Ecology

1.Holway, D. A. & Suarez, A. V. Animal behavior: An essential component of invasion biology. TREE 14, 328–330 (1999).CAS 
PubMed 

Google Scholar 
2.Chapple, D. G., Simmonds, S. M. & Wong, B. B. M. Can behavioral and personality traits influence the success of unintentional species introductions? Trends Ecol. Evol. 27, 57–64 (2012).PubMed 

Google Scholar 
3.Weis, J. & Sol, D. Behaviour and the Invasion Process. in Biological Invasions and Animal Behaviour 5–116 (Cambridge University Press, 2016).4.Cote, J., Fogarty, S., Weinersmith, K., Brodin, T. & Sih, A. Personality traits and dispersal tendency in the invasive mosquitofish (Gambusia affinis). Proc. R. Soc. B Biol. Sci. 277, 1571–1579 (2010).
Google Scholar 
5.Myles-Gonzalez, E., Burness, G., Yavno, S., Rooke, A. & Fox, M. G. To boldly go where no goby has gone before: Boldness, dispersal tendency, and metabolism at the invasion front. Behav. Ecol. 26, 1083–1090 (2015).
Google Scholar 
6.Mutascio, H. E., Pittman, S. E. & Zollner, P. A. Investigating movement behavior of invasive Burmese pythons on a shy–bold continuum using individual-based modeling. Perspect. Ecol. Conserv. 15, 25–31 (2017).
Google Scholar 
7.Chuang, A. Living Life on the Edge: The Role of Invasion Processes in Shaping Personalities in a Non-Native Spider Species (The University of Tennessee, Knoxville, 2019). https://doi.org/10.1017/CBO9781107415324.004.Book 

Google Scholar 
8.Blackburn, T. M. et al. A proposed unified framework for biological invasions. Trends Ecol. Evol. 26, 333–339 (2011).PubMed 

Google Scholar 
9.Pintor, L. M., Sih, A. & Kerby, J. L. Behavioral correlations provide a mechanism for explaining high invader densities and increased impacts on native prey. Ecology 90, 581–587 (2009).PubMed 

Google Scholar 
10.Petren, K. & Case, T. J. An experimental demonstration of exploitation competition in an ongoing invasion. Ecology 77, 118–132 (1996).
Google Scholar 
11.Wright, T. F., Eberhard, J. R., Hobson, E. A., Avery, M. L. & Russello, M. A. Behavioral flexibility and species invasions: The adaptive flexibility hypothesis. Ethol. Ecol. Evol. 22, 393–404 (2010).
Google Scholar 
12.Dick, J. T. A. Role of behaviour in biological invasions and species distributions; lessons from interactions between the invasive Gammarus pulex and the native G. duebeni (Crustacea: Amphipoda). Contrib. Zool. 77, 91–98 (2008).
Google Scholar 
13.Dick, J. T. A. et al. Invader Relative Impact Potential: A new metric to understand and predict the ecological impacts of existing, emerging and future invasive alien species. J. Appl. Ecol. 54, 1259–1267 (2017).
Google Scholar 
14.Dick, J. T. A., Elwood, R. W. & Montgomery, W. I. The behavioural basis of a species replacement: differential aggresssion and predation between the introduced Gammarus pulex and the native G. duebeni celticus (Amphipoda). Behav. Ecol. Sociobiol. 37, 393–398 (1995).
Google Scholar 
15.Dick, J. T. A. et al. Ecological impacts of an invasive predator explained and predicted by comparative functional responses. Biol. Invasions 15, 837–846 (2013).
Google Scholar 
16.Dick, J. T. A. et al. Advancing impact prediction and hypothesis testing in invasion ecology using a comparative functional response approach. Biol. Invasions 16, 735–753 (2014).
Google Scholar 
17.Iacarella, J. C., Dick, J. T. A. & Ricciardi, A. A spatio-temporal contrast of the predatory impact of an invasive freshwater crustacean. Divers. Distrib. 21, 803–812 (2015).
Google Scholar 
18.Toscano, B. J. & Griffen, B. D. Trait-mediated functional responses: Predator behavioural type mediates prey consumption. J. Anim. Ecol. 83, 1469–1477 (2014).PubMed 

Google Scholar 
19.MacCrimmon, H. R. World distribution of rainbow trout (Salmo gairdneri): further observations. J. Fish. Res. Board Canada 28, 663–704 (1971).
Google Scholar 
20.MacCrimmon, H. R., Marshall, T. L. & Gots, B. L. World distribution of brown trout, Salmo trutta: further observations. J. Fish. Res. Board Canada 27, 811–818 (1970).
Google Scholar 
21.Crawford, S. S. & Muir, A. M. Global introductions of salmon and trout in the genus Oncorhynchus: 1870–2007. Rev. Fish Biol. Fish. 18, 313–344 (2008).
Google Scholar 
22.Crowl, T. A., Townsend, C. R. & Mcintosh, A. R. The impact of introduced brown and rainbow trout on native fish: The case of Australasia. Rev. Fish Biol. Fish. 241, 217–241 (1992).
Google Scholar 
23.Hasegawa, K. Invasions of rainbow trout and brown trout in Japan: A comparison of invasiveness and impact on native species. Ecol. Freshw. Fish 29, 419–428 (2020).
Google Scholar 
24.Cambray, J. A. The global impact of alien trout species—A review; with reference to their impact in South Africa. African J. Aquat. Sci. 28, 61–67 (2003).
Google Scholar 
25.Dunham, J. B., Wheeler, A. & Rosenberger, A. Assessing the consequences of nonnative trout in headwater ecosystems in western North America. Fisheries 29, 37–41 (2004).
Google Scholar 
26.Fausch, K. D., Taniguchi, Y., Nakano, S., Grossman, G. D. & Townsend, C. R. Flood disturbance regimes influence rainbow trout invasion success among five holarctic regions. Ecol. Appl. 11, 1438–1455 (2001).
Google Scholar 
27.Anderson, R. M. & Nehring, R. B. Effects of a catch-and-release regulation on a wild trout population in Colorado and its acceptance by Anglers. North Am. J. Fish. Manag. 4, 257–265 (1984).
Google Scholar 
28.Young, K. A. et al. A trial of two trouts: Comparing the impacts of rainbow and brown trout on a native galaxiid. Anim. Conserv. 13, 399–410 (2010).
Google Scholar 
29.Conrad, J. L., Weinersmith, K. L., Brodin, T., Saltz, J. B. & Sih, A. Behavioural syndromes in fishes: A review with implications for ecology and fisheries management. J. Fish Biol. 78, 395–435 (2011).CAS 
PubMed 

Google Scholar 
30.Mowles, S. L., Cotton, P. A. & Briffa, M. Consistent crustaceans: The identification of stable behavioural syndromes in hermit crabs. Behav. Ecol. Sociobiol. 66, 1087–1094 (2012).
Google Scholar 
31.Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: An ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).PubMed 

Google Scholar 
32.Bell, A. M. Behavioural differences between individuals and two populations of stickleback (Gasterosteus aculeatus). J. Evol. Biol. 18, 464–473 (2005).CAS 
PubMed 

Google Scholar 
33.Bourne, G. R. & Sammons, A. J. Boldness, aggression and exploration: evidence for a behavioural syndrome in male pentamorphic livebearing fish, Poecilia parae. AACL Bioflux 1, 39–50 (2008).
Google Scholar 
34.Lukas, J. et al. Consistent behavioral syndrome across seasons in an invasive freshwater fish. Front. Ecol. Evol. 8, 466 (2021).ADS 

Google Scholar 
35.Gjedrem, T., Gjøen, H. M. & Gjerde, B. Genetic origin of Norwegian farmed Atlantic salmon. Aquaculture 98, 41–50 (1991).
Google Scholar 
36.Huntingford, F. & Adams, C. Behavioural syndromes in farmed fish: Implications for production and welfare. Behaviour 142, 1207–1221 (2005).
Google Scholar 
37.Alvarez, D. & Nicieza, A. G. Predator avoidance behaviour in wild and hatchery-reared brown trout : The role of experience and domestication. J. Fish Biol. 63, 1565–1577. https://doi.org/10.1046/j.1095-8649.2003.00267.x (2003).Article 

Google Scholar 
38.Geffroy, B. et al. Evolutionary dynamics in the anthropocene: Life history and intensity of human contact shape antipredator responses. PLoS Biol. 18, 1–17 (2020).
Google Scholar 
39.Lincoln, R. F. & Scott, A. P. Production of all-female triploid rainbow trout. Aquaculture 30, 375–380 (1983).
Google Scholar 
40.Maxime, V. The physiology of triploid fish: Current knowledge and comparisons with diploid fish. Fish Fish. 9, 67–78 (2008).
Google Scholar 
41.Chatterji, R., Longley, D., Sandford, D., Roberts, D. & Stubbing, D. Performance of stocked triploid and diploid brown trout and their effects on wild brown trout in UK rivers. (2008).42.Benfey, T. J. The physiology and behavior of triploid fishes. Rev. Fish. Sci. 7, 39–67 (1999).
Google Scholar 
43.Carter, C. G. et al. Food consumption, feeding behaviour, and growth of triploid and diploid Atlantic salmon, Salmo salar L., parr.. Can. J. Zool. 72, 609–617 (1994).
Google Scholar 
44.Weber, G. M., Hostuttler, M. A., Cleveland, B. M. & Leeds, T. D. Growth performance comparison of intercross-triploid, induced triploid, and diploid rainbow trout. Aquaculture 433, 85–93 (2014).
Google Scholar 
45.Øverli, Ø., Pottinger, T. G., Carrick, T. R., Øverli, E. & Winberg, S. Differences in behaviour between rainbow trout selected for high- and low-stress responsiveness. J. Exp. Biol. 205, 391–395 (2002).PubMed 

Google Scholar 
46.Sadoul, B., Leguen, I., Colson, V., Friggens, N. C. & Prunet, P. A multivariate analysis using physiology and behavior to characterize robustness in two isogenic lines of rainbow trout exposed to a confinement stress. Physiol. Behav. 140, 139–147 (2015).CAS 
PubMed 

Google Scholar 
47.Adriaenssens, B. & Johnsson, J. I. Learning and context-specific exploration behaviour in hatchery and wild brown trout. Appl. Anim. Behav. Sci. 132, 90–99 (2011).
Google Scholar 
48.Näslund, J. & Johnsson, J. I. State-dependent behavior and alternative behavioral strategies in brown trout (Salmo trutta L.) fry. Behav. Ecol. Sociobiol. 70, 2111–2125 (2016).PubMed 
PubMed Central 

Google Scholar 
49.Mortensen, E. Density-dependent mortality of trout fry (Salmo trutta L.) and its relationship to the management of small streams. J. Fish Biol. 11, 613–617 (1977).
Google Scholar 
50.Armstrong, J. D. & Nislow, K. H. Critical habitat during the transition from maternal provisioning in freshwater fish, with emphasis on Atlantic salmon (Salmo salar) and brown trout (Salmo trutta). J. Zool. 269, 403–413 (2006).
Google Scholar 
51.Walsh, R. N. & Cummins, R. A. The open-field test: A critical review. Psychol. Bull. 83, 482–504 (1976).CAS 
PubMed 

Google Scholar 
52.Adriaenssens, B. & Johnsson, J. I. Shy trout grow faster: Exploring links between personality and fitness-related traits in the wild. Behav. Ecol. 22, 135–143 (2010).
Google Scholar 
53.Sneddon, L. U. The bold and the shy: Individual differences in rainbow trout. J. Fish Biol. 62, 971–975 (2003).
Google Scholar 
54.Adriaenssens, B. Individual variation in behaviour: personality and performance of brown trout in the wild (University of Gothenburg, 2010).55.Elias, A., Thrower, F. & Nichols, K. M. Rainbow trout personality: Individual behavioural variation in juvenile Oncorhynchus mykiss. Behaviour 155, 205–230 (2018).
Google Scholar 
56.Dick, J. T. A. et al. Functional responses can unify invasion ecology. Biol. Invasions 19, 1667–1672 (2017).
Google Scholar 
57.Sloman, K. A., Metcalfe, N. B., Taylor, A. C. & Gilmour, K. M. Plasma cortisol concentrations before and after social stress in rainbow trout and brown trout. Physiol. Biochem. Zool. 74, 383–389 (2001).CAS 
PubMed 

Google Scholar 
58.Sadoul, B., Blumstein, D. T., Alfonso, S. & Geffroy, B. Human protection drives the emergence of a new coping style in animals. PLoS Biol. 19, 1–11 (2021).
Google Scholar 
59.Campbell, J. M., Carter, P. A., Wheeler, P. A. & Thorgaard, G. H. Aggressive behavior, brain size and domestication in clonal rainbow trout lines. Behav. Genet. 45, 245–254 (2015).PubMed 

