Ecology

Isogai A. TEMPO-catalyzed oxidation of polysaccharides. Polym J. https://doi.org/10.1038/s41428-021-00580-1.Kadokawa J-I. Glucan phosphorylase-catalyzed enzymatic synthesis of unnatural oligosaccharides and polysaccharides using nonnative substrates. Polym J. https://doi.org/10.1038/s41428-021-00584-x.Zhong C, Nidetzky B. Precision synthesis of reducing-end thiol-modified cellulose enabled by enzyme selection. Polym J. https://doi.org/10.1038/s41428-021-00599-4.Sakurai Y, Sawada T, Serizawa T. Phosphorylase-catalyzed synthesis and self-assembled structures of cellulose oligomers in the presence of protein denaturants. Polym J. https://doi.org/10.1038/s41428-021-00592-x.Sato T, Yang J, Terao K Micellar structure of hydrophobically modified polysaccharides in aqueous solution. Polym J. https://doi.org/10.1038/s41428-021-00561-4.Chen H, Liu N, He F, Liu Q, Xu X. Specific β-glucans in chain conformations and their biological functions. Polym J. https://doi.org/10.1038/s41428-021-00587-8.Li H, Mumtaz M, Isono T, Satoh T, Chen W-C, Borsali R. Self-assembly of carbohydrate-based block copolymer systems: glyconanoparticles and highly nanostructured thin films. Polym J. https://doi.org/10.1038/s41428-021-00604-w.Kinose Y, Sakakibara K, Tsuji Y. Conformational characteristics of regioselectively PEG/PS-grafted cellulosic bottlebrushes in solution: cross-sectional structure and main-chain stiffness. Polym J. https://doi.org/10.1038/s41428-021-00594-9.Kar H, Sun J, Clewett CFM, Thongsai N, Paoprasert P, Dwyer JH, et al. Uniform amphiphilic cellulose nanocrystal films. Polym J. https://doi.org/10.1038/s41428-021-00611-x.Vadanan SV, Basu A, Lim S. Bacterial cellulose production, functionalization and development of hybrid materials with synthetic biology. Polym J. https://doi.org/10.1038/s41428-021-00606-8.Kitagishi H, Mao Q. Capture of carbon monoxide using a heme protein model: from biomimetic chemistry of heme proteins to physiological and therapeutic applications. Polym J. https://doi.org/10.1038/s41428-021-00591-y.Arai T, Aiki Y, Sato T. Accelerated transgene expression of pDNA/polysaccharide complexes by solid-phase reverse transfection and analysis of the cell transfection mechanism. Polym J. https://doi.org/10.1038/s41428-021-00603-x.Sumiya K, Izumi H, Matsunaga T, Tanaka M, Sakurai K. Delivery of therapeutic oligonucleotides targeting Dectin-1 using quantized complexes. Polym J. https://doi.org/10.1038/s41428-021-00595-8.Takada K, Komuro A, Ali MA, Singh M, Okajima M, Matsumura K, et al. Cell-adhesive gels made of sacran/collagen complexes. Polym J. https://doi.org/10.1038/s41428-021-00593-w.Higaki Y, Takahara A. Structure and properties of polysaccharide/imogolite hybrids. Polym J. https://doi.org/10.1038/s41428-021-00588-7.Tanabe K, Izawa H, Ifuku S. Preparation and recycling property of nanofiber-reinforced polystyrene molded product using the emulsion-forming ability of chitin nanofibers. Polym J. https://doi.org/10.1038/s41428-021-00586-9.Zheng C, Wang J, Jiang H, Ma Y, Shao Z. Green synthesis of polyacrylamide/polyanionic cellulose hydrogels composited with Zr-based coordination polymer and their enhanced mechanical and adsorptive properties. Polym J. https://doi.org/10.1038/s41428-021-00590-z.Sagawa T, Oishi M, Yataka Y, Sato R, Iijima K, Hashizume M. Control of molecular permeability of polysaccharides composite films utilizing molecular imprinting approach. Polym J. https://doi.org/10.1038/s41428-021-00605-9.Khoerunnisa F, Sihombing M, Nurhayati M, Dara F, Triadi HA, Nasir M, et al. Poly(ether sulfone)-based ultrafiltration membranes with enhanced permeability and antifouling properties by employing chitosan/ammonium chloride. Polym J. https://doi.org/10.1038/s41428-021-00607-7.Suzuki S, Togo A, Iwata T. Dry-jet wet spinning of β-1,3-glucan and α-1,3-glucan. Polym J. https://doi.org/10.1038/s41428-021-00573-0.Venanzi M, Kimura S. Special issue: peptide materials. Polym J. 2013;45:467–467.CAS 
Article 

Google Scholar 
Serizawa T. Special issue: biorelated polymers and materials. Polym J. 2014;46:435–435.CAS 
Article 

Google Scholar 
Ikeda M, Kuzuya K, Matsusaki M, Tanaka K. Special issue: biofunctional gels. Polym J. 2020;52:821–821.CAS 
Article 

Google Scholar  More

Polyzos, S. & Tsiotas, D. The contribution of transport infrastructures to the economic and regional development: A review of the conceptual framework. Theor. Empir. Res. Urban Manag. 15, 5–23 (2020).
Google Scholar 
Ledec, G. & Posas, P. J. Biodiversity conservation in road projects: Lessons from World Bank experience in Latin America. Transp. Res. Rec. 1819, 198–202 (2003).Article 

Google Scholar 
Hughes, A. C. Understanding and minimizing environmental impacts of the Belt and Road Initiative. Conserv. Biol. 33, 883–894 (2019).Article 

Google Scholar 
Seiler, A. in COST 341—habitat fragmentation due to transportation infrastructure: the European review (eds Trocmé, M. et al.) Ch. 3, 31–50 (Office for Official Publications of the European Communities, 2002).Marcantonio, M., Rocchini, D., Geri, F., Bacaro, G. & Amici, V. Biodiversity, roads, & landscape fragmentation: Two Mediterranean cases. Appl. Geogr. 42, 63–72. https://doi.org/10.1016/j.apgeog.2013.05.001 (2013).Article 

Google Scholar 
Plămădeal, V. & Slobodeaniuc, S. Negative impact of railway transport on the ambient environment. J. Eng. Sci. https://doi.org/10.5281/zenodo.2640044 (2019).Lala, F. et al. Wildlife roadkill in the Tsavo Ecosystem, Kenya: Identifying hotspots, potential drivers, and affected species. Heliyon 7, e06364 (2021).Article 

Google Scholar 
Laurance, W. F. et al. A global strategy for road building. Nature 513, 229–232. https://doi.org/10.1038/nature13717 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Laurance, W. F., Goosem, M. & Laurance, S. G. W. Impacts of roads and linear clearings on tropical forests. Trends Ecol. Evol. 24, 659–669. https://doi.org/10.1016/j.tree.2009.06.009 (2009).Article 
PubMed 

Google Scholar 
Clair, C. C. S., Whittington, J., Forshner, A., Gangadharan, A. & Laskin, D. N. Railway mortality for several mammal species increases with train speed, proximity to water, and track curvature. Sci. Rep. 10, 20476. https://doi.org/10.1038/s41598-020-77321-6 (2020).CAS 
Article 

Google Scholar 
Kušta, T., Ježek, M. & Keken, Z. Mortality of large mammals on railway tracks. Sci. Agric. Bohem. 42, 12–18 (2011).
Google Scholar 
Dorsey, B. & Olsson, M. Handbook of Road Ecology (eds van der Ree, R. et al.) Ch. 26, 219–227 (Wiley, 2015).Barrientos, R. & Borda-de-Água, L. Railway Ecology (eds Borda-de-Água, L. et al.) Ch. 4, 43–64 (Springer Open, 2017).Lucas, P. S., de Carvalho, R. G. & Grilo, C. Railway Ecology Ch. Chapter 6, 81–99 (2017).Barrientos, R., Ascensão, F., Beja, P., Pereira, H. M. & Borda-de-Água, L. Railway ecology vs. road ecology: Similarities and differences. Eur. J. Wildl. Res. 65, 1–9. https://doi.org/10.1007/s10344-018-1248-0 (2019).Article 

Google Scholar 
Jasińska, K. D. et al. Linking habitat composition, local population densities and traffic characteristics to spatial patterns of ungulate-train collisions. J. Appl. Ecol. 56, 2630–2640. https://doi.org/10.1111/1365-2664.13495 (2019).Article 

Google Scholar 
Smith, D. J., Ree, R. v. d. & Rosell, C. Handbook of Road Ecology (eds van der Ree, R. et al.) Ch. 21, 172–183 (Wiley, 2015).Gilhooly, P. S., Nielsen, S. E., Whittington, J. & Clair, C. C. S. Wildlife mortality on roads and railways following highway mitigation. Ecosphere 10, e02597 (2019).Article 

Google Scholar 
Clevenger, A. P., Chruszcz, B. & Gunson, K. E. Highway mitigation fencing reduces wildlife-vehicle collisions. Wildl. Soc. Bull. 29, 646–653 (2001).
Google Scholar 
Simpson, N. O. et al. Overpasses and underpasses: Effectiveness of crossing structures for migratory ungulates. J. Wildl. Manag. 80, 1370–1378. https://doi.org/10.1002/jwmg.21132 (2016).Article 

Google Scholar 
Seidler, R. G., Green, D. S. & Beckmann, J. P. Highways, crossing structures and risk: Behaviors of Greater Yellowstone pronghorn elucidate efficacy of road mitigation. Glob. Ecol. Conserv. 15, e00416. https://doi.org/10.1016/j.gecco.2018.e00416 (2018).Article 

