Ecology

1.
Aschoff, J. Circadian activity pattern with two peaks. Ecology 47, 657–662 (1966).
Article  Google Scholar 
2.
Daan, S. & Aschoff, J. Circadian rhythms of locomotor activity in captive birds and mammals: Their variations with season and latitude. Oecologia 18, 269–316 (1975).
ADS  Article  PubMed  PubMed Central  Google Scholar 

3.
Katandukila, J. V., Bennett, N. C., Chimimba, C. T., Faulkes, C. G. & Oosthuizen, M. K. Locomotor activity patterns of captive East African root rats, Tachyoryctes splendens (Rodentia: Spalacidae), from Tanzania, East Africa. J. Mamm. 94, 1393–1400 (2013).
Article  Google Scholar 

4.
Bennie, J. J., Duffy, J. P., Inger, R. & Gaston, K. J. Biogeography of time partitioning in mammals. Proc. Natl. Acad. Sci. U.S.A. 111, 13727–13732 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

5.
Cloudsley-Thompson, J. L. Rhythmic Activity in Animal Physiology and Behaviour (Academic Press, Cambridge, 1961).
Google Scholar 

6.
Aschoff, J., Gercke, H., Pohl, P., Rieger, P. V. & Wever, S. P. U. R. Interdependent parameters of circadian activity rhythms in birds and man. In Biochronometry (ed. Menaker, M.) 3–29 (National Academy of Science, Washington, DC, 1971).
Google Scholar 

7.
Risenhoover, K. L. Winter activity patterns of moose in interior Alaska. J. Wildl. Manage. 50, 727–734 (1986).
Article  Google Scholar 

8.
Castillo-Ruiz, A., Paul, M. J. & Schwartz, W. J. In search of a temporal niche: Social interactions. Prog. Brain Res. 199, 267–280 (2012).
Article  PubMed  PubMed Central  Google Scholar 

9.
Kronfeld-Schor, N., Visser, M. E., Salis, L. & van Gils, J. A. Chronobiology of interspecific interactions in a changing world. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. https://doi.org/10.1098/rstb.2016.0248 (2017).
Article  Google Scholar 

10.
Farsi, H. et al. Validation of locomotion scoring as a new and inexpensive technique to record circadian locomotor activity in large mammals. Heliyon 4, e00980–e00980 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

11.
El Allali, K. et al. Smartphone and a freely available application as a new tool to record locomotor activity rhythm in large mammals and humans. Chronobiol. Int. 36, 1047–1057 (2019).
Article  PubMed  Google Scholar 

12.
Schmidt-Nielsen, K., Schmidt-Nielsen, B., Jarnum, S. A. & Houpt, T. R. Body temperature of the camel and its relation to water economy. Am. J. Physiol. 188, 103–112 (1957).
CAS  Article  PubMed  Google Scholar 

13.
Bouâouda, H. et al. Daily regulation of body temperature rhythm in the camel (Camelus dromedarius) exposed to experimental desert conditions. Physiol. Rep. 2, e12151 (2014).
Article  PubMed  PubMed Central  Google Scholar 

14.
El Allali, K. et al. Entrainment of the circadian clock by daily ambient temperature cycles in the camel (Camelus dromedarius). Am. J. Physiol. Regul. Integr. Comp. Physiol. 304, R1044–R1052 (2013).
Article  CAS  PubMed  Google Scholar 

15.
Farsi, H. et al. Melatonin rhythm and other outputs of the master circadian clock in the desert goat (Capra hircus) are entrained by daily cycles of ambient temperature. J. Pineal Res. 68, e12634 (2020).
CAS  Article  PubMed  Google Scholar 

16.
Ebling, F. J., Lincoln, G. A., Wollnik, F. & Anderson, N. Effects of constant darkness and constant light on circadian organization and reproductive responses in the ram. J. Biol. Rhythms 3, 365–384 (1988).
CAS  Article  PubMed  Google Scholar 

17.
Johnson, R. F., Randall, S. & Randall, W. Freerunning and entrained circadian rhythms in activity, eating and drinking in the cat. J. Interdiscipl. Cycle Res. 14, 315–327 (1983).
Article  Google Scholar 

18.
Jilge, B., Hörnicke, H. & Stähle, H. Circadian rhythms of rabbits during restrictive feeding. Am. J. Physiol. 253, R46–R54 (1987).
CAS  PubMed  Google Scholar 

19.
Decoursey, G. & Decoursey, P. J. Adaptive aspects of activity rhythms in bats. Biol. Bull. 126, 14–27 (1964).
Article  Google Scholar 

20.
Erkert, H. G., Nagel, B. & Stephani, I. Light and social effects on the free-running circadian activity rhythm in common marmosets (Callithrix jacchus; Primates): Social masking, pseudo-splitting, and relative coordination. Behav. Ecol. Sociobiol. 18, 443–452 (1986).
Article  Google Scholar 

21.
O’Reilly, H., Armstrong, S. M. & Coleman, G. J. Restricted feeding and circadian activity rhythms of a predatory marsupial, Dasyuroides byrnei. Physiol. Behav. 38, 471–476 (1986).
CAS  Article  PubMed  Google Scholar 

22.
Boulos, Z., Frim, D. M., Dewey, L. K. & Moore-Ede, M. C. Effects of restricted feeding schedules on circadian organization in squirrel monkeys. Physiol. Behav. 45, 507–515 (1989).
CAS  Article  PubMed  Google Scholar 

23.
Mahoney, M., Bult, A. & Smale, L. Phase response curve and light-induced fos expression in the suprachiasmatic nucleus and adjacent hypothalamus of Arvicanthis niloticus. J. Biol. Rhythms 16, 149–162 (2001).
CAS  Article  PubMed  Google Scholar 

24.
Alagaili, A. N., Bennett, N. C., Amor, N. M. & Hart, D. W. The locomotory activity patterns of the arid-dwelling desert hedgehog, Paraechinus aethiopicus, from Saudi Arabia. J. Arid Environ. 177, 104141 (2020).
ADS  Article  Google Scholar 

25.
Verwey, M., Robinson, B. & Amir, S. Recording and analysis of circadian rhythms in running-wheel activity in rodents. J. Vis. Exp. https://doi.org/10.3791/50186 (2013).
Article  PubMed  PubMed Central  Google Scholar 

26.
Refinetti, R. Early research on circadian rhythms. In Circadian Physiology 2nd edn (ed. Refinetti, R.) 1–667 (CRC Taylor and Frabcis Group, Boca Raton, 2006).
Google Scholar 

27.
Goldman, B. D., Goldman, S. L., Riccio, A. P. & Terkel, J. Circadian patterns of locomotor activity and body temperature in blind mole-rats, Spalax ehrenbergi. J. Biol. Rhythms 12, 348–361 (1997).
CAS  Article  PubMed  Google Scholar 

28.
Kopp, C. et al. Effects of a daylight cycle reversal on locomotor activity in several inbred strains of mice. Physiol. Behav. 63, 577–585 (1998).
CAS  Article  PubMed  Google Scholar 

29.
Giannetto, C., Casella, S., Caola, G. & Piccione, G. Photic and non-photic entrainment on daily rhythm of locomotor activity in goats. Anim. Sci. J. 81, 122–128 (2010).
Article  PubMed  Google Scholar 

30.
Piccione, G., Giannetto, C., Casella, S. & Caola, G. Daily locomotor activity in five domestic animals. Anim. Biol. 60, 15–24 (2010).
Article  Google Scholar 

31.
Challet, E. Minireview: Entrainment of the suprachiasmatic clockwork in diurnal and nocturnal mammals. Endocrinology 148, 5648–5655 (2007).
CAS  Article  PubMed  PubMed Central  Google Scholar 

32.
Dibner, C., Schibler, U. & Albrecht, U. The mammalian circadian timing system: Organization and coordination of central and peripheral clocks. Annu. Rev. Physiol. 72, 517–549 (2010).
CAS  Article  PubMed  PubMed Central  Google Scholar 

33.
Tanaka, M., Ichitani, Y., Okamura, H., Tanaka, Y. & Ibata, Y. The direct retinal projection to VIP neuronal elements in the rat SCN. Brain Res. Bull. 31, 637–640 (1993).
CAS  Article  PubMed  PubMed Central  Google Scholar 

34.
Jacomy, H., Burlet, A. & Bosler, O. Vasoactive intestinal peptide neurons as synaptic targets for vasopressin neurons in the suprachiasmatic nucleus. Double-label immunocytochemical demonstration in the rat. Neuroscience 88, 859–870 (1999).
CAS  Article  PubMed  PubMed Central  Google Scholar 

35.
Aton, S. J., Colwell, C. S., Harmar, A. J., Waschek, J. & Herzog, E. D. Vasoactive intestinal polypeptide mediates circadian rhythmicity and synchrony in mammalian clock neurons. Nat. Neurosci. 8, 476–483 (2005).
CAS  Article  PubMed  PubMed Central  Google Scholar 

36.
Reppert, S. M. & Weaver, D. R. Comparing clockworks: Mouse versus fly. J. Biol. Rhythms 15, 357–364 (2000).
CAS  Article  PubMed  PubMed Central  Google Scholar 

37.
Reppert, S. M. & Weaver, D. R. Molecular analysis of mammalian circadian rhythms. Annu. Rev. Physiol. 63, 647–676 (2001).
CAS  Article  PubMed  PubMed Central  Google Scholar 

38.
Shearman, L. P. et al. Interacting molecular loops in the mammalian circadian clock. Science 288, 1013–1019 (2000).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

39.
Okamura, H., Yamaguchi, S. & Yagita, K. Molecular machinery of the circadian clock in mammals. Cell Tissue Res. 309, 47–56 (2002).
CAS  Article  Google Scholar 

40.
Takahashi, J. S., Hong, H. K., Ko, C. H. & McDearmon, E. L. The genetics of mammalian circadian order and disorder: Implications for physiology and disease. Nat. Rev. Genet. 9, 764–775 (2008).
CAS  Article  PubMed  PubMed Central  Google Scholar 

