Ecology

1.Sanderson, E. W. et al. The human footprint and the last of the wild. Bioscience 52, 891 (2002).
Google Scholar 
2.Halpern, B. S. et al. A global map of human impact on marine ecosystems. Science 319, 948–952 (2008).ADS 
CAS 
PubMed 

Google Scholar 
3.Birk, S. et al. Impacts of multiple stressors on freshwater biota across spatial scales and ecosystems. Nat. Ecol. Evol. 4, 1060–1068 (2020).PubMed 

Google Scholar 
4.EU Water Framework Directive Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000, establishing a framework for Community action in the field of water policy (2000).5.Valiente-Banuet, A. et al. Beyond species loss: The extinction of ecological interactions in a changing world. Funct. Ecol. 29, 299–307 (2015).
Google Scholar 
6.Powers, R. P. & Jetz, W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Change 9, 323–329 (2019).ADS 

Google Scholar 
7.Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145–148 (2004).ADS 
CAS 
PubMed 

Google Scholar 
8.Haddeland, I. et al. Global water resources affected by human interventions and climate change. Proc. Natl. Acad. Sci. U.S.A. 111, 3251–3256 (2014).ADS 
CAS 
PubMed 

Google Scholar 
9.Stanton, J. C., Shoemaker, K. T., Pearson, R. G. & Akçakaya, H. R. Warning times for species extinctions due to climate change. Glob. Change Biol. 21, 1066–1077 (2015).ADS 

Google Scholar 
10.Jarić, I., Lennox, R. J., Kalinkat, G., Cvijanović, G. & Radinger, J. Susceptibility of European freshwater fish to climate change: Species profiling based on life-history and environmental characteristics. Glob. Change Biol. 25, 448–458 (2019).ADS 

Google Scholar 
11.Gaston, K. J., Jackson, S. F., Cantú-Salazar, L. & Cruz-Piñón, G. The ecological performance of protected areas. Annu. Rev. Ecol. Evol. Syst. 39, 93–113 (2008).
Google Scholar 
12.Kati, V. et al. Hotspots, complementarity or representativeness? Designing optimal small-scale reserves for biodiversity conservation. Biol. Conserv. 120, 471–480 (2004).
Google Scholar 
13.Everall, N. C. et al. Comparability of macroinvertebrate biomonitoring indices of river health derived from semi-quantitative and quantitative methodologies. Ecol. Indic. 78, 437–448 (2017).
Google Scholar 
14.Gieswein, A., Hering, D. & Lorenz, A. W. Development and validation of a macroinvertebrate-based biomonitoring tool to assess fine sediment impact in small mountain streams. Sci. Total Environ. 652, 1290–1301 (2019).ADS 
PubMed 

Google Scholar 
15.Coates, S., Waugh, A., Anwar, A. & Robson, M. Efficacy of a multi-metric fish index as an analysis tool for the transitional fish component of the Water Framework Directive. Mar. Pollut. Bull. 55, 225–240 (2007).CAS 
PubMed 

Google Scholar 
16.Feld, C. K. & Hering, D. Community structure or function: Effects of environmental stress on benthic macroinvertebrates at different spatial scales. Freshw. Biol. 52, 1380–1399 (2007).
Google Scholar 
17.Dobson, A., Lafferty, K. D., Kuris, A. M., Hechinger, R. F. & Jetz, W. Homage to Linnaeus: How many parasites? How many hosts? Proc. Natl. Acad. Sci. 105, 11482–11489 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
18.Carlson, C. J., Zipfel, C. M., Garnier, R. & Bansal, S. Global estimates of mammalian viral diversity accounting for host sharing. Nat. Ecol. Evol. 3, 1070–1075 (2019).PubMed 

Google Scholar 
19.Poulin, R. Parasite biodiversity revisited: Frontiers and constraints. Int. J. Parasitol. 44, 581–589 (2014).PubMed 

Google Scholar 
20.Thomas, F. et al. Parasites and ecosystem engineering: What roles could they play? Oikos 84, 167 (1999).
Google Scholar 
21.Hudson, P. J., Dobson, A. P. & Lafferty, K. D. Is a healthy ecosystem one that is rich in parasites? Trends Ecol. Evol. 21, 381–385 (2006).PubMed 

Google Scholar 
22.Lefèvre, T. et al. The ecological significance of manipulative parasites. Trends Ecol. Evol. 24, 41–48 (2009).PubMed 

Google Scholar 
23.Frainer, A., McKie, B. G., Amundsen, P. A., Knudsen, R. & Lafferty, K. D. Parasitism and the biodiversity-functioning relationship. Trends Ecol. Evol. 33, 260–268 (2018).PubMed 

Google Scholar 
24.Lafferty, K. D. & Morris, A. K. Altered behavior of parasitized Killifish increases susceptibility to predation by bird final hosts. Ecology 77, 1390–1397 (1996).
Google Scholar 
25.Mouritsen, K. N. & Poulin, R. Parasitism, community structure and biodiversity in intertidal ecosystems. Parasitology 124, 101–117 (2002).
Google Scholar 
26.Marcogliese, D. J. Parasites: Small players with crucial roles in the ecological theater. EcoHealth 1, 151–164 (2004).
Google Scholar 
27.Lagrue, C. & Poulin, R. Intra- and interspecific competition among helminth parasites: Effects on Coitocaecum parvum life history strategy, size and fecundity. Int. J. Parasitol. 38, 1435–1444 (2008).PubMed 

Google Scholar 
28.Rosenkranz, M., Poulin, R. & Selbach, C. Behavioural impacts of trematodes on their snail host: Species-specific effects or generalised response? Ethology 124, 790–795 (2018).
Google Scholar 
29.Lafferty, K. D. et al. Parasites in food webs: The ultimate missing links. Ecol. Lett. 11, 533–546 (2008).PubMed 
PubMed Central 

Google Scholar 
30.Thieltges, D. W. et al. Parasites as prey in aquatic food webs: Implications for predator infection and parasite transmission. Oikos 122, 1473–1482 (2013).
Google Scholar 
31.Thieltges, D. W., Jensen, K. T. & Poulin, R. The role of biotic factors in the transmission of free-living endohelminth stages. Parasitology 135, 407–426 (2008).CAS 
PubMed 

Google Scholar 
32.Kuris, A. M. et al. Ecosystem energetic implications of parasite and free-living biomass in three estuaries. Nature 454, 515–518 (2008).ADS 
CAS 
PubMed 

Google Scholar 
33.Preston, D. L., Orlofske, S. A., Lambden, J. P. & Johnson, P. T. J. Biomass and productivity of trematode parasites in pond ecosystems. J. Anim. Ecol. 82, 509–517 (2013).PubMed 

Google Scholar 
34.Soldánová, M., Selbach, C. & Sures, B. The early worm catches the bird? Productivity and patterns of Trichobilharzia szidati cercarial emission from Lymnaea stagnalis. PLoS ONE 11, e0149678 (2016).PubMed 
PubMed Central 

Google Scholar 
35.Vidal-Martínez, V. M., Pech, D., Sures, B., Purucker, S. T. & Poulin, R. Can parasites really reveal environmental impact? Trends Parasitol. 26, 44–51 (2010).PubMed 

Google Scholar 
36.Shea, J. et al. The use of parasites as indicators of ecosystem health as compared to insects in freshwater lakes of the Inland Northwest. Ecol. Indic. 13, 184–188 (2012).
Google Scholar 
37.Sures, B., Nachev, M., Selbach, C. & Marcogliese, D. J. Parasite responses to pollution: What we know and where we go in ‘environmental parasitology’. Parasit. Vectors 10, 65 (2017).PubMed 
PubMed Central 

Google Scholar 
38.Esch, G. W. The transmission of digenetic trematodes: Style, elegance, complexity. Integr. Comp. Biol. 42, 304–312 (2002).PubMed 

Google Scholar 
39.Byers, J. E., Altman, I., Grosse, A. M., Huspeni, T. C. & Maerz, J. C. Using parasitic trematode larvae to quantify an elusive vertebrate host. Conserv. Biol. 25, 85–93 (2010).PubMed 

Google Scholar 
40.Moore, C. S., Gittman, R. K., Puckett, B. J., Wellman, E. H. & Blakeslee, A. M. H. If you build it, they will come: Restoration positively influences free-living and parasite diversity in a restored tidal marsh. Food Webs 25, e00167 (2020).
Google Scholar 
41.Dougherty, E. R. et al. Paradigms for parasite conservation. Conserv. Biol. 30, 724–733 (2016).MathSciNet 
PubMed 

Google Scholar 
42.Carlson, C. J. et al. A global parasite conservation plan. Biol. Conserv. 250, 108596 (2020).
Google Scholar 
43.Kwak, M. L., Heath, A. C. G. & Cardoso, P. Methods for the assessment and conservation of threatened animal parasites. Biol. Conserv. 248, 108696 (2020).
Google Scholar 
44.Votýpka, J., Kment, P., Yurchenko, V. & Lukeš, J. Endangered monoxenous trypanosomatid parasites: A lesson from island biogeography. Biodivers. Conserv. 29, 3635–3667 (2020).
Google Scholar 
45.Carlson, C. J. et al. Parasite biodiversity faces extinction and redistribution in a changing climate. Sci. Adv. 3, e1602422 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
46.Lafferty, K. D. Biodiversity loss decreases parasite diversity: Theory and patterns. Philos. Trans. R. Soc. B Biol. Sci. 367, 2814–2827 (2012).
Google Scholar 
47.Cizauskas, C. A. et al. Parasite vulnerability to climate change: An evidence-based functional trait approach. R. Soc. Open Sci. 4, 160535 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
48.Watson, D. M., Milner, K. V. & Leigh, A. Novel application of species richness estimators to predict the host range of parasites. Int. J. Parasitol. 47, 31–39 (2017).PubMed 

