Philippa Kaur

Lindenmayer, D. et al. A checklist of attributes for effective monitoring of threatened species and threatened ecosystems. J. Environ. Manage. 262, 110312 (2020).PubMed 

Google Scholar 
Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).PubMed 

Google Scholar 
IUCN. The IUCN Red List of Threatened Species. Version 2019-3. http://www.iucnredlist.org (2021).Adams, M. J. et al. Trends in amphibian occupancy in the United States. PLoS ONE 8, e64347 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar 
Corn, P. S. Climate change and amphibians. Anim. Biodivers. Conserv. 28, 59–67 (2005).
Google Scholar 
Kiesecker, J. M., Blaustein, A. R. & Belden, L. K. Complex causes of amphibian population declines. Nature 410, 681–684 (2001).ADS 
CAS 

Google Scholar 
Baldwin, R. F. & deMaynadier, P. G. Assessing threats to pool-breeding amphibian habitat in an urbanizing landscape. Biol. Conserv. 142, 1628–1638 (2009).Borzée, A., Kyong, C. N., Kil, H. K. & Jang, Y. Impact of water quality on the occurrence of two endangered Korean anurans: Dryophytes suweonensis and Pelophylax chosenicus. Herpetologica 74, 1–7 (2018).
Google Scholar 
Stuart, S. N. et al. Status and trends of amphibian declines and extinctions worldwide. Science 306, 1783–1786 (2004).ADS 
CAS 

Google Scholar 
Caro, T., Rowe, Z., Berger, J., Wholey, P. & Dobson, A. An inconvenient misconception: climate change is not the principal driver of biodiversity loss. Conserv. Lett. e12868 (2022).Daszak, P. et al. Emerging infectious diseases and amphibian population declines. Emerg. Infect. 5, 735–748 (1999).CAS 

Google Scholar 
Fellers, G., Green, D. E. & Longcore, J. Oral chytridiomycosis in the mountain yellow-legged frog (Rana muscosa). Copeia 2001, 945–953Blaustein, A. R. et al. Effects of ultraviolet radiation on amphibians: field experiments. Am. Zool. 38, 799–812 (1999).
Google Scholar 
Langhelle, A., Lindell, M. J. & Nyström, P. Effects of ultraviolet radiation on amphibian embryonic and larval development. J. Herpetol. 33, 449–456 (1999).
Google Scholar 
Beebee, T. J. C. Amphibians breeding and climate. Nature 374, 219–220 (1995).ADS 
CAS 

Google Scholar 
Donnelly, M. A. & Crump, M. L. Potential effects of climate change on two neotropical amphibian assemblages. In Potential Impacts of Climate Change on Tropical Forest Ecosystems (ed. Markham, A.) 401–421 (Springer Netherlands, 1998).Carey, C. & Alexander, M. A. Climate change and amphibian declines: is there a link? Divers. Distrib. 9, 111–121 (2003).
Google Scholar 
Fisher, R. N. & Shaffer, H. B. The decline of amphibians in California’s Great Central Valley. Conserv. Biol. 10, 1387–1397 (1996).
Google Scholar 
Sparling, D. W., Donald, W., Linder, G. & Bishop, C. A. Ecotoxicology of Amphibians and Reptiles. (SETAC Press, 2000).Rouse, M. J. & Daellenbach, U. S. Rethinking research methods for the resource-based perspective: isolating sources of sustainable competitive advantage. Strat. Manag. J. 20, 487–494 (1999).
Google Scholar 
Bridges, C. M. & Boone, M. D. The interactive effects of UV-B and insecticide exposure on tadpole survival, growth and development. Biol. Conserv. 113, 49–54 (2003).
Google Scholar 
Schmeller, D. S. et al. National responsibilities in European species conservation: a methodological review. Conserv. Biol. 22, 593–601 (2008).PubMed 

Google Scholar 
Anderson, S. Area and endemism. Q. Rev. Biol. 69, 451–471 (1994).
Google Scholar 
Strayer, D. L. & Dudgeon, D. Freshwater biodiversity conservation: recent progress and future challenges. J. N. Am. Benthol. Soc. 29, 344–358 (2010).
Google Scholar 
Gorman, C. E., Potts, B. M., Schweitzer, J. A. & Bailey, J. K. Shifts in species interactions due to the evolution of functional differences between endemics and non-endemics: an endemic syndrome hypothesis. PLoS ONE 9, e111190 (2014).Mace, G. M. et al. Quantification of extinction risk: IUCN’s system for classifying threatened species. Conserv. Biol. 22, 1424–1442 (2008).PubMed 

Google Scholar 
Fontaine, B. et al. The European Union’s 2010 target: putting rare species in focus. Biol. Conserv. 139, 167–185 (2007).
Google Scholar 
Saeed, M. et al. Rise in temperature causes decreased fitness and higher extinction risks in endemic frogs at high altitude forested wetlands in northern Pakistan. J. Therm. Biol. 95, 102809 (2021).McDonald, L. L. Sampling rare populations. In Sampling Rare or Elusive Species: Concepts, Designs, and Techniques for Estimating Population Parameters (ed. Thompson W. L.) 11–42 (Island Press, 2004).Dodd Jr. K. Monitoring Amphibians in Great Smoky Mountains National Park (USGS Survey Circular, 2003).Qu, C. & Stewart, K. A. Evaluating monitoring options for conservation : traditional and environmental DNA tools for a critically endangered mammal. Sci. Nat. 106, 9 (2019).
Google Scholar 
Deiner, K. et al. Environmental DNA metabarcoding: transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895 (2017).PubMed 

Google Scholar 
Schmidt, B. R., Kery, M., Ursenbacher, S., Hyman, O. J. & Collins, J. P. Site occupancy models in the analysis of environmental DNA presence/absence surveys: a case study of an emerging amphibian pathogen. Methods Ecol. Evol. 4, 646–653 (2013).
Google Scholar 
Iknayan, K. J., Tingley, M. W., Furnas, B. J. & Beissinger, S. R. Detecting diversity: emerging methods to estimate species diversity. Trends Ecol. Evol. 29, 97–106 (2014).PubMed 

Google Scholar 
Kéry, M. & Schmidt, B. R. Imperfect detection and its consequences for monitoring for conservation. Community Ecol. 9, 207–216 (2008).
Google Scholar 
Mackenzie, D. I. et al. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248–2255 (2002).
Google Scholar 
Mackenzie, D. I. & Royle, J. A. Designing occupancy studies: general advice and allocating survey effort. J. Appl. Ecol. 42, 1105–1114 (2005).
Google Scholar 
Ficetola, G. F., Miaud, C., Pompanon, F. & Taberlet, P. Species detection using environmental DNA from water samples. Biol. Lett. 4, 423–425 (2008).PubMed 
PubMed Central 

Google Scholar 
Darling, J. A. & Mahon, A. R. From molecules to management: adopting DNA-based methods for monitoring biological invasions in aquatic environments. Environ. Res. 111, 978–988 (2011).CAS 
PubMed 

Google Scholar 
Goldberg, C. S., Pilliod, D. S., Arkle, R. S. & Waits, L. P. Molecular detection of vertebrates in stream water: a demonstration using Rocky Mountain tailed frogs and Idaho giant salamanders. PLoS ONE 6, e22746 (2011).Williams, M. R. et al. Isothermal amplification of environmental DNA (eDNA) for direct field-based monitoring and laboratory confirmation of Dreissena sp. PLoS ONE 12, e0186462 (2017).Agersnap, S. et al. Monitoring of noble, signal and narrow-clawed crayfish using environmental DNA from freshwater samples. PLoS ONE 12, e0179261 (2017).Barnes, M. A. & Turner, C. R. The ecology of environmental DNA and implications for conservation genetics. Conserv. Genet. 17, 1–17 (2016).CAS 

Google Scholar 
Bohmann, K. et al. Environmental DNA for wildlife biology and biodiversity monitoring. Trends Ecol. Evol. 29, 358–367 (2014).PubMed 