41.
Mohawk, J. A., Green, C. B. & Takahashi, J. S. Central and peripheral circadian clocks in mammals. Annu. Rev. Neurosci. 35, 445–462 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

42.
Rensing, L. & Ruoff, P. Temperature effect on entrainment, phase shifting, and amplitude of circadian clocks and its molecular bases. Chronobiol. Int. 19, 807–864 (2002).
CAS  Article  PubMed  Google Scholar 

43.
Aschoff, J. & Tokura, H. Circadian activity rhythms in squirrel monkeys: Entrainment by temperature cycles 1. J. Biol. Rhythms 1, 91–99 (1986).
CAS  Article  PubMed  Google Scholar 

44.
Pálková, M., Sigmund, L. & Erkert, H. G. Effect of ambient temperature on the circadian activity rhythm in common marmosets, Callithrix j jacchus (primates). Chronobiol. Int. 16, 149–161 (1999).
Article  PubMed  Google Scholar 

45.
Rajaratnam, S. M. W. & Redman, J. R. Entrainment of activity rhythms to temperature cycles in diurnal palm squirrels. Physiol. Behav. 63, 271–277 (1998).
CAS  Article  PubMed  Google Scholar 

46.
Refinetti, R. Entrainment of circadian rhythm by ambient temperature cycles in mice. J. Biol. Rhythms 25, 247–256 (2010).
Article  Google Scholar 

47.
van Jaarsveld, B., Bennett, N. C., Hart, D. W. & Oosthuizen, M. K. Locomotor activity and body temperature rhythms in the Mahali mole-rat (C. h. mahali): The effect of light and ambient temperature variations. J. Therm. Biol. 79, 24–32 (2019).
Article  Google Scholar 

48.
Schmidt-Nielsen, K. The physiology of the camel. Sci. Am. 201, 140–151 (1959).
CAS  Article  PubMed  PubMed Central  Google Scholar 

49.
Wu, H. et al. Camelid genomes reveal evolution and adaptation to desert environments. Nat. Commun. 5, 5188 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

50.
Samara, E. M. Unraveling the relationship between the topographic distribution patterns of skin temperature and perspiration response in dromedary camels. J. Therm. Biol 84, 311–315 (2019).
Article  PubMed  Google Scholar 

51.
Tibary, A. & El Allali, K. Dromedary camel: A model of heat resistant livestock animal. Theriogenology 154, 203–211 (2020).
Article  PubMed  Google Scholar 

52.
Lindberg, R. G. & Hayden, P. Thermoperiodic entrainment of arousal from torpor in the little pocket mouse, Perognathus longimembris. Chronobiologia 1, 356–361 (1974).
CAS  PubMed  Google Scholar 

53.
Erkert, H. G. & Rothmund, E. Differences in temperature sensitivity of the circadian systems of homoeothermic and heterothermic neotropical bats. Comp. Biochem. Physiol. 68A, 383–390 (1980).
Google Scholar 

54.
Pohl, H. Temperature cycles as zeitgeber for the circadian clock of two burrowing rodents, the normothermic antelope ground squirrel and the heterothermic Syrian Hamster. Biol. Rhythm Res. 29, 311–325 (1998).
Article  Google Scholar 

55.
Cain, J. W., Krausman, P. R., Rosenstock, S. S. & Turner, J. C. Mechanisms of thermoregulation and water balance in Desert Ungulates. Wildl. Soc. Bull. 1973–2006(34), 570–581 (2006).
Article  Google Scholar 

56.
Mengistu, U., Dahlborn, K. & Olsson, K. Mechanisms of water economy in lactating Ethiopian Somali goats during repeated cycles of intermittent watering. Anim. Int. J. Anim. Biosci. 1, 1009–1017 (2007).
CAS  Article  Google Scholar 

57.
Gauthier-Pilters, H. Aspects of dromedary ecology and ethology. In The Camelid (ed. Cockrill, W. R.) (Scandinavian Institute of African Studies, Uppsala, 1984).
Google Scholar 

58.
Miller, G. D., Cochran, M. H. & Smith, E. L. Nighttime activity of desert bighorn sheep. Desert Bighorn Council Trans. 28, 23–25 (1984).
Google Scholar 

59.
Hayes, C. L. & Krausman, P. R. Nocturnal activity of female desert mule deer. J. Wildl. Manage. 57, 897–904 (1993).
Article  Google Scholar 

60.
Davimes, J. G. et al. Temporal niche switching in Arabian oryx (Oryx leucoryx): Seasonal plasticity of 24h activity patterns in a large desert mammal. Physiol. Behav. 177, 148–154 (2017).
CAS  Article  PubMed  Google Scholar 

61.
Davimes, J. G. et al. Seasonal variations in sleep of free-ranging Arabian oryx (Oryx leucoryx) under natural hyperarid conditions. Sleep https://doi.org/10.1093/sleep/zsy038 (2018).
Article  PubMed  Google Scholar 

62.
El Allali, K. et al. Seasonal variations in the nycthemeral rhythm of plasma melatonin in the camel (Camelus dromedarius). J. Pineal Res. 39, 121–128 (2005).
Article  CAS  PubMed  Google Scholar 

63.
Mrosovsky, N. Circannual cycles in golden-mantled ground squirrels: Phase shift produced by low temperature. J. Comp. Physiol. 136, 349–353 (1980).
Article  Google Scholar 

64.
Mrosovsky, N. Circannual cycles in golden-mantled ground squirrels: Experiments with food deprivation and effects of temperature on periodicity. J. Comp. Physiol. 136, 355–360 (1980).
Article  Google Scholar 

65.
Mrosovsky, N. Thermal effects on the periodicity, phasing and peristance of circannual cycles. In Living in the Cold (eds Heller, H. C. et al.) 403–410 (Elsevier, New York, 1986).
Google Scholar 

66.
Mrosovsky, N. Circannual cycles in golden-mantled ground squirrels: fall and spring cold pulses. J. Comp. Physiol. 167, 683–689 (1990).
Article  Google Scholar 

67.
Canguilhem, B., Schieber, J. P. & Koch, A. Circannual weight rhythm of the European hamster (Cricetus cricetus). Respective influence of the photoperiod and external temperature during its course. Arch. Sci. Physiol. 27, 67–90 (1973).
CAS  Google Scholar 

68.
Jallageas, M. & Assenmacher, I. External factors controlling annual testosterone and thyroxine cycles in the edible dormouse Glis glis. Comp. Biochem. Physiol. A Physiol. 77, 161–167 (1984).
CAS  Article  Google Scholar 

69.
Touitou, Y., Smolensky, M. H. & Portaluppi, F. Ethics, standards, and procedures of animal and human chronobiology research. Chronobiol. Int. 23, 1083–1096 (2006).
Article  PubMed  Google Scholar  More

1.
Claussnitzer, M. et al. A brief history of human disease genetics. Nature 577, 179–189 (2020).
ADS  CAS  PubMed  PubMed Central  Google Scholar 
2.
Hiller, M. et al. A “forward genomics” approach links genotype to phenotype using independent phenotypic losses among related species. Cell Rep. 2, 817–823 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

3.
Wasser, S. K. et al. Genetic assignment of large seizures of elephant ivory reveals Africa’s major poaching hotspots. Science 349, 84–87 (2015).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

4.
Wright, B. et al. Development of a SNP-based assay for measuring genetic diversity in the Tasmanian devil insurance population. BMC Genomics 16, 791 (2015).
PubMed  PubMed Central  Google Scholar 

5.
Lappalainen, T., Scott, A. J., Brandt, M. & Hall, I. M. Genomic analysis in the age of human genome sequencing. Cell 177, 70–84 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

6.
Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

7.
Lindblad-Toh, K. et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478, 476–482 (2011).
CAS  PubMed  PubMed Central  Google Scholar 

8.
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

9.
IUCN. The IUCN Red List of Threatened Species. Version 2019-2 http://www.iucnredlist.org (2019).

10.
Ryder, O. A. & Onuma, M. Viable cell culture banking for biodiversity characterization and conservation. Annu. Rev. Anim. Biosci. 6, 83–98 (2018).
PubMed  Google Scholar 

11.
Weisenfeld, N. I. et al. Comprehensive variation discovery in single human genomes. Nat. Genet. 46, 1350–1355 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

12.
Putnam, N. H. et al. Chromosome-scale shotgun assembly using an in vitro method for long-range linkage. Genome Res. 26, 342–350 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

13.
Kim, J. et al. Reconstruction and evolutionary history of eutherian chromosomes. Proc. Natl Acad. Sci. USA 114, E5379–E5388 (2017).
CAS  PubMed  Google Scholar 

14.
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

15.
Balasubramanian, S. et al. Using ALoFT to determine the impact of putative loss-of-function variants in protein-coding genes. Nat. Commun. 8, 382 (2017).
ADS  PubMed  PubMed Central  Google Scholar 

16.
Meadows, J. R. S. & Lindblad-Toh, K. Dissecting evolution and disease using comparative vertebrate genomics. Nat. Rev. Genet. 18, 624–636 (2017).
CAS  PubMed  Google Scholar 

17.
Cooper, G. M. & Shendure, J. Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. Nat. Rev. Genet. 12, 628–640 (2011).
CAS  PubMed  Google Scholar 

18.
Baiz, M. D., Tucker, P. K., Mueller, J. L. & Cortés-Ortiz, L. X-linked signature of reproductive isolation in humans is mirrored in a howler monkey hybrid zone. J. Hered. 111, 419–428 (2020).
PubMed  PubMed Central  Google Scholar 

19.
Dobzhansky, T. & Dobzhansky, T. G. Genetics and the Origin of Species (Columbia Univ. Press, 1937).

20.
Herrera-Álvarez, S., Karlsson, E., Ryder, O. A., Lindblad-Toh, K. & Crawford, A. J. How to make a rodent giant: genomic basis and tradeoffs of gigantism in the capybara, the world’s largest rodent. Preprint at https://doi.org/10.1101/424606 (2018).

