Philippa Kaur

Podos, J. Correlated evolution of morphology and vocal signal structure in Darwin’s finches. Nature 409, 185–188 (2001).ADS 
CAS 
PubMed 

Google Scholar 
Bradbury, J. W. & Vehrencamp, S. Principles of Animal Communication (Sinauer Associated, 1998).
Google Scholar 
Podos, J. & Warren, P. S. The evolution of geographic variation in birdsong. Adv. Study Behav. 37, 403–458 (2007).
Google Scholar 
Charlton, B. D., Owen, M. A. & Swaisgood, R. R. Coevolution of vocal signal characteristics and hearing sensitivity in forest mammals. Nat. Commun. 10, 1–7 (2019).CAS 

Google Scholar 
Soltis, J. Vocal communication in African elephants (Loxodonta africana). Zoo Biol. 29, 192–209 (2010).PubMed 

Google Scholar 
King, S. L. & Janik, V. M. Bottlenose dolphins can use learned vocal labels to address each other. Proc. Natl. Acad. Sci. 110, 13216–13221 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ravignani, A. et al. Ontogeny of vocal rhythms in harbor seal pups: An exploratory study. Curr. Zool. 65, 107–120 (2019).PubMed 

Google Scholar 
Rauber, R. & Manser, M. B. Effect of group size and experience on the ontogeny of sentinel calling behaviour in meerkats. Anim. Behav. 171, 129–138 (2021).
Google Scholar 
Janik, V. M. & Slater, P. J. Vocal learning in mammals. Adv. Study Behav. 26, 59–100 (1997).
Google Scholar 
Fitch, W. T. Production of vocalizations in mammals. In Encyclopedia of Language and Linguistics 2nd edn, Vol. 1 115–121 (Elsevier, 2006).
Google Scholar 
Fletcher, N. H. A frequency scaling rule in mammalian vocalization. In Handbook of Behavioral Neuroscience Vol. 19 51–56 (Elsevier, 2010).
Google Scholar 
Fant, G. Acoustic Theory of Speech Production (Mouton, 1960).
Google Scholar 
Taylor, A. & Reby, D. The contribution of source–filter theory to mammal vocal communication research. J. Zool. 280, 221–236 (2010).
Google Scholar 
Fitch, W. T., Neubauer, J. & Herzel, H. Calls out of chaos: The adaptive significance of nonlinear phenomena in mammalian vocal production. Anim. Behav. 63, 407–418 (2002).
Google Scholar 
Domning, D. P. & Hayek, L. A. C. Interspecific and intraspecific morphological variation in manatees (Sirenia: Trichechus). Mar. Mamm. Sci. 2, 87–144 (1986).
Google Scholar 
Anderson, P. K. & Barclay, R. M. R. Acoustic signals of solitary Dugongs: Physical characteristics and behavioral correlates. J. Mamm. 76, 1226–1237 (1995).
Google Scholar 
Sousa-Lima, R., Paglia, A. P. & Da Fonseca, G. Signature information and individual recognition in the isolation calls of Amazonian manatees, Trichechus inunguis (Mammalia: Sirenia). Anim. Behav. 63, 301–310 (2002).
Google Scholar 
Sousa-Lima, R. S., Paglia, A. P. & Fonseca, G. A. B. Gender, age, and identity in the isolation calls of Antillean manatees (Trichechus manatus manatus). Aquat. Mamm. 34, 109–122 (2008).
Google Scholar 
O’Shea, T. J. & Poché, L. B. Aspects of underwater sound communication in Florida manatees (Trichechus manatus latirostris). J. Mamm. 87, 1061–1071 (2006).
Google Scholar 
Rosas, F. C. W. Biology, conservation and status of the Amazonian manatee Trichechus inunguis. Mamm. Rev. 24, 49–59 (1994).
Google Scholar 
Meirelles, A. C. O. & Carvalho, V. L. Peixe-boi marinho: biologia e conservação no Brasil. Aquasis, Bambu Editora e Artes Gráficas, São Paulo (2016).Alvarez-Alemán, A., Beck, C. A. & Powell, J. A. First report of a Florida manatee (Trichechus manatus latirostris) in Cuba. Aquat. Mamm. 36, 148 (2010).
Google Scholar 
Castelblanco-Martínez, D. N. et al. First documentation of long-distance travel by a Florida manatee to the Mexican Caribbean. Ethol. Ecol. Evol. 1–12 (2021).Packard, J. M. & Wetterqvist, O. F. Evaluation of manatee habitat systems on the northwestern Florida coast. Coast. Manag. 14, 279–310 (1986).
Google Scholar 
Luna, F. D. O. et al. Genetic connectivity of the West Indian manatee in the southern range and limited evidence of hybridization with Amazonian manatees. Front. Mar. Sci. 7, 1089 (2021).
Google Scholar 
Hartman, D. Ecology and behavior of the manatee (Trichechus manatus) in Florida. Spec. Publ. Am. Soc. Mammal. 5, 153 (1979).
Google Scholar 
D’AffonsecaNeto, J. A. & Vergara-Parente, J. E. Sirenia (peixe-boi-da-Amazônia, Peixe-boi-marinho). In Tratado de Animais Selvagens: medicina veterinária (eds Cubas, Z. S. et al.) 701–714 (Roca, 2006).
Google Scholar 
Deutsch, C. J., Reid, J. P., Bonde, R. K., Easton, D. E., Kochman, H. I. & O’Shea, T. J. Seasonal movements, migratory behavior, and site fidelity of West Indian manatees along the Atlantic coast of the United States. Wildl. Monogr. 1–77 (2003).Laist, D. W. & Reynolds, J. E. III. Influence of power plants and other warm-water refuges on Florida manatees. Mar. Mamm. Sci. 21, 739–764 (2005).
Google Scholar 
Reynolds, J. E. Aspects of the social behaviour and herd structure of a semi-isolated colony of West Indian manatees, Trichechus manatus. Mammalia 45, 431–452 (1981).
Google Scholar 
Dantas, G. A. Ontogenia do padrão vocal individual do peixe-boi da Amazônia Trichechus inunguis (Sirenia, trichechidae). Dissertação (Instituto Nacional de Pesquisas da Amazônia, 2009).
Google Scholar 
Brady, B., Moore, J. & Love, K. Behavior related vocalizations of the Florida manatee (Trichechus manatus latirostris). Mar. Mamm. Sci. 1–15 (2021).Nowacek, D. P., Casper, B. M., Wells, R. S., Nowacek, S. M. & Mann, D. A. Intraspecific and geographic variation of West Indian manatee (Trichechus manatus spp.) vocalizations. J. Acoust. Soc. Am. 114, 66–69 (2003).ADS 
PubMed 

Google Scholar 
Rycyk, A. M. et al. First characterization of vocalizations and passive acoustic monitoring of the vulnerable African manatee (Trichechus senegalensis). J. Acoust. Soc. Am. 150, 3028–3037 (2021).ADS 
PubMed 

Google Scholar 
Landrau-Giovannetti, N., Mignucci-Giannoni, A. A. & Reidenberg, J. S. Acoustical and anatomical determination of sound production and transmission in West Indian (Trichechus manatus) and Amazonian (T. inunguis) manatees. Anat. Rec. 297, 1896–1907 (2014).
Google Scholar 
Morton, E. On the occurrence and significance of motivation-structural rules in some bird and mammal sounds. Am. Nat. 111, 855–869 (1977).
Google Scholar 
Borges, J. C. et al. Growth pattern differences of captive born Antillean manatee (Trichechus manatus) calves and those rescued in the Brazilian northeastern coast. J. Zoo Wildl. Med. 43, 494–500 (2012).PubMed 

Google Scholar 
Lima, D. S., Vergara-Parente, J. E., Young, R. J. & Paszkiewicz, E. Training of Antillean manatee Trichechus manatus manatus Linnaeus, 1758 as a management technique for individual welfare. Lat. Am. J. Mar. Mamm. 4, 61–68 (2005).
Google Scholar 
K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology. Raven Pro: Interactive Sound Analysis Software (Version 1.5) [Computer software]. https://ravensoundsoftware.com/ (The Cornell Lab of Ornithology, 2022).Zollinger, S. A., Podos, J., Nemeth, E., Goller, F. & Brumm, H. On the relationship between, and measurement of, amplitude and frequency in birdsong. Anim. Behav. 84, e1–e9 (2012).
Google Scholar 
Sokal, R. R. & Rohlf, F. J. Biometry (W. H. Freeman and Co., 1995).MATH 

Google Scholar 
Charrier, I. & Harcourt, R. G. Individual vocal identity in mother and pup Australian sea lions (Neophoca cinerea). J. Mamm. 87, 929–938 (2006).
Google Scholar 
Green, S. & Salkind, N. J. Using SPSS for Windows and Macintosh: Analyzing and Understanding Data 4th edn. (Prentice Hall, 2003).
Google Scholar 
Charlton, B. D. et al. Cues to body size in the formant spacing of male koala (Phascolarctos cinereus) bellows: Honesty in an exaggerated trait. J. Exp. Biol. 214(20), 3414–3422 (2011).PubMed 

Google Scholar 
IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27.0.Best, R. C. The aquatic mammals and reptiles of the Amazon. In The Amazon 371–412 (Springer, 1984).
Google Scholar 
Gerhardt, H. C. The evolution of vocalization in frogs and toads. Annu. Rev. Ecol. Syst. 25, 293–324 (1994).
Google Scholar 
Mendoza, P. et al. Growth curve of Amazonian manatee (Trichechus inunguis) in captivity. Aquat. Mamm. 45 (2019).Schwarz, L. K. Methods and models to determine perinatal status of Florida manatee carcasses. Mar. Mamm. Sci. 24, 881–898 (2008).
Google Scholar 
Hauser, M. D., Chomsky, N. & Fitch, W. T. The faculty of language: What is it, who has it, and how did it evolve?. Science 298, 1569–1579 (2002).ADS 
CAS 
PubMed 