Google Scholar 
Sigsgaard, E. E., Carl, H., Møller, P. R. & Thomsen, P. F. Monitoring the near-extinct European weather loach in Denmark based on environmental DNA from water samples. Biol. Conserv. 183, 46–52 (2015).
Google Scholar 
Bedwell, M. E., Hopkins, K. V. S., Dillingham, C. & Goldberg, C. S. Evaluating Sierra Nevada yellow-legged frog distribution using environmental DNA. J. Wildl. Mangaement 85, 945–952 (2021).
Google Scholar 
Eiler, A., Löfgren, A., Hjerne, O., Nordén, S. & Saetre, P. Environmental DNA (eDNA) detects the pool frog (Pelophylax lessonae) at times when traditional monitoring methods are insensitive. Sci. Rep. 8, 5452 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Brozio, S. et al. Development and application of an eDNA method to detect the critically endangered Trinidad golden tree frog (Phytotriades auratus) in bromeliad phytotelmata. PLoS ONE 12, e0170619 (2017).Pellet, J. & Schmidt, B. R. Monitoring distributions using call surveys: estimating site occupancy, detection probabilities and inferring absence. Biol. Conserv. 123, 27–35 (2005).
Google Scholar 
Weir, L. A., Royle, J. A., Nanjappa, P. & Jung, R. E. Modeling anuran detection and site occupancy on North American Amphibian Monitoring Program (NAAMP) routes in Maryland. J. Herpetol. 39, 627–639 (2005).
Google Scholar 
Fiske, I. J. & Chandler, R. B. Unmarked: an R package for fitting hierarchical models of wildlife occurrence and abundance. J. Stat. Softw. 43, 1–23 (2011).
Google Scholar 
Goldberg, C. S. et al. Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods Ecol. Evol. 7, 1299–1307 (2016).
Google Scholar 
Holland, M. M. & Parsons, T. J. Mitochondrial DNA sequence analysis – validation and use for forensic casework. Forensic Sci. Rev. 11, 21–50 (1999).CAS 
PubMed 

Google Scholar 
Willerslev, E. et al. Diverse plant and animal genetic records from Holocene and Pleistocene sediments. Science 300, 791–795 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Waits, L. P. & Paetkau, D. Noninvasive genetic sampling tools for wildlife biologists: a review of applications and recommendations for accurate data collection. J. Wildl. Manage. 69, 1419–1433 (2006).
Google Scholar 
Shokralla, S. et al. Next-generation DNA barcoding: using next-generation sequencing to enhance and accelerate DNA barcode capture from single specimens. Mol. Ecol. Resour. 14, 892–901 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mills, L. S., Pilgrim, K. L., Schwartz, M. K. & McKelvey, K. Identifying lynx and other North American felids based on mtDNA analysis. Conserv. Genet. 1, 285–288 (2000).CAS 

Google Scholar 
Hajibabaei, M. et al. A minimalist barcode can identify a specimen whose DNA is degraded. Mol. Ecol. Notes 6, 959–964 (2006).CAS 

Google Scholar 
Kim, P., Kim, D., Yoon, T. J. & Shin, S. Early detection of marine invasive species, Bugula neritina (Bryozoa: Cheilostomatida), using species-specific primers and environmental DNA analysis in Korea. Mar. Environ. Res. 139, 1–10 (2018).CAS 
PubMed 

Google Scholar 
Dejean, T. et al. Persistence of environmental DNA in freshwater ecosystems. PLoS ONE 6, e23398 (2011).Xia, Z. et al. Early detection of a highly invasive bivalve based on environmental DNA (eDNA). Biol. Invasions 20, 437–447 (2018).
Google Scholar 
Torresdal, J. D., Farrell, A. D. & Goldberg, C. S. Environmental DNA detection of the golden tree frog (Phytotriades auratus) in bromeliads. PLoS ONE 12, e0168787 (2017).Biggs, J. et al. Using eDNA to develop a national citizen science-based monitoring programme for the great crested newt (Triturus cristatus). Biol. Conserv. 183, 19–28 (2015).
Google Scholar 
Pilliod, D. S., Goldberg, C. S., Arkle, R. S. & Waits, L. P. Estimating occupancy and abundance of stream amphibians using environmental DNA from filtered water samples. Can. J. Fish. Aquat. Sci. 70, 1123–1130 (2013).CAS 

Google Scholar 
Smith, D. H. V., Jones, B., Randall, L. & Prescott, D. R. C. Difference in detection and occupancy between two anurans: the importance of species-specific monitoring. Herpetol. Conserv. Biol. 9, 267–277 (2014).
Google Scholar 
Bayley, P. B. & Peterson, J. T. An approach to estimate probability of presence and richness of fish species. Trans. Am. Fish. Soc. 130, 620–633 (2004).
Google Scholar 
Mehta, S. V., Haight, R. G., Homans, F. R., Polasky, S. & Venette, R. C. Optimal detection and control strategies for invasive species management. Ecol. Econ. 61, 237–245 (2007).
Google Scholar 
Scott, Jr., N. J. & Woodward, B. D. Surveys at breeding sites. In Measuring and Monitoring Biological Diversity: Standard Methods for Amphibians (eds. Heyer, W. R., Donnelly, M. A., McDiarmid, R. W., Hayek, L. C., & Foster, M. S.) 118–125 (Smithsonian Institution Press, 1994).Dejean, T. et al. Improved detection of an alien invasive species through environmental DNA barcoding: the example of the American bullfrog Lithobates catesbeianus. J. Appl. Ecol. 49, 953–959 (2012).
Google Scholar 
Goldberg, C. S., Sepulveda, A., Ray, A., Baumgardt, J. & Waits, L. P. Environmental DNA as a new method for early detection of New Zealand mudsnails (Potamopyrgus antipodarum). Freshw. Sci. 32, 792–800 (2013).
Google Scholar 
Mahon, A. R. et al. Validation of eDNA surveillance sensitivity for detection of Asian carps in controlled and field experiments. PLoS ONE 8, e58316 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Khan, M. S. Amphibians and Reptiles of Pakistan (Krieger Publishing Company, 2006).Ruppert, K. M., Davis, D. R., Rahman, M. S. & Kline, R. J. Development and assessment of an environmental DNA (eDNA) assay for a cryptic Siren (Amphibia: Sirenidae). Environ. Adv. 7, 100163 (2022).
Google Scholar 
Hobbs, J., Round, J. M., Allison, M. J. & Helbing, C. C. Expansion of the known distribution of the coastal tailed frog, Ascaphus truei, in British Columbia, Canada, using robust eDNA detection methods. PLoS ONE 14, e0213849 (2019).Barata, I. M., Griffiths, R. A., Fogell, D. J. & Buxton, A. S. Comparison of eDNA and visual surveys for rare and cryptic bromeliad-dwelling frogs. Herpetol. J. 31, 1–9 (2021).
Google Scholar 
Ahmed, W. et al. Site occupancy of two endemic stream frogs in different forest types in Pakistan. Herpetol. Conserv. Biol. 15, 506–511 (2020).
Google Scholar 
Richmond, O. M. W., Hines, J. E. & Beissinger, S. R. Two-species occupancy models: a new parameterization applied to co-occurrence of secretive rails. Ecol. Appl. 20, 2036–2046 (2010).PubMed 

Google Scholar 
Shea, C. P., Eaton, M. J. & MacKenzie, D. I. Implementation of an occupancy-based monitoring protocol for a widespread and cryptic species, the New England cottontail (Sylvilagus transitionalis). Wildl. Res. 46, 222–235 (2019).
Google Scholar 
Rota, C. T. et al. A multispecies occupancy model for two or more interacting species. Methods Ecol. Evol. 7, 1164–1173 (2016).
Google Scholar 
Ohler, A. & Dubois, A. Phylogenetic relationships and generic taxonomy of the tribe Paini (Amphibia, Anura, Ranidae, Dicroglossinae). Zoosystema 28, 769–784 (2006).
Google Scholar 
Jiang, J. et al. Phylogenetic relationships of the tribe Paini (Amphibia, Anura, Ranidae) based on partial sequences of mitochondrial 12s and 16s rRNA genes. Zool. Res. 362, 353–362 (2005).
Google Scholar 
Rais, M. et al. A note on recapture of Nanorana vicina (Anura: Amphibia) from Murree, Pakistan. J. Anim. Plant Sci. 24, 455–458 (2014).
Google Scholar 
Siddiqui, M. F., Ahmed, M., Khan, N. & Khan, I. A. A quantitative description of moist temperate conifer forests of Himalayan region of Pakistan and Azad Kashmir. Int. J. Biotechnol. 7, 175–185 (2010).
Google Scholar 
Beck, H. E. et al. Present and future köppen-geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).Sheikh, M. I. & Hafeez, S. M. Forest and Forestry in Pakistan (A-one Publishers, 2001).Lodhi, A. Conservation of leopards in Ayubia National Park, Pakistan (MS Thesis) (University of Montana, 2007).Palumbi, S. R. Nucleic acids II: the polymerase chain reaction. In Molecular Systematics, 2nd Edition (eds. Hillis, D. M. et al.) 205–247 (Sinauer, 1996).Vences, M., Thomas, M., Van Der Meijden, A., Chiari, Y. & Vieites, D. R. Comparative performance of the 16S rRNA gene in DNA barcoding of amphibians. Front. Zool. 2, 5 (2005).Pounds, J. A. & Crump, M. L. Amphibian declines and climate disturbance: the case of the golden toad and the harlequin frog. Conserv. Biol. 8, 72–85 (1994).
Google Scholar 
R Core Team. R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. https://www.R-project.org/ (2021).Hutchinson, R. A., Valente, J. J., Emerson, S. C., Betts, M. G. & Dietterich, T. G. Penalized likelihood methods improve parameter estimates in occupancy models. Methods Ecol. Evol. 6, 949–959 (2015).
Google Scholar 
Clipp, H. L., Evans, A. L., Kessinger, B. E., Kellner, K., & Rota, C. T. A penalized likelihood for multispecies occupancy models improves predictions of species interactions. Ecology 102, e03520 (2021).PubMed 

Google Scholar  More

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

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

NEWS
01 April 2022

Tropical forests have big climate benefits beyond carbon storage

Study finds that trees cool the planet by one-third of a degree through biophysical mechanisms such as humidifying the air.

