlements, and Villages to Dense Settlements.Sampling effort, reporting bias and incomplete dataA potential issue in quantifying the effects of different predictor variables on the number of zoonosis events is the need to account for the differences in survey effort, reporting bias and incomplete disease data among regions1,21,28. There is a high probability that zoonosis discovery is spatially biased by uneven levels of surveillance across countries, as the global allocation of scientific resources has been focused on rich and developed countries. We thus included the Infectious Disease Vulnerability Index (IDVI), which is a comprehensive metric reflecting the demographic, health care, public health, socioeconomic, and political factors that may have an impact on the capacity of surveillance and detection of infectious diseases in each country60. Second, we followed the methods of a previous study21 to control for reporting biases. We incorporated PubMed citations per disease for each jurisdiction using a Python-based PubCrawler21. In addition, we added the longitude and latitude of the geographic centroid of administrative units to control for spatial autocorrelation as there would be a higher probability of having similar diseases in nearby than distant administrative units61.Statistical analysisThe number of zoonosis events, native potential host richness, established alien animal richness and human population density were log-transformed to improve linearity. A potential issue in our data analysis is that the numbers of zoonosis events and the numbers of native and alien animal species are strongly influenced by geographical area, as larger countries or regions may host more native or alien animal species and more disease events. We therefore calculated the density of native or alien species richness and the number of zoonosis events using the total number divided by the geographical area of each jurisdiction. Furthermore, the number of zoonosis events may also be influenced by the degree of local disease surveillance. We thus obtained the residuals from a regression correlating zoonosis event density and all disease event density, and used them as the dependent variable for further analyses (Fig. 1). As some of our variables may be expected to be nonlinear, we performed generalized additive mixed model (GAMM) analyses following Mollentze & Streicker 2020’s framework25 to quantify the relationships between different predictor variables and the number of zoonosis events. We started with a full model with zoonosis event density controlling for overall disease surveillance as the response variable and 13 smoothed fixed effects (Fig. 1 and Supplementary Data 4): GEnS, human population density, density of native species richness, biodiversity loss, density of alien zoonotic host richness, density of alien non-zoonotic host richness, climate (temperature and precipitation) change, land-use change, IDVI, PubMed citations, longitude and latitude of geographic centroid of administrative units. The reason why we included the density of alien non-zoonotic host richness as a covariate is because this variable can serve as a positive control for propagule pressure, allowing us to more explicitly test whether zoonotic alien hosts contribute to zoonoses beyond propagule pressure associated with non-zoonotic alien hosts, which cannot directly increase zoonotic diseases. These predictor variables were not highly collinear as their correlation coefficients based on Pearson rank correlation analyses were all More

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    Drivers of tropical forest loss between 2008 and 2019

    The crowdsourcing campaign was organized as a competition with prizes offered to those who contributed the most, based on a combination of quality and quantity. The Geo-Wiki platform (www.geo-wiki.org), a web platform dedicated to engaging citizens in environmental monitoring, was used as the tool to perform the campaign. A customized user interface was prepared for the campaign (Fig. 2), where participants were shown a random location in the tropics (here broadly defined as the area between 30 degrees of latitude north and south of the equator, i.e., including part of the subtropics), where a blue 1 × 1 km box showed the location to be visually interpreted. The Global Forest Change (GFC) tree loss map (v1.7)10 was overlaid on the imagery to show all areas where tree loss was detected at any point between 2008 and 2019. The tree loss area was shaded in red and the map itself was aggregated to 100 m for fast rendering.Fig. 2Customized Geo-Wiki interface for the ‘Drivers of Tropical Forest Loss’ crowdsourcing campaign showing: (a) Tools available to participants such as the NDVI and Sentinel time-series profiles, visualizing the location on Google Earth and exploring the imagery time-series, reviewing the quick-start guide and exploring examples to identify specific drivers of forest loss as well as contacting IIASA staff via chat or email; (b) country and continent of the location as well as dates of the imagery shown; (c) campaign statistics; (d) available background imagery; and (e) tasks to be undertaken by the participants along with buttons to submit or skip the location.Full size imageThe year 2008 was selected as the start date because the RED states that date as the cut-off year for conversion from high-carbon areas, i.e., forest, to other land uses7. In order to capture the main drivers of forest loss, but also include potential additional drivers such as the existence of roads as precursors of deforestation, the participants were asked to complete three steps: 1) To select the predominant tree loss driver visible inside the tree loss pixels in the blue box from a list of nine specific drivers; 2) to select all other tree loss drivers visible inside the tree loss pixels in the blue box from a list of five more general drivers, and 3) to mark if roads, trails, or buildings were visible in the blue box. The list of specific and general drivers as well as their definitions is shown in Table 1. The Geo-Wiki interface allowed participants to switch between different background imagery such as ESRI, Google Maps, and Bing Maps as well as Sentinel 2 satellite imagery. The different sources of imagery allowed the participants to see the location at different resolutions and in different periods of time. It also provided participants with information about the current country and the continent as well as the dates of the background imagery. Furthermore, it provided the participants with links for displaying NDVI and Sentinel time series, and to see the location and explore the historical imagery using the Google Earth platform. All these tools were meant to help with easier identification of the forest loss drivers by allowing participants to look at the locations during different times and at different spatial resolutions.Table 1 List and description of the available list