21.
Abegglen, L. M. et al. Potential mechanisms for cancer resistance in elephants and comparative cellular response to DNA damage in humans. J. Am. Med. Assoc. 314, 1850–1860 (2015).
CAS  Google Scholar 

22.
Casewell, N. R. et al. Solenodon genome reveals convergent evolution of venom in eulipotyphlan mammals. Proc. Natl Acad. Sci. USA 116, 25745–25755 (2019).
CAS  PubMed  Google Scholar 

23.
Beichman, A. C. et al. Aquatic adaptation and depleted diversity: a deep dive into the genomes of the sea otter and giant otter. Mol. Biol. Evol. 36, 2631–2655 (2019).
CAS  PubMed  Google Scholar 

24.
Damas, J. et al. Broad host range of SARS-CoV-2 predicted by comparative and structural analysis of ACE2 in vertebrates. Proc. Natl Acad. Sci. USA 117, 22311–22322 (2020).
CAS  PubMed  Google Scholar 

25.
Xue, Y. et al. Mountain gorilla genomes reveal the impact of long-term population decline and inbreeding. Science 348, 242–245 (2015).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

26.
Ceballos, F. C., Joshi, P. K., Clark, D. W., Ramsay, M. & Wilson, J. F. Runs of homozygosity: windows into population history and trait architecture. Nat. Rev. Genet. 19, 220–234 (2018).
CAS  PubMed  Google Scholar 

27.
Spielman, D., Brook, B. W. & Frankham, R. Most species are not driven to extinction before genetic factors impact them. Proc. Natl Acad. Sci. USA 101, 15261–15264 (2004).
ADS  CAS  PubMed  Google Scholar 

28.
Vinson, J. P. et al. Assembly of polymorphic genomes: algorithms and application to Ciona savignyi. Genome Res. 15, 1127–1135 (2005).
PubMed  PubMed Central  Google Scholar 

29.
MacManes, M. D. & Lacey, E. A. The social brain: transcriptome assembly and characterization of the hippocampus from a social subterranean rodent, the colonial tuco-tuco (Ctenomys sociabilis). PLoS ONE 7, e45524 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

30.
Jones, K. E. et al. PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90, 2648 (2009).
Google Scholar 

31.
Cardillo, M. Biological determinants of extinction risk: why are smaller species less vulnerable? Anim. Conserv. 6, 63–69 (2003).
Google Scholar 

32.
Natesh, M. et al. Empowering conservation practice with efficient and economical genotyping from poor quality samples. Methods Ecol. Evol. 10, 853–859 (2019).
PubMed  PubMed Central  Google Scholar 

33.
Lowry, D. B. et al. Breaking RAD: an evaluation of the utility of restriction site-associated DNA sequencing for genome scans of adaptation. Mol. Ecol. Resour. 17, 142–152 (2017).
CAS  PubMed  Google Scholar 

34.
Shapiro, B. Pathways to de-extinction: how close can we get to resurrection of an extinct species? Funct. Ecol. 31, 996–1002 (2017).
Google Scholar 

35.
Benazzo, A. et al. Survival and divergence in a small group: the extraordinary genomic history of the endangered Apennine brown bear stragglers. Proc. Natl Acad. Sci. USA 114, E9589–E9597 (2017).
CAS  PubMed  Google Scholar 

36.
Saremi, N. F. et al. Puma genomes from North and South America provide insights into the genomic consequences of inbreeding. Nat. Commun. 10, 4769 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

37.
Armstrong, J. et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature https://doi.org/10.1038/s41586-020-2871-y (2020).

38.
Haeussler, M. et al. The UCSC genome browser database: 2019 update. Nucleic Acids Res. 47, D853–D858 (2019).
CAS  PubMed  Google Scholar 

39.
Rands, C. M., Meader, S., Ponting, C. P. & Lunter, G. 8.2% of the human genome is constrained: variation in rates of turnover across functional element classes in the human lineage. PLoS Genet. 10, e1004525 (2014).
PubMed  PubMed Central  Google Scholar 

40.
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
ADS  Google Scholar 

41.
GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
PubMed Central  Google Scholar 

42.
Regev, A. et al. The human cell atlas. eLife 6, e27041 (2017).
PubMed  PubMed Central  Google Scholar 

43.
Lewin, H. A. et al. Earth BioGenome project: sequencing life for the future of life. Proc. Natl Acad. Sci. USA 115, 4325–4333 (2018).
CAS  PubMed  Google Scholar 

44.
Koepfli, K.-P., Paten, B., the Genome 10K Community of Scientists & O’Brien, S. J. The Genome 10K project: a way forward. Annu. Rev. Anim. Biosci. 3, 57–111 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

45.
Teeling, E. C. et al. Bat biology, genomes, and the Bat1K project: to generate chromosome-level genomes for all living bat species. Annu. Rev. Anim. Biosci. 6, 23–46 (2018).
PubMed  Google Scholar 

46.
Feng, S. et al. Dense sampling of bird diversity increases power of comparative genomics. Nature https://doi.org/10.1038/s41586-020-2873-9 (2020).

47.
Kumar, S., Stecher, G., Suleski, M. & Hedges, S. B. TimeTree: a resource for timelines, timetrees, and divergence times. Mol. Biol. Evol. 34, 1812–1819 (2017).
CAS  PubMed  Google Scholar 

48.
Wilson, D. E. & Reeder, D. M. (eds) Mammal Species of the World. A Taxonomic and Geographic Reference 3rd edn (Johns Hopkins Univ. Press, 2005).

49.
Vlieghe, D. et al. A new generation of JASPAR, the open-access repository for transcription factor binding site profiles. Nucleic Acids Res. 34, D95–D97 (2006).
CAS  PubMed  Google Scholar 

50.
Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).
PubMed  PubMed Central  Google Scholar 

51.
Farré, M. et al. A near-chromosome-scale genome assembly of the gemsbok (Oryx gazella): an iconic antelope of the Kalahari desert. Gigascience 8, giy162 (2019).
PubMed Central  Google Scholar 

52.
McKenna, A. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

53.
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
CAS  PubMed  PubMed Central  Google Scholar 

54.
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
PubMed  PubMed Central  Google Scholar 

55.
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
CAS  PubMed  PubMed Central  Google Scholar 

56.
Benaglia, T., Chauveau, D., Hunter, D. & Young, D. mixtools: an R package for analyzing finite mixture models. J. Stat. Softw. 32, 1–29 (2009).
Google Scholar 

57.
R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (2019).

58.
Paten, B. et al. Cactus: algorithms for genome multiple sequence alignment. Genome Res. 21, 1512–1528 (2011).
CAS  PubMed  PubMed Central  Google Scholar 

59.
Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 17, 132 (2016).
PubMed  PubMed Central  Google Scholar 

60.
Smit, A. F. A., Hubley, R. & Green, P. RepeatMasker Open-4.0. http://www.repeatmasker.org/ (2013–2015).

61.
Hubisz, M. J., Pollard, K. S. & Siepel, A. PHAST and RPHAST: phylogenetic analysis with space/time models. Brief. Bioinform. 12, 41–51 (2011).
CAS  PubMed  Google Scholar 

62.
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
MathSciNet  MATH  Google Scholar 

63.
Benson, D. A. et al. GenBank. Nucleic Acids Res. 41, D36–D42 (2013).
CAS  PubMed  Google Scholar 

64.
Nguyen, N. et al. Comparative assembly hubs: web-accessible browsers for comparative genomics. Bioinformatics 30, 3293–3301 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

65.
Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

66.
Pinheiro, E. C., Taddei, V. A., Migliorini, R. H. & Kettelhut, I. C. Effect of fasting on carbohydrate metabolism in frugivorous bats (Artibeus lituratus and Artibeus jamaicensis). Comp. Biochem. Physiol. B Biochem. Mol. Biol. 143, 279–284 (2006).
PubMed  Google Scholar 

67.
Gordon, L. M. et al. Amorphous intergranular phases control the properties of rodent tooth enamel. Science 347, 746–750 (2015).
ADS  CAS  PubMed  Google Scholar 

68.
Hindle, A. G. & Martin, S. L. Intrinsic circannual regulation of brown adipose tissue form and function in tune with hibernation. Am. J. Physiol. Endocrinol. Metab. 306, E284–E299 (2014).
CAS  PubMed  Google Scholar 

69.
Stanford, K. I. et al. Brown adipose tissue regulates glucose homeostasis and insulin sensitivity. J. Clin. Invest. 123, 215–223 (2013).
CAS  PubMed  Google Scholar 

70.
Chondronikola, M. et al. Brown adipose tissue improves whole-body glucose homeostasis and insulin sensitivity in humans. Diabetes 63, 4089–4099 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

71.
Saito, M. et al. High incidence of metabolically active brown adipose tissue in healthy adult humans: effects of cold exposure and adiposity. Diabetes 58, 1526–1531 (2009).
CAS  PubMed  PubMed Central  Google Scholar  More