Freda Kreier

Freda Kreier

View author publications

You can also search for this author in PubMed
 Google Scholar

Twitter

Facebook

Email

Tropical forests create cloud cover that reflects sunlight and cools the air.Credit: Thomas Marent/Minden Pictures

Tropical forests have a crucial role in cooling Earth’s surface by extracting carbon dioxide from the air. But only two-thirds of their cooling power comes from their ability to suck in CO2 and store it, according to a study1. The other one-third comes from their ability to create clouds, humidify the air and release cooling chemicals.
How much can forests fight climate change?
This is a larger contribution than expected for these ‘biophysical effects’ says Bronson Griscom, a forest climate scientist at the non-profit environmental organization Conservation International, headquartered in Arlington, Virginia. “For a while now, we’ve assumed that carbon dioxide alone is telling us essentially all we need to know about forest–climate interactions,” he says. But this study confirms that tropical forests have other significant ways of plugging into the climate system, he says.The analysis, published in Frontiers in Forests and Global Change on 24 March1, could enable scientists to improve their climate models, while helping governments to devise better conservation and climate strategies.The findings underscore growing concerns about rampant deforestation across the tropics. Scientists warn that one-third of the world’s tropical forests have been mown down in the past few centuries, and another one-third has been degraded by logging and development. This, when combined with climate change, could transform vast swathes of forest into grasslands2.“This study gives us even more reasons why tropical deforestation is bad for the climate,” says Nancy Harris, forest-research director at the World Resources Institute in Washington DC.More than a carbon spongeForests are major players in the global carbon cycle because they soak up CO2 from the atmosphere as they grow. Tropical forests, in particular, store around one-quarter of all terrestrial carbon on the planet, making them “centrepieces for climate policy” in their home countries, Griscom says.
Tropical forests may be carbon sources, not sinks
“There’s clear evidence that the tropics are producing excellent climate benefits for the entire planet,” says Deborah Lawrence, an environmental scientist at the University of Virginia in Charlottesville and a co-author of the latest study. She and her colleagues analysed the cooling capacity of forests around the globe, in particular considering biophysical effects alongside carbon storage. Tropical forests, they found, can cool Earth by a whole 1 °C — and biophysical effects contribute significantly.Although scientists knew about these effects, they hadn’t understood to what extent the various factors counter global warming.Trees in the tropics provide shade, but they also act as giant humidifiers by pulling water from the ground and emitting it from their leaves, which helps to cool the surrounding area in a way similar to sweating, Griscom says.“If you go into a forest, it immediately is a considerably cooler environment,” he says.This transpiration, in turn, creates the right conditions for clouds, which like snow and ice in the Arctic, can reflect sunlight higher into the atmosphere and further cool the surroundings. Trees also release organic compounds — for example, pine-scented terpenes — that react with other chemicals in the atmosphere to sometimes create a net cooling effect.Locally coolTo quantify these effects, Lawrence and her colleagues compared how the various effects of forests around the world feed into the climate system, breaking down their contributions in bands of ten degrees of latitude. When they considered only the biophysical effects, the researchers found that the world’s forests collectively cool the surface of the planet by around 0.5 °C.
When will the Amazon hit a tipping point?
Tropical forests are responsible for most of that cooling. But this band of trees across Latin America, Central Africa and southeast Asia is under increasing pressure from climate change and deforestation. Both of these human-caused impacts can lead rainforests to dry out, says Christopher Boulton, a geographer at the University of Exeter, UK. Last month, he and his colleagues published a review2 of nearly 30 years’ worth of satellite images of the Amazon, the largest rainforest in the world. By measuring the biomass of the vegetation in the images, the team discovered that three-quarters of the Amazon is losing resilience — the ability to recover from an extreme weather event such as a drought.Threats to tropical rainforests are dangerous not only for the global climate, but also for communities that neighbour the forests, Lawrence says. She and her colleagues found that the cooling caused by biophysical effects was especially significant locally. Having a rainforest nearby can help to protect an area’s agriculture and cities from heatwaves, Lawrence says. “Every tenth of a degree matters in limiting extreme weather. And where you have forests, the extremes are minimized.”Governments across the tropics have struggled to conserve their forests despite more than two decades of global campaigns to halt deforestation, promote sustainable development and protect the climate. Lawrence says that her team’s findings make it clear that protecting forests is a matter of self-interest, and has immediate benefits for local communities.

doi: https://doi.org/10.1038/d41586-022-00934-6

ReferencesLawrence, D., Coe, M., Walker, W., Verchot, L. & Vandecar, K. Front. For. Glob. Change https://doi.org/10.3389/ffgc.2022.756115 (2022).Article 

Google Scholar 
Boulton, C. A., Lenton, T. M. & Boers, N. Nature Clim. Change 12, 271–278 (2022).Article 

Google Scholar 
Download references

Related Articles

How much can forests fight climate change?

When will the Amazon hit a tipping point?

Tropical forests may be carbon sources, not sinks

Illegal mining in the Amazon hits record high amid Indigenous protests

Subjects

Climate sciences

Climate change

Conservation biology

Latest on:

Climate sciences

Funding battles stymie ambitious plan to protect global biodiversity
News 31 MAR 22

Trends in Europe storm surge extremes match the rate of sea-level rise
Article 30 MAR 22

From the archive: fishy business in 1972 and 1922
News & Views 29 MAR 22

Climate change

Funding battles stymie ambitious plan to protect global biodiversity
News 31 MAR 22

Trends in Europe storm surge extremes match the rate of sea-level rise
Article 30 MAR 22

The race to upcycle CO2 into fuels, concrete and more
News Feature 29 MAR 22

Jobs

Postdoctoral Fellow in Electrochemical CO2 reduction

The University of British Columbia (UBC)
Kelowna, Canada

Staff Member in Project Coordination (for 19 h/week)

Jülich Research Centre (FZJ)
Erlangen-Nürnberg, Germany

Student assistant IT administration (m/f/d)

Alfred Wegener Institute – Helmholtz Centre for Polar and Marine Research (AWI)
Potsdam, Germany

Physicist (postdoctoral researcher) (all genders) in the field of Theoretical Astrophysics