Google Scholar 
60.Berejikian, B. A., Mathews, S. B. & Quinn, T. P. Effects of hatchery and wild ancestry and rearing environments on the development of agonistic behavior in steelhead trout (Oncorhynchus mykiss) fry. Can. J. Fish. Aquat. Sci. 53, 2004–2014 (1996).
Google Scholar 
61.Laverty, C. et al. Assessing the ecological impacts of invasive species based on their functional responses and abundances. Biol. Invasions 19, 1653–1665 (2017).
Google Scholar 
62.Alexander, M. E., Dick, J. T. A., Weyl, O. L. F., Robinson, T. B. & Richardson, D. M. Existing and emerging high impact invasive species are characterized by higher functional responses than natives. Biol. Lett. 10, 20130946 (2014).PubMed 
PubMed Central 

Google Scholar 
63.Dickey, J. W. E., Cuthbert, R. N., Steffen, G. T., Dick, J. T. A. & Briski, E. Sea freshening may drive the ecological impacts of emerging and existing invasive non-native species. Divers. Distrib. 27, 144–156 (2021).
Google Scholar 
64.Sadler, J., Pankhurst, P. M. & King, H. R. High prevalence of skeletal deformity and reduced gill surface area in triploid Atlantic salmon (Salmo salar L.). Aquaculture 198, 369–386 (2001).
Google Scholar 
65.Benfey, T. J. & Biron, M. Acute stress response in triploid rainbow trout (Oncorhynchus mykiss) and brook trout (Salvelinus fontinalis). Aquaculture 184, 167–176 (2000).CAS 

Google Scholar 
66.Sadler, J., Pankhurst, N. W., Pankhurst, P. M. & King, H. Physiological stress responses to confinement in diploid and triploid Atlantic salmon. J. Fish Biol. 56, 506–518 (2000).
Google Scholar 
67.Berrebi, P., Splendiani, A., Palm, S. & Berna, R. Genetic diversity of domestic brown trout stocks in Europe. Aquaculture 544, 737043 (2021).CAS 

Google Scholar 
68.Gross, R., Lulla, P. & Paaver, T. Genetic variability and differentiation of rainbow trout (Oncorhynchus mykiss) strains in northern and Eastern Europe. Aquaculture 272, 139–146 (2007).
Google Scholar 
69.Whelan, K. Assessing and mitigating the impact of a major rainbow trout escape on the wild salmon and trout populations of the Mourne river system, Northern Ireland. (2017).70.Shelton, J. et al. Temperature mediates the impact of non-native rainbow trout on native freshwater fishes in South Africa’s Cape Fold Ecoregion. Biol. Invasions 20, 2927–2944 (2018).
Google Scholar 
71.Michelangeli, M. et al. Sex-dependent personality in two invasive species of mosquitofish. Biol. Invasions 22, 1353–1364 (2020).
Google Scholar 
72.Friard, O. & Gamba, M. BORIS: A free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330 (2016).
Google Scholar 
73.R Core Team. R: A language and environment for statistical computing. (2018).74.RStudio Team. RStudio Team (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA. http://www.rstudio.com/. 2019 (2020).75.Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R. Springer https://doi.org/10.1086/648138 (2008).Article 
MATH 

Google Scholar 
76.Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 18637 (2015).
Google Scholar 
77.Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A Grammar of Data Manipulation. R package version. Media https://doi.org/10.1007/978-0-387-98141-3 (2019).Article 

Google Scholar 
78.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).MATH 

Google Scholar 
79.Barton, K. MuMIn: Multi-Model Inference. 2020 (2020).80.Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. emmeans: estimated marginal means, aka least-squares means. R package version 1.5.2-1 (2020).81.Pritchard, D. frair: tools for functional response analysis. R package version 0.0.100 (2017).82.Juliano, S. A. Predation and functional response curves. in Design and Analysis of Ecological Experiments (eds. Scheiner, S. & Gurevitch, J.) Chapter 10 (2001).83.Rogers, D. Random search and insect population models. J. Anim. Ecol. 41, 369–383 (1972).
Google Scholar 
84.Bolker, B. M. Rogers random predator equation: extensions and estimation by numerical integration. 1–20 (2012). More

1.Angel, S. et al. The dimensions of global urban expansion: Estimates and projections for all countries, 2000–2050. Prog. Plan. 75, 53–107 (2011).
Google Scholar 
2.Grimm, N. B. et al. Global change and the ecology of cities. Science 319, 756–760 (2008).ADS 
CAS 
PubMed 

Google Scholar 
3.McKinney, M. L. Urbanization as a major cause of biotic homogenization. Biol. Conserv. 127, 247–260 (2006).
Google Scholar 
4.Groffman, P. M. et al. Ecological homogenization of urban USA. Front. Ecol. Environ. 12, 74–81 (2014).
Google Scholar 
5.Bolnick, D. I. et al. (Non)Parallel evolution. Annu. Rev. Ecol. Evol. Syst. 49, 303–330 (2018).
Google Scholar 
6.Donihue, C. M. & Lambert, M. R. Adaptive evolution in urban ecosystems. Ambio 44, 194–203 (2015).PubMed 

Google Scholar 
7.Johnson, M. T. J. & Munshi-South, J. Evolution of life in urban environments. Science 358, eaam8327 (2017).
Google Scholar 
8.Rivkin, L. R. et al. A roadmap for urban evolutionary ecology. Evol. Appl. 12, 384–398 (2019).PubMed 

Google Scholar 
9.Santangelo, J. S. et al. Urban environments as a framework to study parallel evolution. In Urban Evolutionary Biology (eds Szulkin, M. et al.) (Oxford University Press, 2020).
Google Scholar 
10.Cosentino, B. J., Moore, J.-D., Karraker, N. E., Ouellet, M. & Gibbs, J. P. Evolutionary response to global change: Climate and land use interact to shape color polymorphism in a woodland salamander. Ecol. Evol. 7, 5426–5434 (2017).PubMed 
PubMed Central 

Google Scholar 
11.Koprowski, J. L., Munroe, K. E. & Edelman, A. J. Gray not grey: Ecology of Sciurus carolinensis in their native range in North America. In Grey Squirrels: Ecology and Management of an Invasive Species in Europe (eds Shuttleworth, C. M. et al.) (European Squirrel Initiative, 2016).
Google Scholar 
12.McRobie, H., Thomas, A. & Kelly, J. The genetic basis of melanism in the gray squirrel (Sciurus carolinensis). J. Hered. 100, 709–714 (2009).CAS 
PubMed 

Google Scholar 
13.Gibbs, J. P., Buff, M. F. & Cosentino, B. J. The biological system: Urban wildlife, adaptation and evolution: Urbanization as a driver of contemporary evolution in gray squirrels (Sciurus carolinensis). In Understanding Urban Ecology (eds Hall, M. A. & Balogh, S.) (Springer, 2019).
Google Scholar 
14.Lehtinen, R. M. et al. Dispatches form the neighborhood watch: Using citizen science and field survey data to document color morph frequency in space and time. Ecol. Evol. 10, 1526–1538 (2020).PubMed 
PubMed Central 

Google Scholar 
15.Perlut, N. G. Long-distance dispersal by eastern gray squirrels in suburban habitats. Northeast. Nat. 27, 195–200 (2020).
Google Scholar 
16.Goheen, J. R., Swihart, R. K., Gehring, T. M. & Miller, M. S. Forces structuring tree squirrel communities in landscapes fragmented by agriculture: Species differences in perceptions of forest connectivity and carrying capacity. Oikos 102, 95–103 (2003).
Google Scholar 
17.Ducharme, M. B., Larochelle, J. & Richard, D. Thermogenic capacity in gray and black morphs of the gray squirrel, Sciurus carolinensis. Physiol. Zool. 62, 1273–1292 (1989).
Google Scholar 
18.Linnen, C. R. & Hoekstra, H. E. Measuring natural selection on genotypes and phenotypes in the wild. Cold Spring Harb. Symp. Quant. Biol. 74, 155–168 (2010).PubMed Central 

Google Scholar 
19.Campbell-Staton, S. C. et al. Parallel selection on thermal physiology facilitates repeated adaptation of city lizards to urban heat islands. Nat. Ecol. Evol. 4, 652–658 (2020).PubMed 

Google Scholar 
20.Reid, N. M. et al. The genomic landscape of rapid repeated evolutionary adaptation to toxic pollution in wild fish. Science 354, 1305–1308 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
21.Bowers, M. A. & Breland, B. Foraging of gray squirrels on an urban-rural gradient: Use of the GUD to assess anthropogenic impact. Ecol. Appl. 6, 1135–1142 (1996).
Google Scholar 
22.McCleery, R. A., Lopez, R. R., Silvy, N. J. & Gallant, D. L. Fox squirrel survival in urban and rural environments. J. Wildl. Manage. 72, 133–137 (2008).
Google Scholar 
23.Benson, E. The urbanization of the eastern gray squirrel in the United States. J. Am. Hist. 100, 691–710 (2013).
Google Scholar 
24.Leveau, L. United colours of the city: A review about urbanization impact on animal colours. Austral Ecol. 46, 670–679 (2021).
Google Scholar 
25.Ducrest, A.-L., Keller, L. & Roulin, A. Pleiotropy in the melanocortin system, coloration, and behavioural syndromes. Trends Ecol. Evol. 23, 502–510 (2008).PubMed 

Google Scholar 
26.Stothart, M. R. & Newman, A. E. M. Shades of grey: Host phenotype dependent effect of urbanization on the bacterial microbiome of a wild mammal. Anim. Microbiome. 3, 46 (2021).PubMed 
PubMed Central 

Google Scholar 
27.Vasemägi, A. The adaptive hypothesis of clinal variation revisited: Single-locus clines as a result of spatially restricted gene flow. Genetics 173, 2411–2414 (2006).PubMed 
PubMed Central 

Google Scholar 
28.Merrick, M. J., Evans, K. L. & Bertolino, S. Urban grey squirrel ecology, associated impacts, and management challenges. In Grey Squirrels: Ecology and Management of an Invasive Species in Europe (eds Shuttleworth, C. M. et al.) (European Squirrel Initiative, 2016).
Google Scholar 
29.Chipman, R., Slate, D., Rupprecht, C. & Mendoza, M. Downside risk of wildlife translocation. In Towards the Elimination of Rabies in Eurasia (eds Dodet, B. et al.) (Dev. Biol Basel, Karger, 2008).
Google Scholar 
30.Allen, D. L. Michigan Fox Squirrel Management (Michigan Department of Conservation, 1943).
Google Scholar 
31.Schorger, A. W. Squirrels in early Wisconsin. Trans. Wis. Acad. Sci. Arts Lett. 39, 195–247 (1949).
Google Scholar 
32.Robertson, G. I. Distribution of Color Morphs of Sciurus carolinensis in Eastern North America (University of Western Ontario, 1973).
Google Scholar 
33.MacCleery, D. W. American Forests: A History of Resiliency and Recovery (Forest History Society, 2011).
Google Scholar 
34.Foster, D. R. et al. Wildlands and Woodlands: A Vision for the New England Landscape (Harvard University Press, 2010).
Google Scholar 
35.Thompson, R. T., Carpenter, D. N., Cogbill, C. V. & Foster, D. R. Four centuries of change in northeastern United States forests. PLoS ONE 8(9), e72540 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
36.Lambert, M. R. et al. Adaptive evolution in cities: Progress and misconceptions. Trends Ecol. Evol. 36, 239–257 (2021).PubMed 

Google Scholar 
37.Farquhar, D. N. Some Aspects of Thermoregulation as Related to the Geographic Distribution of the Northern Melanic Phase of the Grey Squirrel (York University, 1974).
Google Scholar 
38.Innes, S. & Lavigne, D. M. Comparative energetics of coat colour polymorphs in the eastern gray squirrel Sciurus carolinensis. Can. J. Zool. 57, 585–592 (1979).
Google Scholar 
39.Santangelo, J. S. et al. Predicting the strength of urban-rural clines in a Mendelian polymorphism along a latitudinal gradient. Evol. Lett. 4, 212–225 (2020).PubMed 
PubMed Central 

Google Scholar 
40.Fidino, M. et al. Landscape-scale differences among cities alter common species’ responses to urbanization. Ecol. Appl. 31, e02253 (2021).PubMed 