Google Scholar 
49.Vannatta, J. T. & Minchella, D. J. Parasites and their impact on ecosystem nutrient cycling. Trends Parasitol. 34, 452–455 (2018).CAS 
PubMed 

Google Scholar 
50.Jorge, F. & Poulin, R. Poor geographical match between the distributions of host diversity and parasite discovery effort. Proc. R. Soc. B Biol. Sci. 285, 20180072 (2018).
Google Scholar 
51.Faltýnková, A. Larval trematodes (Digenea) in molluscs from small water bodies near České Budějovice, Czech Republic. Acta Parasitol. 50, 49–55 (2005).
Google Scholar 
52.Żbikowska, E. Digenea species in chosen populations of freshwater snails in northern and central part of Poland. Wiadomości Parazytol. 53, 301–308 (2007).
Google Scholar 
53.Schwelm, J., Soldánová, M., Vyhlídalová, T., Sures, B. & Selbach, C. Small but diverse: Larval trematode communities in the small freshwater planorbids Gyraulus albus and Segmentina nitida (Gastropoda: Pulmonata) from the Ruhr River, Germany. Parasitol. Res. 117, 241–255 (2018).CAS 
PubMed 

Google Scholar 
54.Selbach, C., Soldánová, M., Feld, C. K., Kostadinova, A. & Sures, B. Hidden parasite diversity in a European freshwater system. Sci. Rep. 10, 2694 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
55.Gibson, D. I. & Bray, R. A. The evolutionary expansion and host-parasite relationships of the Digenea. Int. J. Parasitol. 24, 1213–1226 (1994).CAS 
PubMed 

Google Scholar 
56.Gordy, M. A., Kish, L., Tarrabain, M. & Hanington, P. C. A comprehensive survey of larval digenean trematodes and their snail hosts in central Alberta, Canada. Parasitol. Res. 115, 3867–3880 (2016).PubMed 

Google Scholar 
57.Soldánová, M. et al. Molecular analyses reveal high species diversity of trematodes in a sub-Arctic lake. Int. J. Parasitol. 47, 327–345 (2017).PubMed 

Google Scholar 
58.Faltýnková, A. & Haas, W. Larval trematodes in freshwater molluscs from the Elbe to Danube rivers (Southeast Germany): Before and today. Parasitol. Res. 99, 572–582 (2006).PubMed 

Google Scholar 
59.Faltýnková, A., Našincová, V. & Kablásková, L. Larval trematodes (Digenea) of the great pond snail, Lymnaea stagnalis (L.), (Gastropoda, pulmonata) in Central Europe: A survey of species and key to their identification. Parasite 14, 39–51 (2007).PubMed 

Google Scholar 
60.Faltýnková, A., Našincová, V. & Kablásková, L. Larval trematodes (Digenea) of planorbid snails (Gastropoda: Pulmonata) in Central Europe: A survey of species and key to their identification. Syst. Parasitol. 69, 155–178 (2008).PubMed 

Google Scholar 
61.Faltýnková, A., Sures, B. & Kostadinova, A. Biodiversity of trematodes in their intermediate mollusc and fish hosts in the freshwater ecosystems of Europe. Syst. Parasitol. 93, 283–293 (2016).PubMed 

Google Scholar 
62.Soldánová, M., Selbach, C., Sures, B., Kostadinova, A. & Perez-del-Olmo, A. Larval trematode communities in Radix auricularia and Lymnaea stagnalis in a reservoir system of the Ruhr River. Parasit. Vectors 3, 56 (2010).PubMed 
PubMed Central 

Google Scholar 
63.Kamiya, T., O’Dwyer, K., Nakagawa, S. & Poulin, R. Host diversity drives parasite diversity: Meta-analytical insights into patterns and causal mechanisms. Ecography (Cop.) 37, 689–697 (2014).
Google Scholar 
64.Johnson, P. T. J. & Thieltges, D. W. Diversity, decoys and the dilution effect: How ecological communities affect disease risk. J. Exp. Biol. 213, 961–970 (2010).CAS 
PubMed 

Google Scholar 
65.Lagrue, C. & Poulin, R. Local diversity reduces infection risk across multiple freshwater host-parasite associations. Freshw. Biol. 60, 2445–2454 (2015).
Google Scholar 
66.Song, Z. & Proctor, H. Parasite prevalence in intermediate hosts increases with waterbody age and abundance of final hosts. Oecologia 192, 311–321 (2020).ADS 
PubMed 

Google Scholar 
67.Welsh, J. E., Van Der Meer, J., Brussaard, C. P. D. & Thieltges, D. W. Inventory of organisms interfering with transmission of a marine trematode. J. Mar. Biol. Assoc. U.K. 94, 697–702 (2014).
Google Scholar 
68.Gopko, M., Mironova, E., Pasternak, A., Mikheev, V. & Taskinen, J. Freshwater mussels (Anodonta anatina) reduce transmission of a common fish trematode (eye fluke, Diplostomum pseudospathaceum). Parasitology 144, 1971–1979 (2017).CAS 
PubMed 

Google Scholar 
69.Vielma, S., Lagrue, C., Poulin, R. & Selbach, C. Non-host organisms impact transmission at two different life stages in a marine parasite. Parasitol. Res. 118, 111–117 (2019).PubMed 

Google Scholar 
70.Kudlai, O., Stunženas, V. & Tkach, V. The taxonomic identity and phylogenetic relationships of Cercaria pugnax and C. helvetica XII (Digenea: Lecithodendriidae) based on morphological and molecular data. Folia Parasitol. (Praha) 62, 1–7 (2015).
Google Scholar 
71.Dunn, R. R., Harris, N. C., Colwell, R. K., Koh, L. P. & Sodhi, N. S. The sixth mass coextinction: Are most endangered species parasites and mutualists? Proc. R. Soc. B Biol. Sci. 276, 3037–3045 (2009).
Google Scholar 
72.Selbach, C., Soldánová, M., Georgieva, S., Kostadinova, A. & Sures, B. Integrative taxonomic approach to the cryptic diversity of Diplostomum spp. in lymnaeid snails from Europe with a focus on the ‘Diplostomum mergi’ species complex. Parasit. Vectors 8, 300 (2015).PubMed 
PubMed Central 

Google Scholar 
73.Marcogliese, D. J. Parasites of the superorganism: Are they indicators of ecosystem health? Int. J. Parasitol. 35, 705–716 (2005).PubMed 

Google Scholar 
74.MacKenzie, K., Williams, H. H., Williams, B., McVicar, A. H. & Siddall, R. Parasites as indicators of water quality and the potential use of helminth transmission in marine pollution studies. Adv. Parasitol. 35, 85–144 (1995).CAS 
PubMed 

Google Scholar 
75.Anderson, T. K. & Sukhdeo, M. V. K. Qualitative community stability determines parasite establishment and richness in estuarine marshes. PeerJ 2013, 1–14 (2013).
Google Scholar 
76.Neutel, A. M. Stability in real food webs: Weak links in long loops. Science 296, 1120–1123 (2002).ADS 
CAS 
PubMed 

Google Scholar 
77.Glöer, P. Süßwassergastropoden Nord- und Mitteleuropas: Mollusca I: Bestimmungsschlüssel, Lebensweise, Verbreitung (ConchBooks, 2002).
Google Scholar 
78.Welter-Schultes, F. European Non-marine Molluscs, a Guide for Species Identification (Planet Poster Editions, 2012).
Google Scholar 
79.Brühne, M. & Scharbert, A. Die Erschließung des Bienener Altrheins für die Rheinfischfauna. Naturschutz und Biologische Vielfalt (2005).80.Hugghins, E. J. Life history of a strigeid trematode, Hysteromorpha triloba (Rudolphi, 1819) Lutz, 1931. II. Sporocyst through adult. Trans. Am. Microsc. Soc. 73, 221 (1954).
Google Scholar 
81.Našincová, V. & Scholz, T. The life cycle of Asymphylodora tincae (Modeer 1790) (Trematoda: Monorchiidae): A unique development in monorchiid trematodes. Parasitol. Res. 80, 192–197 (1994).PubMed 

Google Scholar 
82.Georgieva, S. et al. New cryptic species of the ‘revolutum’ group of Echinostoma (Digenea: Echinostomatidae) revealed by molecular and morphological data. Parasit. Vectors 6, 64 (2013).PubMed 
PubMed Central 

Google Scholar 
83.Grabner, D. S. et al. Invaders, natives and their enemies: Distribution patterns of amphipods and their microsporidian parasites in the Ruhr Metropolis, Germany. Parasit. Vectors 8, 419 (2015).PubMed 
PubMed Central 