Helmholtz Centre for Heavy Ion Research GmbH (GSI)
Darmstadt, Germany More

To explore how environmental gradients shape the distribution of cyanophages and picocyanobacteria, we conducted high-resolution surveys in surface waters along five oceanic transects on three cruises covering thousands of kilometres in the North Pacific Ocean in the spring or early summer of 2015, 2016 and 2017 (Fig. 1a–c). These cruises, two of which were out-and-back, passed through distinct regimes from warm, saline and nutrient-poor waters of the North Pacific Subtropical Gyre to cooler, less saline and nutrient-rich waters of higher latitudes influenced by the subpolar gyre (Fig. 1d–i)27. The shift between the two gyres was marked by abrupt changes in trophic indicators such as particulate carbon concentrations (Fig. 1g) and a chlorophyll front (defined as the 0.2 mg m−3 chlorophyll contour28; Fig. 1a–c). As such, the inter-gyre transition zone, defined by salinity and temperature thresholds29 (Fig. 1d), was distinct from both the subtropical and subpolar gyre ecosystems28.Fig. 1: Gradients in environmental conditions across the North Pacific gyres.a–c, Transects of three cruises overlaid on monthly averaged satellite-derived sea-surface chlorophyll in March 2015 (a), April 2016 (b) and June 2017 (c). d, Temperature–salinity diagram showing the boundaries of the subtropical and subpolar gyres (black dashed lines) based on the salinity thresholds reported by Roden29. e–i, Temperature (e), salinity (f) as well as the levels of particulate carbon (g), phosphate (h) and nitrate + nitrite (i) as a function of latitude. The coloured dashed lines show the position of the 0.2 mg m−3 chlorophyll contour. For environmental variables plotted against temperature, see Supplementary Fig. 3.Full size imageUnexpected Prochlorococcus declineProchlorococcus concentrations in the oligotrophic waters of the subtropical gyre were 1.5–3.0 × 105 cells ml−1, comprising an average of approximately 29% of the total bacteria (Extended Data Fig. 1) and numerically dominating the phytoplankton community in all three cruises (Extended Data Fig. 2). Prochlorococcus abundance remained high in the southern region of the transition zone in 2015 and 2016, decreasing precipitously to less than 2,000 cells ml−1 north of the chlorophyll front, generally constituting 80% of cyanophages measured, with the remainder consisting of T7-like clade A and TIM5-like cyanophages (Fig. 3 and Extended Data Fig. 4). Cyanophage abundances correlated positively with total picocyanobacteria in the subtropical gyre (Pearson’s coefficient of multiple correlation (r) = 0.54, P = 0.02, n = 26; Fig. 2d), suggesting that cyanophages were limited by the availability of susceptible hosts in this region and were not regulating picocyanobacterial populations. On average, less than 1% of the cyanobacterial populations were infected (Fig. 4), with higher infection rates by T4-like cyanophages than T7-like cyanophages (Extended Data Figs. 5 and 6). These instantaneous measurements of infection were used to estimate the daily rates of mortality39 (Methods and Supplementary Discussion), which suggests that 0.5–6% of picocyanobacterial populations were lysed by viruses each day (Extended Data Fig. 7). This implicates other factors, such as grazing45, as the major causes of cyanobacterial mortality in the North Pacific Subtropical Gyre.Fig. 3: Cyanophage community composition across the North Pacific gyres.a–c, Cyanophage abundance for the March 2015 (a), April 2016 (b) and June 2017 (c) transects. Insets: T7-like clade A and TIM5-like cyanophage abundances on an expanded scale (similar to the main images, the units for the vertical axes are ×105 viruses ml−1). The grey shaded regions show the position of the virus hotspot. See Extended Data Fig. 4 for the confidence intervals and out-and-back reproducibility and Supplementary Fig. 4 for cyanophage lineages plotted against latitude.Full size imageFig. 4: Viral infection patterns of picocyanobacteria in the North Pacific Ocean.a–f, Viral infection levels (black) of Prochlorococcus (a,c,e) and Synechococcus (b,d,f) plotted against temperature for the March 2015 (a,b), April 2016 (c,d) and June 2017 (e,f) transects. Insets: infection levels on an expanded scale. The solid lines show infection (red), Prochlorococcus (green) and Synechococcus (pink) averaged and plotted for every 0.5 °C. The dashed lines and shaded regions show the position of the chlorophyll front and the virus hotspot, respectively. For plots by latitude and the upper and lower bounds of infection, see Extended Data Figs. 5 and 6.Full size imageWithin the transition zone we observed a steep latitudinal increase in the abundance of cyanophages for every transect, which we define as a cyanophage hotspot (Fig. 2c and Extended Data Figs. 2 and 4). The cyanophage abundances in this hotspot were between three- and tenfold greater than in the subtropical gyre (Fig. 2c). Notably, cyanophages were approximately 25% more abundant (an increase of approximately 5 × 105 viruses ml−1) in the hotspot on the 2017 cruise relative to the other two cruises, reaching a maximum of 2 × 106 viruses ml−1. The hotspot peaked at temperatures of 15–16 °C on all transects, regardless of the geographical location, season or the exact pattern of the Prochlorococcus and Synechococcus distributions (Fig. 2c). Notably, the numbers of T7-like clade B cyanophages increased sharply in the transition zone to become the most abundant lineage, whereas T4-like cyanophages increased more modestly (Fig. 3 and Extended Data Fig. 4). The change in the cyanophage community structure was particularly pronounced in June 2017, when T7-like cyanophages were up to 2.3-fold more abundant than T4-like cyanophages (Fig. 3c). The switch in the relative abundance of T4-like and T7-like clade B cyanophages was diagnostic of the cyanophage hotspot compared with patterns in the subtropical and subpolar gyres.To begin assessing whether cyanophages negatively affected cyanobacterial populations in the hotspot, we tested the relationship between the abundance of cyanophages and total cyanobacteria. This showed a significant negative correlation between cyanophage and cyanobacterial abundances across all three cruises (Pearson’s r = −0.56, two-sided P = 0.0005, n = 34). This relationship was particularly distinct in 2017, when cyanobacteria were at their overall lowest abundances and cyanophages at their highest (Pearson’s r = −0.65, two-sided P = 0.004, n = 18). This suggests that viruses are one of the key regulators of picocyanobacteria in the region of the hotspot. However, no significant correlation was found across all regimes and all years (Pearson’s r = −0.008, two-sided P = 0.9, n = 87; Fig. 2d), indicating that factors other than viruses are likely to be more important in regulating the abundances of cyanobacteria in other regimes.Our single-cell infection measurements allowed us to directly evaluate active viral infection and its impact on picocyanobacteria in the transition zone. Viral infection spiked in this region each year with infection levels that were an average of two- to ninefold higher than those in the subtropical gyre (Fig. 4 and Extended Data Figs. 5,6 and 8). Infection peaked within the temperature range of 12–18 °C and was associated with a concomitant dip in Prochlorococcus abundances in all three cruises (Fig. 4 and Extended Data Fig. 5). These findings provide independent support for the strong negative correlation between cell and virus abundances (Fig. 2d) being the result of virus-induced mortality.Lineage-specific infection was also distinct in the transition zone relative to the subtropical gyre. Infection by T7-like clade B cyanophages generally increased to reach (2015 and 2016) or exceed (2017) those of T4-like cyanophages (Extended Data Figs. 5 and 6). In addition, the ratio of the abundances of T7-like clade B cyanophages to the number of cells they infected was 2.6-fold greater in the hotspot than the subtropics, whereas this ratio was similar in both regions for T4-like cyanophages. Together, these results indicate that, within the hotspot, the T4-like cyanophages displayed increased levels of infection, whereas the T7-like cyanophages displayed both increased levels of infection and produced more viruses per infection, suggesting that T7-like clade B cyanophages are better adapted to conditions in the transition zone (see below).Of the three cruises, the highest levels of viral infection were observed in June 2017, with up to 9.5% and 8.9% of Prochlorococcus and Synechococcus infected, respectively (Fig. 4e,f). This dramatic increase in infection mirrored the massive decline in Prochlorococcus abundances (Fig. 4e and Extended Data Fig. 5i). We estimate that viruses killed 10–30% of Prochlorococcus and Synechococcus cells daily at these high instantaneous levels of infection (Extended Data Fig. 6) based on the expected number of infection cycles cyanophages were able to complete at the light and temperature conditions in the transition zone (Methods and Supplementary Discussion). Given that Prochlorococcus is estimated to double every 2.8 ± 0.8 d at the low temperatures in this region12, we estimate that 21–51% of the population was infected and killed in the interval before cell division. Synechococcus is expected to have faster growth rates at these temperatures, doubling every 1.1 ± 0.2 d (refs. 12,46). Thus, we estimate that less of the Synechococcus population (9–31%) was killed before division.Under quasi-steady state conditions, abiotic controls on the growth rate of Prochlorococcus are balanced by mortality due to viral lysis, grazing and other mortality agents39,45,47. Based on the high levels of virus-mediated mortality, the parallel pattern between Prochlorococcus’ death and viral infection, and the negative correlation between cyanophage and picocyanobacterial abundances in the transition zone, we propose that enhanced viral infection in 2017 disrupted this balance, leading to the unexpected decline in Prochlorococcus populations. Grazing and other mortality agents not investigated here could also have contributed to additional mortality beyond the steady state, resulting in further losses of Prochlorococcus. In contrast to Prochlorococcus, Synechococcus maintained large populations despite high levels of infection (Fig. 4f), presumably due to their faster growth rates enabling them to maintain a positive net growth despite enhanced mortality. These findings suggest that virus-mediated mortality in 2017 was an important factor in limiting the geographic range of Prochlorococcus that resulted in a massive loss of habitat of approximately 550 km.Cyanophage abundances and infection levels dropped sharply in the higher-latitude waters north of the hotspot (Figs. 2c, 4 and Extended Data Figs. 1d,h and 2). The abundances of both T7-like clade B and T4-like cyanophages declined precipitously, yet T4-like cyanophages were the dominant cyanophage lineage (Fig. 3). T7-like clade A cyanophages generally increased locally at the northern border of the hotspot and became the dominant T7-like