Google Scholar 
Marler, P. & Peters, S. Developmental overproduction and selective attrition: New processes in the epigenesis of birdsong. Dev. Psychol. J. Int. Soc. Dev. Psychol. 15.4, 369–378 (1982).
Google Scholar 
Casey, C., Reichmuth, C., Costa, D. P. & Le Boeuf, B. The rise and fall of dialects in northern elephant seals. Proc. R. Soc. B 285, 2018–2176 (2018).
Google Scholar 
Hunter, M. E. et al. Puerto Rico and Florida manatees represent genetically distinct groups. Conserv. Genet. 13, 1623–1635 (2012).
Google Scholar 
Castelblanco-Martínez, D. N. et al. Analysis of body condition indices reveals different ecotypes of the Antillean manatee. Sci. Rep. 11, 1–14 (2021).
Google Scholar 
McCracken, K. G. & Sheldon, F. H. Avian vocalizations and phylogenetic signal. Proc. Natl. Acad. Sci. 94, 3833–3836 (1997).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Moron, J. R. et al. Whistle variability of Guiana dolphins in South America: Latitudinal variation or acoustic adaptation? Mar. Mamm. Sci. 1–32 (2018)Luís, A. R. et al. Vocal universals and geographic variations in the acoustic repertoire of the common bottlenose dolphin. Sci. Rep. 11, 1–9 (2021).
Google Scholar 
Ey, E. & Fisher, J. The ‘Acoustic adaptations hypothesis’ a review of the evidence from birds, anurans and mammals. Bioacoustics 19, 21–48 (2009).
Google Scholar 
Miksis-Olds, J. L. & Tyack, P. L. Manatee (Trichechus manatus) vocalization usage in relation to environmental noise levels. J. Acoust. Soc. Am. 125, 1806–1815 (2009).ADS 
PubMed 

Google Scholar 
Sun, W., Wang, Z., Jamalabdollahi, M. & Reza Zekavat, S. A. Experimental study on the difference between acoustic communication channels in freshwater rivers/lakes and in oceans. In 2014 48th Asilomar Conference on Signals, Systems and Computers, 333–337 (2004)Rivera Chavarría, M., Castro, J. & Camacho, A. The relationship between acoustic habitat, hearing and tonal vocalizations in the Antillean manatee (Trichechus manatus manatus, Linnaeus, 1758). Biol. Open 4, 1237–1242 (2015).PubMed 
PubMed Central 

Google Scholar 
Gaspard, J. C. III. et al. Audiogram and auditory critical ratios of two Florida manatees (Trichechus manatus latirostris). J. Exp. Biol. 215, 1442–1447 (2012).PubMed 

Google Scholar 
Gerstein, E. R., Gerstein, L., Forsythe, S. E. & Blue, J. E. The underwater audiogram of the West Indian manatee (Trichechus manatus). J. Acoust. Soc. Am. 105, 3575–3583 (1999).ADS 
CAS 
PubMed 

Google Scholar 
Klishin, V., Pezo, R., Popov, V., Ya, A. & Supin. Some characteristics of hearing of the Brazilian manatee, Trichechus inunguis. Aquat. Mamm. 16 (1990).Johnson, M., de Soto, N. A. & Madsen, P. T. Studying the behaviour and sensory ecology of marine mammals using acoustic recording tags: A review. Mar. Ecol. Prog. Ser. 395, 55–73 (2009).ADS 

Google Scholar  More

Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).Article 

Google Scholar 
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).Article 
CAS 
PubMed 

Google Scholar 
Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).Article 

Google Scholar 
Hector, A. et al. Plant diversity and productivity experiments in European grasslands. Science 286, 1123–1127 (1999).Article 
CAS 
PubMed 

Google Scholar 
Soliveres, S. et al. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536, 456–459 (2016).Article 
CAS 
PubMed 

Google Scholar 
Gross, N. et al. Functional trait diversity maximizes ecosystem multifunctionality. Nat. Ecol. Evol. 1, 0132 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
van der Plas, F. et al. Towards the development of general rules describing landscape heterogeneity–multifunctionality relationships. J. Appl. Ecol. 56, 168–179 (2019).Article 

Google Scholar 
Jochum, M. et al. The results of biodiversity–ecosystem functioning experiments are realistic. Nat. Ecol. Evol. 4, 1485–1494 (2020).Article 
PubMed 

Google Scholar 
Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264 (2017).Article 
CAS 
PubMed 

Google Scholar 
van der Plas, F. et al. Biotic homogenization can decrease landscape-scale forest multifunctionality. Proc. Natl Acad. Sci. USA 113, E2549–E2549 (2016).
Google Scholar 
Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).Article 
CAS 
PubMed 

Google Scholar 
Hautier, Y. et al. Local loss and spatial homogenization of plant diversity reduce ecosystem multifunctionality. Nat. Ecol. Evol. 2, 50–56 (2018).Article 
PubMed 

Google Scholar 
Srivastava, D. S. & Vellend, M. Biodiversity–ecosystem function research: is it relevant to conservation? Annu. Rev. Ecol. Evol. Syst. 36, 267–294 (2005).Article 

Google Scholar 
Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mori, A. S., Isbell, F. & Seidl, R. β-Diversity, community assembly, and ecosystem functioning. Trends Ecol. Evol. 33, 549–564 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Chase, J. M. & Knight, T. M. Scale-dependent effect sizes of ecological drivers on biodiversity: why standardised sampling is not enough. Ecol. Lett. 16, 17–26 (2013).Article 
PubMed 

Google Scholar 
Chase, J. M. et al. Embracing scale-dependence to achieve a deeper understanding of biodiversity and its change across communities. Ecol. Lett. 21, 1737–1751 (2018).Article 
PubMed 

Google Scholar 
Barry, K. E. et al. The future of complementarity: disentangling causes from consequences. Trends Ecol. Evol. 34, 167–180 (2019).Article 
PubMed 

Google Scholar 
Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).Article 
CAS 
PubMed 

Google Scholar 
Hagan, J. G., Vanschoenwinkel, B. & Gamfeldt, L. We should not necessarily expect positive relationships between biodiversity and ecosystem functioning in observational field data. Ecol. Lett. 24, 2537–2548 (2021).Article 
PubMed 

Google Scholar 
Brose, U. & Hillebrand, H. Biodiversity and ecosystem functioning in dynamic landscapes. Philos. Trans. R. Soc. B 371, 20150267 (2016).Article 

Google Scholar 
Isbell, F. et al. Benefits of increasing plant diversity in sustainable agroecosystems. J. Ecol. 105, 871–879 (2017).Article 

Google Scholar 
Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes-eight hypotheses. Biol. Rev. 87, 661–685 (2012).Article 
PubMed 

Google Scholar 
Ricotta, C. On beta diversity decomposition: trouble shared is not trouble halved. Ecology 91, 1981–1983 (2010).Article 
PubMed 

Google Scholar 
Kraft, N. J. B. et al. Disentangling the drivers of β diversity along latitudinal and elevational gradients. Science 333, 1755–1758 (2011).Article 
CAS 
PubMed 

Google Scholar 
Gonthier, D. J. et al. Biodiversity conservation in agriculture requires a multi-scale approach. Proc. R. Soc. Lond. B 281, 20141358 (2014).
Google Scholar 
Flynn, D. F. et al. Loss of functional diversity under land use intensification across multiple taxa. Ecol. Lett. 12, 22–33 (2009).Article 
PubMed 

Google Scholar 
Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).Article 
CAS 
PubMed 

Google Scholar 
Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).Article 
CAS 
PubMed 

Google Scholar 
Allan, E. et al. Land use intensification alters ecosystem multifunctionality via loss of biodiversity and changes to functional composition. Ecol. Lett. 18, 834–843 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Le Provost, G. et al. Land-use history impacts functional diversity across multiple trophic groups. Proc. Natl Acad. Sci. USA 117, 1573–1579 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Adl, S. M., Coleman, D. C. & Read, F. Slow recovery of soil biodiversity in sandy loam soils of Georgia after 25 years of no-tillage management. Agric. Ecosyst. Environ. 114, 323–334 (2006).Article 

Google Scholar 
Le Provost, G. et al. Contrasting responses of above- and belowground diversity to multiple components of land-use intensity. Nat. Commun. 12, 3918 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
James, L. A. Legacy effects. Oxford Bibliographies in Environmental Science https://doi.org/10.1093/OBO/9780199363445-0019 (2015).Lamy, T., Liss, K. N., Gonzalez, A. & Bennett, E. M. Landscape structure affects the provision of multiple ecosystem services. Environ. Res. Lett. 11, 124017 (2016).Article 

Google Scholar 
Alsterberg, C. et al. Habitat diversity and ecosystem multifunctionality—the importance of direct and indirect effects. Sci. Adv. 3, e1601475 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Tscharntke, T., Klein, A. M., Kruess, A., Steffan-Dewenter, I. & Thies, C. Landscape perspectives on agricultural intensification and biodiversity—ecosystem service management. Ecol. Lett. 8, 857–874 (2005).Article 

Google Scholar 
Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. Nat. Commun. 6, 8568 (2015).Article 
PubMed 

Google Scholar 
Benton, T. G., Vickery, J. A. & Wilson, J. D. Farmland biodiversity: is habitat heterogeneity the key? Trends Ecol. Evol. 18, 182–188 (2003).Article 

Google Scholar 
Bullock, J. M., Aronson, J., Newton, A. C., Pywell, R. F. & Rey-Benayas, J. M. Restoration of ecosystem services and biodiversity: conflicts and opportunities. Trends Ecol. Evol. 26, 541–549 (2011).Article 
PubMed 

Google Scholar 
Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 5, eaax0121 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Mitchell, M. G. E., Bennett, E. M. & Gonzalez, A. Linking landscape connectivity and ecosystem service provision: current knowledge and research gaps. Ecosystems 16, 894–908 (2013).Article 

Google Scholar 
Fischer, M. et al. Implementing large-scale and long-term functional biodiversity research: The Biodiversity Exploratories. Basic Appl. Ecol. 11, 473–485 (2010).Article 

Google Scholar 
Blüthgen, N. et al. A quantitative index of land-use intensity in grasslands: Integrating mowing, grazing and fertilization. Basic Appl. Ecol. 13, 207–220 (2012).Article 