Google Scholar 
Huijser, M. P. et al. Effectiveness of short sections of wildlife fencing and crossing structures along highways in reducing wildlife–vehicle collisions and providing safe crossing opportunities for large mammals. Biol. Conserv. 197, 61–68. https://doi.org/10.1016/j.biocon.2016.02.002 (2016).Article 

Google Scholar 
Olsson, M. P. O. & Widen, P. Effects of highway fencing and wildlife crossings on moose Alces alces movements and space use in southwestern Sweden. Wildl. Biol. 14, 111–117 (2008).Article 

Google Scholar 
Donaldson, B. Use of highway underpasses by large mammals and other wildlife in Virginia: Factors influencing their effectiveness. Transp. Res. Rec. 157–164, 2007. https://doi.org/10.3141/2011-17 (2011).Article 

Google Scholar 
Foster, M. L. & Humphrey, S. R. Use of highway underpasses by Florida panthers and other wildlife. Wildl. Soc. Bull. 23, 95–100 (1995).
Google Scholar 
Caldwell, M. R. & Klip, J. M. K. Wildlife interactions within highway underpasses. J. Wildl. Manag. 84, 227–236. https://doi.org/10.1002/jwmg.21801 (2019).Article 

Google Scholar 
Clevenger, A. P. & Waltho, N. Performance indices to identify attributes of highway crossing structures facilitating movement of large mammals. Biol. Conserv. 121, 453–464. https://doi.org/10.1016/j.biocon.2004.04.025 (2005).Article 

Google Scholar 
Mcdonald, W. & Clair, C. C. S. Elements that promote highway crossing structure use by small mammals in Banff National Park. J. Appl. Ecol. 41, 82–93 (2004).Article 

Google Scholar 
Mata Estacio, C., Hervás Bengoechea, I., Herranz Barrera, J., Suárez Cardona, F. & Arrazola, J. E. M. International Conference on Ecology and Transportation (ICOET 2003) Federal Highway Administration.Sawyer, H., Lebeau, C. & Hart, T. Mitigating roadway impacts to migratory mule deer—A case study with underpasses and continuous fencing. Wildl. Soc. Bull. 36, 492–498. https://doi.org/10.1002/wsb.166 (2012).Article 

Google Scholar 
Rodriguez, A., Crema, G. & Delibes, M. Use of non-wildlife passages across a high speed railway by terrestrial vertebrates. J. Appl. Ecol. 33, 1527–1540 (1996).Article 

Google Scholar 
Yanes, M., Velasco, J. M. & Sufirez, F. Permeability of roads and railways to vertebrates: The importance of culverts. Biol. Conserv. 71, 217–222 (1995).Article 

Google Scholar 
Rodriguez, A., Crema, G. & Delibes, M. Factors affecting crossing of red foxes and wildcats through non-wildlife passages across a high-speed railway. Ecography 2, 287–294 (1997).Article 

Google Scholar 
Weeks, S. Handbook of Road Ecology (eds van der Ree, R. et al.) Ch. 43, 353–356 (Wiley, 2015).Okita-Ouma, B. et al. Effectiveness of wildlife underpasses and culverts in connecting elephant habitats: A case study of new railway through Kenya’s Tsavo National Parks. Afr. J. Ecol. 59(3), 624–640 (2021).Article 

Google Scholar 
Collinson, W., Davies-Mostert, H., Roxburgh, L. & van der Ree, R. Status of road ecology research in Africa: Do we understand the impacts of roads, and how to successfully mitigate them?. Front. Ecol. Evol. 7, 479. https://doi.org/10.3389/fevo.2019.00479 (2019).ADS 
Article 

Google Scholar 
Wang, Y., Guan, L., Chen, J. & Kong, Y. Influences on mammals frequency of use of small bridges and culverts along the Qinghai-Tibet railway, China. Ecol. Res. 33, 879–887. https://doi.org/10.1007/s11284-018-1578-0 (2018).Article 

Google Scholar 
Ng, S. J., Dole, J. W., Sauvajot, R. M., Riley, S. P. D. & Valone, T. J. Use of highway undercrossings by wildlife in southern California. Biol. Conserv. 115, 499–507. https://doi.org/10.1016/s0006-3207(03)00166-6 (2004).Article 

Google Scholar 
Mata, C., Hervas, I., Herranz, J., Suarez, F. & Malo, J. E. Are motorway wildlife passages worth building? Vertebrate use of road-crossing structures on a Spanish motorway. J. Environ. Manag. 88, 407–415. https://doi.org/10.1016/j.jenvman.2007.03.014 (2008).CAS 
Article 

Google Scholar 
Mata, C., Herranz, J. & Malo, J. E. Attraction and avoidance between predators and prey at wildlife crossings on roads. Diversity 12, 166. https://doi.org/10.3390/d12040166 (2020).Article 

Google Scholar 
Stewart, L., Russell, B., Zelig, E., Patel, G. & Whitney, K. S. Wildlife crossing design influences effectiveness for small and large mammals in Banff National Park. Case Stud. Environ. 4, 1231752. https://doi.org/10.1525/cse.2020.1231752 (2020).Article 

Google Scholar 
Mysłajek, R. W., Nowak, S., Kurek, K., Tołkacz, K. & Gewartowska, O. Utilisation of a wide underpass by mammals on an expressway in the Western Carpathians, S Poland. Folia Zool. 65, 225–232. https://doi.org/10.25225/fozo.v65.i3.a8.2016 (2016).Article 

Google Scholar 
Clevenger, A. P. & Waltho, N. factors influencing the effectiveness of wildlife underpasses in Banff National Park, Alberta, Canada. Conserv. Biol. 14, 47–56 (2000).Article 

Google Scholar 
Laurance, W. F., Sloan, S., Weng, L. & Sayer, J. A. Estimating the environmental costs of Africa’s massive “development corridors”. Curr. Biol. 25, 3202–3208. https://doi.org/10.1016/j.cub.2015.10.046 (2015).CAS 
Article 
PubMed 

Google Scholar 
van der Ree, R., Gagnon, J. W. & Smith, D. J. Handbook of Road Ecology (eds van der Ree, R. et al.) Ch. 20, 159–171 (Wiley, 2015).Ascensão, F. & Mira, A. Factors affecting culvert use by vertebrates along two stretches of road in southern Portugal. Ecol. Res. 22, 57–66. https://doi.org/10.1007/s11284-006-0004-1 (2006).Article 

Google Scholar 
Hepenstrick, D., Thiel, D., Holderegger, R. & Gugerli, F. Genetic discontinuities in roe deer (Capreolus capreolus) coincide with fenced transportation infrastructure. Basic Appl. Ecol. 13, 631–638. https://doi.org/10.1016/j.baae.2012.08.009 (2012).Article 

Google Scholar 
Wilson, R. E., Farley, S. D., McDonough, T. J., Talbot, S. L. & Barboza, P. S. A genetic discontinuity in moose (Alces alces) in Alaska corresponds with fenced transportation infrastructure. Conserv. Genet. 16, 791–800. https://doi.org/10.1007/s10592-015-0700-x (2015).Article 

Google Scholar 
Jaeger, J. A. G. & Fahrig, L. Effects of road fencing on population persistence. Conserv. Biol. 18, 1651–1657 (2004).Article 

Google Scholar 
Ngene, S., Lala, F., Nzisa, M., Kimitei, K., Mukeka, J., Kiambi, S., Davidson, Z., Bakari, S., Lyimo, E. & Khayale, C. (eds Arusha Kenya Wildlife Service (KWS) and Tanzania Wildlife Research Institute (TAWIRI)) (2017).World Resources Institute, Department of Resource Surveys and Remote Sensing Ministry of Environment and Natural Resources Kenya, Central Bureau of Statistics Ministry of Planning and National Development Kenya & International Livestock Research Institute. Nature’s Benefits in Kenya, An Atlas of Ecosystems and Human Well-Being (World Resources Institute, 2007).Wijngaarden, W. V. Elephants, trees, grass, grazers: relationships between climate, soils, vegetation, and large herbivores in a semi-arid savanna ecosystem (Tsavo, Kenya) Doctor of Philosophy thesis, Landbouwhogeschool te Wageningen (1985).Stuart, C. Field Guide to Tracks & Signs of Southern, Central & East African Wildlife (Penguin Random House South Africa, 2013).
Google Scholar 
Murie, O. J. & Elbroch, M. A Field Guide to Animal Tracks Vol. 3 (Houghton Mifflin Harcourt, 2005).
Google Scholar 
Kerley, G. I. H., Pressey, R. L., Cowling, R. M., Boshoff, A. F. & Sims-Castley, R. Options for the conservation of large and medium-sized mammals in the Cape Floristic Region hotspot, South Africa. Biol. Conserv. 112, 169–190. https://doi.org/10.1016/S0006-3207(02)00426-3 (2003).Article 

Google Scholar 
R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/ (2021).Hayward, M. W., Hayward, G. J., Tambling, C. J. & Kerley, G. I. Do lions Panthera leo actively select prey or do prey preferences simply reflect chance responses via evolutionary adaptations to optimal foraging?. PLoS ONE 6, e23607 (2011).ADS 
CAS 
Article 

Google Scholar 
De Boer, W. F. et al. Spatial distribution of lion kills determined by the water dependency of prey species. J. Mammal. 91, 1280–1286 (2010).Article 

Google Scholar 
Hayward, M. W. & Kerley, G. I. H. Prey preferences of the lion (Panthera leo). J. Zool. 267, 309–322. https://doi.org/10.1017/S0952836905007508 (2005).Article 