Google Scholar 
41.Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: Challenges and benefits. Annu. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).
Google Scholar 
42.Alberti, M. Global urban signatures of phenotypic change in animal and plant populations. Proc. Natl. Acad. Sci. U.S.A. 114, 8951–8956 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
43.United States Census Bureau. 2019 TIGER/Line Shapefiles (machine-readable data files) https://www2.census.gov/geo/tiger/TIGER2019/UAC/ (2019).44.XX. Statistics Canada. Population Centre Boundary File, Census year 2016 https://www150.statcan.gc.ca/n1/en/catalogue/92-166-X (2017).45.Aiello-Lammens, M. E. et al. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).
Google Scholar 
46.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2020).47.Brown de Colstoun, E. C. et al. Documentation for the Global Man-made Impervious Surface (GMIS) Dataset from Landsat (NASA Socioeconomic Data and Applications Center, 2017).
Google Scholar 
48.Steele, M. A. & Koprowski, J. L. North American Tree Squirrels (Smithsonian Books, 2001).
Google Scholar 
49.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).ADS 
CAS 
PubMed 

Google Scholar 
50.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
51.Hijmans, R. L. raster: Geographic data analysis and modeling. R package version 3.3–13. https://CRAN.R-project.org/package=raster (2020).52.Baston, D. exactextractr: Fast extraction from raster datasets using polygons. R package version 0.5.1. https://CRAN.R-project.org/package=exactextractr (2020).53.Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6, e4794 (2018).PubMed 
PubMed Central 

Google Scholar 
54.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
55.Gelman, A. & Su, Y. arm: Data analysis using regression and multilevel/hierarchical models. R package version 1.11–2. https://CRAN.R-project.org/package=arm (2020).56.Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge University Press, 2007).
Google Scholar 
57.Crase, B., Liedloff, A. C. & Wintle, B. A. A new method for dealing with residual spatial autocorrelation in species distribution models. Ecography 35, 879–888 (2012).
Google Scholar 
58.Bivand, R. S. & Wong, D. W. S. Comparing implementations of global and local indicators of spatial association. TEST 27, 716–748 (2018).MathSciNet 
MATH 

Google Scholar 
59.Bardos, D. C., Guillera-Arroita, G. & Wintle, B. A. Valid auto-models for spatially autocorrelated occupancy and abundance data. Methods Ecol. Evol. 6, 1137–1149 (2015).
Google Scholar  More

Time-to-event models for wild animals generally model exposure of individuals to natural conditions that may affect the risk of mortality and disappearance. Most models neglect to consider seasons of high human activity that may affect such risks, or interactions between endpoint hazards (reflected in incidences) that may illuminate ecology. For many large carnivores, which suffer from low natural mortality yet are also subject to high risk of anthropogenic mortality and poaching, seasons of anthropogenic activity may be as important as natural ones in mediating cause-specific mortality and disappearance.Importantly, such anthropogenic seasons of higher mortality need not be specific to the animals being studied, especially if the species is controversial and much mortality illegal: our anthropogenic seasons consist of state hunting and hounding seasons for species other than wolves (i.e., deer or bear hunting, and hounding; not wolf hunting), but that mediate human activity on the landscape during those seasons. Our results support the hypothesis that increases in poaching risk during hunting seasons may be attributable to the surge of individuals with inclination to poach on the landscape14,18,29. Alternatively, it could also suggest enhanced criminal activity of a few poachers during the same periods. We temper this increase in poaching risk by establishing snow cover as a major environmental factor strongly associated with poaching. Moreover, our time-to-event analyses illuminate how to evaluate the effects that such anthropogenic seasons may have on risk of mortality and disappearance of monitored animals throughout their lifetime, and how considering such seasons may elucidate the mechanisms behind anthropogenic mortality and disappearance.Additionally, our analysis period precedes and completely excludes any established public wolf hunting seasons. Hence, our modeled anthropogenic seasons represent the periods of most relevant anthropogenic activity for wolves, as hypothesized by other studies14,29,33 and suggested by social science studies on inclinations to poach self-reported by both deer hunters and bear hunters, as well as acceptance of poaching by hunters and farmers30,31,32.Our analyses show increases in the hazard of disappearances of collared wolves (LTF) relative to the baseline period (which excludes environmental and anthropogenic risks) for all seasons. The highest hazard of LTF occurs during the snow season, whereas increases in hazard are lower (and similar) for the two seasons that included hounding and hunting. LTF may experience changes in hazard due to changes in the hazard of any/all of its components: migration, collar failure, or cryptic poaching.Constant and steep increases in LTF hazard throughout a wolf’s lifetime suggests mechanisms other than migration regulating LTF hazard, given migration for adults is most frequent by yearlings and younger adults, around 1.5 to 2.2 years34,35,36. Moreover, only migration out of state would end monitoring, not routine extraterritorial movements of radio-collared wolves. That our seasonal LTF curves depict the cumulative hazards more than doubling beyond those t generally associated with dispersal (~ t  More

1.Nakamura, A. et al. Forests and their canopies: Achievements and horizons in canopy science. Trends Ecol. Evol. 32, 438–451 (2017).PubMed 

Google Scholar 
2.Scheffers, B. R. et al. Microhabitats reduce animal’s exposure to climate extremes. Glob. Change Biol. 20, 495–503 (2014).ADS 

Google Scholar 
3.Lefsky, M. A. et al. Estimates of forest canopy height and aboveground biomass using ICESat. Geophys. Res. Lett. 32, L22S02 (2005).
Google Scholar 
4.Ellwood, M. D. F. & Foster, W. A. Doubling the estimate of invertebrate biomass in a rainforest canopy. Nature 429, 549–551 (2004).ADS 
CAS 
PubMed 

Google Scholar 
5.Dial, R. et al. Arthropod abundance, canopy structure, and microclimate in a Bornean lowland tropical rain forest. Biotropica 38, 643–652 (2006).
Google Scholar 
6.Valencia, R. et al. High tree alpha-diversity in Amazonian Ecuador. Biodivers. Conserv. 3, 21–28 (1994).
Google Scholar 
7.Stone, M. J. et al. Edge effects and beta diversity in ground and canopy beetle communities of fragmented subtropical forest. PLoS ONE 13, e0193369 (2018).PubMed 
PubMed Central 

Google Scholar 
8.Nadkarni, N. M. Diversity of species and interactions in the upper tree canopy of forest ecosystems. Am. Zool. 34, 70–78 (1994).
Google Scholar 
9.Stanton, D. E. et al. Rapid nitrogen fixation by canopy microbiome in tropical forest determined by both phosphorus and molybdenum. Ecology 100(9), e02795 (2019).PubMed 

Google Scholar 
10.Basset, Y. et al. (eds) Arthropods of Tropical Forests. Spatio-Temporal Dynamics and Resource Use in the Canopy (Cambridge University Press, 2003).
Google Scholar 
11.Schowalter, T. D. et al. Post-hurricane successional dynamics in abundance and diversity of canopy arthropods in a tropical rainforest. Environ. Entomol. 46, 11–20 (2017).CAS 
PubMed 

Google Scholar 
12.Silva, R. R. & Brandão, C. R. F. Morphological patterns and community organization in leaf-litter ant assemblages. Ecol. Monogr. 80, 107–124 (2010).
Google Scholar 
13.McCaig, T., Sam, L., Nakamura, L. & Stork, N. E. Is insect vertical distribution in rainforests better explained by distance from the canopy top or distance from the ground?. Biodivers. Conserv. 29, 1081–1103 (2020).
Google Scholar 
14.Floren, A. & Linsenmair, K. E. The influence of anthropogenic disturbances on the structure of arboreal arthropod communities. Plant Ecol. 153, 153–167 (2001).
Google Scholar 
15.Adis, J. et al. Canopy fogging of an overstory tree—Recommendations for standardization. Ecotropica 4, 93–97 (1998).
Google Scholar 
16.Bar-Ness, Y. D. et al. Sampling forest canopy arthropod biodiversity with three novel minimal-cost trap designs. Aust. J. Entomol. 51, 12–21. https://doi.org/10.1111/j.1440-6055.2011.00836.x (2012).Article 

Google Scholar 
17.Erwin, T. L. Canopy arthropod biodiversity: A chronology of sampling techniques and results. Rev. Peru. Entomol. 2, 71–77 (1990).
Google Scholar 
18.Floren, A. Sampling arthropods from the canopy by insecticidal knockdown. In Manual on Field Recording Techniques and Protocols for All Taxa Biodiversity Inventories, Part 1 Vol. 8 (eds Eymann, J., Degref, J., Häuser, C. et al.) 158–172 (ABC Taxa, 2010).
Google Scholar 
19.Leather, S. R. (ed.) Insect Sampling in Forest Ecosystems (Blackwell Science, 2005).
Google Scholar 
20.Lowman, M., Moffett, M. & Rinker, H. B. A new technique for taxonomic and ecological sampling in rain forest canopies. Selbyana 14, 75–79 (1993).
Google Scholar 
21.Lowman, M. D., Kitching, R. L. & Carruthers, G. Arthropod sampling in Australian subtropical rain forest: How accurate are some of the more common techniques?. Selbyana 17, 36–42 (1996).
Google Scholar 
22.Lowman, M. D., Schowalter, T. D. & Franklin, J. F. Methods in Forest Canopy Research (University of California Press, 2012).
Google Scholar 
23.Majer, J. D. & Recher, H. F. Invertebrate communities on Western Australian eucalypts—A comparison of branch clipping and chemical knockdown procedures. Aust. J. Ecol. 13, 269–278. https://doi.org/10.1111/j.1442-9993.1988.tb00974.x (1988).Article 

Google Scholar 
24.Ozanne, C. M. P. Techniques and methods for sampling canopy insects. In Insect Sampling in forest ecosystems (ed. Leather, S. R.) 146–165 (Blackwell, 2005).
Google Scholar 
25.Paarmann, W. & Stork, N. E. Canopy fogging, a method of collecting living insects for investigation of life history strategies. J. Nat. Hist. 21, 563–566. https://doi.org/10.1080/00222938700770341 (1987).Article 

Google Scholar 
26.Parker, G. G., Smith, A. P. & Hogan, K. P. Access to the upper forest canopy with a large tower crane. Bioscience 42, 664–670. https://doi.org/10.2307/1312172 (1992).Article 

Google Scholar 
27.Skvarla, M. J., Larson, J. L., Fisher, J. R. & Dowling, A. P. G. A review of terrestrial and canopy malaise traps. Ann. Entomol. Soc. Am. 114(1), 27–47. https://doi.org/10.1093/aesa/saaa044 (2021).Article 

Google Scholar 
28.Stork, N. E. Australian tropical forest canopy crane: New tools for new frontiers. Aust. Ecol. 32, 4–9. https://doi.org/10.1111/j.1442-9993.2007.01740.x (2007).Article 

Google Scholar 
29.Basset, Y. et al. IBISCA-Panama, a large-scale study of arthropod beta-diversity and vertical stratification in a lowland rainforest: Rationale, study sites and field protocols. Bull. Inst. R. Sci. Nat. Belg. Entomol. 77, 39–69 (2007).
Google Scholar 
30.Basset, Y., Cizek, L. & Cuénoud, P. Arthropod diversity in a tropical forest. Science 338, 1481–1484. https://doi.org/10.1126/science.1226727 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar 
31.Kitching, R. L. et al. The biodiversity of arthropods from Australian rainforest canopies: General introduction, methods, sites and ordinal results. Aust. J. Ecol. 18, 181–191. https://doi.org/10.1111/j.1442-9993.1993.tb00442.x (1993).Article 

Google Scholar 
32.Lindo, Z. & Winchester, N. N. Oribatid mite communities and foliar litter decomposition in canopy suspended soils and forest floor habitats of western red cedar forests, Vancouver Island, Canada. Soil Biol. Biochem. 39, 2957–2966. https://doi.org/10.1016/j.soilbio.2007.06.009 (2007).CAS 
Article 

Google Scholar 
33.Schowalter, T. D. Canopy arthropod communities in relation to forest age and alternative harvest practices in western Oregon. For. Ecol. Manage 78, 115–125 (1995).
Google Scholar 
34.Southwood, T. R. E., Moran, V. C. & Kennedy, C. E. J. The assessment of arboreal insect fauna: Comparisons of knockdown sampling and faunal lists. Ecol. Entomol. 7, 331–340. https://doi.org/10.1111/j.1365-2311.1982.tb00674.x (1982).Article 

Google Scholar 
35.Stork, N. E. Guild structure of arthropods from Bornean rain forest trees. Ecol. Entomol. 12, 69–80. https://doi.org/10.1111/j.1365-2311.1987.tb00986.x (1987).Article 

Google Scholar 
36.Stork, N. E. et al. (eds) Canopy Arthropods (Chapman & Hall, 1997).
Google Scholar 
37.DeVries, P. J. Stratification of fruit-feeding nymphalid butterflies in a Costa Rican rain forest. J. Res. Lepid. 26, 98–108 (1988).ADS 