Google Scholar 
Vogt, J. et al. Eleven years’ data of grassland management in Germany. Biodivers. Data J. 7, e36387 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Manning, P. et al. Redefining ecosystem multifunctionality. Nat. Ecol. Evol. 2, 427–436 (2018).Article 
PubMed 

Google Scholar 
Linders, T. E. W. et al. Stakeholder priorities determine the impact of an alien tree invasion on ecosystem multifunctionality. People Nat. 3, 658–672 (2021).Article 

Google Scholar 
Nathan, R. Long-distance dispersal of plants. Science 313, 786–788 (2006).Article 
CAS 
PubMed 

Google Scholar 
Manning, P. et al. Grassland management intensification weakens the associations among the diversities of multiple plant and animal taxa. Ecology 96, 1492–1501 (2015).Article 

Google Scholar 
Clough, Y. et al. Density of insect-pollinated grassland plants decreases with increasing surrounding land-use intensity. Ecol. Lett. 17, 1168–1177 (2014).Article 
PubMed 

Google Scholar 
Vickery, J. A. et al. The management of lowland neutral grasslands in Britain: effects of agricultural practices on birds and their food resources. J. Appl. Ecol. 38, 647–664 (2001).Article 

Google Scholar 
López-Jamar, J., Casas, F., Díaz, M. & Morales, M. B. Local differences in habitat selection by Great Bustards Otis tarda in changing agricultural landscapes: implications for farmland bird conservation. Bird. Conserv. Int. 21, 328–341 (2011).Article 

Google Scholar 
Wells, K., Böhm, S. M., Boch, S., Fischer, M. & Kalko, E. K. Local and landscape-scale forest attributes differ in their impact on bird assemblages across years in forest production landscapes. Basic Appl. Ecol. 12, 97–106 (2011).Article 

Google Scholar 
Bommarco, R., Lindborg, R., Marini, L. & Öckinger, E. Extinction debt for plants and flower-visiting insects in landscapes with contrasting land use history. Divers. Distrib. 20, 591–599 (2014).Article 

Google Scholar 
Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).Article 
PubMed 

Google Scholar 
Lee, M., Manning, P., Rist, J., Power, S. A. & Marsh, C. A global comparison of grassland biomass responses to CO2 and nitrogen enrichment. Philos. Trans. R. Soc. B 365, 2047–2056 (2010).Article 
CAS 

Google Scholar 
Smith, P. Do grasslands act as a perpetual sink for carbon? Glob. Change Biol. 20, 2708–2711 (2014).Article 

Google Scholar 
Wagg, C., Bender, S. F., Widmer, F. & van der Heijden, M. G. A. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 111, 5266–5270 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bradford, M. A. et al. Discontinuity in the responses of ecosystem processes and multifunctionality to altered soil community composition. Proc. Natl Acad. Sci. USA 111, 14478–14483 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schaub, S. et al. Plant diversity effects on forage quality, yield and revenues of semi-natural grasslands. Nat. Commun. 11, 768 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mace, G. M., Norris, K. & Fitter, A. H. Biodiversity and ecosystem services: a multilayered relationship. Trends Ecol. Evol. 27, 19–26 (2012).Article 
PubMed 

Google Scholar 
Peter, S., Le Provost, G., Mehring, M., Müller, T. & Manning, P. Cultural worldviews consistently explain bundles of ecosystem service prioritisation across rural Germany. People Nat. 4, 218–230 (2022).Article 

Google Scholar 
Emmerson, M. et al. How agricultural intensification affects biodiversity and ecosystem services. Adv. Ecol. Res. 55, 43–97 (2016).Article 

Google Scholar 
Gonzalez, A. et al. Scaling-up biodiversity–ecosystem functioning research. Ecol. Lett. 23, 757–776 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Loreau, M., Mouquet, N. & Gonzalez, A. Biodiversity as spatial insurance in heterogeneous landscapes. Proc. Natl Acad. Sci. USA 100, 12765–12770 (2003).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Anderson, B. J. et al. Spatial covariance between biodiversity and other ecosystem service priorities. J. Appl. Ecol. 46, 888–896 (2009).Article 

Google Scholar 
Maes, J. et al. Mapping ecosystem services for policy support and decision making in the European Union. Ecosyst. Serv. 1, 31–39 (2012).Article 

Google Scholar 
Metzger, J. P. et al. Considering landscape-level processes in ecosystem service assessments. Sci. Total Environ. 796, 149028 (2021).Article 
CAS 
PubMed 

Google Scholar 
Costanza, R. et al. Twenty years of ecosystem services: how far have we come and how far do we still need to go? Ecosyst. Serv. 28, 1–16 (2017).Article 

Google Scholar 
DeFries, R. & Nagendra, H. Ecosystem management as a wicked problem. Science 356, 265–270 (2017).Article 
CAS 
PubMed 

Google Scholar 
Díaz, S. et al. Assessing nature’s contributions to people. Science 359, 270–272 (2018).Article 
PubMed 

Google Scholar 
Schenk, N. et al. Assembled ecosystem measures from grassland EPs (2008–2018) for multifunctionality synthesis—June 2020. Version 40. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27087 (2022).Michael Scherer-Lorenzen, M. & Mueller, S. Acoustic diversity index based on environmental sound recordings on all forest EPs, HAI, 2016. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27568 (2020).Michael Scherer-Lorenzen, M. & Mueller, S. Acoustic diversity index based on environmental sound recordings on all forest EPs, Alb, 2016. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27569 (2020).Michael Scherer-Lorenzen, M. & Mueller, S. Acoustic diversity index based on environmental sound recordings on all forest EPs, SCH, 2016. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27570 (2020).Penone, C. et al. Assembled RAW diversity from grassland EPs (2008–2020) for multidiversity synthesis—November 2020. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27707 (2021).Penone, C. et al. Assembled species information from grassland EPs (2008–2020) for multidiversity synthesis—November 2020. Version 3. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27706 (2021).Junge, X., Schüpbach, B., Walter, T., Schmid, B. & Lindemann-Matthies, P. Aesthetic quality of agricultural landscape elements in different seasonal stages in Switzerland. Landsc. Urban Plan. 133, 67–77 (2015).Article 

Google Scholar 
Lindemann-Matthies, P., Junge, X. & Matthies, D. The influence of plant diversity on people’s perception and aesthetic appreciation of grassland vegetation. Biol. Conserv. 143, 195–202 (2010).Article 

Google Scholar 
Haines-Young, R. & Potschin, M. B. Common International Classification of Ecosystem Services (CICES) V5.1 and Guidance on the Application of the Revised Structure. https://cices.eu/content/uploads/sites/8/2018/01/Guidance-V51-01012018.pdf (2018)Byrnes, J. E. et al. Investigating the relationship between biodiversity and ecosystem multifunctionality: challenges and solutions. Methods Ecol. Evol. 5, 111–124 (2014).Article 

Google Scholar 
Neyret, M. et al. Assessing the impact of grassland management on landscape multifunctionality. Ecosyst. Serv. 52, 101366 (2021).Article 

Google Scholar 
Ferraro, D. M. et al. The phantom chorus: birdsong boosts human well-being in protected areas. Proc. R. Soc. B 287, 20201811 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Graves, R. A., Pearson, S. M. & Turner, M. G. Species richness alone does not predict cultural ecosystem service value. Proc. Natl Acad. Sci. USA 114, 3774–3779 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chan, K. M. A., Satterfield, T. & Goldstein, J. Rethinking ecosystem services to better address and navigate cultural values. Ecol. Econ. 74, 8–18 (2012).Article 

Google Scholar 
Villamagna, A. M., Angermeier, P. L. & Bennett, E. M. Capacity, pressure, demand, and flow: a conceptual framework for analyzing ecosystem service provision and delivery. Ecol. Complex. 15, 114–121 (2013).Article 

Google Scholar 
Bolliger, R., Prati, D., Fischer, M., Hoelzel, N. & Busch, V. Vegetation Records for Grassland EPs, 2008–2018. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/24247 (2020).Le Provost, G. & Manning, P. Cover of all vascular plant species in representative 2×2 quadrats of the major surrounding homogeneous vegetation zones in a 75-m radius of the 150 grassland EPs, 2017–2018. Version 4. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27846 (2021).Koleff, P., Gaston, K. J. & Lennon, J. J. Measuring beta diversity for presence–absence data. J. Anim. Ecol. 72, 367–382 (2003).Article 

Google Scholar 
Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 19, 134–143 (2010).Article 

Google Scholar 
Ostrowski, A., Lorenzen, K., Petzold, E. & Schindler, S. Land use intensity index (LUI) calculation tool of the Biodiversity Exploratories project for grassland survey data from three different regions in Germany since 2006, BEXIS 2 module. Zenodo https://doi.org/10.5281/zenodo.3865579 (2020).Thiele, J., Weisser, W. & Scherreiks, P. Historical land use and landscape metrics of grassland EP. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/25747 (2020).Steckel, J. et al. Landscape composition and configuration differently affect trap-nesting bees, wasps and their antagonists. Biol. Conserv. 172, 56–64 (2014).Article 

Google Scholar 
Westphal, C., Steckel, J. & Rothenwöhrer, C. InsectScale / LANDSCAPES – Landscape heterogeneity metrics (grassland EPs, radii 500 m–2000 m, 2009) – shape files. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/24046 (2019).Fahrig, L. et al. Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecol. Lett. 14, 101–112 (2011).Article 
PubMed 

Google Scholar 
Sirami, C. et al. Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions. Proc. Natl Acad. Sci. USA 116, 16442–16447 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gessler, P. E., Moore, I. D., Mckenzie, N. J. & Ryan, P. J. Soil–landscape modelling and spatial prediction of soil attributes. Int. J. Geogr. Inf. Syst. 9, 421–432 (1995).Article 

Google Scholar 
Zinko, U., Seibert, J., Dynesius, M. & Nilsson, C. Plant species numbers predicted by a topography-based groundwater flow index. Ecosystems 8, 430–441 (2005).Article 
CAS 

Google Scholar 
Moeslund, J. E. et al. Topographically controlled soil moisture drives plant diversity patterns within grasslands. Biodivers. Conserv. 22, 2151–2166 (2013).Article 