Google Scholar 
84.Bush, A. O., Lafferty, K. D., Lotz, J. M. & Shostak, A. W. Parasitology meets ecology on its own terms: Margolis et al. revisited. J. Parasitol. 83, 575 (1997).CAS 
PubMed 

Google Scholar 
85.Niewiadomska, K. Family Cyathocotylidae Mühling, 1898. In Keys to the Trematoda Vol. 1 (eds Gibson, D. I. et al.) 201–214 (CABI Publishing Wallingford & Natural History Museum, 2002).
Google Scholar 
86.Möhl, K. et al. Biology of Alaria spp. and human exposition risk to Alaria mesocercariae-a review. Parasitol. Res. 105, 1–15 (2009).PubMed 

Google Scholar 
87.Brown, R., Soldánová, M., Barrett, J. & Kostadinova, A. Small-scale to large-scale and back: Larval trematodes in Lymnaea stagnalis and Planorbarius corneus in Central Europe. Parasitol. Res. 108, 137–150 (2011).PubMed 

Google Scholar 
88.Niewiadomska, K. Family Diplostomidae Poirier, 1886. In Keys to the Trematoda Vol. 1 (eds Gibson, D. I. et al.) 167–196 (CABI Publishing Wallingford & Natural History Museum, 2002).
Google Scholar 
89.Tkach, V. V., Kudlai, O. & Kostadinova, A. Molecular phylogeny and systematics of the Echinostomatoidea Looss, 1899 (Platyhelminthes: Digenea). Int. J. Parasitol. 46, 171–185 (2016).PubMed 

Google Scholar 
90.Kostadinova, A. & Gibson, D. A redescription of Uroproctepisthmium bursicola (Creplin, 1837) n. comb. (Digenea: Echinostomatidae), and re-evaluations of the genera Episthmium Lühe, 1909 and Uroproctepisthmium Fischthal & Kuntz, 1976. Syst. Parasitol. 50, 63–67 (2001).CAS 
PubMed 

Google Scholar 
91.Kudlai, O. Biology of Neoacanthoparyphium echinatoides (Trematoda, Echinostomatidae) in north-western Priazov’ye (Ukraine). Vestn. Zool. 23, 102–106 (2009).
Google Scholar 
92.Selbach, C. et al. Morphological and molecular data for larval stages of four species of Petasiger Dietz, 1909 (Digenea: Echinostomatidae) with an updated key to the known cercariae from the Palaearctic. Syst. Parasitol. 89, 153–166 (2014).PubMed 

Google Scholar 
93.Bray, R. A. Family Lissorchiidae Magath, 1917. In Keys to the Trematoda Vol. 3 (eds Bray, R. A. et al.) 177–186 (CABI Publishing Wallingford & Natural History Museum, 2008).
Google Scholar 
94.Zikmundová, J., Georgieva, S., Faltýnková, A., Soldánová, M. & Kostadinova, A. Species diversity of Plagiorchis Lühe, 1899 (Digenea: Plagiorchiidae) in lymnaeid snails from freshwater ecosystems in central Europe revealed by molecules and morphology. Syst. Parasitol. 88, 37–54 (2014).PubMed 

Google Scholar 
95.Kanarek, G., Zaleśny, G., Sitko, J. & Tkach, V. V. The systematic position and structure of the genus Leyogonimus Ginetsinskaya, 1948 (Platyhelminthes: Digenea) with comments on the taxonomy of the superfamily Microphalloidea Ward, 1901. Acta Parasitol. 62, 617–624 (2017).CAS 
PubMed 

Google Scholar 
96.Tkach, V. V., Snyder, S. D. & Świderski, Z. On the phylogenetic relationships of some members of Macroderoididae and Ochetosomatidae (Digenea, Plagiorchioidea). Acta Parasitol. 46, 267–275 (2001).
Google Scholar 
97.Roy, C. L. & St-Louis, V. Spatio-temporal variation in prevalence and intensity of trematodes responsible for waterfowl die-offs in faucet snail-infested waterbodies of Minnesota, USA. Int. J. Parasitol. Parasites Wildl. 6, 162–176 (2017).PubMed 
PubMed Central 

Google Scholar 
98.Kostadinova, A. Family Echinostomatidae Looss, 1899. In Keys to the Trematoda Vol. 2 (eds Jones, A. et al.) 9–64 (CABI Publishing Wallingford & Natural History Museum, 2005).
Google Scholar 
99.Niewiadomska, K. Family Strigeidae Railliet, 1919. In Keys to the Trematoda Vol. 1 (eds Gibson, D. I. et al.) 231–241 (CABI Publishing & Natural History Museum, 2002).
Google Scholar  More

1.Drucker, P. Indians of the Northwest Coast (McGraw-Hill, 1955).Book 

Google Scholar 
2.Kroeber, A. Culture and natural areas of Native North America (University of California Press, 1939).
Google Scholar 
3.Introduction, S. W. In Handbook of North American Indians volume 7: Northwest Coast (ed. Suttles, W.) 1–15 (Smithsonian Institution, 1990).
Google Scholar 
4.Suttles, W. Coping with abundance: subsistence on the Northwest Coast. In Coast Salish Essays (ed. Suttles, W.) (Talon Books, 1987).
Google Scholar 
5.Barnett, H. G. The Coast Salish of British Columbia (University of Oregon Press, 1955).
Google Scholar 
6.Ames, K. The Northwest Coast: Complex hunter-gatherers, ecology, and social evolution. Annu. Rev. Anthropol. 23, 209–229 (1994).Article 

Google Scholar 
7.Carlson, R. L., Szpak, P. & Richards, M. The Pender Canal site and the beginnings of the Northwest Coast cultural system. Can. J. Archaeol. 41, 1–29 (2017).
Google Scholar 
8.Cannon, A. & Yang, D. Y. Early storage and sedentism on the Pacific Northwest Coast: ancient DNA analysis of salmon remains from Namu, British Columbia. Am. Antiquity 71, 123–140 (2006).Article 

Google Scholar 
9.Matson, R. G. The evolution of Northwest Coast subsistence. In Research in Economic Anthropology Supplement 6: Long-Term Subsistence Change in Prehistoric North America (eds Croes, D. et al.) 366–428 (JAI Press Inc., 1992).
Google Scholar 
10.Caldwell, M. et al. A bird’s eye view of northern Coast Salish intertidal resource management features, southern British Columbia. J. Island Coast. Archaeol. 7, 219–233 (2012).Article 

Google Scholar 
11.Caldwell, M. & Lepofsky, D. Indigenous marine resource management on the Northwest Coast of North America. Ecol. Process. 2(1), 12 (2013).
Google Scholar 
12.Croes, D. R. (ed.). The Qwu?gwes Archaeological Site and Fish Trap (45TN240), and Tested Homestead (45TN396), Eleven-year South Puget Sound Community College Summer Field School Investigations with the Squaxin Island Tribe—Final Report. Report on file, Washington State Department of Archaeology and Historic Preservation, Olympia (2013).13.Lepofsky, D. et al. Shellfish mariculture on the Northwest Coast of North America. Am. Antiq. 80, 236–259 (2015).Article 

Google Scholar 
14.Mathews, D. L. & Turner, N. J. Ocean cultures: northwest coast ecosystems and indigenous management systems. In Conservation for the Anthropocene Ocean: Interdisciplinary Science in Support of Nature and People (eds Levin, P. S. & Poe, M. R.) 169–201 (Academic Press, 2017).Chapter 

Google Scholar 
15.Williams, J. Clam gardens: aboriginal mariculture on Canada’s West Coast (New Star Books, 2006).
Google Scholar 
16.Campbell, S. & Butler, V. Archaeological evidence for resilience of Pacific Northwest salmon populations and the socioecological system over the last~7,500 years. Ecol. Soc. 15(1), 17 (2000).Article 

Google Scholar 
17.Thornton, T., Deur, D. & Kitka, H. Cultivation of salmon and other marine resources on the Northwest Coast of North America. Hum. Ecol. 43, 189–199 (2015).Article 

Google Scholar 
18.Thornton, T. The ideology and practice of Pacific Herring cultivation among the Tlingit and Haida. Hum. Ecol. 43, 213–223 (2015).Article 

Google Scholar 
19.Petersen, J. R. et al. Use of the traditional halibut hook of the Makah Tribe, the čibu⋅d, reduces bycatch in recreational halibut fisheries. PeerJ 8, e9288 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
20.Ritchie, M. & Angelbeck, B. “Coyote broke the dams”: Power, reciprocity, and conflict in fish weir narratives and implications for traditional and contemporary fisheries. Ethnohistory 67(2), 191–220 (2020).Article 