Google Scholar 
Davidson, Z. et al. Seasonal diet and prey preference of the African lion in a waterhole-driven semi-arid Savanna. PLoS ONE 8, e55182. https://doi.org/10.1371/journal.pone.0055182 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Patterson, B. D., Kasiki, S. M., Selempo, E. & Kays, R. W. Livestock predation by lions (Panthera leo) and other carnivores on ranches neighboring Tsavo National ParkS, Kenya. Biol. Conserv. 119, 507–516. https://doi.org/10.1016/j.biocon.2004.01.013 (2004).Article 

Google Scholar 
Hayward, M. W. et al. Prey preferences of the leopard (Panthera pardus). J. Zool. 270, 298–313. https://doi.org/10.1111/j.1469-7998.2006.00139.x (2006).Article 

Google Scholar 
Ogara, W. O. et al. Determination of carnivores prey base by scat analysis in Samburu community group ranches in Kenya. Afr. J. Environ. Sci. Technol. 4, 540–546 (2010).
Google Scholar 
Hayward, M. W. Prey preferences of the spotted hyaena (Crocuta crocuta) and degree of dietary overlap with the lion (Panthera leo). J. Zool. 270, 606–614. https://doi.org/10.1111/j.1469-7998.2006.00183.x (2006).Article 

Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
Barton, K. & Barton, M. K. Package ‘MuMIn’. Version 1, 18 (2015).
Google Scholar 
Williams, E. M. Giraffe stature and neck elongation: Vigilance as an evolutionary mechanism. Biology 5, 35 (2016).Article 

Google Scholar 
Shorrocks, B. The Giraffe: Biology, Ecology, Evolution and Behaviour (Wiley, 2016).Book 

Google Scholar 
Mata, C., Bencini, R., Chambers, B. K. & Malo, J. E. Handbook of Road Ecology (eds Smith, D. J. & van der Ree, C. G. R.) Ch. 23, 190–197 (Wiley, 2015).Harris, I. M., Mills, H. R. & Bencini, R. Multiple individual southern brown bandicoots (Isoodonobesulus fusciventer) and foxes (Vulpes vulpes) use underpasses installed at a new highway in Perth, Western Australia. Wildl. Res. 37, 127–133 (2010).Article 

Google Scholar 
Fehlmann, G. et al. Extreme behavioural shifts by baboons exploiting risky, resource-rich, human-modified environments. Sci. Rep. 7, 1–8 (2017).CAS 
Article 

Google Scholar 
McLennan, M. R., Spagnoletti, N. & Hockings, K. J. The implications of primate behavioral flexibility for sustainable human-primate coexistence in anthropogenic habitats. Int. J. Primatol. 38, 105–121. https://doi.org/10.1007/s10764-017-9962-0 (2017).Article 

Google Scholar 
Riley, E. P. Flexibility in diet and activity patterns of Macaca tonkeana in response to anthropogenic habitat alteration. Int. J. Primatol. 28, 107–133. https://doi.org/10.1007/s10764-006-9104-6 (2007).Article 

Google Scholar 
Johnson-Ulrich, L., Yirga, G., Strong, R. L. & Holekamp, K. E. The effect of urbanization on innovation in spotted hyenas. Anim. Cogn. 24, 1027–1038. https://doi.org/10.1007/s10071-021-01494-4 (2021).Article 
PubMed 

Google Scholar 
Holekamp, K. E. & Dloniak, S. M. Intraspecific variation in the behavioral ecology of a tropical carnivore, the spotted hyena. Adv. Study Behav. 42, 189–229 (2010).Article 

Google Scholar 
Devens, C. H. et al. Estimating leopard density across the highly modified human-dominated landscape of the Western Cape, South Africa. Oryx 55, 34–45. https://doi.org/10.1017/S0030605318001473 (2021).Article 

Google Scholar 
Van Cleave, E. K. et al. Diel patterns of movement activity and habitat use by leopards (Panthera pardus pardus) living in a human-dominated landscape in central Kenya. Biol. Conserv. 226, 224–237. https://doi.org/10.1016/j.biocon.2018.08.003 (2018).Article 

Google Scholar 
Odden, M., Athreya, V., Rattan, S. & Linnell, J. D. C. Adaptable neighbours: Movement patterns of GPS-collared leopards in human dominated landscapes in India. PLoS ONE 9, e112044. https://doi.org/10.1371/journal.pone.0112044 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Athreya, V., Odden, M., Linnell, J. D. C., Krishnaswamy, J. & Karanth, K. U. A cat among the dogs: Leopard Panthera pardus diet in a human-dominated landscape in western Maharashtra, India. Oryx 50, 156–162. https://doi.org/10.1017/S0030605314000106 (2016).Article 

Google Scholar 
Suraci, J. P. et al. Behavior-specific habitat selection by African lions may promote their persistence in a human-dominated landscape. Ecology 100, e02644. https://doi.org/10.1002/ecy.2644 (2019).Article 
PubMed 

Google Scholar 
Daniels, S. E., Fanelli, R. E., Gilbert, A. & Benson-Amram, S. Behavioral flexibility of a generalist carnivore. Anim. Cogn. 22, 387–396 (2019).Article 

Google Scholar 
Murray, M. H. & St. Clair, C. C. Individual flexibility in nocturnal activity reduces risk of road mortality for an urban carnivore. Behav. Ecol. 26, 1520–1527. https://doi.org/10.1093/beheco/arv102 (2015).Article 

Google Scholar 
Galanti, V., Preatoni, D., Martinoli, A., Wauter, L. A. & Tosi, G. Space and habitat use of the African elephant in the Tarangire-Manyara ecosystem, Tanzania: Implications for conservation. Mamm. Biol. 71, 99–114. https://doi.org/10.1016/j.mambio.2005.10.001 (2006).Article 

Google Scholar 
Douglas-Hamilton, I., Krink, T. & Vollrath, F. Movements and corridors of African elephants in relation to protected areas. Naturwissenschaften 92, 158–163. https://doi.org/10.1007/s00114-004-0606-9 (2005).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Coe, P. K. et al. Identifying migration corridors of mule deer threatened by highway development. Wildl. Soc. Bull. 39, 256–267. https://doi.org/10.1002/wsb.544 (2015).Article 

Google Scholar 
Spinage, C. A. Territoriality and social organization of the Uganda defassa waterbuck Kobus defassa ugandae. J. Zool. Lond. 159, 329–361 (1969).Article 

Google Scholar 
Mizutani, F. & Jewell, P. A. Home-range and movements of leopards (Panthera pardus) on a livestock ranch in Kenya. J. Zool. Lond. 244, 269–286 (1998).Article 

Google Scholar 
Riley, S. P. et al. A southern California freeway is a physical and social barrier to gene flow in carnivores. Mol. Ecol. 15, 1733–1741. https://doi.org/10.1111/j.1365-294X.2006.02907.x (2006).CAS 
Article 
PubMed 

Google Scholar 
Sells, S. N. & Mitchell, M. S. The economics of territory selection. Ecol. Model. 438, 109329. https://doi.org/10.1016/j.ecolmodel.2020.109329 (2020).Article 

Google Scholar 
Valls-Fox, H. et al. Water and cattle shape habitat selection by wild herbivores at the edge of a protected area. Anim. Conserv. 21, 365–375. https://doi.org/10.1111/acv.12403 (2018).Article 

Google Scholar 
Hibert, F. et al. Spatial avoidance of invading pastoral cattle by wild ungulates: Insights from using point process statistics. Biodivers. Conserv. 19, 2003–2024 (2010).Article 

Google Scholar 
Stewart, K. M., Bowyer, R. T., Kie, J. G., Cimon, N. J. & Johnson, B. K. Temporospatial distributions of elk, mule deer, and cattle: Resource partitioning and competitive displacement. J. Mammal. 83, 229–244. https://doi.org/10.1644/1545-1542(2002)083%3c0229:Tdoemd%3e2.0.Co;2 (2002).Article 

Google Scholar 
Leeuw, J. D. et al. Distribution and diversity of wildlife in northern Kenya in relation to livestock and permanent water points. Biol. Conserv. 100, 297–306 (2001).Article 

Google Scholar 
Donaldson, B. Use of highway underpasses by large mammals and other wildlife in Virginia. Transp. Res. Rec 157–164, 2007. https://doi.org/10.3141/2011-17 (2011).Article 

Google Scholar 
Dodd, N. L., Gagnon, J. W., Manzo, A. L. & Schweinsburg, R. E. Video surveillance to assess highway underpass use by elk in Arizona. J. Wildl. Manag. 71, 637–645. https://doi.org/10.2193/2006-340 (2007).Article 

Google Scholar 
Gordon, K. M. & Anderson, S. H. International Conference on Ecology and Transportation https://escholarship.org/uc/item/2wv1v6dz.Bond, A. R. & Jones, D. N. Temporal trends in use of fauna-friendly underpasses and overpasses. Wildl. Res. 35, 103–112. https://doi.org/10.1071/WR07027 (2008).Article 

Google Scholar 
Altmann, J., Schoeller, D., Altmann, S. A., Muruthi, P. & Sapolsky, R. M. Body size and fatness of free-living baboons reflect food availability and activity levels. Am. J. Primatol. 30, 149–161. https://doi.org/10.1002/ajp.1350300207 (1993).Article 
PubMed 