Google Scholar 
38.Hill, C. J., Gillison, A. N. & Jones, R. E. The spatial distribution of rain forest butterflies at three sites in North Queensland, Australia. J. Trop. Ecol. 8, 37–46 (1992).
Google Scholar 
39.Medina, M. C., Robbins, R. K. & Lamas, G. Vertical stratification of flight by Ithomiinae butterflies (Lepidoptera: Nymphalidae) at Pakitza, Manu National Park, Peru. In Manu—The Biodiversity of Southeastern Peru (eds Wilson, D. E. & Sandoval, A.) 211–216 (Smithsonian Institution, 1996).
Google Scholar 
40.DeVries, P. J., Murray, D. & Lande, R. Species diversity in vertical, horizontal, and temporal dimensions of a fruitfeeding butterfly community in an Ecuadorian rainforest. Biol. J. Linn. Soc. 62, 343–364. https://doi.org/10.1111/j.1095-8312.1997.tb01630.x (1997).Article 

Google Scholar 
41.DeVries, P. J., Murray, D. & Lande, R. Species diversity in vertical, horizontal, and temporal dimensions of a fruit-feeding butterfly community in an Ecuadorian rain forest. Biol. J. Linn. Soc. 62, 343–364 (1997).
Google Scholar 
42.Beccaloni, G. W. Vertical stratification of ithomiine butterfly (Nymphalidae: Ithomiinae) mimicry complexes: The relationship between adult flight height and larval host-plant height. Biol. J. Linn. Soc. 62, 313–341 (1997).
Google Scholar 
43.Schulze, C. H., Linsenmair, K. E. & Fiedler, K. Understorey versus canopy: Patterns of vertical stratification and diversity among Lepidoptera in a Bornean Rain Forest. Plant Ecol. 153, 133–152. https://doi.org/10.1023/A:1017589711553 (2001).Article 

Google Scholar 
44.Fordyce, J. A. & DeVries, P. J. A tale of two communities: Eotropical butterfly assemblages show higher beta diversity in the canopy compared to the understory. Oecologia 181, 235–243. https://doi.org/10.1007/s00442-016-3562-0 (2016).ADS 
Article 
PubMed 

Google Scholar 
45.Santos, J. P., Iserhard, C. A., Carreira, J. Y. O. & Freitas, A. V. L. Monitoring fruit-feeding butterfly assemblages in two vertical strata in seasonal Atlantic Forest: Temporal species turnover is lower in the canopy. J. Trop. Ecol. 33(5), 345–355 (2017).
Google Scholar 
46.Lourido, G. M., Motta, C. S., Graça, M. B. & Rafael, J. A. Diversity patterns of hawkmoths (Lepidoptera: Sphingidae) in the canopy of an ombrophilous forest in Central Amazon, Brazil. Acta Amazon. 48, 117–125 (2018).
Google Scholar 
47.Araujo, P. F., Freitas, A. V. L., Gonçalves, G. A. S. & Ribeiro, D. B. Vertical stratification on a small scale: The distribution of fruit-feeding butterflies in a semi-deciduous Atlantic forest in Brazil. Stud. Neotrop. Fauna Environ. 56, 10–39 (2021).
Google Scholar 
48.Charles, E. & Basset, Y. Vertical stratification of leaf-beetle assemblages (Coleoptera: Chrysomelidae) in two forest types in Panama. J. Trop. Ecol. 21, 329–336. https://doi.org/10.1017/S0266467405002300 (2005).Article 

Google Scholar 
49.Grimbacher, P. S. & Stork, N. E. Vertical stratification of feeding guilds and body size in beetle assemblages from an Australian tropical rainforest. Aust. Ecol. 32, 77–85. https://doi.org/10.1111/j.1442-9993.2007.01735.x (2007).Article 

Google Scholar 
50.Floren, A. & Schmidl, J. (eds) Canopy Arthropod Research in Europe: Basic and Applied Studies from the High Frontier (Bioform Entomology & Equipment, 2008).
Google Scholar 
51.Stork, N. E. et al. Vertical stratification of beetles in tropical rainforests as sampled by light traps in North Queensland, Australia. Austral Ecol. 41(2), 168–178 (2015).
Google Scholar 
52.Tregidgo, D. J., Qie, L., Barlow, J., Sodhi, N. S. & Lee-Hong, L. S. Vertical stratification responses of an arboreal dung beetle species to tropical forest fragmentation in Malaysia. Biotropica 42, 521–552 (2010).
Google Scholar 
53.Davis, A. J., Sutton, S. L. & Brendell, M. J. D. Vertical distribution of beetles in a tropical rainforest in Sulawesi: The role of the canopy in contributing to Biodiversity. Sepilok Bull. 13 & 14, 59–83 (2011).
Google Scholar 
54.Heatwole, H. Changes in ant assemblages across an arctic treeline. Rev d’Entomol du Quebec 34, 10–22 (1989).
Google Scholar 
55.Roubik, D. W. Tropical pollinators in the canopy and understory: Field data and theory for stratum “preferences”. J. Ins. Behav. 6, 659–673. https://doi.org/10.1007/BF01201668 (1993).Article 

Google Scholar 
56.Longino, J. T. & Colwell, R. K. Biodiversity assessment using structured inventory: Capturing the ant fauna of a tropical rain forest. Ecol. Appl. 7, 1263–1277. https://doi.org/10.1890/1051-0761(1997)007[1263:BAUSIC]2.0.CO;2 (1997).Article 

Google Scholar 
57.Vance, A. C. C., Smith, S. M., Malcolm, J. R., Huber, J. & Bellocq, M. I. Differences between forest type and vertical strata in the diversity and composition of hymenopteran families and mymarid genera in Northeastern Temperate Forests. Environ. Entomol. 36, 1073–1083. https://doi.org/10.1603/0046-225X(2007)36[1073:DBFTAV]2.0.CO;2 (2007).CAS 
Article 
PubMed 

Google Scholar 
58.Hernández-Flores, J. et al. Effect of forest disturbance on ant (Hymenoptera: Formicidae) diversity in a Mexican tropical dry forest canopy. Insect Conserv. Diver. 14(3), 393–402. https://doi.org/10.1111/icad.12466 (2020).Article 

Google Scholar 
59.Roberts, H. R. Arboreal Orthoptera in the rain forest of Costa Rica collected with insecticide: A report on the grasshoppers (Acrididae) including new species. Proc. Acad. Nat. Sci. Phila. 125, 46–66 (1973).
Google Scholar 
60.Rodgers, D. J. & Kitching, R. L. Vertical stratification of rainforest collembolan (Collembola: Insecta) assemblages: Description of ecological patterns and hypotheses concerning their generation. Ecography 21, 392–400. https://doi.org/10.1111/j.1600-0587.1998.tb00404.x (1998).Article 

Google Scholar 
61.Krab, E. J., Oorsprong, H., Berg, M. P. & Cornelissen, J. H. C. Turning northern peatlands upside down: Disentangling microclimate and substrate quality effects on vertical distribution of Collembola. Funct. Ecol. 24, 1362–1369. https://doi.org/10.1111/j.1365-2435.2010.01754.x (2010).Article 

Google Scholar 
62.Coots, C., Lambdin, P., Grant, J., Rhea, R. & Mockford, E. Vertical stratification and co-occurrence patterns of the psocoptera community associated with Eastern Hemlock, Tsuga canadensis (L.) Carrière, in the Southern Appalachians. Forests 3, 127–136. https://doi.org/10.3390/f3010127 (2012).Article 

Google Scholar 
63.Wardhaugh, C. W. et al. Vertical stratification in the spatial distribution of the beech scale insect (Ultracoelostoma assimile) in Nothofagus tree canopies in New Zealand. Ecol. Entomol. 31, 185–195 (2006).
Google Scholar 
64.Brown, B. V. et al. Comprehensive inventory of true flies (Diptera) at a tropical site. Commun. Biol. 1, 1–8 (2018).ADS 

Google Scholar 
65.Borkent, A. et al. Remarkable fly (Diptera) diversity in a patch of Costa Rican cloud forest: Why inventory is a vital science. Zootaxa 4402, 53–90 (2018).PubMed 

Google Scholar 
66.Hebert, P. D. N. et al. Counting animal species with DNA barcodes: Canadian insects. Philos. Trans. R. Soc. Lond. Ser. B. 371, 20150333 (2016).
Google Scholar 
67.Basset, Y. et al. Arthropod distribution in a tropical rainforest: Tackling a four dimensional puzzle. PLoS ONE 10, e0144110 (2015).PubMed 
PubMed Central 

Google Scholar 
68.MacArthur, R. H. Population ecology of some warblers of northeastern coniferous forests. Ecology 39, 599–619 (1958).
Google Scholar 
69.Higuchi, N. et al. Governos locais amazônicos e as questões climáticas globais 103 (INPA/edição dos autores, 2009).
Google Scholar 
70.Brown, B. V. Malaise trap catches and the crisis in Neotropical dipterology. Am. Entomol. 51, 180–183 (2005).
Google Scholar 
71.Gressitt, J. L. & Gressitt, M. K. An improved Malaise trap. Pacific Insects 4, 87–90 (1962).
Google Scholar 
72.van Achterberg, K. Can Townes type Malaise traps be improved? Some recent developments. Entomologische Berichten 69, 129–135 (2009).
Google Scholar 
73.R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (Accessed 20 October 2021); https://www.R-project.org/.
74.Konietschke, F. (2011). nparcomp: nparcomp-package. R package version 1.0-1. (Accessed 20 October 2021); http://CRAN.R-project.org/package=nparcomp75.Alboukadel Kassambara (2020). ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.3.0. (Accessed 20 October 2021); https://CRAN.R-project.org/package=ggpubr76.Watson, J. E. M. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).PubMed 

Google Scholar 
77.Gibson, L. et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–381 (2011).ADS 
CAS 
PubMed 

Google Scholar 
78.Qin, Y. et al. Improved estimates of forest cover and loss in the Brazilian Amazon in 2000–2017. Nat. Sustain. 2, 764–772 (2019).
Google Scholar 
79.Gardner, T. A. et al. Predicting the uncertain future of tropical forest species in a data vacuum. Biotropica 39, 25–30 (2007).
Google Scholar  More

1.Macdonald IR, Guinasso NL, Sassen R, Brooks JM, Lee L, Scott KT. Gas hydrate that breaches the sea-floor on the continental-slope of the Gulf-of-Mexico. Geology. 1994;22:699–702.CAS 

Google Scholar 
2.Kvenvolden KA. A review of the geochemistry of methane in natural gas hydrate. Org Geochem. 1995;23:997–1008.CAS 

Google Scholar 
3.Milkov AV. Molecular and stable isotope compositions of natural gas hydrates: a revised global dataset and basic interpretations in the context of geological settings. Org Geochem. 2005;36:681–702.CAS 

Google Scholar 
4.Cragg BA, Parkes RJ, Fry JC, Weightman AJ, Rochelle PA, Maxwell JR. Bacterial populations and processes in sediments containing gas hydrates (ODP Leg 146: Cascadia Margin). Earth Planet Sc Lett. 1996;139:497–507.CAS 

Google Scholar 
5.Yoshioka H, Maruyama A, Nakamura T, Higashi Y, Fuse H, Sakata S, et al. Activities and distribution of methanogenic and methane-oxidizing microbes in marine sediments from the Cascadia Margin. Geobiology. 2010;8:223–33.CAS 
PubMed 

Google Scholar 
6.Yoshioka H, Sakata S, Cragg BA, Parkes RJ, Fujii T. Microbial methane production rates in gas hydrate-bearing sediments from the eastern Nankai Trough, off central Japan. Geochem J. 2009;43:315–21.CAS 

Google Scholar 
7.Heuer VB, Inagaki F, Morono Y, Kubo Y, Spivack AJ, Viehweger B, et al. Temperature limits to deep subseafloor life in the Nankai Trough subduction zone. Science. 2020;370:1230–4.CAS 
PubMed 

Google Scholar 
8.Wellsbury P, Goodman K, Cragg BA, Parkes RJ. The geomicrobiology of deep marine sediments from Blake Ridge containing methane hydrate (sites 994, 995 and 997). Proc Ocean Drill Program Sci results. 2000;164:379–91.
Google Scholar 
9.Bidle KA, Kastner M, Bartlett DH. A phylogenetic analysis of microbial communities associated with methane hydrate containing marine fluids and sediments in the Cascadia margin (ODP site 892B). Fems Microbiol Lett. 1999;177:101–8.CAS 
PubMed 

Google Scholar 
10.Reed DW, Fujita Y, Delwiche ME, Blackwelder DB, Sheridan PP, Uchida T, et al. Microbial communities from methane hydrate-bearing deep marine sediments in a forearc basin. Appl Environ Microbiol. 2002;68:3759–70.CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Briggs BR, Inagaki F, Morono Y, Futagami T, Huguet C, Rosell-Mele A, et al. Bacterial dominance in subseafloor sediments characterized by methane hydrates. FEMS Microbiol Ecol. 2012;81:88–98.CAS 
PubMed 