Google Scholar 
Keddy, P. A. Assembly and response rules: two goals for predictive community ecology. J. Veg. Sci. 3, 157–164 (1992).Article 

Google Scholar 
Myers, M. C., Mason, J. T., Hoksch, B. J., Cambardella, C. A. & Pfrimmer, J. D. Birds and butterflies respond to soil-induced habitat heterogeneity in experimental plantings of tallgrass prairie species managed as agroenergy crops in Iowa, USA. J. Appl. Ecol. 52, 1176–1187 (2015).Article 

Google Scholar 
Carvalheiro, L. G. et al. Soil eutrophication shaped the composition of pollinator assemblages during the past century. Ecography 43, 209–221 (2020).Article 

Google Scholar 
Schöning, I., Klötzing, T., Schrumpf, M., Solly, E. & Trumbore, S. Mineral soil pH values of all experimental plots (EP) of the Biodiversity Exploratories project from 2011, Soil (core project). Version 8. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/14447 (2021).Sørensen, R., Zinko, U. & Seibert, J. On the calculation of the topographic wetness index: evaluation of different methods based on field observations. Hydrol. Earth Syst. Sci. 10, 101–112 (2006).Article 

Google Scholar 
Le Provost, G. et al. Aggregated environmental and land-use covariates of the 150 grassland EPs used in ‘Contrasting responses of above- and belowground diversity to multiple components of land-use intensity’. Version 5. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/31018 (2021).R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2020).Grace, J. B. Structural equation modeling for observational studies. J. Wildl. Manag. 72, 14–22 (2008).Article 

Google Scholar 
Grace, J. B. Structural Equation Modeling and Natural Systems (Cambridge University Press, 2006).Rosseel, Y. Lavaan: an R package for structural equation modeling and more. Version 0.5–12 (BETA). J. Stat. Softw. 48, 1–36 (2012).Article 

Google Scholar 
Le Bagousse-Pinguet, Y. et al. Phylogenetic, functional, and taxonomic richness have both positive and negative effects on ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 116, 8419–8424 (2019).Article 
PubMed 
PubMed Central 

Google Scholar  More

Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).Article 
PubMed 
CAS 

Google Scholar 
Spang, A. et al. Complex archaea that bridge the gap between prokaryotes and eukaryotes. Nature 521, 173–179 (2015).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Tyson, G. W. et al. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428, 37–43 (2004).Article 
PubMed 
CAS 

Google Scholar 
Anantharaman, K. et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat. Commun. 7, 13219 (2016).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).Article 
PubMed 
CAS 

Google Scholar 
Tully, B. J. & Graham, E. D. & Heidelberg, J. F. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci. Data 5, 170203 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Stewart, R. D. et al. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen. Nat. Commun. 9, 870 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Pasolli, E. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography and lifestyle. Cell 176, 649–662 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 39, 499–509, https://doi.org/10.1038/s41587-020-0718-6 (2021).Article 
PubMed 
CAS 

Google Scholar 
Gilbert, J. A., Jansson, J. K. & Knight, R. The Earth Microbiome project: successes and aspirations. BMC Biol 12, 69 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Saheb Kashaf, S., Almeida, A., Segre, J. A. & Finn, R. D. Recovering prokaryotic genomes from host-associated, short-read shotgun metagenomic sequencing data. Nat. Protoc. 16, 2520–2541 (2021).Article 
PubMed 
CAS 

Google Scholar 
Chong, J., Liu, P., Zhou, G. & Xia, J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat. Protoc. 15, 799–821 (2020).Article 
PubMed 
CAS 

Google Scholar 
Arkin, A. P. et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat. Biotechnol. 36, 566–569 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Sayers, E. W. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 49, D10–D17 (2021).Article 
PubMed 
CAS 

Google Scholar 
Kluyver, T., et al. Jupyter Notebooks – a publishing format for reproducible computational workflows. In: Loizides F, Schmidt B, editors. Positioning and Power in Academic Publishing: Players, Agents and Agendas. p. 87–90 (2016).Banfield, J. Development of a Knowledgebase to Integrate, Analyze, Distribute, and Visualize Microbial Community Systems Biology Data. (2015). Report number: DOE-UCB-4918, OSTI ID: 1167269.Chen, I.-M. A. et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res 47, D666–D677 (2019).Article 
PubMed 
CAS 

Google Scholar 
Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res 44, W3–W10 (2016).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Devisetty, U. K., Kennedy, K., Sarando, P., Merchant, N. & Lyons, E. Bringing your tools to CyVerse discovery environment using Docker. F1000Res. 5, 1442 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, L., Lu, Z., Van Buren, P. & Ware, D. SciApps: a bioinformatics workflow platform powered by XSEDE and CyVerse. in Proceedings of the Practice and Experience on Advanced Research Computing 1–5 (Association for Computing Machinery, 2018).Eren, A. M. et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat. Microbiol. 6, 3–6 (2021).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Wattam, A. R. et al. Improvements to PATRIC, the all-bacterial bioinformatics database and analysis resource center. Nucleic Acids Res 45, D535–D542 (2017).Article 
PubMed 
CAS 

Google Scholar 
Mitchell, A. L. et al. MGnify: the microbiome analysis resource in 2020. Nucleic Acids Res. 48, D570–D578 (2020).PubMed 
CAS 

Google Scholar 
Wu, Y.-W. et al. Ionic liquids impact the bioenergy feedstock-degrading microbiome and transcription of enzymes relevant to polysaccharide hydrolysis. mSystems 1, e00120–16 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Rajeev, L. et al. Dynamic cyanobacterial response to hydration and dehydration in a desert biological soil crust. ISME J 7, 2178–2191 (2013).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Foster, I. Globus Online: accelerating and democratizing science through cloud-based services. IEEE Internet Comput 15, 70–73 (2011).Article 

Google Scholar 
Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res 27, 824–834 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Zhang, H. et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res 46, W95–W101 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).PubMed Central 

Google Scholar 
Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinforma 10, 421 (2009).Article 

Google Scholar 
Nordberg, H. et al. The genome portal of the Department of Energy Joint Genome Institute: 2014 updates. Nucleic Acids Res 42, D26–D31 (2014).Article 
PubMed 
CAS 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).Article 

Google Scholar 
Menzel, P., Ng, K. L. & Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7, 11257 (2016).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Freitas, T. A. K., Li, P.-E., Scholz, M. B. & Chain, P. S. G. Accurate read-based metagenome characterization using a hierarchical suite of unique signatures. Nucleic Acids Res 43, e69 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol 20, 257 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).Article 
PubMed 
CAS 

Google Scholar 
Milanese, A. et al. Microbial abundance, activity and population genomic profiling with mOTUs2. Nat. Commun. 10, 2014 (2019).Article 

Google Scholar 
Youngblut, N. D. & Ley, R. E. Struo2: efficient metagenome profiling database construction for ever-expanding microbial genome datasets. Peer J 9, e12198 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ondov, B. D., Bergman, N. H. & Phillippy, A. M. Interactive metagenomic visualization in a Web browser. BMC Bioinform 12, 385 (2011).Article 

Google Scholar 
Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).Article 
PubMed 
CAS 

Google Scholar 
Peng, Y., Leung, H. C. M., Yiu, S. M. & Chin, F. Y. L. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428 (2012).Article 
PubMed 
CAS 

Google Scholar 
Orakov, A. et al. GUNC: detection of chimerism and contamination in prokaryotic genomes. Genome Biol 22, 178 (2021).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075 (2013).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).Article 
PubMed 
CAS 

Google Scholar 
Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).Article 
PubMed 
CAS 

Google Scholar 
Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 25, 1043–1055 (2015).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Delcher, A. L., Salzberg, S. L. & Phillippy, A. M. Using MUMmer to identify similar regions in large sequence sets. Curr. Protoc. Bioinform. Chapter 10, Unit 10.3 (2003).
Google Scholar 
Darling, A. C. E., Mau, B., Blattner, F. R. & Perna, N. T. Mauve: multiple alignment of conserved genomic sequence with rearrangements. Genome Res 14, 1394–1403 (2004).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Parks, D. H. et al. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res 50, D785–D794 (2022).Article 
PubMed 
CAS 

Google Scholar 
Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Brettin, T. et al. RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci. Rep. 5, 8365 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Overbeek, R. et al. The SEED and the rapid annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res 42, D206–D214 (2014).Article 
PubMed 
CAS 

Google Scholar 
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).Article 
PubMed 
CAS 

Google Scholar 
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform 11, 119 (2010).Article 

Google Scholar 
Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).Article 
PubMed 
CAS 

Google Scholar 
Rinke, C. et al. A standardized archaeal taxonomy for the Genome Taxonomy Database. Nat. Microbiol. 6, 946–959 (2021).Article 
PubMed 
CAS 

Google Scholar 
Haft, D. H. et al. RefSeq: an update on prokaryotic genome annotation and curation. Nucleic Acids Res 46, D851–D860 (2018).Article 
PubMed 
CAS 

Google Scholar 
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Shaffer, M. et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res 48, 8883–8900 (2020).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Galperin, M. Y., Makarova, K. S., Wolf, Y. I. & Koonin, E. V. Expanded microbial genome coverage and improved protein family annotation in the COG database. Nucleic Acids Res 43, D261–D269 (2015). (Database Issue).Article 
PubMed 
CAS 

Google Scholar 
El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res 47, D427–D432 (2019).Article 
PubMed 
CAS 

Google Scholar 
Haft, D. H. et al. TIGRFAMs and Genome Properties in 2013. Nucleic Acids Res 41, D387–D395 (2013). (Database issue).Article 
PubMed 
CAS 

Google Scholar 
Eddy, S. R. Accelerated Profile HMM Searches. PLoS Comput. Biol. 7, e1002195 (2011).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P. M. & Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res 42, D490–D495 (2014).Article 
PubMed 
CAS 

Google Scholar 
Chivian, D., Dehal, P. S., Keller, K. & Arkin, A. P. MetaMicrobesOnline: phylogenomic analysis of microbial communities. Nucleic Acids Res 41, D648–D654 (2013).Article 
PubMed 
CAS 