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In this episode:
00:46 Sensory pollution and bird reproduction
Light- and noise-pollution have been shown to affect the behaviour of birds. However, it’s been difficult to work out whether these behavioural changes have led to bird species thriving or declining. Now, researchers have assembled a massive dataset that can begin to give some answers. Research article: Senzaki et al.
10:17 Coronapod
Interim results from a phase III trial show compelling evidence that a coronavirus vaccine candidate can prevent COVID-19. However, amid the optimism there remain questions to be answered – we discuss these, and what the results might mean for other vaccines in development. News: What Pfizer’s landmark COVID vaccine results mean for the pandemic
23:29 Research Highlights
A tiny bat breaks a migration record, and researchers engineer a mouse’s sense of place. Research Highlight: The record-setting flight of a bat that weighs less than a toothbrush; Research Article: Robinson et al.
25:39 Organised crime in fisheries
When you think of fishing, organised crime probably isn’t the first thing that springs to mind. However, billions of dollars every year from the fishing industry are lost to criminal enterprises. We discuss some of the impacts and what can be done about it. Research Article: Witbooi et al.
32:13 Briefing Chat
We discuss some highlights from the Nature Briefing. This time, a time-capsule discovered on the Irish coast provides a damning indictment of Arctic warming, and some human remains challenge the idea of ‘man-the-hunter’. The Guardian: Arctic time capsule from 2018 washes up in Ireland as polar ice melts; Science: Woman the hunter: Ancient Andean remains challenge old ideas of who speared big game
Subscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.
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Survey area
The U.S. AMLR Program conducted five winter surveys (August and September 2012–2016) around the northern Antarctic Peninsula aboard the U.S. National Science Foundation research vessel/ice breaker (RVIB) Nathaniel B. Palmer (Fig. 1)19. We surveyed a historical grid of 110 fixed stations located 20–40 km apart around the northern Antarctic Peninsula and South Shetland Islands, which was divided into four sampling areas: the Elephant Island Area (EI; 43,865 km2), the South Area (the Bransfield Strait, SA; 24,479 km2), the West Area (the west shelf immediately north of Livingston and King George Islands, WA; 38,524 km2), and the Joinville Island Area (JI; 18,151 km2). In 2016, we also surveyed the Gerlache Strait (GS; 24,479 km2).
At-sea sampling
Detailed methods for all at-sea sampling by the U.S. AMLR Program have been previously reported19. At each sampling station, we performed a Conductivity-Temperature-Depth (CTD) cast to 750 m or to within 10 m of the bottom in shallower areas (SBE9/11; Sea-Bird Electronics). The CTD rosette was equipped with 24 10 l Niskin bottles triggered to close on the upcast at 750, 200, 100, 75, 50, 40, 30, 20, 15, and 5 m. We defined the Upper Mixed Layer (UML) depth (m) as the depth at which the density of the water changed by 0.05 kg m−3 relative to the mean density of the upper 10 m of the water column20. UML temperature and salinities were defined to be means over the depth range of the UML. We defined daylight conditions according to three categories. Day (D) was defined as one hour after local sunrise to one hour before local sunset; night (N) was defined as one hour after sunset to one hour before sunrise; and Twilight (T) was defined as one hour before and after sunrise and sunset.
Chlorophyll-a (hereafter chl-a) was determined from water samples collected between 5 and 200 m21,22. Samples (285 ml) were filtered at a pressure differential of  More

1.
Götzenberger, L. et al. Ecological assembly rules in plant communities—Approaches, patterns and prospects. Biol. Rev. 87, 111–127. https://doi.org/10.1111/j.1469-185X.2011.00187.x (2012).
Article  PubMed  Google Scholar 
2.
Díaz, S. et al. Incorporating plant functional diversity effects in ecosystem service assessments. P. Natl. Axad. Sci. USA 104, 20684–20689. https://doi.org/10.1073/pnas.0704716104 (2007).
ADS  Article  Google Scholar 

3.
McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185. https://doi.org/10.1016/j.tree.2006.02.002 (2006).
Article  PubMed  Google Scholar 

4.
Nobel, I.R. & Slatyer, R.O. Post-fire succession of plants in Mediterranean ecosystems in: Proceedings of the Symposium on the Environmental Consequences of Fire and Fuel Management in Mediterranean Ecosystems (eds. Mooney, H.A. & Conrad, C.E.) 27–36 (California Palo Alto, 1977).

5.
Cornwell, W. K. & Ackerly, D. D. Community assembly and shifts in plant trait distributions across an environmental gradient in coastal California. Ecol. Monogr. 79, 109–126. https://doi.org/10.1890/07-1134.1 (2009).
Article  Google Scholar 

6.
Kraft, N. J. B., Valencia, R. & Ackerly, D. D. Functional traits and niche-based tree community assembly in an amazonian forest. Science 322, 580–582. https://doi.org/10.1126/science.1160662 (2008).
ADS  CAS  Article  PubMed  Google Scholar 

7.
MacArthur, R. & Levins, R. The limiting similarity, convergence, and divergence of coexisting species. Am. Nat. 101, 377–385. https://doi.org/10.1086/282505 (1967).
Article  Google Scholar 

8.
Pásztor, L., Botta-Dukát, Z., Magyar, G., Czárán, T. & Meszéna, G. Theory-Based Ecology: A Darwinian Approach (Oxford University Press, Oxford, 2016).
Google Scholar 

9.
Diamond, J.M. Assembly of Species Communities in Ecology and Evolution of Communities (eds. Cody, M.L. & Diamond, J.M.) 342–444 (Belknap Press, 1975).

10.
Litchman, E., Klausmeier, C. A., Schofield, O. M. & Falkowski, P. G. The role of functional traits and trade-offs in structuring phytoplankton communities: Scaling from cellular to ecosystem level. Ecol. Lett. 10, 1170–1181. https://doi.org/10.1111/j.1461-0248.2007.01117.x (2007).
Article  PubMed  Google Scholar 

11.
Reynolds, C. S., Huszár, V., Kruk, C., Naselli-Flores, L. & Melo, S. Towards a functional classification of the freshwater phytoplankton. J. Plankton Res. 24, 417–428. https://doi.org/10.1093/plankt/24.5.417 (2002).
Article  Google Scholar 

12.
Salmaso, N. & Padisák, J. Morpho-functional groups and phytoplankton development in two deep lakes (Lake Garda, Italy and Lake Stechlin, Germany). Hydrobiologia 578, 97–112. https://doi.org/10.1007/s10750-006-0437-0 (2007).
Article  Google Scholar 

13.
Salmaso, N., Naselli-Flores, L. & Padisák, J. Functional classifications and their application in phytoplankton ecology. Freshw. Biol. 60, 603–619. https://doi.org/10.1111/fwb.12520 (2015).
Article  Google Scholar 

14.
Borics, G., Tóthmérész, B., Lukács, B. A. & Várbíró, G. Functional groups of phytoplankton shaping diversity of shallow lake ecosystems. Hydrobiologia 698, 251–262. https://doi.org/10.1007/s10750-012-1129-6 (2012).
Article  Google Scholar 

15.
Vellend, M. Conceptual synthesis in community ecology. Q. Rev. Biol. 85, 183–206. https://doi.org/10.1086/652373 (2010).
Article  PubMed  PubMed Central  Google Scholar 

16.
Padisák, J., Vasas, G. & Borics, G. Phycogeography of freshwater phytoplankton: Traditional knowledge and new molecular tools. Hydrobiologia 764, 3–27. https://doi.org/10.1007/s10750-015-2259-4 (2016).
CAS  Article  Google Scholar 

17.
Padisák, J. Cylindrospermopsis raciborskii (Woloszynska) Seenayya et Subba Raju, an expanding, highly adaptive cyanobacterium: worldwide distribution and review of its ecology. Arch. Hydrobiol. 107, 563–593 (1997).
Google Scholar 

18.
Méndez, V., Assaf, M., Masó-Puigdellosas, A., Campos, D. & Horsthemke, W. Demographic stochasticity and extinction in populations with Allee effect. Phys. Rev. E. 99, 022101. https://doi.org/10.1103/PhysRevE.99.022101 (2019).
ADS  Article  Google Scholar 

19.
Parvinen, K., Dieckmann, U., Gyllenberg, M. & Metz, J. A. Evolution of dispersal in metapopulations with local density dependence and demographic stochasticity. J. Evolut. Biol. 16, 143–153. https://doi.org/10.1046/j.1420-9101.2003.00478.x (2003).
CAS  Article  Google Scholar 

20.
Borics, G., Abonyi, A., Salmaso, N. & Ptacnik, R. Freshwater phytoplankton diversity: Models, drivers and implications for ecosystem properties. Hydrobiologia https://doi.org/10.1007/s10750-020-04332-9 (2020).
Article  PubMed  PubMed Central  Google Scholar 

21.
Naselli-Flores, L., Padisák, J., Dokulil, M. T. & Chorus, I. Equilibrium/steady-state concept in phytoplankton ecology. Hydrobiologia 502, 395–403. https://doi.org/10.1023/B:HYDR.0000004297.52645.59 (2003).
Article  Google Scholar 

22.
Weiher, E. & Keddy, P.A. Assembly rules, null models, and trait dispersion: New questions from old patterns. Oikos 74, 159–164 (1995). https://www.jstor.org/stable/3545686.