Google Scholar 
12.Kendall MM, Boone DR. Cultivation of methanogens from shallow marine sediments at Hydrate Ridge, Oregon. Archaea. 2006;2:31–38.CAS 
PubMed 
PubMed Central 

Google Scholar 
13.Fry JC, Parkes RJ, Cragg BA, Weightman AJ, Webster G. Prokaryotic biodiversity and activity in the deep subseafloor biosphere. FEMS Microbiol Ecol. 2008;66:181–96.CAS 
PubMed 

Google Scholar 
14.Nunoura T, Takaki Y, Shimamura S, Kakuta J, Kazama H, Hirai M, et al. Variance and potential niche separation of microbial communities in subseafloor sediments off Shimokita Peninsula, Japan. Environ Microbiol. 2016;18:1889–906.CAS 
PubMed 

Google Scholar 
15.Mikucki JA, Liu Y, Delwiche M, Colwell FS, Boone DR. Isolation of a methanogen from deep marine sediments that contain methane hydrates, and description of Methanoculleus submarinus sp. nov. Appl Environ Microbiol. 2003;69:3311–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Weng C-Y, Chen S-C, Lai M-C, Wu S-Y, Lin S, Yang TF, et al. Methanoculleus taiwanensis sp. nov., a methanogen isolated from deep marine sediment at the deformation front area near Taiwan. Int J Syst Evol Micr. 2015;65:1044–9.CAS 

Google Scholar 
17.Kendall MM, Liu Y, Sieprawska-Lupa M, Stetter KO, Whitman WB, Boone DR. Methanococcus aeolicus sp. nov., a mesophilic, methanogenic archaeon from shallow and deep marine sediments. Int J Syst Evol Microbiol. 2006;56:1525–9.CAS 
PubMed 

Google Scholar 
18.Strąpoć D, Ashby M, Wood L, Levinson R, Huizinga B. Significant contribution of methyl/methanol-utilising methanogenic pathway in a subsurface biogas environment. In: Skovhus T, Whitby C, editors. Applied microbiology and molecular biology in oilfield systems. Dordrecht: Springer; 2010. p. 211–6.19.Guo H, Liu R, Yu Z, Zhang H, Yun J, Li Y, et al. Pyrosequencing reveals the dominance of methylotrophic methanogenesis in a coal bed methane reservoir associated with Eastern Ordos Basin in China. Int J Coal Geol. 2012;93:56–61.CAS 

Google Scholar 
20.Katayama T, Yoshioka H, Muramoto Y, Usami J, Fujiwara K, Yoshida S, et al. Physicochemical impacts associated with natural gas development on methanogenesis in deep sand aquifers. ISME J. 2015;9:436–46.CAS 
PubMed 

Google Scholar 
21.Yanagawa K, Tani A, Yamamoto N, Hachikubo A, Kano A, Matsumoto R, et al. Biogeochemical cycle of methanol in anoxic deep-sea sediments. Microbes Environ. 2016;31:190–3.PubMed 
PubMed Central 

Google Scholar 
22.Colwell F, Matsumoto R, Reed D. A review of the gas hydrates, geology, and biology of the Nankai Trough. Chem Geol. 2004;205:391–404.CAS 

Google Scholar 
23.Uchida T, Waseda A, Namikawa T. Methane accumulation and high concentration of gas hydrate in marine and terrestrial sandy sediments. In: Collett T, Johnson A, Knapp C, Boswell R, editors. Natural gas hydrates: energy resource potential and associated geologic hazards. Tulsa: American Association of Petroleum Geologists Memoir 89; 2009. p. 401–13.24.Katayama T, Yoshioka H, Takahashi HA, Amo M, Fujii T, Sakata S. Changes in microbial communities associated with gas hydrates in subseafloor sediments from the Nankai Trough. FEMS Microbiol Ecol. 2016;92:fiw093.PubMed 

Google Scholar 
25.Oba M, Sakata S, Fujii T. Archaeal polar lipids in subseafloor sediments from the Nankai Trough: Implications for the distribution of methanogens in the deep marine subsurface. Org Geochem. 2015;78:153–60.CAS 

Google Scholar 
26.Noguchi S, Shimoda N, Takano O, Oikawa N, Inamori T, Saeki T, et al. 3-D internal architecture of methane hydrate-bearing turbidite channels in the eastern Nankai Trough, Japan. Mar Pet Geol. 2011;28:1817–28.
Google Scholar 
27.Fujii T, Suzuki K, Takayama T, Tamaki M, Komatsu Y, Konno Y, et al. Geological setting and characterization of a methane hydrate reservoir distributed at the first offshore production test site on the Daini-Atsumi Knoll in the eastern Nankai Trough, Japan. Mar Pet Geol. 2015;66:310–22.CAS 

Google Scholar 
28.Kanno T, Fukuhara M, Osawa O, Chee S, Takekoshi M, Wang X, et al. Estimation of geothermal gradient in marine gas-hydrate-bearing formation in the Eastern Nankai Trough. Beijing, China: Proceedings of the 8th International Conference on Gas Hydrates (ICGH8–2014); 2014.29.Kaneko M, Takano Y, Ogawa NO, Sato Y, Yoshida N, Ohkouchi N. Estimation of methanogenesis by quantification of coenzyme F430 in marine sediments. Geochem J. 2016;50:453–60.CAS 

Google Scholar 
30.Kaneko M, Takano Y, Chikaraishi Y, Ogawa NO, Asakawa S, Watanabe T, et al. Quantitative analysis of coenzyme F430 in environmental samples: a new diagnostic tool for methanogenesis and anaerobic methane oxidation. Anal Chem. 2014;86:3633–8.CAS 
PubMed 

Google Scholar 
31.Katayama T, Kamagata Y Cultivation of Methanogens. Hydrocarbon and lipid microbiology protocols. In: McGenity T, Timmis K, Nogales B, editors. Springer protocols handbooks. Berlin, Heidelberg: Springer; 2016. p. 177–95.32.Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–41.CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007;35:7188–96.CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol. 2011;28:2731–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Jobb G, von Haeseler A, Strimmer K. TREEFINDER: a powerful graphical analysis environment for molecular phylogenetics. BMC Evol Biol. 2004;4:18.PubMed 
PubMed Central 

Google Scholar 
36.Whiticar MJ. Carbon and hydrogen isotope systematics of bacterial formation and oxidation of methane. Chem Geol. 1999;161:291–314.CAS 

Google Scholar 
37.Scheller S, Goenrich M, Thauer RK, Jaun B. Methyl-coenzyme M reductase from methanogenic archaea: Isotope effects on the formation and anaerobic oxidation of methane. J Am Chem Soc. 2013;135:14975–84.CAS 
PubMed 

Google Scholar 
38.Diekert G, Konheiser U, Piechulla K, Thauer RK. Nickel requirement and factor F430 content of methanogenic bacteria. J Bacteriol. 1981;148:459–64.CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Mayr S, Latkoczy C, Krüger M, Günther D, Shima S, Thauer RK, et al. Structure of an F430 variant from archaea associated with anaerobic oxidation of methane. J Am Chem Soc. 2008;130:10758–67.CAS 
PubMed 

Google Scholar 
40.House CH, Orphan VJ, Turk KA, Thomas B, Pernthaler A, Vrentas JM, et al. Extensive carbon isotopic heterogeneity among methane seep microbiota. Environ Microbiol. 2009;11:2207–15.CAS 
PubMed 

Google Scholar 
41.Lloyd KG, Alperin MJ, Teske A. Environmental evidence for net methane production and oxidation in putative ANaerobic MEthanotrophic (ANME) archaea. Environ Microbiol. 2011;13:2548–64.CAS 
PubMed 

Google Scholar 
42.Laso-Pérez R, Wegener G, Knittel K, Widdel F, Harding KJ, Krukenberg V, et al. Thermophilic archaea activate butane via alkyl-coenzyme M formation. Nature. 2016;539:396–401.PubMed 

Google Scholar 
43.Inagaki F, Nunoura T, Nakagawa S, Teske A, Lever M, Lauer A, et al. Biogeographical distribution and diversity of microbes in methane hydrate-bearing deep marine sediments on the Pacific Ocean Margin. Proc Natl Acad Sci USA. 2006;103:2815–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Marchesi JR, Weightman AJ, Cragg BA, Parkes RJ, Fry JC. Methanogen and bacterial diversity and distribution in deep gas hydrate sediments from the Cascadia Margin as revealed by 16S rRNA molecular analysis. FEMS Microbiol Ecol. 2001;34:221–8.CAS 
PubMed 

Google Scholar 
45.Nunoura T, Inagaki F, Delwiche ME, Colwell FS, Takai K. Subseafloor microbial communities in methane hydrate-bearing sediment at two distinct locations (ODP Leg 204) in the Cascadia Margin. Microbes Environ. 2008;23:317–25.PubMed 

Google Scholar 
46.Cord-Ruwisch R, Ollivier B. Interspecific hydrogen transfer during methanol degradation by Sporomusa acidovorans and hydrogenophilic anaerobes. Arch Microbiol. 1986;144:163–5.CAS 

Google Scholar 
47.Heijthuijsen JHFG, Hansen TA. Interspecies hydrogen transfer in co-cultures of methanol-utilizing acidogens and sulfate-reducing or methanogenic bacteria. FEMS Microbiol Ecol. 1986;2:57–64.
Google Scholar 
48.Eichler B, Schink B. Oxidation of primary aliphatic alcohols by Acetobacterium carbinolicum sp. nov., a homoacetogenic anaerobe. Arch Microbiol. 1984;140:147–52.CAS 

Google Scholar 
49.Parkes RJ, Cragg B, Roussel E, Webster G, Weightman A, Sass H. A review of prokaryotic populations and processes in sub-seafloor sediments, including biosphere: geosphere interactions. Mar Geol. 2014;352:409–25.CAS 

Google Scholar 
50.Imachi H, Aoi K, Tasumi E, Saito Y, Yamanaka Y, Saito Y, et al. Cultivation of methanogenic community from subseafloor sediments using a continuous-flow bioreactor. ISME J. 2011;5:1913–25.CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Newberry CJ, Webster G, Cragg BA, Parkes RJ, Weightman AJ, Fry JC. Diversity of prokaryotes and methanogenesis in deep subsurface sediments from the Nankai Trough, Ocean Drilling Program Leg 190. Environ Microbiol. 2004;6:274–87.PubMed 

Google Scholar 
52.Orsi WD, Edgcomb VP, Christman GD, Biddle JF. Gene expression in the deep biosphere. Nature 2013;499:205–8.CAS 
PubMed 

Google Scholar 
53.Vigneron A, L’Haridon S, Godfroy A, Roussel EG, Cragg BA, Parkes RJ, et al. Evidence of active methanogen communities in shallow sediments of the sonora margin cold seeps. Appl Environ Microbiol. 2015;81:3451–9.CAS 
PubMed 
PubMed Central 

Google Scholar  More

1.De Queiroz, K. Species concepts and species delimitation. Syst. Biol. 56, 879–886. https://doi.org/10.1080/10635150701701083 (2007).Article 
PubMed 

Google Scholar 
2.Coyne, J. A. & Orr, H. A. Speciation (Sinauer Associates Inc, 2004).
Google Scholar 
3.Endler, J. A. Gene flow and population differentiation: studies of clines suggest that differentiation along environmental gradients may be independent of gene flow. Science 179, 243–250 (1973).CAS 
PubMed 
ADS 

Google Scholar 
4.Mayr, E. Systematics and the Origin of Species, from the Viewpoint of a Zoologist (Harvard University Press, 1999).
Google Scholar 
5.Richardson, J. L., Urban, M. C., Bolnick, D. I. & Skelly, D. K. Microgeographic adaptation and the spatial scale of evolution. Trends Ecol. Evol. 29, 165–176 (2014).PubMed 

Google Scholar 
6.Nosil, P. Ernst Mayr and the integration of geographic and ecological factors in speciation. Biol. J. Lin. Soc. 95, 26–46 (2008).
Google Scholar 
7.Kisel, Y. & Barraclough, T. G. Speciation has a spatial scale that depends on levels of gene flow. Am. Nat. 175, 316–334 (2010).PubMed 

Google Scholar 
8.Leliaert, F. et al. DNA-based species delimitation in algae. Eur. J. Phycol. 49, 179–196 (2014).
Google Scholar 
9.Carstens, B. C., Pelletier, T. A., Reid, N. M. & Satler, J. D. How to fail at species delimitation. Mol. Ecol. 22, 4369–4383 (2013).PubMed 