Google Scholar 
Kiffner, C. et al. Road-based line distance surveys overestimate densities of olive baboons. PLoS ONE 17, e0263314. https://doi.org/10.1371/journal.pone.0263314 (2022).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Strandburg-Peshkin, A., Farine, D. R., Crofoot, M. C. & Couzin, I. D. Habitat and social factors shape individual decisions and emergent group structure during baboon collective movement. Elife 6, e19505. https://doi.org/10.7554/eLife.19505 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Bohrer, G., Beck, P. S., Ngene, S. M., Skidmore, A. K. & Douglas-Hamilton, I. Elephant movement closely tracks precipitation driven vegetation dynamics in a Kenyan forest-savanna landscape. Mov. Ecol. 2, 2 (2014).Article 

Google Scholar 
Merkle, J. A. et al. Large herbivores surf waves of green-up during spring. Proc. Biol. Sci. 283, 20160456. https://doi.org/10.1098/rspb.2016.0456 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Middleton, A. D. et al. Green-wave surfing increases fat gain in a migratory ungulate. Oikos 127, 1060–1068. https://doi.org/10.1111/oik.05227 (2018).Article 

Google Scholar 
Bartlam-Brooks, H. L. A., Beck, P. S. A., Bohrer, G. & Harris, S. In search of greener pastures: Using satellite images to predict the effects of environmental change on zebra migration. J. Geophys. Res. Biogeosci. 118, 1427–1437. https://doi.org/10.1002/jgrg.20096 (2013).Article 

Google Scholar 
Bischof, R. et al. A migratory northern ungulate in the pursuit of spring: Jumping or surfing the green wave?. Am. Nat. 180, 407–424. https://doi.org/10.1086/667590 (2012).Article 
PubMed 

Google Scholar 
Aikens, E. O. et al. The greenscape shapes surfing of resource waves in a large migratory herbivore. Ecol. Lett. 20, 741–750. https://doi.org/10.1111/ele.12772 (2017).Article 
PubMed 

Google Scholar 
Mandinyenya, B., Monks, N., Mundy, P. J., Sebata, A. & Chirima, A. Habitat choices of African buffalo (Syncerus caffer) and plains zebra (Equus quagga) in a heterogeneous protected area. Wildl. Res. 47, 106–113. https://doi.org/10.1071/WR18201 (2020).Article 

Google Scholar  More

von Humboldt, A. Views of Nature: Or Contemplations on the Sublime Phenomena of Creation (transl. Otté, E. C. & Bohn, H. G.) (Henry G. Bohn, 1850).Cooney, C. R. et al. Nat. Ecol. Evol., https://doi.org/10.1038/s41559-022-01714-1 (2022).Article 

Google Scholar 
Hawkins, B. A. et al. J. Biogeogr. 39, 825–841 (2012).Article 

Google Scholar 
Pulido-Santacruz, P. & Weir, J. T. Evolution 70, 860–872 (2016).Article 

Google Scholar 
Fine, P. V. A. Annu. Rev. Ecol. Evol. Syst. 46, 369–392 (2015).Article 

Google Scholar 
Storch, D., Bohdalková, E. & Okie, J. Ecol. Lett. 21, 920–937 (2018).Article 

Google Scholar 
Jablonski, D., Roy, K. & Valentine, J. W. Science 314, 102–106 (2006).CAS 
Article 

Google Scholar 
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. Nature 491, 444–448 (2012).CAS 
Article 

Google Scholar 
Kennedy, J. D. et al. J. Biogeogr. 41, 1746–1757 (2014).Article 

Google Scholar 
Pontarp, M. et al. Trends Ecol. Evol. 34, 211–223 (2019).Article 

Google Scholar  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Brito-Morales, I. et al. Towards climate-smart, three-dimensional protected areas for biodiversity conservation in the high seas. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01323-7 (2022). More

Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science https://doi.org/10.1126/science.281.5374.237 (1998).Guidi, L. et al. Plankton networks driving carbon export in the oligotrophic ocean. Nature https://doi.org/10.1038/nature16942 (2016).Henson, S. A., Sanders, R. & Madsen, E. Global patterns in efficiency of particulate organic carbon export and transfer to the deep ocean. Glob. Biogeochem. Cycles https://doi.org/10.1029/2011GB004099 (2012).Azam, F. et al. The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps010257 (1983).Saab, M. A. Day-to-day variation in phytoplankton assemblages during spring blooming in a fixed station along the Lebanese coastline. J. Plankton Res. https://doi.org/10.1093/plankt/14.8.1099 (1992).Djurhuus, A. et al. Environmental DNA reveals seasonal shifts and potential interactions in a marine community. Nat. Commun. https://doi.org/10.1038/s41467-019-14105-1 (2020).Kavanaugh, M. T. et al. Seascapes as a new vernacular for pelagic ocean monitoring, management and conservation. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsw086 (2016).Longhurst, A. R. Ecological Geography of the Sea (Elsevier, 2007).Fay, A. R. & McKinley, G. A. Global open-ocean biomes: mean and temporal variability. Earth Syst. Sci. Data https://doi.org/10.5194/essd-6-273-2014 (2014).Reygondeau, G. et al. Dynamic biogeochemical provinces in the global ocean. Glob. Biogeochem. Cycles https://doi.org/10.1002/gbc.20089 (2013).Richter, D. J. et al. Genomic evidence for global ocean plankton biogeography shaped by large-scale current systems. Preprint at bioRxiv https://doi.org/10.1101/867739 (2020).Dutkiewicz, S. et al. Dimensions of marine phytoplankton diversity. Biogeosciences https://doi.org/10.5194/bg-17-609-2020 (2020).Hellweger, F. L., Van Sebille, E. & Fredrick, N. D. Biogeographic patterns in ocean microbes emerge in a neutral agent-based model. Science https://doi.org/10.1126/science.1254421 (2014).Laso-Jadart, R. et al. Investigating population-scale allelic differential expression in wild populations of Oithona similis (Cyclopoida, Claus, 1866). Ecol. Evol. https://doi.org/10.1002/ece3.6588 (2020).Delmont, T. O. et al. Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade. eLife https://doi.org/10.7554/eLife.46497 (2019).Carradec, Q. et al. A global ocean atlas of eukaryotic genes. Nat. Commun. https://doi.org/10.1038/s41467-017-02342-1 (2018).Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell https://doi.org/10.1016/j.cell.2019.10.014 (2019).Alberti, A. et al. Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition. Sci. Data https://doi.org/10.1038/sdata.2017.93 (2017).Pesant, S. et al. Open science resources for the discovery and analysis of Tara Oceans data. Sci. Data https://doi.org/10.1038/sdata.2015.23 (2015).Karsenti, E. et al. A holistic approach to marine eco-systems biology. PLoS Biol. https://doi.org/10.1371/journal.pbio.1001177 (2011).Duarte, C. M. Seafaring in the 21st century: the Malaspina 2010 circumnavigation expedition. Limnol. Oceanogr. Bull. https://doi.org/10.1002/lob.10008 (2015).Barton, A. D., Irwin, A. J., Finkel, Z. V. & Stock, C. A. Anthropogenic climate change drives shift and shuffle in North Atlantic phytoplankton communities. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1519080113 (2016).Benedetti, F., Guilhaumon, F., Adloff, F. & Ayata, S. D. Investigating uncertainties in zooplankton composition shifts under climate change scenarios in the Mediterranean Sea. Ecography https://doi.org/10.1111/ecog.02434 (2018).Beaugrand, G. et al. Prediction of unprecedented biological shifts in the global ocean. Nat. Clim. Change 9, 237–243 (2019).Article 

Google Scholar 
Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science https://doi.org/10.1126/science.1239352 (2013).Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences https://doi.org/10.5194/bg-10-6225-2013 (2013).Thomas, M. K., Kremer, C. T., Klausmeier, C. A. & Litchman, E. A global pattern of thermal adaptation in marine phytoplankton. Science https://doi.org/10.1126/science.1224836 (2012).Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell https://doi.org/10.1016/j.cell.2019.10.008 (2019).Busseni, G. et al. Large scale patterns of marine diatom richness: drivers and trends in a changing ocean. Glob. Ecol. Biogeogr. https://doi.org/10.1111/geb.13161 (2020).Hutchinson, G. E. Concluding remarks. Cold Spring Harb. Symp. Quant. Biol. 22, 415–427 (1957).Article 

Google Scholar 
Delmont, T. O. et al. Functional repertoire convergence of distantly related eukaryotic plankton lineages revealed by genome-resolved metagenomics. Preprint at bioRxiv https://doi.org/10.1101/2020.10.15.341214 (2020).Delmont, T. O. et al. Heterotrophic bacterial diazotrophs are more abundant than their cyanobacterial counterparts in metagenomes covering most of the sunlit ocean. ISME J. https://doi.org/10.1038/s41396-021-01135-1 (2021).Boyer, et al. World Ocean Database 2013, NOAA Atlas NESDIS 72 (National Oceanic and Atmospheric Administration, 2013); https://doi.org/10.7289/V5NZ85MTSunagawa, S. et al. Tara Oceans: towards global ocean ecosystems biology. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-020-0364-5 (2020).Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0336-3 (2019).van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic Change https://doi.org/10.1007/s10584-011-0148-z (2011).Polovina, J. J., Dunne, J. P., Woodworth, P. A. & Howell, E. A. Projected expansion of the subtropical biome and contraction of the temperate and equatorial upwelling biomes in the North Pacific under global warming. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsq198 (2011).Flombaum, P., Wang, W. L., Primeau, F. W. & Martiny, A. C. Global picophytoplankton niche partitioning predicts overall positive response to ocean warming. Nat. Geosci. https://doi.org/10.1038/s41561-019-0524-2 (2020).Richardson, A. J. In hot water: zooplankton and climate change. ICES J. Mar. Sci. 65, 279–295 (2008).Article 