lineage in two samples between 38 and 39.2° N in 2017 (Fig. 3c and Extended Data Fig. 4). In contrast to all other cyanophages, the abundances of TIM5-like cyanophages increased in waters north of the hotspot (Fig. 3 and Extended Data Fig. 4d,i,m) but remained a minor component of the cyanophage community. No relationship was found between cyanophage and cyanobacterial abundances (Fig. 2d), and less than 1.5% of picocyanobacteria were infected by all cyanophage lineages in these waters (Fig. 4).The cyanophage hotspot in the transition zone is a ridge of high virus activity that separates the subtropical and subpolar gyres. The reproducibility of our observations, which were separated by days to weeks within each cruise (2016 and 2017) and by years among the three cruises (Extended Data Fig. 4), indicates that this virus hotspot is a recurrent feature at the boundary of these two major gyres in the North Pacific Ocean. This suggests that the hotspot forms due to the distinctive environment of the inter-gyre transition zone creating conditions that enhance infection of picocyanobacteria and proliferation of cyanophages. Prochlorococcus in the transition zone may be prone to stress due to being close to the limits of their temperature growth range5,6, which has the potential to increase susceptibility to viral infection. Alternatively, there may be temperature-dependent trade-offs between virus decay and production that lead to replication optima within a narrow temperature range48. Cyanophage infectivity has been observed to decay more slowly at colder temperatures49, which may allow for the accumulation of infective viruses, leading to increased infection. In addition, cyanophage infections may be more productive due to enhanced nutrient supply in the transition zone27 (Fig. 1h,i) relative to the subtropics, given that the cyanophages replicate in hosts with presumably greater intracellular nutrient quota and obtain more extracellular nutrients, both of which may increase progeny production9,10. The environmental factors influencing the production and removal of viruses probably vary in intensity at different times, leading to variability in cyanophage abundance and infection levels. Thus, the putative cyanophage replication optimum in the hotspot may reflect the combined effects of temperature and nutrient conditions that are intrinsically linked to the oceanographic forces that shape the transition zone itself.Changes in the cyanophage community structure over environmental gradients are likely to reflect differences in host range, infection properties and genomic potential to remodel host metabolism9. Our data, together with previous measurements in the North Pacific Subtropical Gyre38,39, indicate that the T4-like cyanophages are the lineage best adapted to the low-nutrient waters of the subtropics (Fig. 2d–f). As these waters are inhabited by hundreds of genomically diverse subpopulations of Prochlorococcus50, the broad host range of many T4-like cyanophages18,19,22,51 may be advantageous for finding a suitable host. T4-like cyanophages also have a large and diverse repertoire of host-derived genes21,51—such as nutrient acquisition, photosynthesis and carbon-metabolism genes—that augment host metabolism52 and may increase fitness in nutrient-poor conditions in the subtropics51. In contrast, T7-like clade A and B cyanophages seem to be better adapted to conditions in the transition zone (Fig. 3). T7-like cyanophages have narrow host ranges19,22,40, with smaller genomes and fewer genes to manipulate the host metabolism23, which may allow them to replicate and produce more progeny in regions with elevated nutrient concentrations relative to subtropical conditions. The maximal abundances of TIM5-like cyanophages were found in the most productive waters at the northern end of the transects where the cyanobacterial abundances were lowest and Synechococcus was the dominant picocyanobacterium. This may be partially due to the narrow host range of TIM5-like cyanophages and their specificity for Synechococcus40,44. Our findings of reproducible lineage-specific responses to changing ocean regimes indicate that cyanophage lineages occupy distinct ecological niches.Temperature and nutrient changes occurring in the transition zone are expected to result in shifts in picocyanobacterial diversity at the sub-genus level (Supplementary Discussion), which we speculate may affect community susceptibility to viral infection. One mechanism for this may be that the picocyanobacteria that thrive in the transition zone are intrinsically more susceptible to viral infection. Another scenario may be related to trade-offs associated with the evolution of resistance to viral infection. The horizontal advection of nutrient-rich waters to the transition zone28 may select for rapidly growing cells adapted for efficient resource utilization. Viral resistance in picocyanobacteria often incurs the cost of reduced growth rates53,54. Thus, competition for nutrients in this region may favour cells with faster growth rates but increased susceptibility to viral infection. Thus, it is probable that the cyanophage distributions do not always follow the cyanobacterial patterns (Extended Data Fig. 2) because of complex interactions between lineage-specific cyanophage traits, host community structure and environmental variables, which may vary seasonally or annually as a result of interannual variability in environmental conditions (see below).Despite consistent features in cyanophage distributions across the North Pacific Ocean, cyanophage infection was higher (Fig. 4 and Extended Data Fig. 7), whereas Prochlorococcus abundances were consistently lower (Fig. 2a), across the June 2017 transects relative to the March 2015 and April 2016 transects. Seasonality and/or climate variability could explain this interannual variability, although the data currently available to assess this are sparse. Viral infection of picocyanobacteria in the subtropical gyre increased from early spring to summer, suggesting a potential seasonal pattern that may extend across the transect (Extended Data Fig. 9a). In addition, the June 2017 transect occurred during a neutral-to-negative El Niño phase with lower sea-surface temperatures relative to the 2015 and 2016 transects, which were in years of a record marine heatwave, followed by a strong El Niño55 (Extended Data Fig. 9b). In 2015 and 2016, the Prochlorococcus abundances were found to be higher than usual in the North Pacific Ocean in this (Fig. 2a) and other studies56,57. Irrespective of the underlying drivers for the observed interannual variability, we speculate that an ecosystem tipping point was reached in the hotspot under the prevailing conditions in June 2017, aided by the higher cyanophage abundances yet smaller Prochlorococcus population sizes. In this scenario, picocyanobacterial populations were subjected to high infection levels that resulted in an accumulation of cyanophages, initiating a stronger than usual positive-feedback loop between infection and virus production, and precipitating the unexpected Prochlorococcus decline. Continued observations in the North Pacific Ocean are needed to evaluate the potential link between seasonality and/or large-scale climate forcing as ultimate drivers affecting virus–host interactions.Predicting basin-scale virus dynamicsMeasurements of cyanobacterial and cyanophage abundances rely on discrete sample collection from shipboard oceanographic expeditions, which limits the geographical and seasonal extent of available data. Therefore, we developed a multiple regression model based on high-resolution satellite data of temperature and chlorophyll to predict cyanophage abundances, a key proxy of cyanobacterial infection (Pearson’s r = 0.61, two-sided P = 1.7 × 10−8, degrees of freedom = 68, n = 70). We used the model to estimate the geographical extent of the virus hotspot. The model accurately predicted the location of the hotspot and cyanophage abundances along a fourth transect in April 2019 (Supplementary Table 1), with the majority of observations falling within the 95% confidence intervals of the model predictions (Fig. 5a–c). Application of the model to the larger region predicted that the virus hotspot formed a boundary extending across the North Pacific Ocean, with lower cyanophage abundances on both sides (Fig. 5d,e and Supplementary Fig. 1). This boundary had the hallmarks of the hotspot with a core that was dominated by T7-like cyanophages and the flanking gyre regions dominated by T4-like cyanophages. Thus, this feature may be more appropriately termed a ‘hot-zone’ due to its substantial projected aerial extent. Assuming the infection levels observed in the hotspot in June 2017 were similar throughout the hot-zone, the potential habitat loss for Prochlorococcus would be about 3.2 × 106 km2, approximately half of the cumulative area loss of the Amazonian rainforest to date58.Fig. 5: Prediction of cyanophage abundances.a–c, Model-based predictions of cyanophage abundances corresponding to the empirically measured total (a), T4-like (b) and T7-like clade B (c) cyanophage abundances along a transect in the North Pacific in April 2019. The shaded regions show the 95% confidence interval for the model predictions. d,e, Predicted total cyanophages (d) and the ratio of T4-like/T7-like clade B cyanophages (e) in June 2017 in the North Pacific Ocean. The black lines indicate the cruise track. The grey areas represent regions with no values due to cloud cover or that were beyond the limits of the predictive model. The hotspot peak corresponds to yellow regions in d and red regions in e.Full size imageVirus hotspot biogeochemistryWith the ability to predict biogeographic patterns of cyanophages, we evaluated the potential biogeochemical implications of virus-mediated picocyanobacterial lysis and release of organic material in sustaining the bacterial community6,7,8,9. The aerial extent of the hot-zone (approximately 4 × 106 km2) is only 14% of the size of the subtropical gyre (2.9 × 107 km2), and yet the total virus-mediated organic matter released from picocyanobacteria in the hot-zone in June 2017 was estimated to be on par with that for the entire North Pacific Subtropical Gyre (Methods and Supplementary Discussion). We estimate that viral lysate released from picocyanobacteria in the subtropical gyre could sustain 4.4 ± 0.8% of the calculated bacterial carbon demand there (Extended Data Fig. 10). In contrast, viral lysate released in the transition zone could sustain an average of 21 ± 12% of the bacterial carbon demand, reaching 33% in some regions (Extended Data Fig. 10), assuming that the bacterial assimilation and growth efficiencies were similar between the subtropical gyre and the hotspot. Thus, local generation of cyanobacterial viral lysate in the transition zone is likely to be an important source of carbon for the heterotrophic bacterial community that can rapidly utilize large molecular weight dissolved organic matter59 and may have contributed to the increase in their abundances south of the chlorophyll front in 2017 (Extended Data Fig. 1a,e). More