23.
Coyle, J. R. et al. Using trait and phylogenetic diversity to evaluate the generality of the stress-dominance hypothesis in eastern North American tree communities. Ecography 37, 814–826. https://doi.org/10.1111/ecog.00473 (2014).
Article  Google Scholar 

24.
Baastrup-Spohr, L., Sand-Jensen, K., Nicolajsen, S. V. & Bruun, H. H. From soaking wet to bone dry: Predicting plant community composition along a steep hydrological gradient. J. Veg. Sci. 26, 619–630. https://doi.org/10.1111/jvs.12280 (2015).
Article  Google Scholar 

25.
Lhotsky, B. et al. Changes in assembly rules along a stress gradient from open dry grasslands to wetlands. J. Ecol. 104, 507–517. https://doi.org/10.1111/1365-2745.12532 (2016).
Article  Google Scholar 

26.
Butterfield, B. J., Bradford, J. B., Munson, S. M. & Gremer, J. R. Aridity increases below-ground niche breadth in grass communities. Plant Ecol. 218, 385–394. https://doi.org/10.1007/s11258-016-0696-4 (2017).
Article  Google Scholar 

27.
Gastauer, M., Saporetti-Junior, A. W., Valladares, F. & Meira-Neto, J. A. Phylogenetic community structure reveals differences in plant community assembly of an oligotrophic white-sand ecosystem from the Brazilian Atlantic Forest. Acta Bot. Bras. 31, 531–538. https://doi.org/10.1590/0102-33062016abb0442 (2017).
Article  Google Scholar 

28.
Chapman, J. & McEwan, R. The role of environmental filtering in structuring appalachian tree communities: Topographic influences on functional diversity are mediated through soil characteristics. Forests 9, 19. https://doi.org/10.3390/f9010019 (2018).
Article  Google Scholar 

29.
Lukács, B. A. et al. Carbon forms, nutrients and water velocity filter hydrophyte and riverbank species differently: A trait-based study. J. Veg. Sci. 30, 471–484 (2019).
Article  Google Scholar 

30.
Kuczynski, L. & Grenouillet, G. Community disassembly under global change: Evidence in favor of the stress-dominance hypothesis. Glob. Chang. Biol. 24, 4417–4427. https://doi.org/10.1111/gcb.14320 (2018).
ADS  Article  PubMed  Google Scholar 

31.
Patrick, L. E. & Stevens, R. D. Phylogenetic community structure of North American desert bats: Influence of environment at multiple spatial and taxonomic scales. J. Anim. Ecol. 85, 1118–1130. https://doi.org/10.1111/1365-2656.12529 (2016).
Article  PubMed  Google Scholar 

32.
Lopez, B. et al. A new framework for inferring community assembly processes using phylogenetic information, relevant traits and environmental gradients. One Ecosyst. 1, e9501. https://doi.org/10.3897/oneeco.1.e9501 (2016).
Article  Google Scholar 

33.
Ács, E. et al. Trait-based community assembly of epiphytic diatoms in saline astatic ponds: a test of the stress-dominance hypothesis. Sci. Rep. 9(1), 15749 (2019).
ADS  Article  PubMed  PubMed Central  Google Scholar 

34.
Downing, J. A. & McCauley, E. The nitrogen:phosphorus relationship in lakes. Limnol. Oceanogr. 37, 936–945 (1992).
ADS  CAS  Article  Google Scholar 

35.
Phillips, G. et al. A phytoplankton trophic index to assess the status of lakes for the Water Framework Directive. Hydrobiologia 704, 75–95. https://doi.org/10.1007/s10750-012-1390-8 (2013).
Article  Google Scholar 

36.
Padisák, J. et al. Dominant species, functional assemblages and frequency of equilibrium phases in late summer phytoplankton assemblages in Hungarian small shallow lakes. Hydrobiologia 502, 157–168. https://doi.org/10.1023/B:HYDR.0000004278.10887.40 (2003).
Article  Google Scholar 

37.
HilleRisLambers, J., Adler, P. B., Harpole, W. S., Levine, J. M. & Mayfield, M. M. Rethinking community assembly through the lens of coexistence theory. Annu. Rev. Ecol. Evol. Syst. 43, 227–248. https://doi.org/10.1146/annurev-ecolsys-110411-160411 (2012).
Article  Google Scholar 

38.
Huisman, J., van Oostveen. P. & Weissing, F.J. Species dynamics in phytoplankton blooms: incomplete mixing and competition for light. Am. Nat. 154, 46–68, https://doi.org/10.1086/303220 (1999).

39.
Bird, D. F. & Kalff, J. Bacterial grazing by planktonic lake algae. Science 231, 493–495. https://doi.org/10.1126/science.231.4737.493 (1986).
ADS  CAS  Article  Google Scholar 

40.
Stoecker, D. K. Mixotrophy among Dinoflagellates. J. Eukaryot. Microbiol. 46, 397–401. https://doi.org/10.1111/j.1550-7408.1999.tb04619.x (1999).
Article  Google Scholar 

41.
Grime, J.P. Plant Strategies, Vegetation Processes, and Ecosystem Properties. (John Wiley and Sons, 2001). ISBN 0-471-49601-4.

42.
Navas, M. & Violle, C. Plant traits related to competition: How do they shape the functional diversity of communities?. Commun. Ecol. 10, 131–137. https://doi.org/10.1556/ComEc.10.2009.1.15 (2009).
Article  Google Scholar 

43.
Reynolds, C. S. The Ecology of Phytoplankton (Cambridge University Press, Cambridge, 2006).
Google Scholar 

44.
Borics, G., Grigorszky, I., Szabó, S. & Padisák, J. Phytoplankton associations in a small hypertrophic fishpond in East Hungary during a change from bottom-up to top-down control. Hydrobiologia 424, 79–90. https://doi.org/10.1023/A:1003948827254 (2000).
Article  Google Scholar 

45.
Mason, N. W. H., de Bello, F., Doležal, J. & Lepš, J. Niche overlap reveals the effects of competition, disturbance and contrasting assembly processes in experimental grassland communities. J. Ecol. 99, 788–796. https://doi.org/10.1111/j.1365-2745.2011.01801.x (2011).
Article  Google Scholar 

46.
Dobosi, Z. & Felméry, L. Climatology, ELTE TTK, Nemzeti Tankönyvkiadó, p. 500 (in Hungarian).

47.
Mihevc, A., Prelovšek, M. & Hajna, N.Z. Introduction to the Dinaric Karst. Inštitut za raziskovanje krasa ZRC SAZU. (2010).

48.
Borics, G. et al. Phytoplankton-based shallow lake types in the Carpathian basin: Steps towards a bottom-up typology. Fund. Appl. Limnol. 184, 23–34. https://doi.org/10.1127/1863-9135/2014/0518 (2014).
Article  Google Scholar 

49.
Borics, G., Abonyi, A., Várbíró, G., Padisák, J. &T-Krasznai, E. Lake stratification in the Carpathian basin and its interesting biological consequences. Inland Waters 5, 173–186, https://doi.org/10.5268/IW-5.2.702 (2015).

50.
Utermöhl, H. Zur Vervollkommnung der quantitative Phytolankton-Methodik. Mitt. Int. Verein. Limnol. 9, 1–38. https://doi.org/10.1080/05384680.1958.11904091 (1958).
Article  Google Scholar 

51.
Török, P. et al. Functional diversity supports the biomass–Diversity humped-back relationship in phytoplankton assemblages. Funct. Ecol. 30, 1593–1602. https://doi.org/10.1111/1365-2435.12631 (2016).
Article  Google Scholar 

52.
Hillebrand, H., Dürselen, C. D., Kirschtel, D., Pollingher, U. & Zohary, T. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35, 403–424. https://doi.org/10.1046/j.1529-8817.1999.3520403.x (1999).
Article  Google Scholar 

53.
MSZ ISO 10260:1993. Water Quality. Measurement of Biochemical Parameters. Spectrometric Determination of the Chlorophyll-a Concentration.

54.
MSZ EN ISO 6878:2004. Water Quality. Determination of Phosphorus. Ammonium Molybdate Spectrometric Method.

55.
ISO 11905-1:1997. Water Quality. Determination of Nitrogen. Part 1: Method Using Oxidative Digestion with Peroxodisulfate.

56.
MSZ ISO 6060:1991.Water Quality. Determination of the Chemical Oxygen Demand.

57.
Botta-Dukát, Z. Cautionary note on calculating standardized effect size (SES) in randomization test. Commun. Ecol. 19, 77–83, https://doi.org/10.1556/168.2018.19.1.8 (2018).

58.
Botta-Dukát, Z. Rao’s quadratic entropy as a measure of functional diversity based on multiple traits. J. Veg. Sci. 16, 533–540. https://doi.org/10.1111/j.1654-1103.2005.tb02393.x (2005).
Article  Google Scholar 

59.
Legendre, P. & Legendre, L.F. Numerical Ecology Vol. 24. (Elsevier, 2012).

60.
Götzenberger, L. et al. Which randomizations detect convergence and divergence in trait‐based community assembly? A test of commonly used null models. J. Veg. Sci. 27, https://doi.org/10.1111/jvs.12452, 1275–1287.

61.
Botta-Dukát, Z. & Czúcz, B. Testing the ability of functional diversity indices to detect trait convergence and divergence using individual-based simulation. Methods Ecol. Evol. 7, 114–126. https://doi.org/10.1111/2041-210X.12450 (2016).
Article  Google Scholar 

62.
Garnier E. et al. Plant functional markers capture ecosystem properties during secondary succession. Ecology 85, e2630–2637, https://doi.org/10.1890/03-0799.

63.
Dray, S. & Dufour, A.B. The ade4 package: Implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20, https://doi.org/10.18637/jss.v022.i04.

64.
Oksanen J. et al. Package ‘vegan’. Community Ecology Package, Version, 2(9) (2013).

65.
Team R. Core (2017) R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2017).
Google Scholar  More

1.
Bohonak, A. J. & Jenkins, D. G. Ecological and evolutionary significance of dispersal by freshwater invertebrates. Ecol. Lett. 6, 783–796 (2003).
Article  Google Scholar 
2.
Clobert, J., Baguette, M., Benton, T. G. & Bullock, J. M. Dispersal Ecology and Evolution (Oxford Univ. Press, 2012).

3.
Heino, J. et al. Metacommunity organisation, spatial extent and dispersal in aquatic systems: Patterns, processes and prospects. Freshw. Biol. 60, 845–869 (2015).
Article  Google Scholar 

4.
Barton, P. S. et al. Guidelines for using movement science to inform biodiversity policy. Environ. Manage. 56, 791–801 (2015).
ADS  Article  Google Scholar 

5.
Heino, J. et al. Integrating dispersal proxies in ecological and environmental research in the freshwater realm. Environ. Rev. 25, 334–349 (2017).
Article  Google Scholar 

6.
Rundle, S. D., Bilton, D. T. & Foggo, A. in Body Size: The Structure and Function of Aquatic Ecosystems (eds. Hildrew, A. G., Raffaelli, D. G. & Edmonds-Brown, R.) 186–209 (Cambridge Univ. Press, 2007).