Google Scholar 
Wrightson, L. & Tagliabue, A. Quantifying the impact of climate change on marine diazotrophy: insights from Earth system models. Front. Mar. Sci. 7, 635 (2020).Article 

Google Scholar 
Zehr, J. P. & Capone, D. G. Changing perspectives in marine nitrogen fixation. Science 368, eaay9514 (2020).CAS 
Article 

Google Scholar 
Luo, Y.-W. et al. Database of diazotrophs in global ocean: abundance, biomass and nitrogen fixation rates. Earth Syst. Sci. Data 4, 47–73 (2012).Article 

Google Scholar 
Eppley, R. W. & Peterson, B. J. Particulate organic matter flux and planktonic new production in the deep ocean. Nature 282, 677–680 (1979).Article 

Google Scholar 
Laws, E. A., Falkowski, P. G., Smith, W. O., Ducklow, H. & McCarthy, J. J. Temperature effects on export production in the open ocean. Glob. Biogeochem. Cycles 14, 1231–1246 (2000).CAS 
Article 

Google Scholar 
Agrawal, R. & Srikant, R. in Proceedings of the 20th International Conference on Very Large Data Bases (eds Bocca, J. B. et al.) 487–499 (Morgan Kaufmann, 1994).Laufkötter, C. et al. Projected decreases in future marine export production: the role of the carbon flux through the upper ocean ecosystem. Biogeosciences 13, 4023–4047 (2016).Article 

Google Scholar 
Iudicone, D. Some may like it hot. Nat. Geosci. https://doi.org/10.1038/s41561-020-0535-z (2020).Gorsky, G. et al. Expanding Tara Oceans protocols for underway, ecosystemic sampling of the ocean–atmosphere interface during Tara Pacific expedition (2016–2018). Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00750 (2019).Istace, B. et al. de novo assembly and population genomic survey of natural yeast isolates with the Oxford Nanopore MinION sequencer. Gigascience https://doi.org/10.1093/gigascience/giw018 (2017).Grand, M. M. et al. Developing autonomous observing systems for micronutrient trace metals. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00035 (2019).Becker, R. A., Wilks, A. R., Brownrigg, R., Minka, T. P. & Deckmyn, A. maps: Draw geographical maps. R version 3.5.0 https://cran.r-project.org/web/packages/maps/index.html (2021).Jaccard, P. Distribution comparée de la flore alpine dans quelques régions des Alpes occidentales et orientales. Bull. Murith. 31, 81–92 (1902).
Google Scholar 
Watson, R. A. A database of global marine commercial, small-scale, illegal and unreported fisheries catch 1950–2014. Sci. Data https://doi.org/10.1038/sdata.2017.39 (2017).Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 11 (Flanders Marine Institute, 2019); https://doi.org/10.14284/386Aumont, O., Ethé, C., Tagliabue, A., Bopp, L. & Gehlen, M. PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies. Geosci. Model Dev. https://doi.org/10.5194/gmd-8-2465-2015 (2015).Bibby, T. S. & Moore, C. M. Silicate:nitrate ratios of upwelled waters control the phytoplankton community sustained by mesoscale eddies in sub-tropical North Atlantic and Pacific. Biogeosciences https://doi.org/10.5194/bg-8-657-2011 (2011).Brun, P., Kiørboe, T., Licandro, P. & Payne, M. R. The predictive skill of species distribution models for plankton in a changing climate. Glob. Change Biol. https://doi.org/10.1111/gcb.13274 (2016).Redfield, A. C. in James Johnstone Memorial Volume (ed. Daniel, R. J.) 176–192 (Liverpool Univ. Press, 1934).Michelangeli, P. A., Vrac, M. & Loukos, H. Probabilistic downscaling approaches: application to wind cumulative distribution functions. Geophys. Res. Lett. https://doi.org/10.1029/2009GL038401 (2009).Ridgeway, G. gbm: Generalized boosted regression models. R version 1.6–3.1 https://cran.r-project.org/web/packages/gbm/gbm.pdf (2010).Breiman, L. & Cutler, A. randomForest: Breiman and Cutler’s random forests for classification and regression. R package 4.1.0 https://www.stat.berkeley.edu/~breiman/RandomForests/ (2012).Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S 4th edn (Springer, 2002).Wood, S. N. Stable and efficient multiple smoothing parameter estimation for generalized additive models. J. Am. Stat. Assoc. https://doi.org/10.1198/016214504000000980 (2004).Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett. https://doi.org/10.1016/j.patrec.2005.10.010 (2006).Biecek, P. DALEX: explainers for complex predictive models. J. Mach. Learn. Res. 19, 1–5 (2018).
Google Scholar 
Jones, M. C. & Cheung, W. W. L. Multi-model ensemble projections of climate change effects on global marine biodiversity. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsu172 (2015).Vallejos, C. A. Exploring a world of a thousand dimensions. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0330-9 (2019).Kaufman, L. and Rousseeuw, P.J. in Statistical Data Analysis Based on the L1 Norm and Related Methods (ed. Dodge, Y.) 405–416 (North-Holland, 1987).Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. https://doi.org/10.1016/0377-0427(87)90125-7 (1987).Orsi, A. H., Whitworth, T. & Nowlin, W. D. On the meridional extent and fronts of the Antarctic Circumpolar Current. Deep Sea Res. Part I https://doi.org/10.1016/0967-0637(95)00021-W (1995).Hubert, L. & Arabie, P. Comparing partitions. J. Classif. https://doi.org/10.1007/BF01908075 (1985).Somerfield, P. J. Identification of the Bray–Curtis similarity index: comment on Yoshioka (2008). Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps07841 (2008).Bloom, S. Similarity indices in community studies: potential pitfalls. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps005125 (1981).Welch, B. L. The generalisation of student’s problems when several different population variances are involved. Biometrika 34, 28–35 (1947).CAS 

Google Scholar 
Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).
Google Scholar 
Mann, H. B. & Whitney, D. R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18, 50–60 (1947).Article 

Google Scholar 
Sthle, L. & Wold, S. Analysis of variance (ANOVA). Chemom. Intell. Lab. Syst. 6, 259–272 (1989).Article 

Google Scholar 
Bozdogan, H. Model selection and Akaike’s Information Criterion (AIC): the general theory and its analytical extensions. Psychometrika 52, 345–370 (1987).Article 

Google Scholar 
Frémont, P. et al. Biogeographies of genomic provinces from ‘Restructuring of plankton genomic biogeography in the surface ocean under climate change’. figshare. https://figshare.com/articles/dataset/Biogeographies_genomic_provinces/19071620 (2022). More

Darwin, C. R. On the Origin of Species, or the Preservation of Favoured Races in the Struggle for Life (John Murray, 1859).Wallace, A. R. Natural Selection and Tropical Nature: Essays on Descriptive and Theoretical Biology 2nd edn (Macmillan, 1895).Darwin, C. R. A Naturalist’s Voyage Round the World (John Murray, 1913).Wallace, A. R. Colour in nature. Nature 19, 580–581 (1879).
Google Scholar 
Dalrymple, R. L. et al. Abiotic and biotic predictors of macroecological patterns in bird and butterfly coloration. Ecol. Monogr. 88, 204–224 (2018).
Google Scholar 
Adams, J. M., Kang, C. & June-Wells, M. Are tropical butterflies more colorful? Ecol. Res. 29, 685–691 (2014).
Google Scholar 
Bailey, S. F. Latitudinal gradients in colors and patterns of passerine birds. Condor 80, 372–381 (1978).
Google Scholar 
Wilson, M. F. & Von Neaumann, R. A. Why are neotropical birds more colourful than North American birds? Avicultural Mag. 78, 141–147 (1972).
Google Scholar 
Dalrymple, R. L. et al. Birds, butterflies and flowers in the tropics are not more colourful than those at higher latitudes. Glob. Ecol. Biogeogr. 24, 1424–1432 (2015).
Google Scholar 
Friedman, N. R. & Remeš, V. Ecogeographical gradients in plumage coloration among Australasian songbird clades. Glob. Ecol. Biogeogr. 26, 261–274 (2017).
Google Scholar 
Dale, J., Dey, C. J., Delhey, K., Kempenaers, B. & Valcu, M. The effects of life history and sexual selection on male and female plumage colouration. Nature 527, 367–370 (2015).CAS 

Google Scholar 
Dunn, P. O., Armenta, J. K. & Whittingham, L. A. Natural and sexual selection act on different axes of variation in avian plumage color. Sci. Adv. 1, e1400155 (2015).PubMed 
PubMed Central 

Google Scholar 
Stoddard, M. C. & Prum, R. O. How colorful are birds? Evolution of the avian plumage color gamut. Behav. Ecol. 22, 1042–1052 (2011).
Google Scholar 
Renoult, J. P., Kelber, A. & Schaefer, H. M. Colour spaces in ecology and evolutionary biology. Biol. Rev. 92, 292–315 (2017).
Google Scholar 
Stoddard, M. C. & Prum, R. O. Evolution of avian plumage color in a tetrahedral color space: a phylogenetic analysis of New World buntings. Am. Nat. 171, 755–776 (2008).
Google Scholar 
Delhey, K. The colour of an avifauna: a quantitative analysis of the colour of Australian birds. Sci. Rep. 5, 18514 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51, 933–938 (2001).
Google Scholar 
Rabosky, D. L. et al. An inverse latitudinal gradient in speciation rate for marine fishes. Nature 559, 392–395 (2018).CAS 