Flores, C. O. et al. Proc. Natl Acad. Sci. USA 108, 288–297 (2011).Article 

Google Scholar 
Carlson, M. C. G. et al. Nat. Microbiol. https://doi.org/10.1038/s41564-022-01088-x (2022).Article 

Google Scholar 
Flombaum, P. et al. Proc. Natl Acad. Sci. USA 110, 9824–9829 (2013).CAS 
Article 

Google Scholar 
Coleman, M. L. & Chisholm, S. W. Trends Microbiol. 15, 398–407 (2007).CAS 
Article 

Google Scholar 
Johnson, Z. I. et al. Science 311, 1737–1740 (2006).CAS 
Article 

Google Scholar 
Martiny, A. C. et al. PLoS ONE 11, e0168291 (2016).Article 

Google Scholar 
Wilhelm, S. W. & Suttle, C. A. Bioscience 49, 781–788 (1999).Article 

Google Scholar 
Follett, C. L. et al. Proc. Natl Acad. Sci. USA 119, e2110993118 (2022).CAS 
Article 

Google Scholar 
Mojica, K. D. A. et al. ISME J. 10, 500–513 (2016).CAS 
Article 

Google Scholar 
Mruwat, N. et al. ISME J. 15, 41–54 (2021).CAS 
Article 

Google Scholar  More

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

Google Scholar 
Intergovernmental Panel on Climate Change. Climate Change 2013: 5th Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2013).
Google Scholar 
Torres, O., Kwiatkowski, L., Sutton, A. J., Dorey, N. & Orr, J. C. Characterizing mean and extreme diurnal variability of ocean CO2 system variables across marine environments. Geophys. Res. Lett. 48, 2 (2021).
Google Scholar 
Dorey, N., Lançon, P., Thorndyke, M. & Dupont, S. Assessing physiological tipping point of sea urchin larvae exposed to a broad range of pH. Glob. Change Biol. 19, 3355–3367 (2013).
Google Scholar 
Hauri, C. et al. Spatiotemporal variability and long-term trends of ocean acidification in the California current system. Biogeosci. Discuss. 9, 10371–10428 (2012).ADS 

Google Scholar 
Dupont, S. & Pörtner, H.-O. A snapshot into ocean acidification research. Mar. Biol. 160, 1765–1771 (2013).CAS 

Google Scholar 
Dupont, S. & Thorndyke, M. Chapter: Direct impacts of near-future ocean acidification on sea urchins. in Climate Change Perspective from the Atlantic: Past, Present and Future (eds. Fernández-Palacios, J. et al.) 461–485 (2013).Byrne, M. & Hernández, J. C. Chapter 16: Sea urchins in a high CO2 world: Impacts of climate warming and ocean acidification across life history stages. in Developments in Aquaculture and Fisheries Science vol. 43 281–297 (Elsevier, 2020).Kroeker, K. J., Kordas, R. L., Crim, R. N. & Singh, G. G. Meta-analysis reveals negative yet variable effects of ocean acidification on marine organisms. Ecol. Lett. 13, 1419–1434 (2010).PubMed 

Google Scholar 
L. Kelley, A., J. Lunden, J., 1 Ocean Acidification Research Center, College of Fisheries and Ocean Sciences, University of Alaska, Fairbanks, Fairbanks, AK, 99775, USA, & 2 Haverford College, Haverford, PA, 19041, USA. Meta-analysis identifies metabolic sensitivities to ocean acidification. AIMS Environ. Sci. 4, 709–729 (2017).Stumpp, M. et al. Acidified seawater impacts sea urchin larvae pH regulatory systems relevant for calcification. Proc. Natl. Acad. Sci. U. S. A. 109, 18192–18197 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stumpp, M. et al. Digestion in sea urchin larvae impaired under ocean acidification. Nat. Clim. Change 3, 1044–1049 (2013).ADS 
CAS 

Google Scholar 
Runcie, D. E. et al. Genomic characterization of the evolutionary potential of the sea urchin Strongylocentrotus droebachiensis facing ocean acidification. Genome Biol. Evol. 8, 272 (2017).
Google Scholar 
Sewell, M. Utilization of lipids during early development of the sea urchin Evechinus chloroticus. Mar. Ecol. Prog. Ser. 304, 133–142 (2005).ADS 
CAS 

Google Scholar 
Lucas, M. I., Walker, G., Holland, D. L. & Crisp, D. J. An energy budget for the free-swimming and metamorphosing larvae of Balanus balanoides (Crustacea: Cirripedia). Mar. Biol. 55, 221–229 (1979).
Google Scholar 
Shilling, F. M., Hoegh-Guldberg, O. & Manahan, D. T. Sources of energy for increased metabolic demand during metamorphosis of the abalone Haliotis rufescens (Mollusca). Biol. Bull. 191, 402–412 (1996).CAS 
PubMed 

Google Scholar 
Meidel, S. K. & Scheibling, R. E. Effects of food type and ration on reproductive maturation and growth of the sea urchin Strongylocentrotus droebachiensis. Mar. Biol. 134, 155–166 (1999).
Google Scholar 
Pearce, C. M. & Scheibling, R. E. Induction of metamorphosis of larvae of the green sea urchin, Strongylocentrotus droebachiensis by coralline red algae. Biol. Bull. 179, 304–311 (1990).CAS 
PubMed 

Google Scholar 
Gosselin, P. & Jangoux, M. From competent larva to exotrophic juvenile: a morphofunctional study of the perimetamorphic period of Paracentrotus lividus (Echinodermata, Echinoida). Zoomorphology 118, 31–43 (1998).
Google Scholar 
Hinegardner, R. T. Growth and development of the laboratory cultured sea urchin. Biol. Bull. 137, 465–475 (1969).CAS 
PubMed 

Google Scholar 
Strathmann, R. R. Length of pelagic period in echinoderms with feeding larvae from the Northeast Pacific. J. Exp. Biol. Ecol. 34, 23–27 (1978).
Google Scholar 
Byrne, M. et al. Unshelled abalone and corrupted urchins: Development of marine calcifiers in a changing ocean. Proc. Biol. Sci. 278, 2376–2383 (2011).PubMed 

Google Scholar 
Dupont, S., Dorey, N., Stumpp, M., Melzner, F. & Thorndyke, M. Long-term and trans-life-cycle effects of exposure to ocean acidification in the green sea urchin Strongylocentrotus droebachiensis. Mar. Biol. 160, 1835–1843 (2013).CAS 

Google Scholar 
Uthicke, S. et al. Impacts of ocean acidification on early life-history stages and settlement of the coral-eating sea star Acanthaster planci. PLoS ONE 8, e82938 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar 
Dupont, S., Lundve, B. & Thorndyke, M. Near future ocean acidification increases growth rate of the lecithotrophic larvae and juveniles of the sea star Crossaster papposus. J. Exp. Zool. Mol. Dev. Evol. 314, 382–389 (2010).
Google Scholar 
Lim, Y.-K., Dang, X. & Thiyagarajan, V. Transgenerational responses to seawater pH in the edible oyster, with implications for the mariculture of the species under future ocean acidification. Sci. Total Environ. 782, 146704 (2021).ADS 
CAS 
PubMed 

Google Scholar 
Hettinger, A. et al. Persistent carry-over effects of planktonic exposure to ocean acidification in the Olympia oyster. Ecology 93, 2758–2768 (2012).PubMed 

Google Scholar 
Hettinger, A. et al. Larval carry-over effects from ocean acidification persist in the natural environment. Glob. Change Biol. https://doi.org/10.1111/gcb.12307 (2013).Article 

Google Scholar 
Albright, R. & Langdon, C. Ocean acidification impacts multiple early life history processes of the Caribbean coral Porites astreoides. Glob. Change Biol. 17, 2478–2487 (2011).ADS 

Google Scholar 
Yuan, X. et al. Elevated CO2 delays the early development of scleractinian coral Acropora gemmifera. Sci. Rep. 8, 2787 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Maboloc, E. A. & Chan, K. Y. K. Parental whole life cycle exposure modulates progeny responses to ocean acidification in slipper limpets. Glob. Change Biol. 2, 15647. https://doi.org/10.1111/gcb.15647 (2021).Article 

Google Scholar 
Mos, B., Byrne, M. & Dworjanyn, S. A. Effects of low and high pH on sea urchin settlement, implications for the use of alkali to counter the impacts of acidification. Aquaculture 528, 735618 (2020).CAS 

Google Scholar 
Harianto, J., Aldridge, J., Torres Gabarda, S. A., Grainger, R. J. & Byrne, M. Impacts of acclimation in warm-low pH conditions on the physiology of the sea urchin Heliocidaris erythrogramma and carryover effects for juvenile offspring. Front. Mar. Sci. 7, 588938 (2021).
Google Scholar 
Houlihan, E. P., Espinel-Velasco, N., Cornwall, C. E., Pilditch, C. A. & Lamare, M. D. Diffusive boundary layers and ocean acidification: Implications for sea urchin settlement and growth. Front. Mar. Sci. 7, 577562 (2020).
Google Scholar 
Norderhaug, K. M. & Christie, H. C. Sea urchin grazing and kelp re-vegetation in the NE Atlantic. Mar. Biol. Res. 5, 515–528 (2009).
Google Scholar 
Dickson, A., Sabine, C. L. & Christian, J. R. Guide to best practices for ocean CO2 measurements. (PICES Special Publication 3;191 pp, 2007).Lavigne, H. & Gattuso, J.-P. seacarb: seawater carbonate chemistry with R. R package version 2.4. http://CRAN.R-project.org/package=seacarb. (2011).R Core Team. R: A language and environment for statistical computing. R: A language and environment for statistical computing (2017).Guillard, R. R. L. & Ryther, J. H. Studies of marine planktonic diatoms. I. Cyclotella nana Hustedt and Detonula confervacea (Cleve) Gran. Can. J. Microbiol. 8, 229–239 (1962).CAS 
PubMed 