of tree loss drivers that participants could select for steps 1 and 2 of the campaign.Full size tableAt the beginning of the campaign, each participant was shown a quick start guide of the interface and the tasks requested. As shown in Fig. 2, this quick start guide could be accessed again at any point during the campaign. Figure 2 also shows that the interface had buttons for four further functions. The first was to see the gallery of examples with access to pre-loaded video-tutorials and examples of images describing each driver of forest loss and how to do visual interpretation and selection of each of these (available at https://application.geo-wiki.org/Application/modules/drivers_forest_change/drivers_forest_change_gallery.html). An illustration of the gallery of examples shown to participants is shown in Figure S1. The second function was to ask experts for help, which automatically sent IIASA experts an email regarding a specific location. The third was to join the expert chat, which led participants to a dedicated chat interface on the Discord messaging platform. Here participants could pose questions and interact with staff and other participants directly. Finally, there was a button to see the leader board as well as the aims, rules and prizes of the campaign (available at https://application.geo-wiki.org/Application/modules/drivers_forest_change/drivers_forest_change.html). When the participants started the campaign, they were shown 10 initial practice locations, where they could try out the user interface (UI) with control points, which showed the participants how to identify the different drivers of forest loss. This set of videos, the images and the training points, together with the gallery of images, were developed to train the participants before and during the campaign.Campaign set-up and data qualityAs the aim of the campaign was to determine the drivers of tree loss across the tropics, the sample locations were selected from the GFC tree loss layer10 for the tropics (between 30 degrees north and south of the equator). No stratification was used since a completely random sample across the tropics was deemed to be the fairest representation of tree loss and their corresponding drivers. The previous map of deforestation drivers6 used a 5 K sample of 10 × 10 km grid cells to produce a global map. Here the sample size was largely driven by the estimated capacity of the crowd. Hence, we aimed to validate ca. 150k 1 × 1 km locations across the tropics, which is a considerably larger sample size than that of Curtis et al.6. In order to reduce noise, the GFC tree loss layer10 was first aggregated to a 100 m resolution from the original 30 m, and 150 K centroids were then randomly selected. From these, a sub sample of 5000 random locations were selected for visual interpretation by six IIASA experts (with backgrounds in remote sensing, agronomy, forestry and geography). Due to time constraints, only 2001 locations were evaluated by at least three different experts. In these locations, agreement was discussed and once a consensus was reached, these locations became the final control or expert data set. The control locations were then used to produce quality scores for each participant as the campaign progressed in order to rank them and determine the final prize winners. The list of prizes offered to the top 30 participants is shown in Table S1 in the Supplementary Information (SI), and a list and rank of motivations mentioned by the participants is shown on Figure S2 in the SI.The control locations were randomly shown to the participants at a ratio of approximately 2 control locations to every 20 non-control locations visited. If the participants correctly selected the predominant tree loss driver (in step 1), they were awarded 20 points; if they selected the wrong answer, they lost 15 points. If participants confused pasture and commercial agriculture or wildfire with other natural disturbances, they lost only 10 points instead of 15. Furthermore, they could win 8 additional points by selecting the correct secondary drivers in step 2. If a mixture of correct and incorrect answers were provided in step 2, the participants gained 2 points for every correct choice and lost 2 points for every incorrect one, with a minimum gain/loss of 0 points. Finally, participants could earn 2 additional points by correctly reporting the existence of roads, trails or buildings in step 3. The scoring system was based on previous Geo-Wiki campaign experiences and aimed to promote focus on the primary driver selection. The points were used to produce a leader board with the total number of points by participant. Additionally, a relative quality score (RQS) was derived from the score received by the users and the potential score that could have been obtained if all control points were correctly interpreted. This is shown in Eq. 1.$${rm{RQS}}=(({{rm{NCP}}}^{ast }15+{rm{SumScore}})/{rm{NCP}})/45$$
    (1)
    where RQS ranges between 0 and 1, NCP is the number of control points visited and SumScore is the number of points obtained.The RQS was crucial in understanding how each participant performed in terms of the quality of their visual interpretations, as this was independent of the number of locations interpreted. Once the campaign ended, an average RQS was used as a minimum criterion for participants to receive a prize, independent of where they were located on the leader board. Additionally, all users who submitted a substantial number of interpretations, i.e., more than 1000 with the minimum required RQS, were invited to become co-authors of the current manuscript, independent of whether they received a monetary prize or not. All these co-authors additionally contributed to the editing and revision of this manuscript. Furthermore, future users of the data set could use the RQS as a key data quality indicator.After the campaign, the data post-processing included eliminating interpretations made by users who broke any of the competition rules. Additionally, during the campaign, some users communicated with IIASA staff using the “Ask Experts” button and pointed out that some control points were mistaken. Consequently, the corresponding points lost were added to the final score of those participants where the correction was made. A total of 18742 validations from 1 participant were removed before the end of the campaign and the user was disqualified since their account was deemed to be shared across several people and computers, which was not allowed. Validations from another user (38,502 out of 40,828) were also removed due to inconsistencies but the user remained in the competition. Before the prizes were awarded to the top 30 users, a questionnaire was administered to all users to gather information about participant characteristics and gauge their motivations. Participation was mandatory for the top 30 users. A summary of the participant backgrounds is provided in Figure S3 in the SI. More