Google Scholar 
Lynch, M. Methods for the analysis of comparative data in evolutionary biology. Evolution 45, 1065–1080 (1991).PubMed 
PubMed Central 

Google Scholar 
Delhey, K. A review of Gloger’s rule, an ecogeographical rule of colour: definitions, interpretations and evidence. Biol. Rev. Camb. Phil. Soc. 94, 1294–1316 (2019).
Google Scholar 
Marchetti, K. Dark habitats and bright birds illustrate the role of the environment in species divergence. Nature 362, 149–152 (1993).
Google Scholar 
Endler, J. A. The color of light in forests and its implications. Ecol. Monogr. 63, 1–27 (1993).
Google Scholar 
Schemske, D. W. in Speciation and Patterns of Diversity Vol. 12 (eds Butlin, R. et al.) 219–239 (Cambridge Univ. Press, 2009).Schemske, D. W., Mittelbach, G. G., Cornell, H. V., Sobel, J. M. & Roy, K. Is there a latitudinal gradient in the importance of biotic interactions? Annu. Rev. Ecol. Evol. Syst. 40, 245–269 (2009).
Google Scholar 
MacArthur, R. H. Patterns of communities in the tropics. Biol. J. Linn. Soc. 1, 19–30 (1969).
Google Scholar 
Hadfield, J. D. & Nakagawa, S. General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi-trait models for continuous and categorical characters. J. Evol. Biol. 23, 494–508 (2010).CAS 

Google Scholar 
Cooney, C. R. et al. Sexual selection predicts the rate and direction of colour divergence in a large avian radiation. Nat. Commun. 10, 1773 (2019).PubMed 
PubMed Central 

Google Scholar 
Cooney, C. R., MacGregor, H. E. A., Seddon, N. & Tobias, J. A. Multi-modal signal evolution in birds: re-assessing a standard proxy for sexual selection. Proc. R. Soc. B 285, 20181557 (2018).PubMed 
PubMed Central 

Google Scholar 
van der Bijl, W. et al. Butterfly dichromatism primarily evolved via Darwin’s, not Wallace’s, model. Evol. Lett. 4, 545–555 (2020).PubMed 
PubMed Central 

Google Scholar 
Darwin, C. R. The Descent of Man, and Selection in Relation to Sex (John Murray, 1871).Tobias, J. A., Montgomerie, R. & Lyon, B. E. The evolution of female ornaments and weaponry: social selection, sexual selection and ecological competition. Phil. Trans. R. Soc. B 367, 2274–2293 (2012).PubMed 
PubMed Central 

Google Scholar 
Galván, I., Negro, J. J., Rodríguez, A. & Carrascal, L. M. On showy dwarfs and sober giants: body size as a constraint for the evolution of bird plumage colouration. Acta Ornithol. 48, 65–80 (2013).
Google Scholar 
Kiltie, R. A. Scaling of visual acuity with body size in mammals and birds. Funct. Ecol. 14, 226–234 (2000).
Google Scholar 
Zahavi, A. & Zahavi, A. The Handicap Principle (Oxford Univ. Press, 1997).Badyaev, A. V. & Hill, G. E. Avian sexual dichromatism in relation to phylogeny and ecology. Annu. Rev. Ecol. Evol. Syst. 34, 27–49 (2003).
Google Scholar 
Simpson, R. K., Johnson, M. A. & Murphy, T. G. Migration and the evolution of sexual dichromatism: evolutionary loss of female coloration with migration among wood-warblers. Proc. R. Soc. B 282, 20150375 (2015).PubMed 
PubMed Central 

Google Scholar 
Helferich, G. Humboldt’s Cosmos (Tantor eBooks, 2011).Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
He, Y. et al. Segmenting biological specimens from photos to understand the evolution of UV plumage in passerine birds. Preprint at bioRxiv https://doi.org/10.1101/2021.07.22.453339 (2021).Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F. & Adam, H. Encoder–decoder with atrous separable convolution for semantic image segmentation. Preprint at arXiv https://doi.org/10.48550/arXiv.1802.02611 (2018).Hussein, B. R., Malik, O. A., Ong, W.-H. & Slik, J. W. F. in Computational Science and Technology Lecture Notes in Electrical Engineering (eds Alfred, R. et al.) 321–330 (Springer Singapore, 2020).Troscianko, J. & Stevens, M. Image calibration and analysis toolbox—a free software suite for objectively measuring reflectance, colour and pattern. Methods Ecol. Evol. 6, 1320–1331 (2015).PubMed 
PubMed Central 

Google Scholar 
Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.5-15 https://CRAN.R-project.org/package=raster (2022).Maia, R., Gruson, H., Endler, J. A., White, T. E. & O’Hara, R. B. pavo 2: new tools for the spectral and spatial analysis of colour in R. Methods Ecol. Evol. 10, 1097–1107 (2019).
Google Scholar 
Stoddard, M. C. et al. Wild hummingbirds discriminate nonspectral colors. Proc. Natl Acad. Sci. USA 117, 15112–15122 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Gomez, D. & Théry, M. Simultaneous crypsis and conspicuousness in color patterns: comparative analysis of a neotropical rainforest bird community. Am. Nat. 169, S42–S61 (2007).
Google Scholar 
Blonder, B. Do hypervolumes have holes? Am. Nat. 187, E93–E105 (2016).
Google Scholar 
Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).CAS 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
Beckmann, M. et al. glUV: a global UV-B radiation data set for macroecological studies. Methods Ecol. Evol. 5, 372–383 (2014).
Google Scholar 
Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004).
Google Scholar 
Tobias, J. A. & Pigot, A. L. Integrating behaviour and ecology into global biodiversity conservation strategies. Phil. Trans. R. Soc. B 374, 20190012 (2019).PubMed 
PubMed Central 

Google Scholar 
Dunn, P. O., Whittingham, L. A. & Pitcher, T. E. Mating systems, sperm competition, and the evolution of sexual dimorphism in birds. Evolution 55, 161–175 (2001).CAS 

Google Scholar 
Bivand, R. S. & Wong, D. W. S. Comparing implementations of global and local indicators of spatial association. TEST 27, 716–748 (2018).
Google Scholar 
Hawkins, B. A. et al. Structural bias in aggregated species-level variables driven by repeated species co-occurrences: a pervasive problem in community and assemblage data. J. Biogeogr. 44, 1199–1211 (2017).
Google Scholar 
Hadfield, J. D. MCMC methods for multi-response generalised linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
Google Scholar 
Healy, K. et al. Ecology and mode-of-life explain lifespan variation in birds and mammals. Proc. R. Soc. B 281, 20140298 (2014).PubMed 
PubMed Central 

Google Scholar 
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021); https://www.R-project.org/ More

Welti, N. et al. Bridging food webs, ecosystem metabolism, and biogeochemistry using ecological stoichiometry theory. Front. Microbiol. 8, 1298 (2017).Article 

Google Scholar 
Ceballos, G. et al. Accelerated modern human-induced species losses: entering the sixth mass extinction. Sci. Adv. 1, e14002 (2015).Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).CAS 
Article 

Google Scholar 
Pauly, D. et al. Towards sustainability in world fisheries. Nature 418, 689–695 (2002).Bellwood, D. R., Streit, R. P., Brandl, S. J. & Tebbett, S. B. The meaning of the term ‘function’ in ecology: a coral reef perspective. Funct. Ecol. 33, 948–961 (2019).Williams, G. J. et al. Coral reef ecology in the Anthropocene. Funct. Ecol. 33, 1014–1022 (2019).Article 

Google Scholar 
Brandl, S. J. et al. Coral reef ecosystem functioning: eight core processes and the role of biodiversity. Front. Ecol. Environ. 17, 445–454 (2019).Article 

Google Scholar 
Cinner, J. E. et al. Meeting fisheries, ecosystem function, and biodiversity goals in a human-dominated world. Science 368, 307–311 (2020).CAS 
Article 

Google Scholar 
Mouillot, D. et al. Functional over-redundancy and high functional vulnerability in global fish faunas on tropical reefs. Proc. Natl Acad. Sci. USA 111, 13757–13762 (2014).CAS 
Article 

Google Scholar 
Mora, C. et al. Global human footprint on the linkage between biodiversity and ecosystem functioning in reef fishes. PLoS Biol. 9, e1000606 (2011).CAS 
Article 

Google Scholar 
Barneche, D. R. et al. Scaling metabolism from individuals to reef-fish communities at broad spatial scales. Ecol. Lett. 17, 1067–1076 (2014).CAS 
Article 

Google Scholar 
McIntyre, P. B. et al. Fish distributions and nutrient cycling in streams: can fish create biogeochemical hotspots? Ecology 89, 2335–2346 (2008).Article 

Google Scholar 
Allgeier, J. E., Layman, C. A., Mumby, P. J. & Rosemond, A. D. Consistent nutrient storage and supply mediated by diverse fish communities in coral reef ecosystems. Glob. Change Biol. 20, 2459–2472 (2014).Article 

Google Scholar 
Morais, R. A. & Bellwood, D. R. Pelagic subsidies underpin fish productivity on a degraded coral reef. Curr. Biol. 29, 1521–1527.e6 (2019).CAS 
Article 

Google Scholar 
Morais, R. A., Connolly, S. R. & Bellwood, D. R. Human exploitation shapes productivity–biomass relationships on coral reefs. Glob. Change Biol. 26, 1295–1305 (2020).Article 

Google Scholar 
Barneche, D. R. et al. Body size, reef area and temperature predict global reef-fish species richness across spatial scales. Glob. Ecol. Biogeogr. 28, 315–327 (2019).Article 