Google Scholar 
21.Royle, T. C. A. et al. An efficient and reliable DNA-based sex identification method for archaeological Pacific salmonid (Oncorhynchus spp.) remains. PLoS ONE 13(3), e0193212 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
22.Royle, T. C. A. et al. Investigating the sex-selectivity of a Middle Ontario Iroquoian Atlantic salmon (Salmo salar) and lake trout (Salvelinus namaycush) fishery through ancient DNA analysis. J. Archaeol. Sci. Rep. 31, 102301 (2020).
Google Scholar 
23.George, G. National Energy Board Hearing Order OH-001-2014. Trans Mountain Pipeline ULC. Trans Mountain Expansion Project. Volume 6 (2014).24.Morin, J. Tsleil-Waututh Nation’s History, Culture and Aboriginal Interests in Eastern Burrard Inlet. Report on file, Gowlings, Lafleur, Henderson LLP, Vancouver (2015).25.Suttles, W. Central Coast Salish. In Handbook of North American Indians Volume 7: Northwest Coast (ed. Suttles, W.) 453–475 (Smithsonian Institution, 1990).26.Hancock, M. J. & Marshall, D.E. Catalogue of Salmon Streams and Spawning Escapements of Statistical Area 28 Howe Sound-Burrard Inlet. Canadian Data Report of Fisheries and Aquatic Sciences No. 557 (1986).27.Harris, G. The salmon and trout streams of Vancouver. Waters J. Vanc. Aquar. 3, 4–23 (1978).
Google Scholar 
28.Ricker, W. E. Effects of the Fishery and of Obstacles to Migration on the Abundance of Fraser River Sockeye Salmon (Oncorhynchus nerka). Canadian Technical Report of Fisheries and Aquatic Sciences No. 1522 (1987).29.Charlton, A. S. The Belcarra Park Site. (Department of Archaeology, Simon Fraser University, 1980).30.Lepofsky, D., Trost, D. & Morin, J. Coast Salish interaction: a view from the inlets. Can. J. Archaeol. 31, 190–223 (2007).
Google Scholar 
31.Morin, J., Lepofsky, D., Ritchie, M., Porcic, M. & Edinborough, K. Assessing continuity in the ancestral territory of the Tsleil-Waututh-Coast Salish, southwest British Columbia, Canada. J. Anthropol. Archaeol. 51, 77–87 (2018).Article 

Google Scholar 
32.Morin, J., Muir, B., Ritchie, M. & Sellers, I. Tsleil-Waututh and Simon Fraser University Archaeological Investigations at Port Moody (Reed Point, Shoreline Park, Old Orchard Park, Slaughterhouse Creek, Carraholly Point, and Barnet Beach). Permit 2014–344. Report on file, British Columbia Archaeology Branch, Victoria (2020).33.Harris, C. Voices of disaster: smallpox around the Strait of Georgia in 1782. Ethnohistory 41, 591–626 (1994).Article 

Google Scholar 
34.Chisholm, B. S. Reconstructions of Prehistoric Diet in British Columbia Using Stable-Carbon Isotopic Analysis. PhD dissertation. (Simon Fraser University, 1986).35.Hanson, D. K. Late Prehistoric Subsistence in the Strait of Georgia Region of the Northwest Coast. Master’s thesis. (Simon Fraser University, 1991).36.Trost, T. Forgotten Waters: A Zooarchaeological Analysis of the Cove Cliff Site (DhRr 18), Indian Arm, British Columbia. Master’s thesis. (Simon Fraser University, 2005).37.Pierson, N. Bridging Troubled Waters: Zooarchaeology and Marine Conservation on Burrard Inlet, Southwest British Columbia. Master’s thesis. (Simon Fraser University, 2011).38.Morin, J. et al. DNA-based species identification of ancient salmonid remains provides new insight into pre-contact Coast Salish salmon fisheries in Burrard Inlet, British Columbia, Canada. J. Archaeol. Sci. Rep. 37, 102956 (2021).
Google Scholar 
39.Sproat, G. June 15, 1877. Copy of minute of decision, Joint Indian Reserve Commission. Signed by Dominion Commissioner Alex Anderson, Provincial Commissioner Arch. McKinley and Joint Commissioner G.M. Sproat. Federal set of JIRC’s Minutes and plans, surveyor’s copy. Aboriginal and Northern Affairs Canada, BC Regional Office Specific Claims Branch, Resource Library, Vancouver. AAND Lands and Trusts registration #15215. Also LAC, RG10, Volume 3612, File 3756-23, Reel C10106 (1877).40.Mortimer, H. & George, D. You Call Me Chief: Impressions of the Life of Dan George (Doubleday, 1981).
Google Scholar 
41.Talbot, M. Old Legends and Customs of the British Columbia Coast Indians. s.n., New Westminster (1952).42.Thornton, M. Indian Lives and Legends (Mitchell Press, 1966).
Google Scholar 
43.MacDonald, C., Drake, D., Doerksen, J. & Cotton, M. Between Forest and Sea: Memories of Belcarra (Belcarra Historical Group, 1998).
Google Scholar 
44.Romanoff, S. Fraser Lillooet Fishing. In A Complex Culture of the British Columbia Plateau, Vancouver (ed. Hayden, B.) 222–265 (University of British Columbia Press, 1992).
Google Scholar 
45.Kennedy, D. & Bouchard, R. Sliammon Life, Sliammon Lands (Talonbooks, 1983).
Google Scholar 
46.Mathisen, O. A. The effect of altered sex ratios on the spawning of red salmon. In Studies of Alaska Red Salmon (ed. Koo, T.) 137–246 (University of Washington Press, 1962).
Google Scholar 
47.Reed, W. J. Sex-selective harvesting of Pacific salmon: a theoretically optimal solution. Ecol. Model. 14, 261–271 (1982).Article 

Google Scholar 
48.Salo, E. O. Life history of chum salmon (Oncorhynchus keta). In Pacific Salmon Life Histories (eds Margolis Groot, C. & Margolis, L.) 231–310 (UBC Press, 1991).
Google Scholar 
49.Jenness, D. The Faith of a Coast Salish Indian (British Columbia Provincial Museum, 1955).50.Richling, B. (ed.) The W̲SÁNEĆ and Their Neighbours: Diamond Jenness on the Coast Salish of Vancouver Island, 1935 (Rock’s Mills Press, 2016).51.Dale, C. & Natcher, D. C. What is old is new again: the reintroduction of Indigenous fishing technologies in British Columbia. Local Environ. 20(11), 1309–1321 (2015).Article 

Google Scholar 
52.Ritchie, M. P. & Springer, C. Harrison River Chum Fishery: The Ethnographic and Archaeological Perspective. Report on file, Sts′ailes, Agassiz (2010).53.Simeone, W. E. & Valentine, E. M. Ahtna Knowledge of Long-Term Changes in Salmon runs in the Upper Copper River Drainage, Alaska (Alaska Department of Fish and Game, Division of Subsistence, 2007).54.Langdon, S. J. Traditional Knowledge and Harvesting of Salmon by Huna and Hinyaa Tlingit. (U.S. Fish and Wildlife Service, Office of Subsistence Management, Fisheries Resource Monitoring Program, 2006).55.Ratner, N. C. et al. Local Knowledge, Customary Practices, and Harvest of Sockeye salmon from the Klawock and Sarkar Rivers, Prince of Wales Island, Alaska (Alaska Department of Fish and Game, Division of Subsistence, 2006)56.Curtis, E. The North American Indian: being a series of volumes picturing and describing the Indians of the United States, the Dominion of Canada, and Alaska, Vol. 13 (Plimpton Press, 1924).
Google Scholar 
57.Kennedy, D. & Bouchard, R. Stl’atl’imx fishing. In A Complex Culture of the British Columbia Plateau (ed. Hayden, B.) 266–354 (UBC Press, 1992).
Google Scholar 
58.Yang, D. Y. & Watt, K. Contamination controls when preparing archaeological remains for ancient DNA analysis. J. Archaeol. Sci. 32(3), 331–336 (2005).Article 

Google Scholar 
59.Speller, C. F. et al. High potential for using DNA from ancient herring bones to inform modern fisheries management and conservation. PLoS ONE 7, e51122 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
60.Yang, D. Y., Eng, B., Waye, J. S., Dudar, J. C. & Saunders, S. R. Technical note: improved DNA extraction from ancient bones using silica-based spin columns. Am. J. Phys. Anthropol. 105(4), 539–543 (1998).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
61.Yang, D. Y., Liu, L., Chen, X. & Speller, C. F. Wild or domesticated: DNA analysis of ancient water buffalo remains from North China. J. Archaeol. Sci. 35(10), 2778–2785 (2008).Article 

Google Scholar 
62.Bertho, S. et al. The unusual rainbow trout sex determination gene hijacked the canonical vertebrate gonadal differentiation pathway. Proc. Natl. Acad. Sci. U.S.A. 115(50), 12781–12786 (2008).Article 
CAS 

Google Scholar 
63.Yano, A. et al. An immune-related gene evolved into the master sex-determining gene in rainbow trout, Oncorhynchus mykiss. Curr. Biol. 22(15), 1423–1428 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Yano, A. et al. The sexually dimorphic on the Y-chromosome gene (sdY) is a conserved male-specific Y-chromosome sequence in many salmonids. Evol. Appl. 6(3), 486–496 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Yang, D., Cannon, A. & Sanders, S. R. DNA species identification of archaeological salmon bone from the Pacific Northwest Coast of North America. J. Archaeol. Sci. 31, 619–631 (2004).Article 