Google Scholar 
10.Schlick-Steiner, B. C. et al. Integrative taxonomy: a multisource approach to exploring biodiversity. Annu. Rev. Entomol. 55, 421–438 (2010).CAS 
PubMed 

Google Scholar 
11.Capblancq, T., Mavárez, J., Rioux, D. & Després, L. Speciation with gene flow: evidence from a complex of alpine butterflies (Coenonympha, Satyridae). Ecol. Evol. 9, 6444–6457 (2019).PubMed 
PubMed Central 

Google Scholar 
12.Pedraza-Marrón, C. d. R. et al. Genomics overrules mitochondrial DNA, siding with morphology on a controversial case of species delimitation. Proc. R. Soc. B 286, 20182924 (2019).PubMed 
PubMed Central 

Google Scholar 
13.Hinojosa, J. C. et al. A mirage of cryptic species: genomics uncover striking mitonuclear discordance in the butterfly Thymelicus sylvestris. Mol. Ecol. 28, 3857–3868 (2019).PubMed 

Google Scholar 
14.Nygren, A. et al. A mega-cryptic species complex hidden among one of the most common annelids in the North East Atlantic. PLoS ONE 13, e0198356 (2018).PubMed 
PubMed Central 

Google Scholar 
15.Thielsch, A., Knell, A., Mohammadyari, A., Petrusek, A. & Schwenk, K. Divergent clades or cryptic species? Mito-nuclear discordance in a Daphnia species complex. BMC Evol. Biol. 17, 1–9 (2017).
Google Scholar 
16.Eyer, P. A. & Hefetz, A. Cytonuclear incongruences hamper species delimitation in the socially polymorphic desert ants of the Cataglyphis albicans group in Israel. J. Evol. Biol. 31, 1828–1842 (2018).CAS 
PubMed 

Google Scholar 
17.Borkent, A. Biology of Disease Vectors. 2nd edn, i–xxiii + 1–785 (Elsevier Academic Press, 2004).18.Mellor, P., Boorman, J. & Baylis, M. Culicoides biting midges: their role as arbovirus vectors. Annu. Rev. Entomol. 45, 307–340 (2000).CAS 
PubMed 

Google Scholar 
19.Rushton, J. & Lyons, N. Economic impact of Bluetongue: a review of the effects on production. Veterinaria italiana 51, 401–406 (2015).PubMed 

Google Scholar 
20.Tabachnick, W. J. Culicoides vriipennis and Bluetongue-Virus eidemiology in the United States. Annu. Rev. Entomol. 41, 23–43. https://doi.org/10.1146/annurev.en.41.010196.000323 (1996).CAS 
Article 
PubMed 

Google Scholar 
21.Wirth, W. W. & Jones, R. H. The North American Subspecies of Culicoides variipennis (Diptera, Heleidae). U. S. Dep. Agric. Tech. Bull 1170, 1–35 (1957).
Google Scholar 
22.Holbrook, F. R. et al. Sympatry in the Culicoides variipennis Complex (Diptera: Ceratopogonidae): a Taxonomic Reassessment. J. Med. Entomol. 37, 65–76. https://doi.org/10.1603/0022-2585-37.1.65 (2000).CAS 
Article 
PubMed 

Google Scholar 
23.Hopken, M. W. Pathogen Vectors at the Wildlife-Livestock Interface: Molecular Approaches to Elucidating Culicoides (Diptera: Ceratopogonidae) Biology (University of Colorado, 2016).
Google Scholar 
24.Shults, P. A Study of the Taxonomy, Ecology, and Systematics of Culicoides Species (Diptera: Ceratopogonidae) Including those Associated with Deer Breeding Facilities in Southeast Texas (Texas A&M University, 2015).
Google Scholar 
25.Velten, R. K. & Mullens, B. A. Field morphological variation and laboratory hybridization of Culicoides variipennis sonorensis and C. v. occidentalis (Diptera:Ceratopogonidae) in southern California. J. Med. Entomol. 34, 277–284 (1997).CAS 
PubMed 

Google Scholar 
26.Fontaine, M. C. et al. Extensive introgression in a malaria vector species complex revealed by phylogenomics. Science 347, 1258522 (2015).PubMed 

Google Scholar 
27.Bolnick, D. I. & Otto, S. P. The magnitude of local adaptation under genotype-dependent dispersal. Ecol. Evol. 3, 4722–4735 (2013).PubMed 
PubMed Central 

Google Scholar 
28.Slatkin, M. Isolation by distance in equilibrium and non-equilibrium populations. Evolution 47, 264–279 (1993).PubMed 

Google Scholar 
29.Pante, E. et al. Species are hypotheses: avoid connectivity assessments based on pillars of sand. Mol. Ecol. 24, 525–544 (2015).PubMed 

Google Scholar 
30.Jacquet, S. et al. Colonization of the Mediterranean basin by the vector biting midge species Culicoides imicola: an old story. Mol. Ecol. 24, 5707–5725. https://doi.org/10.1111/mec.13422 (2015).CAS 
Article 
PubMed 

Google Scholar 
31.Onyango, M. G. et al. Genotyping of whole genome amplified reduced representation libraries reveals a cryptic population of Culicoides brevitarsis in the Northern Territory, Australia. BMC Genomics 17, 769. https://doi.org/10.1186/s12864-016-3124-1 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Onyango, M. G. et al. Delineation of the population genetic structure of Culicoides imicola in East and South Africa. Parasit. Vectors 8, 660. https://doi.org/10.1186/s13071-015-1277-4 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
33.Mignotte, A. et al. High dispersal capacity of Culicoides obsoletus (Diptera: Ceratopogonidae), vector of bluetongue and Schmallenberg viruses, revealed by landscape genetic analyses. Parasit. Vectors 14, 1–14 (2021).
Google Scholar 
34.Sanders, C. J. & Carpenter, S. Assessment of an immunomarking technique for the study of dispersal of Culicoides biting midges. Infect. Genet. Evol. 28, 583–587 (2014).PubMed 

Google Scholar 
35.Kluiters, G., Swales, H. & Baylis, M. Local dispersal of palaearctic Culicoides biting midges estimated by mark-release-recapture. Parasit. Vectors 8, 86 (2015).PubMed 
PubMed Central 

Google Scholar 
36.Ducheyne, E. et al. Quantifying the wind dispersal of Culicoides species in Greece and Bulgaria. Geospat. Health 10, 177–189 (2007).
Google Scholar 
37.Purse, B. V. et al. Climate change and the recent emergence of bluetongue in Europe. Nat. Rev. Microbiol. 3, 171–181 (2005).CAS 
PubMed 

Google Scholar 
38.Jacquet, S. et al. Range expansion of the Bluetongue vector, Culicoides imicola, in continental France likely due to rare wind-transport events. Sci. Rep. https://doi.org/10.1038/srep27247 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
39.Rundle, H. D. & Nosil, P. Ecological speciation. Ecol. Lett. 8, 336–352 (2005).
Google Scholar 
40.Wang, I. J. & Bradburd, G. S. Isolation by environment. Mol. Ecol. 23, 5649–5662 (2014).PubMed 

Google Scholar 
41.Shults, P. A Study of Culicoides Biting Midges in the Subgenus Monoculicoides: Population Genetics, Taxonomy, Systematics, and Control. Ph.D. thesis, Texas A&M University (2021).42.Jewiss-Gaines, A., Barelli, L. & Hunter, F. F. First records of Culicoides sonorensis (Diptera: Ceratopogonidae), a known vector of bluetongue virus, Southern Ontario. J. Med. Entomol. 54, 757–762. https://doi.org/10.1093/jme/tjw215 (2017).CAS 
Article 
PubMed 

Google Scholar 
43.Chan, K. M. & Levin, S. A. Leaky prezygotic isolation and porous genomes: rapid introgression of maternally inherited DNA. Evolution 59, 720–729 (2005).CAS 
PubMed 

Google Scholar 
44.Harrison, R. G. Hybrid zones: windows on evolutionary process. Oxf. Surv. Evol. Biol. 7, 69–128 (1990).
Google Scholar 
45.Harrison, R. G. Animal mitochondrial DNA as a genetic marker in population and evolutionary biology. Trends Ecol. Evol. 4, 6–11 (1989).CAS 
PubMed 

Google Scholar 
46.Després, L. One, Two or More Species? Mitonuclear Discordance and Species Delimitation. Molecular ecology 28(17), 3845–3847 (2019).PubMed 

Google Scholar 
47.Janes, J. K. et al. The K= 2 conundrum. Mol. Ecol. 26, 3594–3602 (2017).PubMed 

Google Scholar 
48.De Meester, L., Vanoverbeke, J., Kilsdonk, L. J. & Urban, M. C. Evolving perspectives on monopolization and priority effects. Trends Ecol. Evol. 31, 136–146 (2016).PubMed 

Google Scholar 
49.Ballard, J. W. O., Chernoff, B. & James, A. C. Divergence of mitochondrial DNA is not corroborated by nuclear DNA, morphology, or behavior in Drosophila simulans. Evolution 56, 527–545 (2002).PubMed 

Google Scholar 
50.Behura, S., Sahu, S., Mohan, M. & Nair, S. Wolbachia in the Asian rice gall midge, Orseolia oryzae (Wood-Mason): Correlation between host mitotypes and infection status. Insect Mol. Biol. 10, 163–171 (2001).CAS 
PubMed 

Google Scholar 
51.Covey, H. et al. Cryptic Wolbachia (Rickettsiales: Rickettsiaceae) detection and prevalence in Culicoides (Diptera: Ceratopogonidae) midge populations in the United States. J. Med. Entomol. 57, 1262–1269. https://doi.org/10.1093/jme/tjaa003 (2020).Article 
PubMed 

Google Scholar 
52.Pagès, N., Muñoz-Muñoz, F., Verdún, M., Pujol, N. & Talavera, S. First detection of Wolbachia-infected Culicoides (Diptera: Ceratopogonidae) in Europe: Wolbachia and Cardinium infection across Culicoides communities revealed in Spain. Parasit. Vectors 10, 582. https://doi.org/10.1186/s13071-017-2486-9 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
53.Pilgrim, J. et al. Cardinium symbiosis as a potential confounder of mtDNA based phylogeographic inference in Culicoides imicola (Diptera: Ceratopogonidae), a vector of veterinary viruses. Parasit. Vectors 14, 100. https://doi.org/10.1186/s13071-020-04568-3 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
54.Hare, M. P. Prospects for nuclear gene phylogeography. Trends Ecol. Evol. 16, 700–706 (2001).
Google Scholar 
55.Onyango, M. G. et al. Assessment of population genetic structure in the arbovirus vector midge, Culicoides brevitarsis (Diptera: Ceratopogonidae), using multi-locus DNA microsatellites. Vet. Res. 46, 108. https://doi.org/10.1186/s13567-015-0250-8 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.Fonseca, D. M., Smith, J. L., Kim, H.-C. & Mogi, M. Population genetics of the mosquito Culex pipiens pallens reveals sex-linked asymmetric introgression by Culex quinquefasciatus. Infect. Genet. Evol. 9, 1197–1203 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Goubert, C., Minard, G., Vieira, C. & Boulesteix, M. Population genetics of the Asian tiger mosquito Aedes albopictus, an invasive vector of human diseases. Heredity 117, 125–134 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Lehmann, T. et al. Microgeographic structure of Anopheles gambiae in western Kenya based on mtDNA and microsatellite loci. Mol. Ecol. 6, 243–253 (1997).CAS 
PubMed 

Google Scholar 
59.Chapuis, M.-P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631. https://doi.org/10.1093/molbev/msl191 (2006).CAS 
Article 
PubMed 

Google Scholar 
60.Manni, M. et al. Molecular markers for analyses of intraspecific genetic diversity in the Asian Tiger mosquito, Aedes albopictus. Parasit. Vectors 8, 1–11 (2015).
Google Scholar 
61.Arntzen, J. W., Jehle, R., Bardakci, F., Burke, T. & Wallis, G. P. Asymmetric viability of reciprocal-cross hybrids between Crested and Marbled Newts (Triturus cristatus and T. marmoratus). Evolution 63, 1191–1202. https://doi.org/10.1111/j.1558-5646.2009.00611.x (2009).Article 
PubMed 

Google Scholar 
62.Gibeaux, R. et al. Paternal chromosome loss and metabolic crisis contribute to hybrid inviability in Xenopus. Nature 553, 337. https://doi.org/10.1038/nature25188 (2018).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
63.Werren, J. H., Baldo, L. & Clark, M. E. Wolbachia: master manipulators of invertebrate biology. Nat. Rev. Microbiol. 6, 741 (2008).CAS 
PubMed 