Google Scholar 
Schiettekatte, N. M. D. et al. Nutrient limitation, bioenergetics and stoichiometry: a new model to predict elemental fluxes mediated by fishes. Funct. Ecol. 34, 1857–1869 (2020).Article 

Google Scholar 
Schramski, J. R., Dell, A. I., Grady, J. M., Sibly, R. M. & Brown, J. H. Metabolic theory predicts whole-ecosystem properties. Proc. Natl Acad. Sci. USA 112, 2617–2622 (2015).CAS 
Article 

Google Scholar 
Morais, R. A. & Bellwood, D. R. Global drivers of reef fish growth. Fish Fish. 19, 874–889 (2018).Article 

Google Scholar 
Hood, J. M., Vanni, M. J. & Flecker, A. S. Nutrient recycling by two phosphorus-rich grazing catfish: the potential for phosphorus-limitation of fish growth. Oecologia 146, 247–257 (2005).Article 

Google Scholar 
Barneche, D. R. & Allen, A. P. The energetics of fish growth and how it constrains food-web trophic structure. Ecol. Lett. 21, 836–844 (2018).Article 

Google Scholar 
Brandl, S. J. et al. Demographic dynamics of the smallest marine vertebrates fuel coral reef ecosystem functioning. Science 364, 1189–1192 (2019).CAS 
Article 

Google Scholar 
Lefcheck, J. S. et al. Tropical fish diversity enhances coral reef functioning across multiple scales. Sci. Adv. 5, eaav6420 (2019).Topor, Z. M., Rasher, D. B., Duffy, J. E. & Brandl, S. J. Marine protected areas enhance coral reef functioning by promoting fish biodiversity. Conserv. Lett. 12, e12638 (2019).Article 

Google Scholar 
Bellwood, D. R., Hughes, T. P. & Hoey, A. S. Sleeping functional group drives coral-reef recovery. Curr. Biol. 16, 2434–2439 (2006).CAS 
Article 

Google Scholar 
Darling, E. S. & D’agata, S. Coral reefs: fishing for sustainability. Curr. Biol. 27, R65–R68 (2017).CAS 
Article 

Google Scholar 
Graham, N. A. J. et al. Human disruption of coral reef trophic structure. Curr. Biol. 27, 231–236 (2017).CAS 
Article 

Google Scholar 
Graham, N. A. J. et al. Dynamic fragility of oceanic coral reef ecosystems. Proc. Natl Acad. Sci. USA 103, 8425–8429 (2006).CAS 
Article 

Google Scholar 
Stuart-Smith, R. D., Brown, C. J., Ceccarelli, D. M. & Edgar, G. J. Ecosystem restructuring along the great barrier reef following mass coral bleaching. Nature 560, 92–96 (2018).CAS 
Article 

Google Scholar 
Burkepile, D. E. et al. Nutrient supply from fishes facilitates macroalgae and suppresses corals in a Caribbean coral reef ecosystem. Sci. Rep. 3, 1493 (2013).CAS 
Article 

Google Scholar 
Graham, N. A. J. et al. Changing role of coral reef marine reserves in a warming climate. Nat. Commun. 11, 2000 (2020).Reynolds, R. W. et al. Daily high-resolution-blended analyses for sea surface temperature. J. Clim. 20, 5473–5496 (2007).Article 

Google Scholar 
Froese, R., Thorson, J. T. & Reyes, R. B. A Bayesian approach for estimating length–weight relationships in fishes. J. Appl. Ichthyol. 30, 78–85 (2014).Article 

Google Scholar 
Froese, R. & Pauly, D. FishBase (2018); https://www.fishbase.in/home.htmParravicini, V. et al. Delineating reef fish trophic guilds with global gut content data synthesis and phylogeny. PLoS Biol. 18, e3000702 (2020).CAS 
Article 

Google Scholar 
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

Google Scholar 
Bürkner, P.-C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).Article 

Google Scholar 
Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. 76, 1–31 (2017).Article 

Google Scholar  More

Levin, L. A. & Le Bris, N. The deep ocean under climate change. Science 350, 766–768 (2015).CAS 

Google Scholar 
Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).
Google Scholar 
Roberts, C. M. et al. Marine reserves can mitigate and promote adaptation to climate change. Proc. Natl Acad. Sci. USA 114, 6167–6175 (2017).CAS 

Google Scholar 
Davies, T. E., Maxwell, S. M., Kaschner, K., Garilao, C. & Ban, N. C. Large marine protected areas represent biodiversity now and under climate change. Sci. Rep. 7, 9569 (2017).CAS 

Google Scholar 
Bates, A. E. et al. Climate resilience in marine protected areas and the ‘protection paradox’. Biol. Conserv. 236, 305–314 (2019).
Google Scholar 
Costello, M. J. & Ballantine, B. Biodiversity conservation should focus on no-take marine reserves: 94% of marine protected areas allow fishing. Trends Ecol. Evol. 30, 507–509 (2015).
Google Scholar 
Ballantine, B. Fifty years on: lessons from marine reserves in New Zealand and principles for a worldwide network. Biol. Conserv. 176, 297–307 (2014).
Google Scholar 
Lester, S. E. et al. Biological effects within no-take marine reserves: a global synthesis. Mar. Ecol. Prog. Ser. 384, 33–46 (2009).
Google Scholar 
Jones, K. R., Watson, J. E. M., Possingham, H. P. & Klein, C. J. Incorporating climate change into spatial conservation prioritisation: a review. Biol. Conserv. 194, 121–130 (2016).
Google Scholar 
Grorud-Colvert, K. et al. The MPA Guide: a framework to achieve global goals for the ocean. Science 373, eabf0861 (2021).CAS 

Google Scholar 
McLeod, E. et al. Integrating climate and ocean change vulnerability into conservation planning. Coast. Manage. 40, 651–672 (2012).
Google Scholar 
Magris, R. A. et al. A blueprint for securing Brazil’s marine biodiversity and supporting the achievement of global conservation goals. Divers. Distrib. 27, 198–215 (2021).
Google Scholar 
Brito-Morales, I. et al. Climate velocity reveals increasing exposure of deep-ocean biodiversity to future warming. Nat. Clim. Change 10, 576–581 (2020).CAS 

Google Scholar 
Tittensor, D. P. et al. Integrating climate adaptation and biodiversity conservation in the global ocean. Sci. Adv. 5, eaay9969 (2019).
Google Scholar 
Burrows, M. T. et al. The pace of shifting climate in marine and terrestrial ecosystems. Science 334, 652–655 (2011).CAS 

Google Scholar 
Burrows, M. T. et al. Geographical limits to species-range shifts are suggested by climate velocity. Nature 507, 492–495 (2014).CAS 

Google Scholar 
Chaudhary, C., Richardson, A. J., Schoeman, D. S. & Costello, M. J. Global warming is causing a more pronounced dip inmarine species richness around the Equator. Proc. Natl Acad. Sci. USA 118, e2015094118 (2021).CAS 

Google Scholar 
Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).
Google Scholar 
Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925 (2013).
Google Scholar 
Levin, N., Kark, S. & Danovaro, R. Adding the third dimension to marine conservation. Conserv. Lett. 11, e12408 (2018).
Google Scholar 
O’Leary, B. C. & Roberts, C. M. Ecological connectivity across ocean depths: implications for protected area design. Glob. Ecol. Conserv. 15, e00431 (2018).
Google Scholar 
Game, E. T. et al. Pelagic protected areas: the missing dimension in ocean conservation. Trends Ecol. Evol. 24, 360–369 (2009).
Google Scholar 
Protected Planet Report 2020 (UNEP-WCMC and IUCN, 2021); https://livereport.protectedplanet.net/Wright, G. et al. Marine spatial planning in areas beyond national jurisdiction. Mar. Policy 132, 103384 (2021).
Google Scholar 
Zero Draft of the Post-2020 Global Biodiversity Framework (Convention on Biological Diversity, 2020).Dunn, D. C. et al. The Convention on Biological Diversity’s ecologically or biologically significant areas: origins, development, and current status. Mar. Policy 49, 137–145 (2014).
Google Scholar 
Claudet, J., Loiseau, C., Sostres, M. & Zupan, M. Underprotected marine protected areas in a global biodiversity hotspot. One Earth 2, 380–384 (2020).
Google Scholar 
Bruno, J. F. et al. Climate change threatens the world’s marine protected areas. Nat. Clim. Change 8, 499–503 (2018).
Google Scholar 
Arafeh-Dalmau, N. et al. Incorporating climate velocity into the design of climate-smart networks of marine protected areas. Methods Ecol. Evol. 12, 1969–1983 (2021).
Google Scholar 
García Molinos, J. et al. Climate velocity and the future global redistribution of marine biodiversity. Nat. Clim. Change 6, 83–88 (2016).
Google Scholar 
Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).CAS 

Google Scholar 
Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101 (2010).CAS 