7.
Macneale, K. H., Peckarsky, B. L. & Likens, G. E. Stable isotopes identify dispersal patterns of stonefly populations living along stream corridors. Freshw. Biol. 50, 1117–1130 (2005).
Article  Google Scholar 

8.
Troast, D., Suhling, F., Jinguji, H., Sahlén, G. & Ware, J. A global population genetic study of Pantala flavescens. PLoS One 11, e0148949 (2016).
Article  CAS  Google Scholar 

9.
French, S. K. & McCauley, S. J. The movement responses of three libellulid dragonfly species to open and closed landscape cover. Insect Conserv. Divers. 12, 437–447 (2019).
Article  Google Scholar 

10.
Arribas, P. et al. Dispersal ability rather than ecological tolerance drives differences in range size between lentic and lotic water beetles (Coleoptera: Hydrophilidae). J. Biogeogr. 39, 984–994 (2012).
Article  Google Scholar 

11.
Lancaster, J. & Downes, B. J. Dispersal traits may reflect dispersal distances, but dispersers may not connect populations demographically. Oecologia 184, 171–182 (2017).
ADS  Article  Google Scholar 

12.
Lancaster, J. & Downes, B. J. Aquatic Entomology (Oxford Univ. Press, 2013).

13.
Stevens, V. M. et al. Dispersal syndromes and the use of life-histories to predict dispersal. Evol. Appl. 6, 630–642 (2013).
Article  Google Scholar 

14.
Outomuro, D. & Johansson, F. Wing morphology and migration status, but not body size, habitat or Rapoport’s rule predict range size in North-American dragonflies (Odonata: Libellulidae). Ecography 42, 309–320 (2019).
Article  Google Scholar 

15.
Tonkin, J. D. et al. The role of dispersal in river network metacommunities: patterns, processes, and pathways. Freshw. Biol. 63, 141–163 (2018).
Article  Google Scholar 

16.
Brown, B. L. & Swan, C. M. Dendritic network structure constrains metacommunity properties in riverine ecosystems. J. Anim. Ecol. 79, 571–580 (2010).
CAS  Article  Google Scholar 

17.
Wikelski, M. et al. Simple rules guide dragonfly migration. Biol. Lett. 2, 325–329 (2006).
Article  Google Scholar 

18.
Schmidt-Kloiber, A. & Hering, D. An online tool that unifies, standardises and codifies more than 20,000 European freshwater organisms and their ecological preferences. Ecol. Indic. 53, 271–282, www.freshwaterecology.info (2015).
Article  Google Scholar 

19.
Serra, S. R. Q., Cobo, F., Graça, M. A. S., Dolédec, S. & Feio, M. J. Synthesising the trait information of European Chironomidae (Insecta: Diptera): towards a new database. Ecol. Indic. 61, 282–292 (2016).
Article  Google Scholar 

20.
Tachet, H., Richoux, P., Bournaud, M. & Usseglio-Polatera, P. Invertébrés d’Eau Douce: Systématique, Biologie, Écologie (CNRS Éditions, 2010).

21.
Vieira, N. K. M. et al. A Database of Lotic Invertebrate Traits for North America (U.S. Geological Survey Data Series 187, 2006).

22.
Chevenet, F., Dolédec, S. & Chessel, D. A fuzzy coding approach for the analysis of long-term ecological data. Freshw. Biol. 31, 295–309 (1994).
Article  Google Scholar 

23.
Schmera, D., Podani, J., Heino, J., Erös, T. & Poff, N. L. R. A proposed unified terminology of species traits in stream ecology. Freshw. Sci. 34, 823–830 (2015).
Article  Google Scholar 

24.
Lancaster, J., Downes, B. J. & Arnold, A. Lasting effects of maternal behaviour on the distribution of a dispersive stream insect. J. Anim. Ecol. 80, 1061–1069 (2011).
Article  Google Scholar 

25.
Jenkins, D. G. et al. Does size matter for dispersal distance? Glob. Ecol. Biogeogr. 16, 415–425 (2007).
Article  Google Scholar 

26.
Harrison, R. G. Dispersal polymorphisms in insects. Annu. Rev. Ecol. Syst. 11, 95–118 (1980).
Article  Google Scholar 

27.
Graham, E. S., Storey, R. & Smith, B. Dispersal distances of aquatic insects: upstream crawling by benthic EPT larvae and flight of adult Trichoptera along valley floors. New Zeal. J. Mar. Freshw. Res. 51, 146–164 (2017).
Article  Google Scholar 

28.
Hoffsten, P. O. Site-occupancy in relation to flight-morphology in caddisflies. Freshw. Biol. 49, 810–817 (2004).
Article  Google Scholar 

29.
Bonada, N. & Dolédec, S. Does the Tachet trait database report voltinism variability of aquatic insects between Mediterranean and Scandinavian regions? Aquat. Sci. 80, 1–11 (2018).
Article  Google Scholar 

30.
Sarremejane, R. et al. DISPERSE, a trait database to assess the dispersal potential of aquatic macroinvertebrates. figshare https://doi.org/10.6084/m9.figshare.c.5000633 (2020).

31.
Lévêque, C., Balian, E. V. & Martens, K. An assessment of animal species diversity in continental waters. Hydrobiologia 542, 39–67 (2005).
Article  Google Scholar 

32.
Green, A. J. & Figuerola, J. Recent advances in the study of long-distance dispersal of aquatic invertebrates via birds. Divers. Distrib. 11, 149–156 (2005).
Article  Google Scholar 

33.
Maasri, A. A global and unified trait database for aquatic macroinvertebrates: the missing piece in a global approach. Front. Environ. Sci. 7, 1–3 (2019).
Article  Google Scholar 

34.
Cañedo-Argüelles, M. et al. Dispersal strength determines meta-community structure in a dendritic riverine network. J. Biogeogr. 42, 778–790 (2015).
Article  Google Scholar 

35.
Datry, T. et al. Metacommunity patterns across three Neotropical catchments with varying environmental harshness. Freshw. Biol. 61, 277–292 (2016).
Article  Google Scholar 

36.
Swan, C. M. & Brown, B. L. Metacommunity theory meets restoration: isolation may mediate how ecological communities respond to stream restoration. Ecol. Appl. 27, 2209–2219 (2017).
Article  Google Scholar 

37.
Sarremejane, R., Mykrä, H., Bonada, N., Aroviita, J. & Muotka, T. Habitat connectivity and dispersal ability drive the assembly mechanisms of macroinvertebrate communities in river networks. Freshw. Biol. 62, 1073–1082 (2017).
Article  Google Scholar 

38.
Jacobson, B. & Peres-Neto, P. R. Quantifying and disentangling dispersal in metacommunities: How close have we come? How far is there to go? Landsc. Ecol. 25, 495–507 (2010).
Article  Google Scholar 

39.
Sarremejane, R. et al. Do metacommunities vary through time? Intermittent rivers as model systems. J. Biogeogr. 44, 2752–2763 (2017).
Article  Google Scholar 

40.
Datry, T., Moya, N., Zubieta, J. & Oberdorff, T. Determinants of local and regional communities in intermittent and perennial headwaters of the Bolivian Amazon. Freshw. Biol. 61, 1335–1349 (2016).
Article  Google Scholar 

41.
Cid, N. et al. A metacommunity approach to improve biological assessments in highly dynamic freshwater ecosystems. Bioscience 70, 427–438 (2020).
Article  Google Scholar 

42.
Datry, T., Bonada, N. & Heino, J. Towards understanding the organisation of metacommunities in highly dynamic ecological systems. Oikos 125, 149–159 (2016).
Article  Google Scholar 

43.
Hermoso, V., Cattarino, L., Kennard, M. J., Watts, M. & Linke, S. Catchment zoning for freshwater conservation: refining plans to enhance action on the ground. J. Appl. Ecol. 52, 940–949 (2015).
Article  Google Scholar 

44.
Thuiller, W. et al. A road map for integrating eco-evolutionary processes into biodiversity models. Ecol. Lett. 16, 94–105 (2013).
Article  Google Scholar 

45.
Mendes, P., Velazco, S. J. E., de Andrade, A. F. A. & De Marco, P. Dealing with overprediction in species distribution models: How adding distance constraints can improve model accuracy. Ecol. Model. 431, 109180 (2020).
Article  Google Scholar 

46.
Willis, S. G. et al. Integrating climate change vulnerability assessments from species distribution models and trait-based approaches. Biol. Conserv. 190, 167–178 (2015).
Article  Google Scholar 

47.
Cooper, J. C. & Soberón, J. Creating individual accessible area hypotheses improves stacked species distribution model performance. Glob. Ecol. Biogeogr. 27, 156–165 (2018).
Article  Google Scholar 

48.
Markovic, D. et al. Europe’s freshwater biodiversity under climate change: distribution shifts and conservation needs. Divers. Distrib. 20, 1097–1107 (2014).
Article  Google Scholar 

49.
Bush, A. & Hoskins, A. J. Does dispersal capacity matter for freshwater biodiversity under climate change? Freshw. Biol. 62, 382–396 (2017).
Article  Google Scholar 

50.
Bohonak, A. J. Dispersal, gene flow, and population structure. Q. Rev. Biol. 74, 21–45 (1999).
CAS  Article  Google Scholar 

51.
Dijkstra, K.-D. B., Monaghan, M. T. & Pauls, S. U. Freshwater biodiversity and aquatic insect diversification. Annu. Rev. Entomol. 59, 143–163 (2014).
CAS  Article  Google Scholar 

52.
Múrria, C. et al. Local environment rather than past climate determines community composition of mountain stream macroinvertebrates across Europe. Mol. Ecol. 26, 6085–6099 (2017).
Article  CAS  Google Scholar 

53.
Statzner, B. & Bêche, L. A. Can biological invertebrate traits resolve effects of multiple stressors on running water ecosystems? Freshw. Biol. 55, 80–119 (2010).
Article  Google Scholar 