Google Scholar 
66.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).67.Kim, K. et al. A real-time PCR-based amelogenin Y allele dropout assessment model in gender typing of degraded DNA samples. Int. J. Legal Med. 127, 55–61 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
68.Sinding, M. et al. Sex determination of baleen whale artefacts: Implications for ancient DNA use in zooarchaeology. J. Archaeol. Sci. Rep. 10, 345–349 (2016).
Google Scholar 
69.Cooper, A. & Poinar, H. Ancient DNA: do it right or not at all. Science 289(5482), 1139 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.McKechnie, I. Investigating the complexities of sustainable fishing at a prehistoric village on western Vancouver Island, British Columbia, Canada. J. Nat. Conserv. 15(3), 208–222 (2007).Article 

Google Scholar 
71.Cannon, A., Yang, D. Y. & Speller, C. Site-specific salmon fisheries on the central coast of British Columbia. In The Archaeology of North Pacific Fisheries (eds Moss, M. & Cannon, A.) 57–74 (University of Alaska Press, 2011).
Google Scholar 
72.McKechnie, I. & Moss, M. Meta-analysis in zooarchaeology expands perspectives on Indigenous fisheries of the Northwest Coast of North America. J. Archaeol. Sci. Rep. 8, 470–485 (2016).
Google Scholar 
73.Orchard, T. J. & Szpak, P. Zooarchaeological and isotopic insights into locally variable economic patterns: a case study from late Holocene southern Haida Gwaii, British Columbia. BC Stud. 187, 107–147 (2015).
Google Scholar 
74.Rodrigues, A. T., McKechnie, I. & Yang, D. Y. Ancient DNA analysis of indigenous rockfish use on the Pacific Coast: implications for marine conservation areas and fisheries management. PLoS ONE 13(2), e0192716 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
75.McGregor, D. Coming full circle: Indigenous knowledge, environment, and our future. Am. Indian Q. 28(3), 385–410 (2004).Article 

Google Scholar 
76.Suttles, W. Economic Life of the Coast Salish of Hario and Rosario Straits. PhD Dissertation. (University of Washington, 1951).77.Caldwell, M. E. Northern Coast Salish Marine Resource Management. PhD dissertation. (University of Alberta, 2015).78.Deur, D. Tending the garden, making the soil: Northwest Coast estuarine gardens as engineered environments. In Keeping It Living: Traditions of Plant Use and Cultivation on the Northwest Coast of North America (eds Deur, D. & Turner, N.) (UBC Press, 2005).
Google Scholar 
79.Hoffmann, T. et al. Engineered feature used to enhance gardening at a 3800-year-old site on the Pacific Northwest coast. Sci. Adv. 2(12), e1601282 (2016).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
80.Lepofsky, D. et al. Documenting pre-contact plant management on the Northwest Coast: an example of prescribed burning in the Central and Upper Fraser Valley, British Columbia. In Keeping It Living: Traditions of Plant Use and Cultivation on the Northwest Coast of North America (eds Deur, D. & Turner, N.) 218–239 (UBC Press, 2005).
Google Scholar 
81.Turner, N. J., Deur, D. & Lepofsky, D. Plant management systems of British Columbia’s First Peoples. BC Stud. 179, 107–133 (2013).
Google Scholar 
82.Turner, N. J., Smith, R. & Jones, J. A fine line between two nations: ownership patterns for plant resources among Northwest Coast indigenous peoples. In Keeping It Living: Traditions of Plant Use and Cultivation on the Northwest Coast of North America (eds Deur, D. & Turner, N. J.) 151–180 (UBC Press, 2005).
Google Scholar 
83.Turner, N. J. & Peacock, S. Solving the perennial paradox: ethnobotanical evidence for plant resource management on the Northwest Coast. In Keeping It Living: Traditions of Plant Use and Cultivation on the Northwest Coast of North America (eds Deur, D. & Turner, N. J.) 101–151 (UBC Press, 2005).
Google Scholar 
84.Limburg, K. E., Walther, Y., Hong, B., Olson, C. & Stora, J. Prehistoric versus modern Baltic Sea cod fisheries: selectivity across the millennia. Proc. R. Soc. B 275, 2659–2665 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
85.Sanchez, G. Indigenous stewardship of marine and estuarine fisheries? Reconstructing the ancient size of Pacific herring through linear regression models. J. Archaeol. Sci. Rep. 29, 102061 (2020).
Google Scholar 
86.Slaney, T. L., Hyatt, K. D., Northcote, T. G. & Fielden, R. J. Status of anadromous salmon and trout in British Columbia and Yukon. Fisheries 21(10), 20–35 (1996).Article 

Google Scholar 
87.Kope, R. & Wainwright, T. Trends in the status of Pacific salmon populations in Washington, Oregon, California, and Idaho. N. Pac. Anadr. Fish Comm. Bull. 1, 1–12 (1998).
Google Scholar 
88.Gustafson, R. G. et al. Pacific salmon extinctions: Quantifying lost and remaining diversity. Conserv. Biol. 21(4), 1009–1020 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
89.Price, M. H., English, K. K., Rosenberger, A. G., MacDuffee, M. & Reynolds, J. D. Canada’s wild salmon policy: an assessment of conservation progress in British Columbia. Can. J. Fish. Aquat. Sci. 74(10), 1507–1518 (2017).Article 

Google Scholar 
90.Gayeski, N. J. et al. The failure of wild salmon management: need for a place-based conceptual foundation. Fisheries 43(7), 303–309 (2018).Article 

Google Scholar 
91.Morales, Q. E., Lepofsky, D. & Berkes, F. Ethnobiology and fisheries: Learning from the past for the present. J. Ethnobiol. 37(3), 369–379 (2017).Article 

Google Scholar 
92.Reid, A. J. et al. Two-eyed seeing: an Indigenous framework to transform fisheries research and management. Fish Fish. 00, 1–19 (2020).
Google Scholar 
93.Atlas, W. I. et al. Indigenous systems of management for culturally and ecologically resilient Pacific salmon (Oncorhynchus spp.) fisheries. Bioscience 71(2), 1–19 (2021).Article 

Google Scholar  More

1.Flemming H-C, Wingender J, Szewzyk U, Steinberg P, Rice SA, Kjelleberg S. Biofilms: an emergent form of bacterial life. Nat Rev Microbiol. 2016;14:563–75.CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Nadell CD, Xavier JB, Foster KR. The sociobiology of biofilms. FEMS Microbiol Rev. 2009;33:206–24.CAS 
PubMed 

Google Scholar 
3.Rumbaugh KP, Sauer K. Biofilm dispersion. Nat Rev Microbiol. 2020;18:571–86.CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Costerton JW, Stewart PS, Greenberg EP. Bacterial biofilms: a common cause of persistent infections. Science. 1999;284:1318–22.CAS 
PubMed 

Google Scholar 
5.Drenkard E, Ausubel FM. Pseudomonas biofilm formation and antibiotic resistance are linked to phenotypic variation. Nature. 2002;416:740–3.CAS 
PubMed 

Google Scholar 
6.de Carvalho CCCR. Marine biofilms: a successful microbial strategy with economic implications. Front Mar Sci. 2018;5:126.7.McDougald D, Rice SA, Barraud N, Steinberg PD, Kjelleberg S. Should we stay or should we go: mechanisms and ecological consequences for biofilm dispersal. Nat Rev Microbiol. 2012;10:39–50.CAS 

Google Scholar 
8.Nathan R, Getz WM, Revilla E, Holyoak M, Kadmon R, Saltz D, et al. A movement ecology paradigm for unifying organismal movement research. Proc Natl Acad Sci USA. 2008;105:19052–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Yan J, Monaco H, Xavier JB. The ultimate guide to bacterial swarming: an experimental model to study the evolution of cooperative behavior. Annu Rev Microbiol. 2019;73:293–312.CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Gokhale S, Conwill A, Ranjan T, Gore J. Migration alters oscillatory dynamics and promotes survival in connected bacterial populations. Nat Commun. 2018;9:5273.CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Hallatschek O, Fisher DS. Acceleration of evolutionary spread by long-range dispersal. Proc Natl Acad Sci USA. 2014;111:E4911–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Birzu G, Hallatschek O, Korolev KS. Fluctuations uncover a distinct class of traveling waves. Proc Natl Acad Sci USA. 2018;115:E3645–54.CAS 
PubMed 
PubMed Central 

Google Scholar 
13.Ping D, Wang T, Fraebel DT, Maslov S, Sneppen K, Kuehn S. Hitchhiking, collapse, and contingency in phage infections of migrating bacterial populations. ISME J. 2020;14:2007–18.PubMed 
PubMed Central 

Google Scholar 
14.Chen L, Noorbakhsh J, Adams RM, Samaniego-Evans J, Agollah G, Nevozhay D, et al. Two-dimensionality of yeast colony expansion accompanied by pattern formation. PLoS Comput Biol. 2014;10:e1003979.PubMed 
PubMed Central 

Google Scholar 
15.Patra P, Kissoon K, Cornejo I, Kaplan HB, Igoshin OA. Colony expansion of socially motile Myxococcus xanthus cells is driven by growth, motility, and exopolysaccharide production. PLoS Comput Biol. 2016;12:e1005010.PubMed 
PubMed Central 