Google Scholar 
64.Servedio, M. R. & Kirkpatrick, M. The effects of gene flow on reinforcement. Evolution 51, 1764–1772. https://doi.org/10.1111/j.1558-5646.1997.tb05100.x (1997).Article 
PubMed 

Google Scholar 
65.Howard, D. J. Reinforcement: origin, dynamics, and fate of an evolutionary hypothesis. Hybrid zones and the evolutionary process, 46–69 (1993).66.Yukilevich, R. Asymmetrical patterns of speciation uniquely support reinforcement in Drosophila. Evolution 66, 1430–1446. https://doi.org/10.1111/j.1558-5646.2011.01534.x (2012).Article 
PubMed 

Google Scholar 
67.Downes, J. A. The Culicoides variipennis complex: a necessary re-alignment of nomenclature (Diptera: Ceratopogonidae). Can. Entomol. 110, 63–69 (1978).
Google Scholar 
68.Toews, D. P. & Brelsford, A. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 21, 3907–3930 (2012).CAS 
PubMed 

Google Scholar 
69.Smith, H. & Mullens, B. A. Seasonal activity, size, and parity of Culicoides occidentalis (Diptera: Ceratopogonidae) in a coastal southern California salt marsh. J. Med. Entomol. 40, 352–355. https://doi.org/10.1603/0022-2585-40.3.352 (2003).Article 
PubMed 

Google Scholar 
70.Linley, J. The effect of salinity on oviposition and egg hatching in Culicoides variipennis sonorensis (Diptera: Ceratopogonidae). J. Am. Mosq. Control Assoc. 2, 79–82 (1986).CAS 
PubMed 

Google Scholar 
71.Gerry, A. C. & Mullens, B. A. Response of Male Culicoides variipennis sonorensis (Diptera: Ceratopogonidae) to carbon dioxide and observations of mating behavior on and near cattle. J. Med. Entomol. 35, 239–244. https://doi.org/10.1093/jmedent/35.3.239 (1998).CAS 
Article 
PubMed 

Google Scholar 
72.Nolan, D. V. et al. Rapid diagnostic PCR assays for members of the Culicoides obsoletus and Culicoides pulicaris species complexes, implicated vectors of bluetongue virus in Europe. Vet. Microbiol. 124, 82–94 (2007).CAS 
PubMed 

Google Scholar 
73.Sebastiani, F. et al. Molecular differentiation of the Old World Culicoides imicola species complex (Diptera, Ceratopogonidae), inferred using random amplified polymorphic DNA markers. Mol. Ecol. 10, 1773–1786 (2001).CAS 
PubMed 

Google Scholar 
74.Carlson, D. Identification of mosquitoes of Anopheles gambiae species complex A and B by analysis of cuticular components. Science 207, 1089–1091 (1980).CAS 
PubMed 
ADS 

Google Scholar 
75.Palacios, G. et al. Characterization of the Sandfly fever Naples species complex and description of a new Karimabad species complex (genus Phlebovirus, family Bunyaviridae). J. Gen. Virol. 95, 292 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Rivas, G., Souza, N. & Peixoto, A. A. Analysis of the activity patterns of two sympatric sandfly siblings of the Lutzomyia longipalpis species complex from Brazil. Med. Vet. Entomol. 22, 288–290 (2008).CAS 
PubMed 

Google Scholar 
77.Wilson, W. C. et al. Current status of bluetongue virus in the Americas. Bluetongue 10, 197–220 (2009).
Google Scholar 
78.Allen, S. E. et al. Epizootic Hemorrhagic Disease in White-Tailed Deer, Canada. Emerg. Infect. Dis. 25, 832–834. https://doi.org/10.3201/eid2504.180743 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
79.McGregor, B. L. et al. Field data implicating Culicoides stellifer and Culicoides venustus (Diptera: Ceratopogonidae) as vectors of epizootic hemorrhagic disease virus. Parasit. Vectors 12, 258. https://doi.org/10.1186/s13071-019-3514-8 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
80.Shults, P., Ho, A., Martin, E. M., McGregor, B. L. & Vargo, E. L. Genetic diversity of Culicoides stellifer (Diptera: Ceratopogonidae) in the Southeastern United States compared with sequences from Ontario, Canada. J. Med. Entomol. 57, 1324–1327. https://doi.org/10.1093/jme/tjaa025 (2020).CAS 
Article 
PubMed 

Google Scholar 
81.Mallet, J. Hybridization as an invasion of the genome. Trends Ecol. Evol. 20, 229–237 (2005).PubMed 

Google Scholar 
82.Ciota, A. T., Chin, P. A. & Kramer, L. D. The effect of hybridization of Culex pipiens complex mosquitoes on transmission of West Nile virus. Parasit. Vectors 6, 1–4 (2013).
Google Scholar 
83.Meiswinkel, R., Gomulski, L., Delécolle, J., Goffredo, M. & Gasperi, G. The taxonomy of Culicoides vector complexes-unfinished business. Vet. Ital. 40, 151–159 (2004).CAS 
PubMed 

Google Scholar 
84.Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics (Oxford, England) 32, 3047–3048. https://doi.org/10.1093/bioinformatics/btw354 (2016).CAS 
Article 

Google Scholar 
85.Andrews, S. Babraham bioinformatics-FastQC a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).86.Rochette, N. C., Rivera-Colón, A. G. & Catchen, J. M. Stacks 2: Analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol. Ecol. 28, 4737–4754 (2019).CAS 
PubMed 

Google Scholar 
87.Morales-Hojas, R. et al. The genome of the biting midge Culicoides sonorensis and gene expression analyses of vector competence for bluetongue virus. BMC Genomics 19, 624. https://doi.org/10.1186/s12864-018-5014-1 (2018).CAS 
Article 
PubMed 
PubMed Central 

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

Google Scholar 
89.Pante, E. et al. Use of RAD sequencing for delimiting species. Heredity 114, 450–459 (2015).CAS 
PubMed 

Google Scholar 
90.Benestan, L. M. et al. Conservation genomics of natural and managed populations: building a conceptual and practical framework. Mol. Ecol. 25, 2967–2977 (2016).PubMed 

Google Scholar 
91.Lischer, H. E. & Excoffier, L. PGDSpider: an automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics (Oxford, England) 28, 298–299 (2012).CAS 

Google Scholar 
92.Pina-Martins, F., Silva, D. N., Fino, J. & Paulo, O. S. Structure_threader: An improved method for automation and parallelization of programs structure, fastStructure and MavericK on multicore CPU systems. Mol. Ecol. Resour. 17, e268–e274 (2017).CAS 
PubMed 

Google Scholar 
93.Raj, A., Stephens, M. & Pritchard, J. K. Variational Inference of Population Structure in Large SNP Datasets. bioRxiv 10, 001073 (2013).
Google Scholar 
94.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.http://www.R-project.org/ (2013).95.Jombart, Thibaut, and Caitlin Collins. A tutorial for discriminant analysis of principal components (DAPC) using adegenet 2.0. 0. London: Imperial College London, MRC Centre for Outbreak Analysis and Modelling (2015).96.Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics (Oxford, England) 30, 1312–1313 (2014).CAS 

Google Scholar 
97.Leaché, A. D., Banbury, B. L., Felsenstein, J., De Oca, A.N.-M. & Stamatakis, A. Short tree, long tree, right tree, wrong tree: New acquisition bias corrections for inferring SNP phylogenies. Syst. Biol. 64, 1032–1047 (2015).PubMed 
PubMed Central 

Google Scholar 
98.Pattengale, N. D., Alipour, M., Bininda-Emonds, O. R., Moret, B. M. & Stamatakis, A. How many bootstrap replicates are necessary?. J. Comput. Biol. 17, 337–354 (2010).MathSciNet 
CAS 
PubMed 

Google Scholar 
99.Trifinopoulos, J., Nguyen, L.-T., von Haeseler, A. & Minh, B. Q. W-IQ-TREE: A fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 44, W232–W235 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
100.Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K., Von Haeseler, A. & Jermiin, L. S. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
101.Nguyen, L.-T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
PubMed 

Google Scholar 
102.Hoang, D. T., Chernomor, O., Von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).CAS 
PubMed 

Google Scholar 
103.Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: Assessing the performance of PhyML 30. Syst. Biol. 59, 307–321. https://doi.org/10.1093/sysbio/syq010 (2010).CAS 
Article 
PubMed 

Google Scholar 
104.Rousset, F. genepop’007: a complete re‐implementation of the genepop software for Windows and Linux. Molecular ecology resources 8(1), 103–106 (2008).
Google Scholar 
105.Rousset, F. Genetic differentiation between individuals. J Evol Biol 13, 58–62 (2000).
Google Scholar 
106.Loiselle, B. A., Sork, V. L., Nason, J. & Graham, C. Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubiaceae). Am. J. Bot. 82, 1420–1425 (1995).
Google Scholar 
107.Hardy, O. & Vekemans, X. SPAGeDi 1.5. A program for Spatial Pattern Analysis of Genetic Diversity. User’s manual http://ebe.ulb.ac.be/ebe/SPAGeDi_files/SPAGeDi_1.5_Manual.pdf. Université Libre de Bruxelles, Brussells, Belgium.[Google Scholar] (2015).108.Jay, F., Sjödin, P., Jakobsson, M. & Blum, M. G. Anisotropic isolation by distance: the main orientations of human genetic differentiation. Mol. Biol. Evol. 30, 513–525 (2013).CAS 
PubMed 

Google Scholar 
109.Piry, S. et al. Mapping Averaged Pairwise Information (MAPI): a new exploratory tool to uncover spatial structure. Methods Ecol. Evol. 7, 1463–1475 (2016).
Google Scholar 
110.Kearse, M. et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics (Oxford, England) 28, 1647–1649. https://doi.org/10.1093/bioinformatics/bts199 (2012).Article 

Google Scholar 
111.Hopken, M. W. Pathogen Vectors at The Wildlife-Livestock Interface: Molecular Approaches to Elucidating Culicoides (Diptera: Ceratopogonidae) Ph.D. thesis, Colorado State University (2016).112.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
113.Bandelt, H. J., Forster, P. & Rohl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48. https://doi.org/10.1093/oxfordjournals.molbev.a026036 (1999).CAS 
Article 
PubMed 

Google Scholar  More

The presents study indicates that Bracon predatorius generally oviposits during early stages of gall development (Fig. 1d) on galls induced by Aceria doctersi mostly on tender leaves (Fig 1a–c) and rarely on petioles and stems13. The number of B. predatorius larvae in parasitized galls ranged from 1–27 (n=93). Eighty-five percent of the examined galls (n=109) were parasitized by B. predatorius. Different development stages of larvae (Fig. 1f,g) and pupae (Fig. 1i) of B. predatorius were found together in some large galls (n=31) (Fig. 1i), which suggests multiple oviposition at different stages of gall development. Dissection of leaf galls two hours after oviposition by B. predatorius always revealed only a single egg (n=8). No live A. doctersi individuals were found close to the parasitoid wasp pupae (Fig. 1h). Aceria doctersi galls parasitised by B. predatorius have also been found in Kodakara (Thrissur district, Kerala) about 100 km away from the type locality in Kozhikode.The larval stages of B. predatorius feed on both juvenile and adults of A. doctersi (Fig 2d–f, Supplementary Video 1) which usually remain close to the erineal hairs on which they feed16; no egg predation occurs. Young larvae of B. predatorius wriggle through in between erineal hairs (Supplementary Video 1). They use their sickle-shaped mandibles (Fig 3b–e) to hunt mites (Supplementary Video 1). Continuous outward and inward movement of mandibles of B. predatorius larvae occurs along with the wriggling movement (Supplementary Video 1). The final instar larvae of B. predatorius are the most active and they feed voraciously at the rate of 5–7 A. doctersi individuals/min (n=8) (Supplementary Video 1).Figure 2Predatory behaviour of Bracon predatorius Ranjith & Quicke sp. nov. (a–c) Relationships between presence/absence and number of B. predatorius, gall size and numbers of mites (median, upper and lower quartiles, 1.5 × interquartile range and outliers): (a) galls without Bracon predatorius (n = 16) are significantly smaller than those with one or more Bracon predatorius (n = 93) (t = 3.7592, DF = 97.265, p-value = 0.000291), (b) galls without Bracon predatorius contain significantly more mites than those with (t = 6.308, DF = 15.877, p-value = 0.0001), (c) mite number as a function of number of Bracon predatorius larvae (only in parasitised galls) with gall volume as co-variate (n = 93, adjusted R2 = 0.4657,F = 21.13 on 3 and 89DF, p-value = 0.0001), gall volume and interaction were non-significant. (d–f) Sequential images of predatory behaviour of Bracon predatorius.Full size imageFigure 3Final instar larval cephalic structure of Bracon predatorius Ranjith & Quicke sp. nov. (a–d) Slide microphotographs of larval head capsule and mandible (a) macerated head capsule in anterior view, (b) head capsule, in dorsal view, (c) head capsule (in part), ventral view, (d) right mandible, in dorsal view, (e) anterior view of living final instar larva of B. predatorius consuming mite.Full size imageUnattacked galls were significantly smaller than those containing B. predatorius (means 217 and 595 respectively; p More