Google Scholar 
Richardson, A. J. In hot water: zooplankton and climate change. ICES J. Mar. Sci. 65, 279–295 (2008).
Google Scholar 
Brito-Morales, I. et al. Climate velocity can inform conservation in a warming world. Trends Ecol. Evol. 33, 441–457 (2018).
Google Scholar 
Jones, K. R. et al. Area requirements to safeguard Earth’s marine species. One Earth 2, 188–196 (2020).
Google Scholar 
Ortuño Crespo, G. & Dunn, D. C. A review of the impacts of fisheries on open-ocean ecosystems. ICES J. Mar. Sci. 74, 2283–2297 (2017).
Google Scholar 
Watson, R. A. A database of global marine commercial, small-scale, illegal and unreported fisheries catch 1950–2014. Sci. Data 4, 170039 (2017).
Google Scholar 
Hanson, J. O. et al. prioritizr: Systematic Conservation Prioritization in R. R package version 5.0 (2021).Visalli, M. E. et al. Data-driven approach for highlighting priority areas for protection in marine areas beyond national jurisdiction. Mar. Policy 122, 103927 (2020).
Google Scholar 
Dunn, D. C. et al. A strategy for the conservation of biodiversity on mid-ocean ridges from deep-sea mining. Sci. Adv. 4, eaar4313 (2018).
Google Scholar 
Irigoien, X. et al. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nat. Commun. 5, 3271 (2014).
Google Scholar 
Costello, M. J. & Chaudhary, C. Marine biodiversity, biogeography, deep-sea gradients, and conservation. Curr. Biol. 27, R511–R527 (2017).CAS 

Google Scholar 
Venegas-Li, R., Levin, N., Possingham, H. & Kark, S. 3D spatial conservation prioritisation: accounting for depth in marine environments. Methods Ecol. Evol. 9, 773–784 (2018).
Google Scholar 
Menini, E. & Van Dover, C. L. An atlas of protected hydrothermal vents. Mar. Policy 108, 103654 (2019).
Google Scholar 
Crespo, G. O. et al. High-seas fish biodiversity is slipping through the governance net. Nat. Ecol. Evol. 3, 1273–1276 (2019).
Google Scholar 
Hanson, J. O. et al. Global conservation of species’ niches. Nature 580, 232–234 (2020).CAS 

Google Scholar 
Barton, A. D. et al. The biogeography of marine plankton traits. Ecol. Lett. 16, 522–534 (2013).
Google Scholar 
Tittensor, D. P. et al. Next-generation ensemble projections reveal higher climate risks for marine ecosystems. Nat. Clim. Change 11, 973–981 (2021).
Google Scholar 
Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).CAS 

Google Scholar 
Daigle, R. M. et al. Operationalizing ecological connectivity in spatial conservation planning with Marxan Connect. Methods Ecol. Evol. 11, 570–579 (2020).
Google Scholar 
Fredston-Hermann, A., Gaines, S. D. & Halpern, B. S. Biogeographic constraints to marine conservation in a changing climate. Ann. N. Y. Acad. Sci. 1429, 5–17 (2018).
Google Scholar 
Cashion, T. et al. Shifting seas, shifting boundaries: dynamic marine protected area designs for a changing climate. PLoS ONE 15, e0241771 (2020).CAS 

Google Scholar 
Ortuño Crespo, G. et al. Beyond static spatial management: scientific and legal considerations for dynamic management in the high seas. Mar. Policy 122, 104102 (2020).
Google Scholar 
Levin, L. A., Amon, D. J. & Lily, H. Challenges to the sustainability of deep-seabed mining. Nat. Sustain. 3, 784–794 (2020).
Google Scholar 
Levin, L. A. et al. Climate change considerations are fundamental to management of deep-sea resource extraction. Glob. Change Biol. 26, 4664–4678 (2020).
Google Scholar 
Morato, T., Watson, R., Pitcher, T. J. & Pauly, D. Fishing down the deep. Fish Fish. 7, 24–34 (2006).
Google Scholar 
Rogers, A. D. & Gianni, M. Implementation of UNGA Resolutions 61/105 and 64/72 in the Management of Deep-Sea Fisheries on the High Seas (DIANE, 2011).Bailey, D. M., Collins, M. A., Gordon, J. D. M., Zuur, A. F. & Priede, I. G. Long-term changes in deep-water fish populations in the Northeast Atlantic: a deeper reaching effect of fisheries? Proc. R. Soc. B 276, 1965–1969 (2009).CAS 

Google Scholar 
NOAA National Geophysical Data Center ETOPO1 1 Arc-Minute Global Relief Model (NOAA National Centers for Environmental Information, 2009).O’Neill, B. C. et al. The roads ahead: narratives for Shared Socioeconomic Pathways describing world futures in the 21st century. Glob. Environ. Change 42, 169–180 (2017).
Google Scholar 
Vrac, M., Stein, M. L., Hayhoe, K. & Liang, X.-Z. A general method for validating statistical downscaling methods under future climate change. Geophys. Res. 34, L18701 (2007).
Google Scholar 
Rogers, A. D. Environmental change in the deep ocean. Annu. Rev. Environ. Resour. 40, 1–38 (2015).
Google Scholar 
Sayre, R. G. et al. A three-dimensional mapping of the ocean based on environmental data. Oceanography 30, 90–103 (2017).
Google Scholar 
Schulzweida, U. CDO User Guide (Max Planck Institute for Meteorology, 2019).R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).Mumby, P. J. et al. Reserve design for uncertain responses of coral reefs to climate change. Ecol. Lett. 14, 132–140 (2011).
Google Scholar 
Magris, R. A., Heron, S. F. & Pressey, R. L. Conservation planning for coral reefs accounting for climate warming disturbances. PLoS ONE 10, e0140828 (2015).
Google Scholar 
Chollett, I., Enríquez, S. & Mumby, P. J. Redefining thermal regimes to design reserves for coral reefs in the face of climate change. PLoS ONE 9, e110634 (2014).
Google Scholar 
Sala, E. et al. Protecting the global ocean for biodiversity, food and climate. Nature 592, 397–402 (2021).CAS 

Google Scholar 
García Molinos, J., Schoeman, D. S., Brown, C. J. & Burrows, M. T. VoCC: an R package for calculating the velocity of climate change and related climatic metrics. Methods Ecol. Evol. 10, 2195–2202 (2019).
Google Scholar 
Iwamura, T., Wilson, K. A., Venter, O. & Possingham, H. P. A climatic stability approach to prioritizing global conservation investments. PLoS ONE 5, e15103 (2010).CAS 

Google Scholar 
Jorda, G. et al. Ocean warming compresses the three-dimensional habitat of marine life. Nat. Ecol. Evol. 4, 109–114 (2020).
Google Scholar 
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).
Google Scholar 
Burrows, M. T. et al. Ocean community warming responses explained by thermal affinities and temperature gradients. Nat. Clim. Change 9, 959–963 (2019).
Google Scholar 
Ball, I. R., Possingham, H. P. & Watts, M. in Spatial Conservation Prioritization: Quantitative Methods and Computational Tools (eds Moilanen, A. et al.) Ch. 14 (Oxford Univ. Press, 2009).Asaad, I., Lundquist, C. J., Erdmann, M. V. & Costello, M. J. Ecological criteria to identify areas for biodiversity conservation. Biol. Conserv. 213, 309–316 (2017).
Google Scholar 
Kaschner, K. et al. AquaMaps: Predicted Range Maps for Aquatic Species (2019).Harris, P. T., Macmillan-Lawler, M., Rupp, J. & Baker, E. K. Geomorphology of the oceans. Mar. Geol. 352, 4–24 (2014).
Google Scholar 
Froese, R. & Pauly, D. FishBase (2021).Palomares, M. L. D. & Pauly, D. SeaLifeBase (2021).Morato, T., Hoyle, S. D., Allain, V. & Nicol, S. J. Seamounts are hotspots of pelagic biodiversity in the open ocean. Proc. Natl Acad. Sci. USA 107, 9707–9711 (2010).CAS 

Google Scholar 
Rowden, A. A. et al. A test of the seamount oasis hypothesis: seamounts support higher epibenthic megafaunal biomass than adjacent slopes. Mar. Ecol. 31, 95–106 (2010).
Google Scholar 
Devred, E., Sathyendranath, S. & Platt, T. Delineation of ecological provinces using ocean colour radiometry. Mar. Ecol. Prog. Ser. 346, 1–13 (2007).CAS 

Google Scholar 
Oliver, M. J. & Irwin, A. J. Objective global ocean biogeographic provinces. Geophys. Res. Lett. 35, L15601 (2008).
Google Scholar 
Costello, M. J. et al. Marine biogeographic realms and species endemicity. Nat. Commun. 8, 1057 (2017).
Google Scholar 
Sutton, T. T. et al. A global biogeographic classification of the mesopelagic zone. Deep Sea Res. 1 126, 85–102 (2017).
Google Scholar 
Global Open Oceans and Deep Seabed (GOODS)—Biogeographic Classification (UNESCO, 2009).Ban, N. C. & Klein, C. J. Spatial socioeconomic data as a cost in systematic marine conservation planning. Conserv. Lett. 2, 206–215 (2009).
Google Scholar 
Tai, T. C., Cashion, T., Lam, V. W. Y., Swartz, W. & Sumaila, U. R. Ex-vessel fish price database: disaggregating prices for low-priced species from reduction fisheries. Front. Mar. Sci. 4, 363 (2017).
Google Scholar 
Gurobi Optimizer Reference Manual (Gurobi Optimization, 2020).Hanson, J. O., Schuster, R., Strimas-Mackey, M. & Bennett, J. R. Optimality in prioritizing conservation projects. Methods Ecol. Evol. 10, 1655–1663 (2019).
Google Scholar 
IUCN Red List of Threatened Species (IUCN, 2020); https://www.iucnredlist.org/enChamberlain, S. rredlist: ‘IUCN’ Red List Client. R package version 0.7.0 (2020).McHugh, M. L. Interrater reliability: the kappa statistic. Biochem. Med. 22, 276–282 (2012).
Google Scholar 
Brito-Morales, I. Towards climate-smart, 3-D protected areas for biodiversity conservation in the high seas (v2.0). Zenodo https://doi.org/10.5281/zenodo.5912047 (2022). More