54.
Strayer, D. L. & Dudgeon, D. Freshwater biodiversity conservation: recent progress and future challenges. J. North Am. Benthol. Soc. 29, 344–358 (2010).
Article  Google Scholar 

55.
Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).
Article  Google Scholar 

56.
R Core Team. R: A language and environment for statistical computing. https://www.r-project.org/ (2020).

57.
Dray, S. & Dufour, A.-B. The ade4 package: Implementing the duality diagram for ecologists. J. Stat. Softw. 1, 1–20 (2007).
Google Scholar  More

Detritivore and leaf litter collection
We collected six phylogenetically diverse species of detritivores in various areas of the Scottish Lowlands in May and June 2018, including three millipede species (Diplopoda), two woodlouse species (Crustacea) and one snail species (Gastropoda). Millipede species include the common pill millipede (Glomeris marginata (Villers, 1789)) collected near Peebles, UK (55°38′45.8″N, 3°07′55.4″W), the striped millipede (Ommatoiulus sabulosus (Linnaeus, 1758)) collected near Dunfermline, UK (56°02′23.7″N, 3°19′49.2″W) and the white-legged millipede (Tachypodoiulus niger (Leach, 1815)) collected near Dundee, UK (56°32′08.5″N, 3°01′51.9″W). Woodlouse species include the common pill woodlouse (Armadillidium vulgare (Latreille, 1804)) collected near Dunfermline, UK (56°01′35.3″N 3°23′14.1″W) and the common rough woodlouse (Porcellio scaber (Latreille, 1804) collected in Stirling, UK (56°07′26.7″N, 3°55′51.2″W). The snail species was the brown-lipped snail (Cepaea nemoralis (Linnaeus, 1758)) collected in Stirling, UK (56°08′07.3″N, 3°55′16.3″W). These species are common in diverse ecosystems across Mediterranean and temperate ecosystems in Europe, where they feed on decomposing litter and produce large amounts of faeces16,38,39,40. Detritivores were kept in plastic boxes and fed with moist litter from various tree species from their respective collection sites before the start of the experiment.
To obtain a gradient of leaf litter quality, we collected leaf litter from six deciduous broadleaf tree species in the Scottish Lowlands. These species include sycamore maple (Acer pseudoplatanus, L.), horse chestnut (Aesculus hippocastanum, L.), common hazel (Corylus avellana, L.), European beech (Fagus sylvatica, L.), English oak (Quercus robur, L.) from a woodland near Dundee, UK (56°32′08.5″N, 3°01′51.9″W) and lime (Tilia platyphyllos, L.) from a woodland in Stirling, UK (56°08′29.5″N, 3°55′14.2″W). Because detritivores are most active in spring and summer in these ecosystems, they feed on partially decomposed litter, which they prefer over freshly fallen litter (David and Gillon8). We thus collected leaf litter from the forest floor in May 2018, air-dried it and stored it in cardboard boxes until use.
Faeces production
To compare the quality and decomposability of leaf litter with faeces derived from the same litter and produced by diverse detritivore species, we set up two series of boxes for the production of the needed material. In the first of these series, we placed each detritivore species together with each litter species to produce the 36 different faeces types (Fig. 1; 6 litter species × 6 detritivore species = 36 faeces types). The second of these series contained the litter species only without any detritivores to produce intact litter from each tree species (6 litter species) under the same conditions for the same amount of time. In total, 42 different substrates were generated. To do so, we placed ca. 30 g of air-dry leaf litter from each species separately in plastic boxes (30 cm × 22 cm × 5.5 cm) to which we added ca. 50 individuals from each detritivore species separately or no detritivore for the intact litter treatment. We sprayed the litter with water to optimise litter moisture for detritivore consumption while avoiding water accumulation at the bottom of the boxes. We kept the boxes at room temperature (ca. 20 °C) for 4 weeks and collected the produced faeces/intact litter twice a week. For the faeces, we placed the content of each box in a large bucket and gently agitated to let detritivores and faeces fall to the bottom of the bucket. After collecting the faeces, we placed all the leaf litter and detritivores back into their boxes and sprayed the litter with water to keep moisture conditions constant. For the intact litter treatment, we followed the same procedure but collected just three random leaves out of the buckets. After each collection step, the combination-specific pools of leaf litter and faeces were dried at 30 °C. At the end of the faeces production period, we manually removed small leaf litter fragments from all combination-specific pools of faeces. Additionally, because detritivores feed on leaf lamina and leave leaf veins mostly uneaten6, we cut out the veins from the species-specific pools of intact leaf litter. This was done to ensure the comparability of quality and decomposability between faeces and intact litter.
Litter and faeces quality
To evaluate the effect of litter conversion into detritivore faeces on organic matter quality, we compared the quality of faeces to that of intact litter by measuring a series of physical and chemical quality parameters on all 42 substrates (6 litter species + 36 faeces types). Chemical characteristics included total carbon (C) and nitrogen (N) concentrations, DOC and TDN concentrations, total tannin concentrations, and 13C solid-state NMR spectra. Physical characteristics included WHC and specific area (surface area per unit of mass). Prior to these measurements, we drew three subsamples from each pool of substrate type. A part of each subsample was ground using a ball mill (TissueLyser II, Qiagen) to measure total C, N and tannin concentration and generate NMR spectra. The other part of each subsample was kept intact and used for all other measurements. All measurements were thus done on these three subsamples per substrate type, except for NMR spectra that were measured once per substrate type on a sample made by pooling all three ground subsamples. This pooling was necessary to obtain a sample large enough for the NMR analyses. Total C and N concentrations were measured with a flash CHN elemental analyser (Flash Smart, ThermoScientific). To measure DOC and TDN, we extracted leachates by placing ca. 30 mg of air-dried material with 25 ml of deionised water in 50 ml Falcon tubes and agitating the tubes horizontally on a reciprocal shaker for 1 h. Water extracts were then filtered through 0.45-μm cellulose nitrate filters to isolate the leachate fraction. Concentrations of DOC and TDN in leachates were measured with a TOC analyser (Shimadzu, Kyoto, Japan) equipped with a supplementary module for N. Tannin concentrations were measured with the protein-precipitable phenolics microplate assay, a microplate protocol adapted from Hagerman and Butler41. We obtained 13C-NMR spectra by applying 13C cross-polarisation magic angle spinning NMR spectroscopy using a 200 MHz spectrometer (Bruker, Billerica, USA). The samples were spun in 7 mm zirconium dioxide rotors at 6.8 kHz with an acquisition time of 0.01024 s. To avoid Hartmann–Hahn mismatches, a ramped 1H impulse was applied during a contact time of 1 ms. We applied a delay time of 2.0 s and the number of scans was set to 1500, yet some of the samples required longer measurements due to the low amount of sample material; in this case, we multiplied the number of scans to 3000, 6000 or 15000. As reference for the chemical shift, tetramethylsilane was used (0 ppm). We used the following chemical shift regions to integrate the spectra: −10–45 ppm alkyl C, 45–110 ppm O/N alkyl C, 110–160 ppm aromatic C, and 160–220 ppm carboxylic C. We measured the WHC by placing ca. 15 mg of air-dried intact material with 1.5 ml of deionised water in 2 ml Eppendorf tubes, agitating the tubes horizontally on a reciprocal shaker for 2 h, retrieving the material and placing it on a Whatman filter to remove excess water, weighing the wet material and reweighing it after drying at 65 °C for 48 h. We measured the specific area of leaf litter, faecal pellets and faeces particles from photographs using a stereomicroscope (ZEISS STEMI 508). For leaf litter and faecal pellets, we took photographs of ca. 20 mg of air-dried intact material. To visualise faeces particles, we weighed ca. 1 mg of air-dried faecal pellets and placed them in a beaker with 20 ml of deionised water for 2 h, allowing complete dissolution of the faecal pellets. We then filtered the faeces particles and photographed the filters under a stereomicroscope. Dimensions of each litter pieces and faecal pellets/faeces particles were measured using the image analysis software (ImageJ, version 1.46r). For all substrate types, we divided the calculated surface area by the dry mass of the sample to obtain the specific area.
Faeces and litter decomposition parameters
To evaluate the effect of litter conversion into detritivore faeces on C and N cycling, we compared the C and N loss of faeces to that of intact litter by incubating all 42 substrates in microcosms under controlled conditions for 6 months (180 days). Microcosms consisted of 250-ml plastic containers filled with 90 mg of air-dry soil collected from a temperate grassland (56°8′40.1″N, 3°54′50.9″W). We chose this soil to avoid any home-field advantage effect as this soil did not receive litter input from any of the studied tree species and none of the selected soil animals were present at this site. About 120 mg of each substrate were placed separately within a small polyvinyl chloride tube (30 mm diameter × 30 mm height) closed in the bottom with a 100-µm mesh and left open on the top. Each tube was then placed on top of the soil within the microcosm. Five replicates per substrate were prepared, resulting in a total of 210 microcosms (42 substrates × 5 replicates). Microcosms were watered by adding water directly over the tube containing faeces/litter so as to reach 70% of soil WHC and incubated at 22 °C and 70% relative humidity in a controlled environment chamber. To limit desiccation while ensuring gas exchange, we drilled four 3-mm holes in each microcosm cap. These microcosms were then weighed weekly and watered to their initial weight at 70% soil WHC. We placed replicates on separated shelves according to a randomised complete block design. Both block positions within the controlled environment chamber and microcosm positions within blocks were randomised weekly. After 180 days, remaining intact litter and faeces in microcosms were collected, dried at 30 °C for 48 h, weighed and ground with a ball mill (TissueLyser II, Qiagen). We measured C and N concentrations in all samples with a flash CHN Elemental Analyser (Flash Smart, ThermoScientific). The percentage of C and N lost after the incubation was calculated as:

$$frac{{M_{rm{i}} times {rm{CN}}_{rm{i}} – M_{rm{f}} times {rm{CN}}_{rm{f}}}}{{M_{rm{i}} times {rm{CN}}_{rm{i}}}} times 100,$$

where Mi and Mf are the initial and final 30 °C dry masses, respectively, and CNi and CNf are the initial and final C or N concentrations, respectively.
Statistics and reproducibility
To visualise how the 11 physicochemical characteristics were related and how their values differed between all substrates, we used a PCA, with all variables centred and standardised prior to ordination. Because NMR spectra were measured on a composite sample combining the three replicates of each substrate, a unique value was attributed to all replicates for each NMR region.
To test our first hypothesis, we tested the overall effect of substrate form (faeces vs. intact litter) on quality (scores on PC1 and PC2) and decomposition (C and N losses) of all substrates using Student’s t tests. To identify the faeces types with significantly different quality (scores on PC1 and PC2) and decomposition (C and N losses) compared to that of the intact litter from which the faeces were derived, we tested the effect of substrate identity (all 42 substrates included as individual levels) on quality (scores on PC1 and PC2) and decomposition (C and N losses) using one-way ANOVAs. We then used Tukey’s honestly significant difference tests to determine significant differences between each faeces type and the corresponding intact litter.
To test our second hypothesis, we expressed the changes in quality and decomposition following litter conversion into detritivore faeces as net differences in quality (scores on PC1 and PC2) and decomposition (C and N losses) between faeces and the litter from which faeces were derived. We then compared the hypothesised role of intact litter quality/decomposition (PC1 and PC2 scores, C and N losses) and the role of detritivore species on changes in quality/decomposition (net differences in PC1 and PC2 scores, C and N losses) by performing ANCOVAs with intact litter quality/decomposition as the continuous variable and detritivore species as categorical variable (all six detritivore species as individual levels). For all ANVOCAs, the variance associated with each term (intact litter quality/decomposition; detritivore species; interaction) was computed by dividing the sum of squares by the total sum of squares.
To evaluate the relation between quality parameters (PC1 and PC2 scores) and C and N losses from intact litter and faeces separately, we determined the relations between intact litter and faeces C and N losses and their scores on PC1 and PC2 with simple linear regressions and visualised these relations by fitting these variables as supplementary variables on the PCA.
For all statistical tests on C and N losses, block was included in the model as a random variable. All data were checked for normal distribution and homoscedasticity of residuals. All analyses were performed using the R software (version 3.5.3).
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

Species distribution modelling
A total of 1548 occurrence locations were obtained for B. fagacearum from the United States Forest Service (provided to us by Erin Bullas-Appleton of the Canadian Food Inspection Agency in July 2018) and the Global Biodiversity Information Facility17. These records were filtered at a 300 arcsecond (approximately 10-km) resolution to remove duplicates and reduce spatial clustering, leaving 1401 unique location records (Fig. 1a). Occurrence locations for two key insect vectors of oak wilt, C. truncatus (Fig. 2a) and C. sayi (Fig. 3a), were obtained from GBIF, publications10,18, and from specimens in the following collections: Atlantic Forestry Centre, Fredericton, NB, Canada; Canadian National Collection of Insects, Arachnids, and Nematodes, Agriculture and Agri-Food Canada Research Centre, Ottawa, ON, Canada; Ontario Forest Research Institute, Sault Ste. Marie, ON, Canada; Gareth S. Powell Collection, Nephi, UT, USA; Reginald Webster Collection, Charters Settlement, New Brunswick, Canada; and the Florida State Collection of Arthropods, Gainesville, FL, USA . After filtering at a 10-km resolution, there were 82 and 58 unique occurrence records for C. truncatus and C. sayi respectively.
Figure 1

Occurrence data (a) used for generating climate suitability models for Bretziella fagacearum. Maps with colour gradients indicate Maxent-derived climate suitability for B. fagacearum for the: 1981–2010 period (b); 2011–2040 period (c); and 2041–2070 period (d). Stippling delineates the ANUCLIM-derived climate envelope for B. fagacearum in each time period. Hatching delineates the current distribution of Quercus in Canada. Climate projections are based on a composite of four climate models and the RCP 4.5 emissions scenario (see text for further details). Maps were generated using ARCGIS v.9.3 (ESRI, Redlands, CA, USA; https://www.esri.com/arcgis/about-arcgis).

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Figure 2

Occurrence data (a) used for generating climate suitability models for Colopterus truncatus. Maps with colour gradients indicate Maxent-derived climate suitability for C. truncatus for the: 1981–2010 period (b); 2011–2040 period (c); and 2041–2070 period (d). Stippling delineates the ANUCLIM-derived climate envelope for C. truncatus in each time period. Hatching delineates the current distribution of Quercus in Canada. Climate projections are based on a composite of four climate models and the RCP 4.5 emissions scenario (see text for further details). Maps were generated using ARCGIS v.9.3 (ESRI, Redlands, CA, USA; https://www.esri.com/arcgis/about-arcgis).

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Figure 3

Occurrence data (a) used for generating climate suitability models for Carpophilus sayi. Maps with colour gradients indicate Maxent-derived climate suitability for C. sayi for the: 1981–2010 period (b); 2011–2040 period (c); and 2041–2070 period (d). Stippling delineates the ANUCLIM-derived climate envelope for C. sayi in each time period. Hatching delineates the current distribution of Quercus in Canada. Climate projections are based on a composite of four climate models and the RCP 4.5 emissions scenario (see text for further details). Maps were generated using ARCGIS v.9.3 (ESRI, Redlands, CA, USA; https://www.esri.com/arcgis/about-arcgis).

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Climate estimates were obtained at each occurrence location by interrogating North American climate models (described in McKenney et al.19) of the 1981–2010 normal period for the following four variables: (1) fall (i.e., September–December) precipitation (FALLPCP); (2) average spring (i.e., March–June) temperature (SPRINGTMP); (3) annual climate moisture index (CMI; a climate-based moisture balance variable; see Hogg20 for details); and (4) Average Minimum Temperature of the Coldest Month (MINTCM). These climate variables were selected based on their reported influences on B. fagacearum (FALLPCP and CMI21,22) C. sayi and C. truncatus (SPRINGTMP10), and insect distributions in general (MINTCM23). Further, none of the selected variables were highly correlated (i.e., r  60 years) for each province in our study area. Focusing on current/near-term oak timber stocks (since we are not estimating B. fagacearum spread), we multiplied merchantable volume over 40 years old—roughly the age at which oak becomes harvestable in Ontario42—by average provincial stumpage values.
Estimating stumpage values was somewhat challenging due to inter-provincial variation in stumpage systems and reporting of stumpage fees. For the province of Québec, we obtained oak-specific stumpage fees for 191 harvest zones for the period April 1, 2019 to March 31, 2020 (Bureau de mise en marché43). Since stumpage fees in Québec vary by wood quality class (i.e., A, B, and C), we further obtained information on the proportion of wood harvested in each class over the same period (unpublished dataset, Bureau de mise en marché des Bois). We then calculated the average stumpage fee for the province by averaging across harvest zones and quality classes, while weighting by the proportion of wood in each quality class. For Ontario, we obtained stumpage fees for two quality classes of hardwoods (i.e., Class 1 and Class 2) and four oak-related product types (Veneer, Sawlogs, Composite, and Firewood) for January 1 to December 31, 2019 (Ontario Ministry of Natural Resources and Forestry44). Given that oak is typically considered a higher value hardwood, and in lieu of information on how oak is partitioned across product types in Ontario, we calculated oak stumpage fees as an average across product types for the Class 1 hardwood category. Note that the stumpage rates employed here include the Renewal and Forest Futures fees that are part of the Ontario stumpage system. Finally, stumpage values for the province of Nova Scotia were obtained for a single hardwood quality class for the period April 1, 2017 to March 31, 2018 (Province of Nova Scotia45). As in Ontario, stumpage fees were averaged across product types. The stumpage values for Nova Scotia were applied to the relatively small amount of oak in the neighbouring Maritime Provinces of New Brunswick and Prince Edward Island (PEI).
Alternatively, gross domestic product (GDP) can provide an estimate of the total economic activity associated with a given industry. Annual GDP estimates for broad categories (e.g., forestry and logging industry, and wood product manufacturing industry) are available for each province at Natural Resources Canada’s forestry statistics website (https://cfs.nrcan.gc.ca/statsprofile/overview/ca). In order to estimate GDP specifically for oak-related timber products, we first multiplied these provincial broad category GDP values by the proportion of the total provincial harvest that was composed of hardwoods (multipliers obtained from published provincial data sources as detailed in the Results section below). This value was then further refined by multiplying by the proportion of hardwoods in the province that was composed of oak species. These estimates were obtained from forest attribute grids13, by summing merchantable volume of (1) oak and (2) all broadleaf species within the industrial forestry limits of each province. Spatial summaries were carried out using the raster and rgdal packages in r. Though admittedly coarse, we felt this approach was the best available given the dearth of readily available economic data for individual tree species/genera; similar approaches have been used previously to estimate economic impacts of invasive species46,47.
These two approaches (i.e., stumpage-based and GDP-based) provide different perspectives on oak-related timber values at risk. The stumpage approach attaches a basic price to standing timber resources, but does not consider downstream economic activities associated with harvest, such as wages, equipment purchases, and capital expenditures. This approach implicitly assumes that substitution possibilities (e.g., other tree species) can fully replace oak-related contributions to the economy with minimal adjustment costs and, as such, is a conservative estimate of potential timber value losses. Alternatively, the GDP approach attempts to include all downstream economic contributions and assumes little or no opportunity for substitution, such that oak timber losses would be accompanied by a proportional reduction in economic activity. We present both estimates here to provide policy-makers with a range of possible impacts. The value of costs through time is generally arrived at using economic discounting; however, here we have no estimates of the timeline associated with oak wilt spread and hence have chosen to report gross, undiscounted values. See Aukema et al.48 for further discussion. More