Google Scholar 
16.Chapman BB, Brönmark C, Nilsson J-Å, Hansson L-A. The ecology and evolution of partial migration. Oikos. 2011;120:1764–75.
Google Scholar 
17.Lundberg P. Partial bird migration and evolutionarily stable strategies. J Theor Biol. 1987;125:351–60.
Google Scholar 
18.Kokko H. Directions in modelling partial migration: how adaptation can cause a population decline and why the rules of territory acquisition matter. Oikos. 2011;120:1826–37.
Google Scholar 
19.Singh NJ, Leonardsson K. Partial migration and transient coexistence of migrants and residents in animal populations. PloS One. 2014;9:e94750.PubMed 
PubMed Central 

Google Scholar 
20.Armbruster CE, Mobley HLT. Merging mythology and morphology: the multifaceted lifestyle of Proteus mirabilis. Nat Rev Microbiol. 2012;10:743.CAS 
PubMed 
PubMed Central 

Google Scholar 
21.Schaffer JN, Pearson MM. Proteus mirabilis and urinary tract infections. Microbiol Spectr. 2015;3. https://doi.org/10.1128/microbiolspec.UTI-0017-2013.22.Jones BV, Young R, Mahenthiralingam E, Stickler DJ. Ultrastructure of Proteus mirabilis swarmer cell rafts and role of swarming in catheter-associated urinary tract infection. Infect Immun. 2004;72:3941–50.CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Li X, Zhao H, Lockatell CV, Drachenberg CB, Johnson DE, Mobley HL. Visualization of Proteus mirabilis within the matrix of urease-induced bladder stones during experimental urinary tract infection. Infect Immun. 2002;70:389–94.CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Stickler DJ. Bacterial biofilms in patients with indwelling urinary catheters. Nat Clin Pr Urol. 2008;5:598–608.CAS 

Google Scholar 
25.Jacobsen SM, Stickler DJ, Mobley HLT, Shirtliff ME. Complicated catheter-associated urinary tract infections due to Escherichia coli and Proteus mirabilis. Clin Microbiol Rev. 2008;21:26–59.CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Harshey RM. Bacterial motility on a surface: many ways to a common goal. Annu Rev Microbiol. 2003;57:249–73.CAS 
PubMed 

Google Scholar 
27.Verstraeten N, Braeken K, Debkumari B, Fauvart M, Fransaer J, Vermant J, et al. Living on a surface: swarming and biofilm formation. Trends Microbiol. 2008;16:496–506.CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Kearns DB. A field guide to bacterial swarming motility. Nat Rev Microbiol. 2010;8:634–44.CAS 
PubMed 
PubMed Central 

Google Scholar 
29.Wu Y, Jiang Y, Kaiser AD, Alber M. Self-organization in bacterial swarming: lessons from myxobacteria. Phys Biol. 2011;8:055003.PubMed 

Google Scholar 
30.Howery KE, Şimşek E, Kim M, Rather PN. Positive autoregulation of the flhDC operon in Proteus mirabilis. Res Microbiol. 2018;169:199–204.CAS 
PubMed 

Google Scholar 
31.Little K, Austerman J, Zheng J, Gibbs KA. Cell shape and population migration are distinct steps of Proteus mirabilis swarming that are decoupled on high-percentage agar. J Bacteriol. 2019;201:e00726–18.CAS 
PubMed 
PubMed Central 

Google Scholar 
32.Furness RB, Fraser GM, Hay NA, Hughes C. Negative feedback from a Proteus class II flagellum export defect to the flhDC master operon controlling cell division and flagellum assembly. J Bacteriol. 1997;179:5585–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Claret L, Hughes C. Functions of the subunits in the FlhD(2)C(2) transcriptional master regulator of bacterial flagellum biogenesis and swarming. J Mol Biol. 2000;303:467–78.CAS 
PubMed 

Google Scholar 
34.Deegan RD, Bakajin O, Dupont TF, Huber G, Nagel SR, Witten TA. Capillary flow as the cause of ring stains from dried liquid drops. Nature. 1997;389:827–9.CAS 

Google Scholar 
35.Andac T, Weigmann P, Velu SKP, Pinçe E, Volpe G, Volpe G, et al. Active matter alters the growth dynamics of coffee rings. Soft Matter. 2019;15:1488–96.CAS 
PubMed 

Google Scholar 
36.Nellimoottil TT, Rao PN, Ghosh SS, Chattopadhyay A. Evaporation-induced patterns from droplets containing motile and nonmotile bacteria. Langmuir. 2007;23:8655–8.CAS 
PubMed 

Google Scholar 
37.Clemmer KM, Rather PN. Regulation of flhDC expression in Proteus mirabilis. Res Microbiol. 2007;158:295–302.CAS 
PubMed 

Google Scholar 
38.Howery KE, Clemmer KM, Rather PN. The Rcs regulon in Proteus mirabilis: implications for motility, biofilm formation, and virulence. Curr Genet. 2016;62:775–89.CAS 
PubMed 

Google Scholar 
39.Howery KE, Clemmer KM, Şimşek E, Kim M, Rather PN. Regulation of the min cell division inhibition complex by the Rcs phosphorelay in Proteus mirabilis. J Bacteriol. 2015;197:2499–507.CAS 
PubMed 
PubMed Central 

Google Scholar 
40.Wang Q, Zhao Y, McClelland M, Harshey RM. The RcsCDB signaling system and swarming motility in Salmonella enterica Serovar Typhimurium: dual regulation of flagellar and SPI-2 virulence genes. J Bacteriol. 2007;189:8447–57.CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Samanta P, Clark ER, Knutson K, Horne SM, Prüß BM. OmpR and RcsB abolish temporal and spatial changes in expression of flhD in Escherichia coli biofilm. BMC Microbiol. 2013;13:182.CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Girgis HS, Liu Y, Ryu WS, Tavazoie S. A comprehensive genetic characterization of bacterial motility. PLoS Genet. 2007;3:e154.PubMed Central 

Google Scholar 
43.Francez-Charlot A, Laugel B, Van Gemert A, Dubarry N, Wiorowski F, Castanié-Cornet MP, et al. RcsCDB His-Asp phosphorelay system negatively regulates the flhDC operon in Escherichia coli. Mol Microbiol. 2003;49:823–32.CAS 
PubMed 

Google Scholar 
44.Rieck VT, Palumbo SA, Witter LD. Glucose availability and the growth rate of colonies of Pseudomonas fluorescens. J Gen Microbiol. 1973;74:1–8.CAS 
PubMed 

Google Scholar 
45.Shao X, Mugler A, Kim J, Jeong HJ, Levin BR, Nemenman I. Growth of bacteria in 3-d colonies. PLoS Comput Biol. 2017;13:e1005679.PubMed 
PubMed Central 

Google Scholar 
46.Warren MR, Sun H, Yan Y, Cremer J, Li B, Hwa T. Spatiotemporal establishment of dense bacterial colonies growing on hard agar. Elife. 2019;8:e41093.PubMed 
PubMed Central 

Google Scholar 
47.Lavrentovich MO, Koschwanez JH, Nelson DR. Nutrient shielding in clusters of cells. Phys Rev E Stat Nonlin Soft Matter Phys. 2013;87:062703. -PubMed 
PubMed Central 

Google Scholar 
48.Dal Co A, van Vliet S, Ackermann M. Emergent microscale gradients give rise to metabolic cross-feeding and antibiotic tolerance in clonal bacterial populations. Philos Trans R Soc Lond B Biol Sci. 2019;374:20190080.CAS 
PubMed 
PubMed Central 

Google Scholar 
49.Huang YH, Ferrières L, Clarke DJ. The role of the Rcs phosphorelay in Enterobacteriaceae. Res Microbiol. 2006;157:206–12.CAS 
PubMed 

Google Scholar 
50.Majdalani N, Gottesman S. The Rcs phosphorelay: a complex signal transduction system. Annu Rev Microbiol. 2005;59:379–405.CAS 
PubMed 

Google Scholar 
51.Fraebel DT, Mickalide H, Schnitkey D, Merritt J, Kuhlman TE, Kuehn S. Environment determines evolutionary trajectory in a constrained phenotypic space. Elife. 2017;6:e24669.PubMed 
PubMed Central 

Google Scholar 
52.Yi X, Dean AM. Phenotypic plasticity as an adaptation to a functional trade-off. Elife. 2016;5:e19307.PubMed 
PubMed Central 

Google Scholar 
53.van Ditmarsch D, Boyle KE, Sakhtah H, Oyler JE, Nadell CD, Déziel É, et al. Convergent evolution of hyperswarming leads to impaired biofilm formation in pathogenic bacteria. Cell Rep. 2013;4:697–708.PubMed 
PubMed Central 

Google Scholar 
54.Auer GK, Oliver PM, Rajendram M, Lin T-Y, Yao Q, Jensen GJ, et al. Bacterial swarming reduces Proteus mirabilis and Vibrio parahaemolyticus cell stiffness and increases β-Lactam susceptibility. mBio. 2019;10:e00210–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
55.Kaiser D. Bacterial swarming: a re-examination of cell-movement patterns. Curr Biol. 2007;17:R561–R70.CAS 
PubMed 