Google Scholar 
Karaoz, U. & Brodie, E. L. microTrait: a toolset for a trait-based representation of microbial genomes. Front. Bioinform. https://doi.org/10.3389/fbinf.2022.918853 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Wood-Charlson, E. M. et al. The National Microbiome Data Collaborative: enabling microbiome science. Nat. Rev. Microbiol. 18, 313–314 (2020).Article 
PubMed 
CAS 

Google Scholar 
Hofmeyr, S. et al. Terabase-scale metagenome coassembly with MetaHipMer. Sci. Rep. 10, 10689 (2020).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Kolmogorov, M. et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat. Methods 17, 1103–1110 (2020).Article 
PubMed 
CAS 

Google Scholar 
Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res 27, 722–736 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Bertrand, D. et al. Hybrid metagenomic assembly enables high-resolution analysis of resistance determinants and mobile elements in human microbiomes. Nat. Biotechnol. 37, 937–944 (2019).Article 
PubMed 
CAS 

Google Scholar 
Chen, L.-X. et al. Accurate and complete genomes from metagenomes. Genome Res 30, 315–333 (2020).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Lui, L. M., Nielsen, T. N. & Arkin, A. P. A method for achieving complete microbial genomes and improving bins from metagenomics data. PLoS Comput Biol 17, e1008972 (2021).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Miller, C. S., Baker, B. J., Thomas, B. C., Singer, S. W. & Banfield, J. F. EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data. Genome Biol 12, R44 (2011).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Chivian, D. et al. Genome extraction from shotgun metagenome sequence data. KBase n/33233/628 https://doi.org/10.25982/33233.606/1831502 (2022).Article 

Google Scholar 
Chivian, D., et al. Moab desert crust – sample 4E. KBase n/62384/334 (2022). https://doi.org/10.25982/62384.253/1831503Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Matsen, F. A., Kodner, R. B. & Armbrust, E. V. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform 11, 538 (2010).Article 

Google Scholar 
Benson, D. A. et al. GenBank. Nucleic Acids Res 46, D41–D47 (2018).Article 
PubMed 
CAS 

Google Scholar 
Ewing, B. & Green, P. Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 8, 186–194 (1998).Article 
PubMed 
CAS 

Google Scholar 
Teiling, C. BaseSpace: Simplifying metagenomic analysis. 26th European Congress of Clinical Microbiology and Infectious Diseases (2016) 10.26226/morressier.56d5ba2ed462b80296c9509dReich, M. et al. The GenePattern notebook environment. Cell Syst 5, 149–151.e1 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Karp, P. D. et al. A comparison of microbial genome web portals. Front. Microbiol. 10, 208 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Yue, Y. et al. Evaluating metagenomics tools for genome binning with real metagenomic datasets and CAMI datasets. BMC Bioinform 21, 334 (2020).Article 
CAS 

Google Scholar 
Nelson, W. C., Tully, B. J. & Mobberley, J. M. Biases in genome reconstruction from metagenomic data. PeerJ 8, e10119 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J 11, 2864–2868 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Li, L., Stoeckert, C. J. Jr & Roos, D. S. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res 13, 2178–2189 (2003).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32, 1792–1797 (2004).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Kumari, S. et al. A KBase case study on genome-wide transcriptomics and plant primary metabolism in response to drought stress in sorghum. Curr. Plant Biol. 28, 100229 (2021).Article 
CAS 

Google Scholar 
Seaver, S. M. D. et al. The ModelSEED biochemistry database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res 49, D575–D588 (2021).Article 
PubMed 
CAS 

Google Scholar 
Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar  More

Experiment results and regression modelThe simulation experiment results based on the design scheme are presented in Table 4, including 24 analysis factors and 7 zero-point experiments for estimating the errors. Quadratic multiple regression analysis of the results in Table 4 was performed using the Design-Expert software, and the regression models between the influencing factors and evaluation indices were established as follows:$$ Y_{{1}} = {1767.57} – {64.29}X_{{1}} + {117.46}X_{{2}} + {324.46}X_{{3}} + {107.87}X_{{4}} – {21.81}X_{{1}} X_{{2}} + {17.94}X_{{1}} X_{{3}} – {41.44}X_{{1}} X_{{4}} + {16.69}X_{{2}} X_{{3}} – {41.19}X_{{2}} X_{{4}} + {73.56}X_{{3}} X_{{4}} + {23.2}{X_{{1}}^{{2}}} – {82.42}{{X_{{2}}}^{{2}}} – {13.17}{{X_{{3}}}^{{2}}} – {53.67}{{X_{{4}}}^{{2}}} $$$$ Y_{{2}} = {1968.14} + {636.42}X_{1} + {34.42}X_{2} + {66}X_{3} + {115.17}X_{{4}} + {28.63}X_{{1}} X_{{2}} + {9.13}X_{{1}} X_{{3}} – { 45.87}X_{{1}} X_{{4}} + {1}0X_{{2}} X_{{3}} + {30.5}X_{{2}} X_{{4}} – {1.75}X_{{3}} X_{{4}} + {55.03}{X_{{1}}^{{2}}} – {8.1}{{X_{{2}}}^{{2}}} – {72.72}{{X_{{3}}}^{2}} + {61.03}{{X_{{4}}}^{{2}}} $$Table 4 Experiment schemes and results.Full size tableThe relationship between the actual values of the efficiency of conveying-soil and the distance of throwing-soil and the predicted values of the regression model is shown in Fig. 7. It can be seen from Fig. 7 that the actual values are basically distributed on the predicted curve, consistent with the trend of the predicted values, and linearly distributed.Figure 7Scatter plot. (a) Scatter plot of actual and predicted distance of throwing-soil. (b) Scatter plot of actual and predicted efficiency of conveying-soil.Full size imageVariance analysis and discussionThe F-test and analysis of variance (ANOVA) were performed on the regression coefficients in the regression models of the evaluation indices Y1 and Y2, and the results are shown in Table 5. According to the significance values P of the lack of fitting in the regression models of the objective functions Y1 and Y2 in Table 5, PL1 = 0.1485  > 0.05 and PL2 = 0.2337  > 0.05 (both were not significant), indicating that no loss factor existed in the regression analysis, and the regression model exhibited a high fitting degree.Table 5 ANOVA results of regression model.Full size tableAccording to the ANOVA, the significance values P of each influencing factor in the test could be determined28. For the evaluation index Y1, the factors X1, X2, X3, X4, X3X4, X22, X42 had extremely significant influences, while the factors X1X4, X2X4 had a significant influence. For the evaluation index Y2, the factors X1, X3, X4, X1X4, X12, X32, X42 had extremely significant influences, and the factors X2, X1X4 had a significant influence. Within the level range of the selected factors, according to the F value of each factor as shown in Table 5, the weight of the factors affecting the efficiency of conveying-soil is feeding speed  > helix angle of auger  > rotating speed of auger  > slope angle. And the weight of the factors affecting the distance of throwing-soil is slope auger  > rotating speed of auger  > feeding speed  > helix angle of auger.In addition, it is obvious that there are interactions between the feeding speed and rotating speed of the auger, slope auger and rotating speed of auger, helix angle of the auger and rotating speed of the auger on the efficiency of conveying-soil Y1. For the distance of throwing-soil Y2, there is an interaction between the slope angle and the rotating speed of the auger.Analysis of response surfaceThe fitting coefficient of the efficiency of conveying-soil is R2 = 0.9714, R2adjust = 0.9263, R2pred = 0.8082, the difference between R2adjust and R2pred is less than 0.2. The fitting coefficient of the distance of throwing-soil is R2 = 0.9873, R2adjust = 0.9742, R2pred = 0.9355, the difference between R2adjust and R2pred is smaller than 0.2. It is indicated that the response surfaces of the two models established have good consistency and predictability for the experimental results29.The response surface is created directly using the Design-Expert software. After entering the data, select “Analysis” module. In the “Model-Graph” menu bar, select “3D-surface” to switch to the 3D view. To express the interactive influence of each factor on the efficiency of conveying-soil Y1 and distance of the throwing-soil Y2, the above two quadratic regression equations of the evaluation indices were subjected to the dimensionality reduction treatment. Two of the factors was set to level 0, while the other two underwent interaction effect analysis to study the influence law on the evaluation indices Y1 and Y2, and the corresponding response surfaces were generated, as illustrated in Fig. 8.Figure 83D response diagram effect of evaluation indices. (a) Effect of interaction between X1 and X2 on efficiency of conveying-soil. (b) Effect of interaction between X2 and X4 on efficiency of conveying-soil. (c) Effect of interaction between X3 and X4 on efficiency of conveying-soil. (d) Effect of interaction between X3 and X4 on distance of throwing-soil.Full size imageIt can be seen in Fig. 8a, when the slope angle was constant, the efficiency of conveying-soil increased with the rotating speed of the auger to a certain value, then the efficiency increase changed more gently. The reasons for this phenomenon are described as follows. On the one hand, the greater the kinetic energy of the soil when leaving the original position, and the thinner the soil was cut, resulting in the smaller the probability of blockage in the spiral blade space. On the other hand, the centrifugal force of soil arriving at the pit mouth is greater, so it does not obstruct in the pit mouth. However, if the rotation speed of the auger was too high and the soil layer cut was too thin, the subsequent soil’s driving effect to the front would be weakened, or even the flow would be interrupted, so the vertical rising speed of the soil would be reduced. When the rotational speed of the auger was constant, the efficiency of conveying-soil decreased with the increase of slope and then slightly increased. With the increase of slope, the time of slope cutting process increased, and there was more soil backfilling on the side of high altitude, which leaded to the reduction of soil discharge efficiency. However, with the increase of slope, the amount of soil slide at the pit mouth was increased, improving the efficiency of soil discharge. Further analysis demonstrated that the response surface for Y1 changed more rapidly in the direction of the rotating speed than in that of the slope angle, indicating that the rotating speed of auger X4 had a more significant influence than the slope angle X1.As can be seen in Fig. 8b, when the helix angle of the auger was fixed, the efficiency of conveying-soil continued to increase with the increase of the rotation speed. When the rotating speed of auger was fixed, the efficiency of conveying-soil increased with the increase of the helix angle and tends to decrease when it reached a certain value. The spiral blades space was the channel of soil movement. This phenomenon was caused by the increase of the gap between the two spiral blades with the increase of the helix angle of the auger, the soil was not easy to produce blockage. Meanwhile, the movement distance of soil was shorter, and the soil with higher kinetic energy was discharged more quickly from the pit. When reaching the pit mouth, the angle of soil throwing was larger and the soil backfilling rate was reduced. However, if the helix angle of auger was too large, the upward support ability and friction of the spiral blade surface to the soil would be reduced. Further analysis demonstrated that the response surface for Y1 changed more rapidly in the direction of the helix angle than the rotating speed of the auger, indicating that the helix angle of the auger X2 had a more significant influence than the rotating speed of the auger X4.When the feeding speed was fixed, the efficiency of throwing-soil continued to increase with the increase of the rotating speed. When the rotating speed of auger was fixed, the efficiency of the throwing-soil with the increase of the feeding speed (see in Fig. 8c). The phenomenon was caused by the faster the feeding speed of the auger, the thickness of soil cut per unit time increased. Furthermore, the subsequent driving force of soil increased, and the soil kinetic energy increased. However, in the actual production, excessive feeding speed would cause soil blockage on the surface of spiral blades. The reason is due to in the simulation process, the soil would not stop moving because of blockage. Further analysis demonstrated that the response surface for Y1 changed more rapidly in the direction of the rotating speed than in that of the feeding speed, indicating that the rotating speed of auger X4 had a more significant influence than the feeding speed X3.When the slope was fixed, the distance of the throwing-soil increased with the increase of rotation speed of the auger, and the increase amplitude increased gradually, as shown in Fig. 8d. The reason for this phenomenon was that the soil had more kinetic energy when it left its original position and the centrifugal force it received when it reaching the pit mouth is greater. When the rotation speed was too low, the soil layer was thin and the subsequent soil driving force was insufficient, resulting in the soil mass per unit area at the pit mouth was light and then the kinetic energy was small. When the rotating speed of auger was fixed, the distance of the throwing-soil increased continuously with the increase of the slope. As the slope increased, the time of soil swipe down process increased and then the rolling distance on the slope increased. Further analysis demonstrated that the response surface for Y2 changed more rapidly in the direction of the slope angle than in that of the rotating speed of auger, indicating that the slope angle X1 had a more significant influence than the rotating speed X3.Comprehensive optimal designAs relative importance and influencing rules of various experimental factors on evaluation indexes were different from each other, evaluation indexes should be taken into comprehensive consideration30. The optimization equation is obtained by the Design-Expert software multi-objective optimization method with Y1 and Y2 as the optimization objective function.$$25le {X}_{1}le 45$$$$10le {X}_{2}le 22$$$$0.04le {X}_{3}le 0.1$$$$30le {X}_{4}le 120$$$${{Y}_{1}}_{mathrm{max}}({X}_{1},{X}_{2},{X}_{3},{X}_{4})$$$${{Y}_{2}}_{min}({X}_{1},{X}_{2},{X}_{3},{X}_{4})$$In practice, the best combination of parameters needs to be selected according to the terrain slope. When the slope was fixed, the Design-Expert software was applied to optimize and solve the above mathematical model. The optimal combination of working parameters affecting the efficiency of conveying-soil Y1 and distance of throwing-soil Y2 for the auger were obtained and are shown in Table 6. If the ground preparation was required before the digging operation, the digging parameters can be designed according to values of Group 6 in Table 6.Table 6 Optimal parameter combinations of several terrain slopes.Full size tableDisturbance of soilA soil disturbance is defined as the loosening, movement and mixing of soil caused by an auger passing through the soil16. In the interface of the EDEM Analyst, add a “Clipping plane” to show the movement of the auger inside the pit. The kinetic energy, soil particle velocity vector, and velocity value of soil particles is observed when the auger in the middle of the soil bin31,32, as shown in Fig. 9.Figure 9The disturbance of the soil effect by spiral blade.Full size imageThe soil was lifted to the surface and then dropped to the lower side. In addition to the volume occupied by the spiral blades, the disturbed area also included the out-of-pit disturbed area caused by the compression of the cutting end of the spiral blade, as shown in the lower left corner of the auger.The kinetic energy and velocity of soil decreased firstly and then increased along the opposite direction of the auger feeding. The cutting end of the auger and the soil-throwing section occurred in the region with high kinetic energy and velocity. This was because the maximum kinetic energy was obtained at the cutting end of the auger, which was gradually consumed in the process of rising. After reaching the dumping end, the soil lost the restraint of the pit wall. When the centrifugal force of soil lost the reaction force, the kinetic energy of soil increased. Too much kinetic energy, however, can cause the soil to spread too far, causing subsequent trouble. The kinetic energy of the soil at the cutting end was related to the rotational speed of the auger. The spiral angle affected the angle between the force and gravity, and then the kinetic energy consumption in the process of soil increased.Verification experimentsTo verify the accuracy of the optimization model for auger working, as well as to evaluate the rationality of the working parameter combination optimized by the virtual experiment, performance verification tests were carried out on the EDEM software. According to the optimized process parameter setting test (as shown in Table 6), the relative error between the theoretical value and the experimental value was obtained. The verification test results are summarized in Table 7. The average relative errors of the efficiency of conveying-soil and the distance of throwing-soil between the Theoretical value and text value were only 4.4%, 9.1%. The simulation model is fairly accurate. The field performance verification experiments were carried out in slope. Figure 10 illustrates the field test and working conditions.Table 7 Results and comparison of validation test.Full size tableFigure 10Operation diagram at the experiment site.Full size image More