Google Scholar 
Stumpp, M., Wren, J., Melzner, F., Thorndyke, M. & Dupont, S. CO2 induced seawater acidification impacts sea urchin larval development I: Elevated metabolic rates decrease scope for growth and induce developmental delay. Comp. Biochem. Physiol. Mol. Integr. Physiol. 160, 331–340 (2011).CAS 

Google Scholar 
His, E., Heyvang, I., Geffard, O. & De Montaudouin, X. A comparison between oyster (Crassostrea gigas) and sea urchin (Paracentrotus lividus) larval bioassays for toxicological studies. Water Res. 33, 1706–1718 (1999).CAS 

Google Scholar 
U. S. National Institutes of Health, Bethesda, Maryland, U. ImageJ, Rasband, W.S., http://imagej.nih.gov/ij/.Smith, M. M., Cruz Smith, L., Cameron, R. A. & Urry, L. The larval stages of the sea urchin, Strongylocentrotus purpuratus. J. Morphol. 269, 713–733 (2008).PubMed 

Google Scholar 
Kahm, M., Hasenbrink, G., Lichtenberg-Frate, H., Ludwig, J. & Kschischo, M. grofit: Fitting Biological Growth Curves with R. J. Stat. Softw., 33(7), 1–21. URL http://www.jstatsoft.org/v33/i07/. (2010).Pinheiro, J., Bates, D., & R-core. Package ‘nlme’: Linear and Nonlinear Mixed Effects Models. Cran-R (2018).Pan, T.-C.F., Applebaum, S. L. & Manahan, D. T. Experimental ocean acidification alters the allocation of metabolic energy. Proc. Natl. Acad. Sci. U. S. A. 112, 4696–4701 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jager, T., Ravagnan, E. & Dupont, S. Near-future ocean acidification impacts maintenance costs in sea-urchin larvae: Identification of stress factors and tipping points using a DEB modelling approach. J. Exp. Mar. Biol. Ecol. 474, 11–17 (2016).
Google Scholar 
Hoegh-Guldberg, O. & Emlet, R. B. Energy use during the development of a lecithotrophic and a planktotrophic echinoid. Biol. Bull. 192, 27–40 (1997).CAS 
PubMed 

Google Scholar 
Vaïtilingon, D. et al. Effects of delayed metamorphosis and food rations on the perimetamorphic events in the echinoid Paracentrotus lividus (Lamarck, 1816) (Echinodermata). J. Exp. Mar. Biol. Ecol. 262, 41–60 (2001).
Google Scholar 
García, E., Clemente, S. & Hernández, J. C. Ocean warming ameliorates the negative effects of ocean acidification on Paracentrotus lividus larval development and settlement. Mar. Environ. Res. 110, 61–68 (2015).PubMed 

Google Scholar 
Wangensteen, O. S., Dupont, S., Casties, I., Turon, X. & Palacín, C. Some like it hot: Temperature and pH modulate larval development and settlement of the sea urchin Arbacia lixula. J. Exp. Mar. Biol. Ecol. 449, 304–311 (2013).
Google Scholar 
García, E., Clemente, S. & Hernández, J. C. Effects of natural current pH variability on the sea urchin Paracentrotus lividus larvae development and settlement. Mar. Environ. Res. 139, 11–18 (2018).PubMed 

Google Scholar 
Marshall, D. J. & Keough, M. J. Variation in the dispersal potential of non-feeding invertebrate larvae: The desperate larva hypothesis and larval size. Mar. Ecol. Prog. Ser. 255, 145–153 (2003).ADS 

Google Scholar 
Huggett, M. J., Williamson, J. E., de Nys, R., Kjelleberg, S. & Steinberg, P. D. Larval settlement of the common Australian sea urchin Heliocidaris erythrogramma in response to bacteria from the surface of coralline algae. Oecologia 149, 604–619 (2006).ADS 
PubMed 

Google Scholar 
Espinel-Velasco, N., Agüera, A. & Lamare, M. Sea urchin larvae show resilience to ocean acidification at the time of settlement and metamorphosis. Mar. Environ. Res. 159, 104977 (2020).CAS 
PubMed 

Google Scholar 
Lamare, M. & Barker, M. Settlement and recruitment of the New Zealand sea urchin Evechinus chloroticus. Mar. Ecol. Prog. Ser. 218, 153–166 (2001).ADS 

Google Scholar 
Martin, S. et al. Early development and molecular plasticity in the Mediterranean sea urchin Paracentrotus lividus exposed to CO2-driven acidification. J. Exp. Biol. 214, 1357–1368 (2011).CAS 
PubMed 

Google Scholar 
Vargas, C. A. et al. Species-specific responses to ocean acidification should account for local adaptation and adaptive plasticity. Nat. Ecol. Evol. 1, 0084 (2017).
Google Scholar 
Espinel-Velasco, N. et al. Effects of ocean acidification on the settlement and metamorphosis of marine invertebrate and fish larvae: a review. Mar. Ecol. Prog. Ser. 606, 237–257 (2018).ADS 

Google Scholar 
Briffa, M., de la Haye, K. & Munday, P. L. High CO2 and marine animal behaviour: potential mechanisms and ecological consequences. Mar. Pollut. Bull. 64, 1519–1528 (2012).CAS 
PubMed 

Google Scholar 
Gaylord, B. et al. Ocean acidification through the lens of ecological theory. Ecology 96, 3–15 (2015).PubMed 