Google Scholar 
56.Inoue T, Shingaki R, Hirose S, Waki K, Mori H, Fukui K. Genome-wide screening of genes required for swarming motility in Escherichia coli K-12. J Bacteriol. 2007;189:950–7.CAS 
PubMed 

Google Scholar 
57.Dong T, Joyce C, Schellhorn H. The role of RpoS in bacterial adaptation. In: El-Sharoud W, editor. Bacterial physiology. Heidelberg: Springer, Berlin; 2008. pp 313-37.58.Phaiboun A, Zhang Y, Park B, Kim M. Survival kinetics of starving bacteria is biphasic and density-dependent. PLoS Comput Biol. 2015;11:e1004198.PubMed 
PubMed Central 

Google Scholar 
59.Majdalani N, Hernandez D, Gottesman S. Regulation and mode of action of the second small RNA activator of RpoS translation, RprA. Mol Microbiol. 2002;46:813–26.CAS 
PubMed 

Google Scholar 
60.Peterson CN, Carabetta VJ, Chowdhury T, Silhavy TJ. LrhA regulates rpoS translation in response to the Rcs phosphorelay system in Escherichia coli. J Bacteriol. 2006;188:3175–81.CAS 
PubMed 
PubMed Central 

Google Scholar 
61.Lok T, Overdijk O, Piersma T. The cost of migration: spoonbills suffer higher mortality during trans-Saharan spring migrations only. Biol Lett. 2015;11:20140944.PubMed 
PubMed Central 

Google Scholar 
62.Flack A, Fiedler W, Blas J, Pokrovsky I, Kaatz M, Mitropolsky M, et al. Costs of migratory decisions: a comparison across eight white stork populations. Sci Adv. 2016;2:e1500931.PubMed 
PubMed Central 

Google Scholar 
63.Rankin MA, Burchsted JCA. The cost of migration in insects. Annu Rev Entomol. 1992;37:533–59.
Google Scholar 
64.Ni B, Colin R, Link H, Endres RG, Sourjik V. Growth-rate dependent resource investment in bacterial motile behavior quantitatively follows potential benefit of chemotaxis. Proc Natl Acad Sci USA. 2020;117:595–601.CAS 
PubMed 

Google Scholar 
65.Amsler CD, Cho M, Matsumura P. Multiple factors underlying the maximum motility of Escherichia coli as cultures enter post-exponential growth. J Bacteriol. 1993;175:6238–44.CAS 
PubMed 
PubMed Central 

Google Scholar 
66.Yokota T, Gots JS. Requirement of adenosine 3’, 5’-cyclic phosphate for flagella formation in Escherichia coli and Salmonella typhimurium. J Bacteriol. 1970;103:513–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Soutourina O, Kolb A, Krin E, Laurent-Winter C, Rimsky S, Danchin A, et al. Multiple control of flagellum biosynthesis in Escherichia coli: role of H-NS protein and the cyclic AMP-catabolite activator protein complex in transcription of the flhDC master operon. J Bacteriol. 1999;181:7500–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
68.Silverman M, Simon M. Characterization of Escherichia coli flagellar mutants that are insensitive to catabolite repression. J Bacteriol. 1974;120:1196–203.CAS 
PubMed 
PubMed Central 

Google Scholar 
69.Mitrophanov AY, Groisman EA. Positive feedback in cellular control systems. Bioessays. 2008;30:542–55.CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Raj A, van Oudenaarden A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell. 2008;135:216–26.71.Ferrières L, Clarke DJ. The RcsC sensor kinase is required for normal biofilm formation in Escherichia coli K-12 and controls the expression of a regulon in response to growth on a solid surface. Mol Microbiol. 2003;50:1665–82.PubMed 

Google Scholar 
72.Guttenplan SB, Kearns DB. Regulation of flagellar motility during biofilm formation. FEMS Microbiol Rev. 2013;37:849–71.CAS 

Google Scholar  More

1.Gelfand, I. et al. Sustainable bioenergy production from marginal lands in the US Midwest. Nature 493, 514–517 (2013).CAS 
PubMed 
Article 

Google Scholar 
2.Sprunger, C. D. & Philip Robertson, G. Early accumulation of active fraction soil carbon in newly established cellulosic biofuel systems. Geoderma 318, 42–51 (2018).CAS 
Article 

Google Scholar 
3.DuPont, S. T. et al. Root traits and soil properties in harvested perennial grassland, annual wheat, and never-tilled annual wheat. Plant Soil 381, 405–420 (2014).CAS 
Article 

Google Scholar 
4.Robertson, G. P. et al. Cellulosic biofuel contributions to a sustainable energy future: Choices and outcomes. Science 356, 6375. https://doi.org/10.1126/science.aal2324 (2017).CAS 
Article 

Google Scholar 
5.Kravchenko, A. N. et al. Microbial spatial footprint as a driver of soil carbon stabilization. Nat. Commun. https://doi.org/10.1038/s41467-019-11057-4 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
6.Yang, Y., Tilman, D., Furey, G. & Lehman, C. Soil carbon sequestration accelerated by restoration of grassland biodiversity. Nat. Commun. 10, 1–7 (2019).Article 

Google Scholar 
7.Lange, M. et al. Plant diversity increases soil microbial activity and soil carbon storage. Nat. Commun. 6, 1–8 (2015).
Google Scholar 
8.Young, I. M. & Crawford, J. W. Interactions and self-organization in the soil-microbe complex. Science 304, 1634–1637 (2004).CAS 
PubMed 
Article 

Google Scholar 
9.Rabot, E., Wiesmeier, M., Schlüter, S. & Vogel, H. J. Soil structure as an indicator of soil functions: A review. Geoderma 314, 122–137 (2018).Article 

Google Scholar 
10.Pohl, M., Alig, D., Körner, C. & Rixen, C. Higher plant diversity enhances soil stability in disturbed alpine ecosystems. Plant Soil 324, 91–102 (2009).CAS 
Article 

Google Scholar 
11.Bodner, G., Leitner, D. & Kaul, H. P. Coarse and fine root plants affect pore size distributions differently. Plant Soil 380, 133–151 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Bacq-Labreuil, A., Crawford, J., Mooney, S. J., Neal, A. L. & Ritz, K. Cover crop species have contrasting influence upon soil structural genesis and microbial community phenotype. Sci. Rep. 9, 1–9 (2019).CAS 
Article 

Google Scholar 
13.Kravchenko, A. N. et al. X-ray computed tomography to predict soil N2O production via bacterial denitrification and N2O emission in contrasting bioenergy cropping systems. GCB Bioenergy 10, 894–909 (2018).CAS 
Article 

Google Scholar 
14.Cambardella, C. A. & Elliott, E. T. Particulate soil organic-matter changes across a grassland cultivation sequence. Soil Sci. Soc. Am. J. 56, 777–783 (1992).Article 

Google Scholar 
15.Gregorich, E. G., Beare, M. H., McKim, U. F. & Skjemstad, J. O. Chemical and biological characteristics of physically uncomplexed organic matter. Soil Sci. Soc. Am. J. 70, 975–985 (2006).CAS 
Article 

Google Scholar 
16.Besnard, E., Chenu, C., Balesdent, J., Puget, P. & Arrouays, D. Fate of particulate organic matter in soil aggregates during cultivation. Eur. J. Soil Sci. 47, 495–503 (1996).CAS 
Article 

Google Scholar 
17.Haddix, M. L. et al. Climate, carbon content, and soil texture control the independent formation and persistence of particulate and mineral-associated organic matter in soil. Geoderma 363, 114160 (2020).CAS 
Article 

Google Scholar 
18.Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: Concept & review. Soil Biol. Biochem. 83, 184–199 (2015).CAS 
Article 

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

Google Scholar 
20.Shi, P. et al. The effects of ecological construction and topography on soil organic carbon and total nitrogen in the Loess Plateau of China. Environ. Earth Sci. 78, 1–8 (2019).Article 

Google Scholar 
21.Cnudde, V. & Boone, M. N. High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications. Earth-Science Rev. 123, 1–17 (2013).Article 

Google Scholar 
22.Wang, W., Kravchenko, A. N., Smucker, A. J. M., Liang, W. & Rivers, M. L. Intra-aggregate pore characteristics: X-ray computed microtomography analysis. Soil Sci. Soc. Am. J. 76, 1159–1171 (2012).CAS 
Article 

Google Scholar 
23.Diel, J., Vogel, H. J. & Schlüter, S. Impact of wetting and drying cycles on soil structure dynamics. Geoderma 345, 63–71 (2019).CAS 
Article 

Google Scholar 
24.Pires, L. F., Auler, A. C., Roque, W. L. & Mooney, S. J. X-ray microtomography analysis of soil pore structure dynamics under wetting and drying cycles. Geoderma 362, 114103 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
25.Negassa, W. C. et al. Properties of soil pore space regulate pathways of plant residue decomposition and community structure of associated bacteria. PLoS ONE 10, 1–22 (2015).Article 

Google Scholar 
26.Quigley, M. Y., Negassa, W. C., Guber, A. K., Rivers, M. L. & Kravchenko, A. N. Influence of pore characteristics on the fate and distribution of newly added carbon. Front. Environ. Sci. 6, 1–13 (2018).Article 