Balvanera, P. et al. Bioscience 64, 49–57 (2014).Article 

Google Scholar 
Hooper, D. U. et al. Ecol. Monogr. 75, 3–35 (2005).Article 

Google Scholar 
Balvanera, P. et al. Ecol. Lett. 9, 1146–1156 (2006).Article 
PubMed 

Google Scholar 
Cardinale, B. J. et al. Am. J. Bot. 98, 572–592 (2011).Article 
PubMed 

Google Scholar 
Cardinale, B. J. B. J. et al. Nature 486, 59–67 (2012).Article 
CAS 
PubMed 

Google Scholar 
Manning, P. et al. in Advances in Ecological Research (eds Eisenhauer N. et al.) 323–356 (Academic, 2019).Le Provost, G. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01918-5 (2022).Felipe-Lucia, M. R. et al. Proc. Natl Acad. Sci. USA 117, 28140–28149 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Foley, J. A. et al. Science 309, 570–574 (2005).Article 
CAS 
PubMed 

Google Scholar 
Cardinale, B. J. et al. Ecology 94, 1697–1707 (2013).Article 
PubMed 

Google Scholar 
Teles da Mota, V. & Pickering, C. J. Outdoor Recreat. Tour. 30, 100295 (2020).Article 

Google Scholar 
Mitchell, M. G. E. et al. Trends Ecol. Evol. 30, 190–198 (2015).Article 
PubMed 

Google Scholar 
Raudsepp-Hearne, C. & Peterson, G. D. Ecol. Soc. 21, 16 (2016).Article 

Google Scholar 
Chaplin-Kramer, R. & Kremen, C. Ecol. Appl. 22, 1936–1948 (2012).Article 
PubMed 