The Levant region, the major land bridge connecting Africa with Eurasia, was a significant dispersal route for Hominins and fauna during the early Pleistocene1,2,3. But while there are numerous Eurasian early Pleistocene sites, fossil hominin remains are rare and present only at four localities dated between 1.1 and 1.9 Mya4,5,6,7,8,9,10,11: Dmanisi (Georgia), Venta Micena (Orce, Granada), Modjokerto and Sangiran (Java, Indonesia), and Sima De Elefante (Atapuerca, Spain) (Supplementary 2: Table 1; Fig. 1a). In contrast, early Pleistocene east African sites containing Homo cranial remains are more abundant, but postcranial remains are scarcer, and the best-preserved skeleton is Nariokotome KNM-WT 1500012,13.Figure 1‘Ubeidya site locality. (a) Map of Africa and Eurasia with major Pleistocene paleoanthropological sites. Black circles denote sites with no osteological remains; red circles denote sites with human osteological remains. (b) The location of the site of ‘Ubeidiya, south of lake Kineret (Sea of Galilee), on the western banks of the Jordan Valley (red circle) (c) aerial photograph of the excavation plan of ‘Ubeidiya with the location of layer II-23 where UB 10749 was recovered.Full size imageIn the Levant, the only site from this time-period with hominin remains is ‘Ubeidiya at the western escarpment of the Jordan Valley which is a part of the broader Rift Valley (Supplementary 1: Fig. 1b,c). The fossil remains include cranial fragments (UB 1703, 1704, 1705, and 1706), two incisor (UB 1700, UB 335) and a molar (UB 1701), identified as Homo cf. erectus/ergaster14,15,16,17,18. It is important to note that some of these fragments were bulldozed out of the ground preceding the first season, while others are considered intrusive and younger than the surroundings deposits17.In 2018 during a reanalysis of the faunal assemblages done by two of the authors (A. B, and M. B.) a complete vertebral body (UB 10749) with hominin characteristics was found. This is the first hominin postcranial remain found at ‘Ubeidiya securely assigned to early Pleistocene deposits (See “Materials and methods”).Here we assess the taxonomic affinity of UB 10749, its serial location along the spinal column, its chronological and physiological age at death, estimate the specimen’s height and weight, and detect any pathological or taphonomic changes. Based on our findings, we explore the unique developmental characteristics of the UB 10749 within the context of early Homo paleobiology and its implications for hominin dispersal out of Africa.Description of the findingUB 10749 is a complete vertebral body (Fig. 2). The superior plate of the vertebra is oval, with an uneven surface, indicating non-ossification of the vertebral endplate. Similarly, the inferior plate is also oval with marked postero-lateral edges. A small pit is found in the center of both superior and inferior plates. The inferior plate is bilaterally wider than the superior plate. The anterior and lateral walls are smooth and slightly concave i.e., their superior and inferior edges are more prominent than the center. There is no evidence of rib attachment to the body on the lateral walls. The posterior wall can be divided into three parts, the center and right and left lateral thirds. The central part is smooth with two nutritional foramina. The two lateral thirds are located at the junction between the vertebral body and the pedicles. Their surface is uneven, indicating that the pedicle had not yet ossified to the vertebral body. In a lateral view, the vertebra shows a lordotic wedging as the height of the anterior wall is greater than that of the posterior wall (Supplementary 2: Table 2). The oval shape of the vertebral body, the concavity of the inferior plate, the lordotic wedging, and the lack of rib bearing facets all indicate a lower lumbar vertebra, i.e., presacral (PS) 1, PS2, or PS3 (corresponding to L5, L4, and L3 in modern humans).Figure 2UB 10749 vertebral body. (a) Superior view; (b) posterior view; (c) inferior view; (d) anterior view.Full size imageA micro-CT (µCT) scan of UB 10749 (Fig. 3) reveals a well-developed cortical bone on the anterior and lateral walls and the central part of the posterior wall. The cancellous bone at the superior and inferior plates is very thin, as is the bone at the lateral thirds of the posterior wall, indicating that these were not yet ossified. The µCT scan also reveals well-developed canals within the vertebral body –Bastons’ venous plexus19 (Fig. 3c). A small pit at the superior and inferior plates is seen in the mid-sagittal and coronal planes of the CT scan (Fig. 3a, b). A thin vertical region that appears black on the µCT connects the two pits, indicating that this area was not yet ossified.Figure 3µCT scan of UB 10749. (a) Midsagittal section. (b) Coronal section. (c) Horizontal section.Full size imageTaxonomic identificationWe compared UB 10749 to a range of mammalian species from, but not limited to, those present in ‘Ubeidiya such as carnivores (e.g., Ursus, Hyeana, Panthera), Artiodactyla (e.g., Hippopotamus, Praemegaceros), Perissodactyla (Rhinocertidae, Equidae), Proboscidea (Mamuthus, Elephas), and Primates (Homo, Pongo, Gorilla, Theropithecus and Papio).UB 10749 lacks the inward indentation on the posterior wall distinctive of Ursus and is short cranio-caudally, as opposed to the longer vertebral bodies of ungulates.The size, the large vertebral plate, and the relatively short vertebral body of UB 10749 indicates that it belongs to hominoidea. The lordotic wedging and the concavity of the inferior plate further suggests that this is a hominin vertebra20,21.To narrow the taxonomic identification, we compared UB 10749 to a range of extant and extinct hominin species, and to Pan as an outgroup (Supplementary 2: Table 3). The analysis revealed that the best index to which best differentiates between lumbar vertebral bodies of Homo and Pan is ‘superior length to posterior height’ (Fig. 4; Supplementary 2: Table 4). This index also differentiates between Homo and Australopithecus22. Compared to the three presacral vertebrae (PS1–PS3) of hominins and Pan, UB 10749 falls within the range of Homo and outside the range of Pan or Australopithecus. It falls near the position of the vertebrae of KNM-WT-15000, an early Pleistocene sub adult specimen from east Africa. Therefore, we conclude that the vertebra at hand most probably belongs to an early Pleistocene Homo.Figure 4Comparison of UB 10749 to other hominoids. Vertebral body ratio (superior length to posterior height) of each of the lower 3 presacral vertebra in modern humans, neandertals, australopith, chimpanzees, KNM-WT 15000 and UB 10749. Note that UB 10749 is consistently falls within the range of Homo, and beyond the range of chimpanzees and australopith.Full size imageSerial allocation of the vertebral bodyIt is well known, especially in Hominoidea, that there is a vast overlap in the shape of adjacent lumbar vertebral bodies23. We conducted three separate analyses to address this problem: (1) Vertebral wedging i.e., the ratio of posterior height/anterior height which significantly separates the vertebral segments PS1, PS2, and PS3 of modern humans (Supplementary 2: Fig. 1; Supplementary 2: Table 4), (2) A principal component analysis (PCA) of vertebral linear indices (Fig. 5a; Supplementary 2: Table 4), and (3) Geometric morphometrics (GM) shape analysis (Fig. 5b). Vertebral wedging sets UB 10749 as PS2. The vertebral linear indices PCA sets the UB 10749 as either PS2 or PS3, and the GM shape analysis sets the vertebra as either PS1 or PS2. Based on these results, we estimate that the serial allocation of UB 10749 is most likely PS2.Figure 5Serial allocation of UB 10749. (a) PCA of vertebral body ratios of modern humans, KNM-WT 15000, STS 14, and UB 10749 (see Supplementary Table 4). Note the overlap between adults and juvenile in each presacral vertebra. UB 10749 falls within the range of PS2–PS3. Note that KNM-WT 15000 and STS 14 follow the same pattern as modern humans. (b) PCA results for GM shape analysis. UB 10749 falls within the range of PS1, but with proximity to PS2. Note that KNM-WT 15000 and STS 14 follow the same pattern as modern humans. In both analysis: Circles denotes juvenile; Squares denotes adults. Red denotes PS1; Blue denotes PS2; Green denotes PS3.Full size imageAge at deathAge at death is estimated based on level of ossification, relative vertebral size, or vertebral shape. The lack of neural canal ossification in UB 10749 indicates an approximate age of 3–6-years-old compared to modern humans24,25 (Supplementary 2: Fig. 2), although it is important to note that several authors report high variability in pedicle ossification, up to 16-years-old26,27. The absence of vertebral endplate ossification also supports the young age of UB 10749, indicating that the vertebra belongs to an individual that had not reached puberty28.In contrast, based on its size alone, UB 10749 would be assigned an older age, probably between 11 and 15-year-old modern human (Fig. 6a: Supplementary 2: Table 5). However, vertebral size is highly variable with age, and we cannot rule out either a younger or older age. Finally, geometric morphometric principal component shape analysis suggests that UB 10749 falls within the range of 6–10-years-old modern humans (Fig. 6b). This is confirmed by a linear discriminant analysis which also places UB 10749 well within the 6–10 years old group (Supplementary 2: Fig. 4). Considering all the above information, we estimate that the age at death for UB 10749 is between 6 and 12-years-old.Figure 6Age at death of UB 10749. (a) Vertebral body size (combined sample of PS1–PS3) of modern humans, KNM-WT 15000 and UB 10749 (see Supplementary 2: Table 5). UB 10749 falls within the range of 11–15 years or the lower end of adults. (b) PCA results for GM shape analysis of modern human, KNM-WT 15000, STS 14, and UB 10749 vertebral bodies. UB 10749 falls within the range of the 6–10 age group. In both analyses: Red, 0–5 years old; Green, 6–10 years old; Blue, 11–15 years old; Brown, 16-adults.Full size imageHeight and weight estimationHeight (stature) and weight at death is estimated based on a range of equations and growth charts for modern humans (Supplementary 2: Tables 6–8). The estimated average height at death of UB 10749, points to a height of 155 cm. This height is comparable to a 13 years-old boy or a 12.5 years-old girl, based on CDC growth charts. A height of 155 cm is above the 95 percentile of 10 years old and above the 75 percentile for 12 years old modern humans29. As the age estimation for UB 10749 is 6–12 years, it seems that this individual was tall for its age.Weight is estimated based on height or based on chronological age. Based on height, UB 10749 was 45–55 kg, while based on age, the weight of UB 10749 was 20–43 kg (Supplementary 2: Table 7). Since height is a stronger predictor for weight than age30, we estimate the weight at death at about 45–50 kg.A single juvenile vertebral body is not a definitive predictor for adult height and weight. Even more so, the growth pattern of early Pleistocene hominins is unknown. Thus, to cautiously estimate the adult height and weight of UB 10749, calculations were based on several methods: modern American (CDC growth charts), modern Sudanese population31, and chimpanzees32.Assuming UB 10749 was 6–12 years old, based on chimpanzees’ growth charts, it would have reached adult height of 155–192 cm and weighted 50–101 kg. Based on modern American and Sudanese growth charts, UB 10749 displays a range of a height between 168 and 247 cm and a weight between 62 and 173 kg (Supplementary 2: Table 8). The average height and weight predication for adult size of UB 10749 is 198 cm and 100 kg. Although we cannot rule out any of the estimations, based on its size at death and predicated adult size, UB 10749 was most likely a large-bodied hominin33,34,35.TaphonomyVery thin fluviatile deposits are evident on the surface of the vertebra, despite being cleaned during the excavation. Aside from that, there is no apparent taphonomic alteration or post-depositional breakage.PathologyThe completeness of the vertebral body and its bilateral symmetry do not suggest pathological processes or developmental deformities that may have affected the vertebra, such as osteoarthritis, disc herniation, spondylosis, tuberculosis, brucellosis, or scoliosis36. However, in the absence of the vertebral arch, we cannot rule out anterior slippage of the vertebral body, i.e., spondylolisthesis or facet joint deformities. The discrepancy between the size of the vertebral body and the level of ossification is puzzling. The size of UB 10749 is equivalent to an 11–15-year-old modern human, and the level of ossification is equivalent to a 3–6-year-old modern human child. This discrepancy might result from several factors, including developmental or pathological conditions, such as: persistent notochondral canal; hypopituitarism; androgen deficiency; genetic mutation24,37,38 (see Supplementary 2 for discussion regarding possible pathology). While these conditions are rare in modern humans, they cannot be ruled out. Another possibility is that UB 10749 displays a different ossification pattern than observed in modern humans or great apes25,39. More