Google Scholar  More

Ebola datasetThe 2018–2020 DRC EVD outbreak lasted over 24 months and spread over 3 distinct spatial and temporal waves. Between the emergency declaration of the EVD outbreak in northern DRC on August 1, 2018 and the outbreak’s official end on June 25, 2020, the DRC Ministry of Health has reported a total of 3481 cases (including confirmed and probable), 1162 recoveries, and 2299 deaths16 in the provinces of Northern Kivu, Southern Kivu, and Ituri. The dataset considered here is a large subset of the entire EVD database compiled by the University of Kinshasa School of Public Health, which comprises 3117 total case records (confirmed and probable) recorded between May 3, 2018, and September 12, 2019. The data included partially de-identified but still detailed patient information, such as each person’s location, date of symptom onset and hospitalization, as well as discharge due to recovery or death. These individual records came from the Ebola treatment centers in 24 different health zones, spread out among the three DRC provinces of Northern Kivu, Southern Kivu, and Ituri.Of the 24 health zones, 77.1% of all cases were from only 6: Beni, Butembo, Katwa, Kalunguta, Mabalako, and Mandima. Only 9.7% of cases were under the age of 18. There is also a slightly larger proportion of females contracting the disease, comprising 57.0% of the cases. Approximately 5% of the cases were health care workers. About one-third of the EVD fatalities were not identified until patient’s death and thus not effectively isolated from the time of infection. Although over 170,000 contacts of confirmed and probable Ebola cases had been monitored across all affected health zones for 21 days after their last known exposure by the end of the epidemic, some of the contact tracing was incomplete due to insecurity that prevented public health response teams from entering some communities. The overall case density map is presented in panel (A) of Fig. 1 with the animated version of the map presented in the online appendix in Fig. A.1. Notice that the high-density areas, particularly Butembo, Katwa, and Beni, are all spatially small health zones corresponding to cities or towns with larger populations.Figure 1DRC Ebola dataset. (A) The spatial distribution of 3481 EVD cases across the northern DRC health zones during Ebola 2018–2020 outbreak. (B) The flowchart of personal records available up to September 12, 2019 available for the current analysis. The total number of available individual disease records was 3080. Map created using open software R17 with geospatial data obtained from18.Full size imageFigure 2Daily incidence and removal rates. Daily incidence (grey bars) and removal counts (red dots) during DRC Ebola 2018–2020 outbreak between August 15, 2018 and September 12, 2020 along with their respective trendlines (loess smoothers). The blue trendline above the plot represents daily effective reproduction number (mathcal{R}_t) defined as the ratio of daily number of new infections to new removals. The vertical lines indicate cut-off dates for data collection in each wave as listed in Table 1.Full size imageTable 1 Observed cases by EVD wave.Full size tableCase alerts and definitionsSince early August, 2018, the DRC Ministry of Health has been collaborating with several international partners to support and enhance EVD response activities through its emergency operations center in Goma. To the extent possible given regional security considerations19, the response teams were deployed to interview patients and their suspected contacts using a standardized case investigation form classifying cases as suspected, probable, or confirmed. A suspected case (whether surviving or not) was defined as one with the acute onset of fever (over 100(^{circ })F) and at least three Ebola-compatible clinical signs or symptoms (headache, vomiting, anorexia, diarrhea, lethargy, stomach pain, muscle or joint aches, difficulty swallowing or breathing, hiccups, unexplained bleeding, or any sudden, unexplained death) in a North Kivu, South Kivu, or Ituri resident or any person who had traveled to these provinces during this period and reported the signs or symptoms defined above. A patient who met the suspected case definition and died but from whom no specimens were available was considered a probable case. A confirmed Ebola case was defined as a suspected case with at least one positive test for Ebola virus using reverse transcription polymerase chain reaction (RT-PCR)20 testing. Patients with suspected Ebola were isolated and transported to an Ebola treatment center for confirmatory testing and treatment2.Onset and removalIn our analysis of the DRC dataset, we focused on dates of symptom onset and removal, with removal defined as either a death/recovery at home or transfer to an Ebola treatment center (ETC). It was assumed that, once in the treatment center, the probability of further infection spread by an isolated individual was very small due to the strict safety protocols—and later due also to vaccination of healthcare personnel and family members who were in contact with the suspected Ebola case. As summarized in panel (B) of Fig. 1, we were able to access 3117 out of 3481 individual records of confirmed and probable Ebola cases. Of these 3117 records, 37 were missing both the onset and recovery dates and were removed from further analysis. In about 30% of the remaining records, either their dates of onset or removal were missing. A detailed flow diagram summarizing the amount of missing data and data processing leading to the final dataset is presented in panel (B) of Fig. 1. The distribution of the original and the partially imputed records across the three waves of infection is provided for further reference in Table 1.Spatial and temporal patternsThroughout the pandemic, the incidence rates exhibited strong spatial and temporal patterns that can be summarized as three distinct waves of infections with approximate boundaries marked by vertical lines in Fig. 1. The distribution of weekly reported cases across the most affected health zones listed in Table 1 is provided in the bar plot and in the corresponding animation in the appendix (see Figure A.1). As seen from the bar chart and the animated plot, the epidemic was initially driven largely by infections in the health zones of Beni, Mandima and Mabalako. After several months, the incidence of new cases in these zones subsided, but the epidemic moved south to the health zones of Katwa and Butembo, where the majority of new infections was registered between weeks 22 to 45 of the epidemic (see Panel (A) in Figure A.1 in the online Appendix). In the final spatial shift, around week 49, the epidemic returned to the health zones of Beni, Mandima, and Mabalako, where it was mostly extinguished around week 60 (September 2019). Isolated Ebola incidences occurred sporadically across northern DRC until end of the outbreak was officially declared in June 2020.The empirical patterns of incidence and removal for EVD cases are summarized in Fig. 2 with the bar and the dot plots representing the daily numbers of new infections and removals, respectively. As seen from the plot, these daily counts closely follow a three-wave temporal pattern in Table 1. This is further evident from the black and red trendlines representing the loess smoothers (see21). The daily ratio of new cases and removals may be interpreted as a crude estimate of the effective reproduction number (mathcal{R}_t) defined more formally in (2) in Model for Data Analysis below. In particular, the blue trendline for (mathcal{R}_t) indicates that towards the end of the observed time period, the number of removals outpaced the number of new infections ((mathcal{R}_t 0) and (r_t = 0) where (beta > 0) is the rate of infection, (gamma > 0) is the rate of recovery and (rho > 0) is the initial amount of infection. In particular, the model implies the existence of the basic reproduction number (mathcal{R}_0) (R-naught), which determines the average speed of disease spread11 and is given by the formula$$mathcal{R}_0=beta /gamma .$$If (mathcal{R}_0 > 1), the proportion of infected initially rises and then subsides, with the final proposition of surviving susceptibles given by (s_infty = 1 – tau > 0) where (tau) is know as the epidemic’s final size. In typical statistical analysis, an estimate of (mathcal{R}_0) is obtained by separately estimating the parameters (beta) and (gamma). Another important quantity related to (1) is the effective reproduction number, which is typically defined as$$begin{aligned} mathcal{R}_t= mathcal{R}_0 s_t. end{aligned}$$
(2)
Although equation (1) is typically considered in the context of an average behavior of a large population, for our purposes we interpret it as defining the individual histories of infection and recovery, according to the idea of the dynamic survival analysis (DSA) discussed recently in10 and24 and also briefly summarized in the Appendix. With the DSA approach, we interpret equation (1) as the so-called stochastic master equation25 describing the change in probability of a randomly selected individual being at time t either susceptible, infected, or removed. These respective probabilities are represented by the scaled proportions (s_t/(1+rho )), (iota _t/(1+rho )), and (r_t/(1+rho )) and evolve according to (1). As outlined in10, the DSA-based interpretation of the classical SIR equations has a number of advantages that make it particularly convenient for analyzing epidemic data consisting of individual histories of infection onsets and removals, which is exactly the type of data available in the DRC Ebola dataset. The fact that the model is individual-based implies also that we can vary the parameters (theta =(beta ,gamma ,rho )) to account for individual covariates and changes in the parameter values over time, as different waves of infection sweep through the population. Finally, for the purpose of our analysis, it is also important to note that the DSA model does not require any knowledge of the size of the susceptible population subjected to the epidemic pressure. For the DRC dataset, that assumption would be difficult to justify due to spatial and temporal heterogeneity of the epidemic and the frequent movements of local populations driven by political conflicts and insecurity. Another element complicating the determination of the size of susceptible population was the ring vaccination campaign that has been conducted since 2019 wherever possible in the northern DRC during periods of relative stability, despite local mistrust and supply issues. This campaign ultimately resulted in over 250,000 vaccinations.Note that, because (s_0 = 1), the values of (mathcal{R}_0) and (mathcal{R}_t) coincide for (t = 0). Moreover, (s_t = exp left( -mathcal{R}_0 int _0^t r_u mathrm {d}u right)) is a decreasing function of time and therefore, so is (mathcal{R}_t). However, in practice, this implication is problematic. Rewriting (mathcal{R}_t = – {dot{s}}_t/ {dot{r}}_t) suggests that a crude but sensible way to estimate (mathcal{R}_t) empirically is to take the ratio of daily number of new infections to new removals. The empirical (mathcal{R}_t) thus estimated will not be necessarily monotonically decreasing. In the light of possibly changing parameters and the effective population size, we have adopted this approach to estimating the daily effective reproduction number (mathcal{R}_t) in Fig. 2.Parameter estimationWe assume that, for each of the three waves of the epidemic, we have a separate and independent set of parameters (theta) and that, in each wave, we observe (n_T) histories (records) of infection. The i-th individual history may be represented either by the times of disease onset and removal ((t_i,T_i)) or by (t_i) or (T_i) times alone ((t_i,circ )) or ((circ ,T_i)) ((circ) denoting missing value). We assume that among the available (n_T) histories we have n complete records ((t_i,T_i)), (n_1) incomplete ones ((t_i,circ )) and (n_2) incomplete ones ((circ ,T_i )). The wave-specific DSA likelihood function for n complete data records is (see Appendix)$$begin{aligned} begin{aligned} {mathcal {L}}_C(theta vert t_1ldots ,t_n,T_1,ldots ,T_n,T)=(s_T-1)^{-n}prod _{i=1}^n {dot{s}}_{t_i}gamma ^{w_i}e^{-gamma (T_i wedge T -t_i)} end{aligned} end{aligned}$$
(3)
where T is the available time horizon and (w_i) is the binary variable indicating whether (T_i) is right-censored (that is, (T_iwedge T =T)) in which case (w_i = 0) and otherwise (w_i = 1). For the remaining (n_1+n_2) records that are partially incomplete, the wave-specific DSA likelihood function is$$begin{aligned} begin{aligned} {mathcal {L}}_I(theta vert t_1ldots ,t_{n_1},T_1,ldots ,T_{n_2},T)= (s_T-1)^{-(n_1+n_2)} gamma ^{n_2}prod _{i=1}^{n_1} {dot{s}}_{t_i} prod _{i=1}^{n_2} (rho e^{-gamma T_i }-iota _{T_i}) end{aligned} end{aligned}$$
(4)
where we assume that (T_i1). Given the wave-specific time horizons (T’s), the set of parameters for each epidemic wave was estimated independently using 2 independent chains of 3000 iterations, with a burn-in period of 1000 iterations. The chains’ convergence assessed using Rubin’s R statistic28. The analysis resulted in approximate samples from the posterior distribution of (theta) for each of the three waves of the epidemic (see e.g., Fig. 4).Ethics statement on human subjects and methodsThe research was conducted in accordance with the relevant guidelines and regulations of the US law and OSU Institutional Review Board. The research activities involving human subjects discussed in the paper meet the US federal exemption criteria under 45 CFR 46 and 21 CFR 56. More