Google Scholar  More

Selection of surveys and extraction of dataWe selected published surveys that produced estimates of sperm whale population size or density (see Supplementary Information for methodology; surveys listed in Table 1). We extracted: the type of survey (ship, aerial; acoustic, visual), the years of data collection; the coordinates of the boundary of the study area; the estimates of g(0) and CV (g(0)) used to correct for availability bias, if given; and an estimate of sperm whale population or density in study area with CV. From these we calculated for each survey the survey area with waters greater than 1000 m deep (typical shallow depth limit of sperm whales3). When no value of g(0) was used (8 ship visual surveys) we corrected the population/density estimate using an assumed generic value of g(0) and recalculated the CV to include uncertainty in g(0) (as in Eq. 1 of8). Three ship visual surveys did calculate a single g(0) estimate: 0.62 (CV 0.35)32; 0.57 (CV 0.28)35; 0.61 (CV 0.25)37. These are consistent and suggest a generic g(0) = 0.60 (CV 0.29), also agreeing with g(0) = 0.60 estimated from pooled surveys in the California Current10.Global habitat of sperm whalesTo extrapolate sperm whale densities from surveyed study areas to the sperm whales’ global habitat, we created a one-degree latitude by one-degree longitude grid. We removed the following grid points as not being prime sperm whale habitat1,3,40: points on land or with central depths less than 1000 m; largely ice-covered points in the Beaufort Sea, and the waters north of Svalbard and Russia; the Black Sea and Red Sea both of which have shallow entrances that appear not to be traversable by sperm whales.Generally, food abundance is a good predictor of species distribution. However, this is not possible for sperm whales as we have no good measures of the abundance or distribution of most of their prey, deep-water squid57. Instead, oceanographic measures have been used to describe sperm whale distributions over various spatial scales with a moderate level of success13,14. We follow this approach. Measures that might predict sperm whale density were collected for each grid point, some at just the surface, others at the surface, 500 m depth, 1000 m depth or an average of the measures at the different depths (Supplementary Table S2). Water depth was the strongest predictor in Mediterranean encounters, when compared to slope and distance to shore13. Temperature and salinity have been used as predictors for the distribution of fish and larger marine animals, which could translate into prey availability and thus density for sperm whales58,59. Primary productivity and dissolved oxygen generally dictate the biomass of wildlife in an area, while nitrate and phosphate levels limit the amount of primary productivity in an area60. Eddy kinetic energy is a measure of the dynamism of physical oceanography which is becoming a commonly used predictor of cetacean habitat61. We did not use: latitude and longitude as these primarily describe the general geographic distribution of the study areas, and geographic aggregates of sperm whale catches62 as these proved to have no predictive power. The mean values of the 14 predictor measures were calculated over calendar months for each grid point, and then over the grid points in each study area.To obtain predictors of the sperm whale density at each grid point, we then made quadratic regressions of the density of sperm whales in each study area (i), d(i), on the mean values of the predictor measures, weighting each study area by its surface area. Because the surveys were conducted over different time periods, the densities were corrected based on the estimated trajectory of global sperm whale populations by multiplying d(i) by the ratio of the global population in 1993 over that in the mid-year of the survey (as in Fig. 4). Predictor variables were selected using forward stepwise selection based upon reduction in AIC.Sperm whale population sizeThe population of sperm whales globally, N, was then calculated as follows:$$N=sum_{k}dleft(kright)cdot aleft(kright),$$
(1)
where a{k} are the parameters of the regression; the summation is over k, the grid points; d(k) is the estimated sperm whale density at grid point k from the habitat suitability model; and a(k) is the area of the 1° cell centred on grid point k. Population estimates for other ocean areas (North Atlantic, North Pacific, Southern Hemisphere) were calculated similarly.The CVs of these population estimates were calculated following the methodology in8, (although there is an error in Eq. (3) of8 such that the squareroot symbol covers both the numerator and denominator rather than just the numerator). The error due to uncertain density estimates for the different surveys is:$$CVleft({D}_{T}right)=frac{sqrt{sum_{i}{left(CV({n}_{i})cdot {n}_{i}right)}^{2}}}{sum_{i}{n}_{i}}.$$
(2)
This is combined with the uncertainty in the extrapolation process (output from the linear models), CV(extrap.), to give an overall CV for the population estimate:$$CVleft(Nright)=sqrt{{CV({D}_{T})}^{2}+{CV(mathrm{extrap}.)}^{2}.}$$
(3)
Post-whaling trend in population sizeWe compiled a database of series of surveys producing population estimates of the same study area during the period 1978 (by which time most commercial sperm whaling had ceased) and 2022. Each series had to span at least 10 years, and all of the surveys in the series had to be comparable in terms of area covered throughout the time span. There also had to have been at least 3 surveys for a data set to be included.The data consisted of the survey area, A, the estimated population in area A in year y (for multi-year surveys, y would be the midpoint of the data collection years), nE(A,y), and the provided CV of that estimate, CV(nE(A,y)). The data series used for these analyses are summarized in Table 3.For each survey area, A, we calculated the trend in logarithmic population size, r(A), over time using weighted linear regression:$${text{Log}}left( {n_{E} left( {A,y} right)} right) , sim {text{ constant}}left( A right) , + rleft( A right) cdot y. left[ {{text{weight }} = { 1}/left( {{1} + {text{ CV}}left( {n_{E} left( {A,y} right)} right)} right)^{{2}} } right]$$
(4)
Table 3 also includes other published estimates of sperm whale population trends, from sighting rates or mark-recapture analyses of photoidentification data, with these estimates also having to span at least 10 years of data collection, and include data collected in three or more different years.Population trajectoryTo examine possible trajectories of the global sperm whale population following the start of commercial whaling in 1712, we used a variant of the theta-logistic, a population model that has been employed in other recent analyses of the population trajectories of large cetaceans45,63. The theta-logistic model is:$$nleft(y+1right)=nleft(yright)+rcdot nleft(yright)left(1-{left(frac{nleft(yright)}{nleft(1711right)}right)}^{theta }right)-fleft(yright)cdot cleft(yright).$$
(5)

Here, n(y) is the population of sperm whales in year y, r is the maximum potential rate of increase of a sperm whale population, and θ describes how the rate of increase varies with population size relative to its basal level before whaling in 1711, n(1711). The recorded catch in year y is c(y) and f(y) is a correction for bias in recorded catches.Whaling reduced the proportion of large breeding males64, likely disrupted the social cohesion of the females3, and may have had other lingering effects which reduced pregnancy or survival, and thus the rate of increase. Poaching has been found to reduce the reproductive output of African elephants, Loxodonta Africana, which have a similar social system to the sperm whales3, and this effect lingers well beyond the effective cessation of poaching46. There is some evidence for these effects of what we call “social disruption” on sperm whale population dynamics20,46,65. We added a term to the theta-logistic to account for such effects:$$nleft(y+1right)=nleft(yright)left[1+rcdot left(1-{left(frac{nleft(yright)}{nleft(1711right)}right)}^{theta }right)-qcdot frac{sum_{t=y-T}^{y}f(t)cdot c(t)}{nleft(y-Tright)}right]-f(y)cdot c(y).$$
(6)

Here, (frac{sum_{t=y-T}^{y}f(t)cdot c(t)}{nleft(y-Tright)}) is the proportion of the population killed over the last T years, and q is the reduction in the rate of increase when almost all the whales have been killed. This reduction is modelled to fall linearly as the proportion killed declines to zero.The global sperm whale population has some geographic structure18. Females appear to rarely move between ocean basins, and males seem to largely stay within one basin. Furthermore, sperm whaling was progressive, moving from ocean area to ocean area as numbers were depleted4. We model this by assuming K largely separate sperm whale subpopulations of equal size. Exploitation in 1712 starts in subpopulation 1 and moves to subpopulations 1 and 2 when the population 1 falls to α% of its initial value, and so on for the other ocean areas. The catch in each year in each area being exploited is pro-rated by the sizes of the different subpopulations being exploited. The population model for subpopulation k, which is one of the KE subpopulations being exploited in year y, is:$$nleft(k,y+1right)=nleft(k,yright)left[1+rcdot left(1-{left(frac{nleft(k,yright)}{nleft(k,1711right)}right)}^{theta }right)-qcdot frac{sum_{t=y-T}^{y}C(k,t)}{nleft(k,y-Tright)}right]-Cleft(k,yright),$$
(7)
where the estimated catch in year y in subpopulation k is given by: (Cleft(k,yright)=f(y)cdot c(y)cdot n(k,y)/sum_{{k}^{mathrm{^{prime}}}= More

Prasad, N. G. & Joshi, A. What have two decades of laboratory life-history evolution studies on Drosophila melanogaster taught us?. J. Genet. 82, 45–76 (2003).CAS 
PubMed 

Google Scholar 
MacMillan, H. A., Walsh, J. P. & Sinclair, B. J. The effects of selection for cold tolerance on cross-tolerance to other environmental stressors in Drosophila melanogaster. Insect Sci. 16, 263–276 (2009).
Google Scholar 
Flatt, T. Life-history evolution and the genetics of fitness components in drosophila melanogaster. Genetics 214(1), 3–48. https://doi.org/10.1534/genetics.119.300160 (2020).Article 
CAS 
PubMed 

Google Scholar 
Hoffmann, A. A. & Parsons, P. A. Selection for increased desiccation resistance in Drosophila melanogaster: Additive genetic control and correlated responses for other stresses. Genetics 122, 837–845 (1989).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nghiem, D., Gibbs, A. G., Rose, M. R. & Bradley, T. J. Postponed aging and desiccation resistance in Drosophila melanogaster. Exp. Gerontol. 35, 957–969 (2000).CAS 
PubMed 

Google Scholar 
Hoffmann, A. A., Scott, M., Partridge, L. & Hallas, R. Overwintering in Drosophila melanogaster: Outdoor field cage experiments on clinal and laboratory selected populations help to elucidate traits under selection. J. Evol. Biol. 16, 614–623 (2003).CAS 
PubMed 

Google Scholar 
Bubliy, O. A. & Loeschcke, V. Correlated responses to selection for stress resistance and longevity in a laboratory population of Drosophila melanogaster. J. Evol. Biol. 18, 789–803 (2005).CAS 
PubMed 

Google Scholar 
Bourg, É. L. & Le Bourg, É. A cold stress applied at various ages can increase resistance to heat and fungal infection in aged Drosophila melanogaster flies. Biogerontology 12, 185–193 (2011).PubMed 

Google Scholar 
Sejerkilde, M., Sørensen, J. G. & Loeschcke, V. Effects of cold- and heat hardening on thermal resistance in Drosophila melanogaster. J. Insect Physiol. 49, 719–726 (2003).CAS 
PubMed 

Google Scholar 
Coulson, S. C. & Bale, J. S. Effect of rapid cold hardening on reproduction and survival of offspring in the housefly Musca domestica. J. Insect Physiol. 38, 421–424 (1992).
Google Scholar 
Bayley, M., Petersen, S. O., Knigge, T., Köhler, H.-R. & Holmstrup, M. Drought acclimation confers cold tolerance in the soil collembolan Folsomia candida. J. Insect Physiol. 47, 1197–1204 (2001).CAS 
PubMed 

Google Scholar 
Wu, B. S. et al. Anoxia induces thermotolerance in the locust flight system. J. Exp. Biol. 205, 815–827 (2002).CAS 
PubMed 

Google Scholar 
Phelan, J. P. et al. Breakdown in correlations during laboratory evolution. I. Comparative analyses of Drosophila populations. Evolution 57, 527–535 (2003).PubMed 

Google Scholar 
Hoffmann, A. A. & Harshman, L. G. Desiccation and starvation resistance in Drosophila: Patterns of variation at the species, population and intrapopulation levels. Heredity 83(Pt 6), 637–643 (1999).PubMed 

Google Scholar 
Sinclair, B. J., Nelson, S., Nilson, T. L., Roberts, S. P. & Gibbs, A. G. The effect of selection for desiccation resistance on cold tolerance of Drosophila melanogaster. Physiol. Entomol. 32, 322–327 (2007).
Google Scholar 
Anderson, A. R., Hoffmann, A. A. & McKechnie, S. W. Response to selection for rapid chill-coma recovery in Drosophila melanogaster: Physiology and life-history traits. Genet. Res. 85, 15–22 (2005).PubMed 

Google Scholar 
Kellett, M., Hoffmann, A. A. & Mckechnie, S. W. Hardening capacity in the Drosophila melanogaster species group is constrained by basal thermotolerance. Funct. Ecol. 19, 853–858 (2005).
Google Scholar 
Overgaard, J., Sørensen, J. G., Petersen, S. O., Loeschcke, V. & Holmstrup, M. Reorganization of membrane lipids during fast and slow cold hardening in Drosophila melanogaster. Physiol. Entomol. 31, 328–335 (2006).CAS 

Google Scholar 
Hoffmann, A. A., Hallas, R., Anderson, A. R. & Telonis-Scott, M. Evidence for a robust sex-specific trade-off between cold resistance and starvation resistance in Drosophila melanogaster. J. Evol. Biol. 18, 804–810 (2005).CAS 
PubMed 

Google Scholar 
Singh, K., Kochar, E. & Prasad, N. G. Egg Viability, Mating Frequency and Male Mating Ability Evolve in Populations of Drosophila melanogaster Selected for Resistance to Cold Shock. PLoS ONE 10, e0129992 (2015).PubMed 
PubMed Central 

Google Scholar 
Singh, K., Kochar, E., Gahlot, P., Bhatt, K. & Prasad, N. G. Evolution of reproductive traits have no apparent life-history associated cost in populations of Drosophila melanogaster selected for cold shock resistance. BMC Ecol. Evol. 21, 1–4 (2021).
Google Scholar 
Salehipour-Shirazi, G., Ferguson, L. V. & Sinclair, B. J. Does cold activate the Drosophila melanogaster immune system?. J. Insect Physiol. 96, 29–34 (2017).CAS 
PubMed 

Google Scholar 
Singh, K., Zulkifli, M. & Prasad, N. G. Identification and characterization of novel natural pathogen of Drosophila melanogaster isolated from wild captured Drosophila spp. Microbes Infect. 18, 813–821 (2016).PubMed 

Google Scholar 
Singh, K., Samant, M. A., Tom, M. T. & Prasad, N. G. Evolution of Pre- and Post-Copulatory Traits in Male Drosophila melanogaster as a Correlated Response to Selection for Resistance to Cold Stress. PLoS ONE 11, e0153629 (2016).PubMed 
PubMed Central 

Google Scholar 
Lefevre, G. J. & Jonsson, U. B. The effect of cold shock on D. melanogaster sperm. Drosophila Inf. Serv. 1962(36), 86–876 (1962).
Google Scholar 
Novitski, E. & Rush, G. Viability and fertility of Drosophila exposed to sub-zero temperatures. Biol. Bull. 97, 150–157 (1949).CAS 
PubMed 

Google Scholar 
Arbogast, R. T. Mortality and Reproduction of Ephestia cautella and Plodia interpunctella 1 Exposed as Pupae to High Temperatures. Environ. Entomol. 10, 708–711 (1981).
Google Scholar 
Saxena, B. P., Sharma, P. R., Thappa, R. K. & Tikku, K. Temperature induced sterilization for control of three stored grain beetles. J. Stored Prod. Res. 28, 67–70 (1992).
Google Scholar 
Collett, J. I. & Jarman, M. G. Adult female Drosophila pseudoobscura survive and carry fertile sperm through long periods in the cold: Populations are unlikely to suffer substantial bottlenecks in overwintering. Evolution 55, 840–845 (2001).CAS 
PubMed 

Google Scholar 
Schnebel, E. M. & Grossfield, J. Mating-temperature range in drosophila. Evolution 38, 1296–1307 (1984).PubMed 

Google Scholar 
Chakir, M., Chafik, A., Moreteau, B., Gibert, P. & David, J. R. Male sterility thermal thresholds in Drosophila: D. simulans appears more cold-adapted than its sibling D. melanogaster. Genetica 114, 195–205 (2002).PubMed 

Google Scholar 
David, J. R. et al. Male sterility at extreme temperatures: A significant but neglected phenomenon for understanding Drosophila climatic adaptations. J. Evol. Biol. 18, 838–846 (2005).CAS 
PubMed 

Google Scholar 
Dolgin, E. S., Whitlock, M. C. & Agrawal, A. F. Male Drosophila melanogaster have higher mating success when adapted to their thermal environment. J. Evol. Biol. 19, 1894–1900 (2006).CAS 
PubMed 

Google Scholar 
David, J. R. Male sterility at high and low temperatures in Drosophila. J. Soc. Biol. 202, 113–117 (2008).PubMed 

Google Scholar 
Zhang, W., Zhao, F., Hoffmann, A. A. & Ma, C.-S. A single hot event that does not affect survival but decreases reproduction in the diamondback moth, plutella xylostella. PLoS ONE 8, e75923 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tucić, N. Genetic capacity for adaptation to cold resistance at different developmental stages of Drosophila melanogaster. Evolution 33, 350–358 (1979).PubMed 

Google Scholar 
Chen, C.-P. & Walker, V. K. Increase in cold-shock tolerance by selection of cold resistant lines in Drosophila melanogaster. Ecol. Entomol. 18, 184–190 (1993).
Google Scholar 
Ring, R. A. & Danks, H. V. Desiccation and cryoprotection: Overlapping adaptations. Cryo Lett. 15, 181–190 (1994).
Google Scholar 
Ring, R. A. & Danks, H. The role of trehalose in cold-hardiness and desiccation. Cryo Lett. 19, 275–282 (1998).CAS 

Google Scholar 
Singh, K. & Prasad, N. G. Cold stress upregulates the expression of heat shock proteins and Frost genes, but evolution of cold stress resistance is apparently not mediated through either heat shock proteins or Frost genes in the cold stress selected population. bioRxiv https://doi.org/10.1101/2022.03.07.483305 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Bubliy, O. A., Kristensen, T. N., Kellermann, V. & Loeschcke, V. Plastic responses to four environmental stresses and cross-resistance in a laboratory population of Drosophila melanogaster. Funct. Ecol. 26, 245–253 (2012).
Google Scholar 
Kristensen, T. N., Loeschcke, V. & Hoffmann, A. A. Can artificially selected phenotypes influence a component of field fitness? Thermal selection and fly performance under thermal extremes. Proc. Biol. Sci. 274, 771–778 (2007).PubMed 

Google Scholar 
Hoffmann, A. A., Anderson, A. & Hallas, R. Opposing clines for high and low temperature resistance in Drosophila melanogaster. Ecol. Lett. 5, 614–618 (2002).
Google Scholar 
Yi, S.-X. & Lee, R. E. Jr. Detecting freeze injury and seasonal cold-hardening of cells and tissues in the gall fly larvae, Eurosta solidaginis (Diptera: Tephritidae) using fluorescent vital dyes. J. Insect Physiol. 49, 999–1004 (2003).CAS 
PubMed 

Google Scholar 
Macmillan, H. A. & Sinclair, B. J. Mechanisms underlying insect chill-coma. J. Insect Physiol. 57, 12–20 (2011).CAS 
PubMed 

Google Scholar 
Marshall, K. E. & Sinclair, B. J. The sub-lethal effects of repeated freezing in the woolly bear caterpillar Pyrrharctia isabella. J. Exp. Biol. 214, 1205–1212 (2011).PubMed 

Google Scholar 
Sinclair, B. J., Ferguson, L. V., Salehipour-shirazi, G. & MacMillan, H. A. Cross-tolerance and cross-talk in the cold: Relating low temperatures to desiccation and immune stress in insects. Integr. Comp. Biol. 53, 545–556 (2013).PubMed 

Google Scholar 
Roxström-Lindquist, K., Terenius, O. & Faye, I. Parasite-specific immune response in adult Drosophila melanogaster: A genomic study. EMBO Rep. 5, 207–212 (2004).PubMed 
PubMed Central 

Google Scholar 
Pham, L. N., Dionne, M. S., Shirasu-Hiza, M. & Schneider, D. S. A specific primed immune response in Drosophila is dependent on phagocytes. PLoS Pathog. 3, e26 (2007).PubMed 
PubMed Central 

Google Scholar 
Mikonranta, L., Mappes, J., Kaukoniitty, M. & Freitak, D. Insect immunity: Oral exposure to a bacterial pathogen elicits free radical response and protects from a recurring infection. Front. Zool. 11, 23 (2014).PubMed 
PubMed Central 

Google Scholar 
Ramløv, H. & Lee, R. E. Jr. Extreme resistance to desiccation in overwintering larvae of the gall fly Eurosta solidaginis (Diptera, tephritidae). J. Exp. Biol. 203, 783–789 (2000).PubMed 

Google Scholar 
Holmstrup, M., Bayley, M. & Ramløv, H. Supercool or dehydrate? An experimental analysis of overwintering strategies in small permeable arctic invertebrates. Proc. Natl. Acad. Sci. 99, 5716–5720 (2002).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chippindale, A. K. et al. Resource acquisition and the evolution of stress resistance in drosophila melanogaster. Evolution 52, 1342 (1998).PubMed 

Google Scholar 
Rose, M. R. Laboratory evolution of postponed senescence in Drosophila melanogaster. Evolution 38, 1004–1010 (1984).ADS 
PubMed 

Google Scholar 
Crill, W. D., Huey, R. B. & Gilchrist, G. W. Within- and between-generation effects of temperature on the morphology and physiology of Drosophila melanogaster. Evolution 50, 1205–1218 (1996).PubMed 

Google Scholar 
Kwan, L., Bedhomme, S., Prasad, N. G. & Chippindale, A. K. Sexual conflict and environmental change: Trade-offs within and between the sexes during the evolution of desiccation resistance. J. Genet. 87, 383–394 (2008).PubMed 

Google Scholar  More

Seguin, R. et al. Nat. Sustain. https://doi.org/10.1038/s41893-022-00981-x (2022).Article 

Google Scholar 
Roberts, C. M. & Polunin, N. V. C. Rev. Fish Biol. Fish. 1, 65–91 (1991).Article 

Google Scholar 
Cinner, J. E. et al. Soc. Nat. Resour. 27, 994–1005 (2014).Article 

Google Scholar 
MacNeil, M. A. et al. Nature 520, 341–344 (2015).Article 
CAS 

Google Scholar 
Morais, R. A. & Bellwood, D. R. Coral Reefs 39, 1221–1231 (2020).Article 

Google Scholar 
Morais, R. A., Connolly, S. R. & Bellwood, D. R. Glob. Change Biol. 26, 1295–1305 (2020).Article 

Google Scholar 
Di Lorenzo, M. et al. Fish Fish. 21, 906–915 (2020).Article 

Google Scholar 
Ban, N. C. et al. Nat. Sustain. 2, 524–532 (2019).Article 

Google Scholar 
Rogers, A. et al. Ecology 99, 450–463 (2018).Article 

Google Scholar 
Robinson, J. P. W. et al. Nat. Ecol. Evol. 3, 183–190 (2019).Article 

Google Scholar  More