Google Scholar 
27.Juyal, A., Otten, W., Baveye, P. C. & Eickhorst, T. Influence of soil structure on the spread of Pseudomonas fluorescens in soil at microscale. Eur. J. Soil Sci. 72, 141–153 (2021).CAS 
Article 

Google Scholar 
28.Kravchenko, A. N., Negassa, W., Guber, A. K. & Schmidt, S. New approach to measure soil particulate organic matter in intact samples using X-ray computed microtomography. Soil Sci. Soc. Am. J. 78, 1177–1185 (2014).Article 

Google Scholar 
29.Peth, S. et al. Localization of soil organic matter in soil aggregates using synchrotron-based X-ray microtomography. Soil Biol. Biochem. 78, 189–194 (2014).CAS 
Article 

Google Scholar 
30.Gee, G. W. & Or, D. 2.4 Particle-Size Analysis (Soil Science Society of America, 2018).
Google Scholar 
31.Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).CAS 
PubMed 
Article 

Google Scholar 
32.Münch, B. & Holzer, L. Contradicting geometrical concepts in pore size analysis attained with electron microscopy and mercury intrusion. J. Am. Ceram. Soc. 91, 4059–4067 (2008).Article 

Google Scholar 
33.Houston, A. N., Otten, W., Baveye, P. C. & Hapca, S. Adaptive-window indicator kriging: A thresholding method for computed tomography images of porous media. Comput. Geosci. 54, 239–248 (2013).Article 

Google Scholar 
34.Doube, M. et al. BoneJ: Free and extensible bone image analysis in ImageJ. Bone 47, 1076–1079 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Houston, A. N. et al. Effect of scanning and image reconstruction settings in X-ray computed microtomography on quality and segmentation of 3D soil images. Geoderma 207–208, 154–165 (2013).Article 

Google Scholar 
36.Milliken, G. A. & Johnson, D. E. Analysis of Messy Data, Volume II: Nonreplicated experiments. Analysis of Messy Data, Volume II: Nonreplicated Experiments (Chaoman/CRC Press, 2017).Book 

Google Scholar 
37.Ladoni, M., Basir, A., Robertson, P. G. & Kravchenko, A. N. Scaling-up: Cover crops differentially influence soil carbon in agricultural fields with diverse topography. Agric. Ecosyst. Environ. 225, 93–103 (2016).Article 

Google Scholar 
38.Ontl, T. A., Hofmockel, K. S., Cambardella, C. A., Schulte, L. A. & Kolka, R. K. Topographic and soil influences on root productivity of three bioenergy cropping systems. New Phytol. 199, 727–737 (2013).CAS 
PubMed 
Article 

Google Scholar 
39.Zhu, M. et al. Effects of topography on soil organic carbon stocks in grasslands of a semiarid alpine region, northwestern China. J. Soils Sediments 19, 1640–1650 (2019).CAS 
Article 

Google Scholar 
40.Shi, P. et al. Land-use types and slope topography affect the soil labile carbon fractions in the Loess hilly-gully area of Shaanxi, China. Arch. Agron. Soil Sci. 66, 638–650 (2020).CAS 
Article 

Google Scholar 
41.Ontl, T. A., Cambardella, C. A., Schulte, L. A. & Kolka, R. K. Factors influencing soil aggregation and particulate organic matter responses to bioenergy crops across a topographic gradient. Geoderma 255–256, 1–11 (2015).Article 

Google Scholar 
42.Kravchenko, A. N. et al. Spatial patterns of extracellular enzymes: Combining X-ray computed micro-tomography and 2D zymography. Soil Biol. Biochem. 135, 411–419 (2019).CAS 
Article 

Google Scholar 
43.Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: Do labile plant inputs form stable soil organic matter?. Glob. Change Biol. 19, 988–995 (2013).Article 

Google Scholar 
44.Oades, J. M. The role of biology in the formation, stabilization and degradation of soil structure. Geoderma 56, 377–400 (1993).Article 

Google Scholar 
45.Kravchenko, A. N. & Guber, A. K. Soil pores and their contributions to soil carbon processes. Geoderma 287, 31–39 (2017).CAS 
Article 

Google Scholar 
46.Wickings, K., Grandy, A. S. & Kravchenko, A. N. Going with the flow: Landscape position drives differences in microbial biomass and activity in conventional, low input, and organic agricultural systems in the Midwestern U.S. Agric. Ecosyst. Environ. 218, 1–10 (2016).Article 

Google Scholar 
47.da Jesus, E. C. et al. Influence of corn, switchgrass, and prairie cropping systems on soil microbial communities in the upper Midwest of the United States. GCB Bioenergy 8, 481–494 (2016).CAS 
Article 

Google Scholar 
48.Poirier, V., Roumet, C. & Munson, A. D. The root of the matter: Linking root traits and soil organic matter stabilization processes. Soil Biol. Biochem. 120, 246–259 (2018).CAS 
Article 

Google Scholar 
49.Toosi, E. R., Kravchenko, A. N., Guber, A. K. & Rivers, M. L. Pore characteristics regulate priming and fate of carbon from plant residue. Soil Biol. Biochem. 113, 219–230 (2017).CAS 
Article 

Google Scholar  More

Eukaryote composition (V9_PR2)Using V9_PR2 we were able to assign a total of 15 668 (shotgun) and 90 689 reads for the shotgun and amplicon data, respectively. These reads represented 14%, 54%, 0 and 32% (shotgun), and 0%, 29%, 0 and 71% (amplicon) unassigned cellular organisms, Bacteria, Archaea and Eukaryota, respectively. Within the eukaryotes, we determined 51 and 64 taxa for shotgun and amplicon data, respectively. Abundant taxa (average abundance >0.1% across all samples; 31 and 27 taxa in shotgun and amplicon, respectively) are shown in Fig. 2. The latter includes 23 taxa (including assignments made on “Eukaryota” level) that were shared between shotgun and amplicon, and four taxa only detected in the amplicon data (Fig. 2C).Fig. 2: Eukaryote composition in five Santa Barbara Basin sediment samples post-alignment with V9_PR2 database.Composition is shown in relative abundances for (A) shotgun, and (B) amplicon data (phylum-level). The surface sample should be considered with caution in both (A) and (B) due to the possibility of contamination (see “Methods”). C Venn diagram showing eukaryote taxa richness (phylum level) in the shotgun and amplicon data after alignment with the V9_PR2 database (diagram areas are proportional to the total number of taxa included, for a list of shared/non-shared taxa see Supplementary Material Fig. 1). Only taxa abundant on average >0.1% are included, as they make up >99% of the eukaryote composition.Full size imageWithin shotgun, the most abundant eukaryotes were Ascomycota (53%), Telonemia (11%), Eukaryota (not further determined, 8%), Polycystinea (4%), Dinophyceae (3.8%), Streptophyta (3.2%), Amoebozoa (3%), Cercozoa (1.6%), Bacillariophyta (1.6%), Arthropoda (1%). In the amplicon data, the most abundant eukaryotes were Ascomycota (33%), Apicomplexa (30%), Dinophyceae (9.5%), Stramenopiles (6.3%), Eukaryota (4.9%), Polycystinea (3.5%), Foraminifera (3.2%), Cercozoa (1.1%) and Chordata (1%). Thus, a total of 10 and 9 taxa were abundant with >1% (average across all samples) in the shotgun and amplicon data, including only five taxa (Ascomycota, Eukaryota, Dinophyceae, Polycystinea, Cercozoa) that were picked up by both methods (i.e., are amongst the shared taxa in Fig. 2C, Supplementary Material Fig. 1). Taxa detected by one method or the other were slightly rarer species (between 0.1 and 1% average relative abundance across all samples; Supplementary Material Table 3).The shotgun EBC detected two taxonomic groups, one prokaryotic (Gammaproteobacteria) and one eukaryotic (Poacea). The amplicon EBC detected 46 taxa, of which 12 were prokaryotes and 34 were eukaryotes, including dinoflagellate taxa (Dinophysis and Alexandrium), Calanoida and Bacillariophyta (copepods and diatoms, respectively; Supplementary Material Table 1). While any reads assigned to EBC taxa were removed from samples, including reads assigned to the Bacillariophyta node, reads assigned to Bacillariophyta at lower taxonomic levels (e.g., Bacillariophycidae, Bacillariaceae, etc.) remain summarised under the phylum-level Bacillariophyta node (Fig. 2).Relationship between Eukaryota composition and V9_PR2 reference sequence lengthV9_PR2 reference sequence-lengths for the relatively abundant taxa ( >0.1% across all samples, including all taxa that were shared and assigned below eukaryote-level, i.e., 22 taxa, see Supplementary Material Table 3) were around the overall average sequence length of the V9_PR2 database (121 bp) (Fig. 3). However, considerable length variation was observed, with most of the abundant taxa being represented by shorter than average reference sequences in the V9_PR2 database, and a few taxa (e.g., Arthropoda, Opisthokonta and Amoebozoa) with a number of reference sequences longer than average (Fig. 3).Fig. 3: Average sequence lengths for individual eukaryote taxa as per in the V9_PR2 database (A) and read counts for these taxa in shotgun (SG) and amplicon (Ampl) data (B).Listed are all taxa that occurred on average >0.1% across all samples in either the shotgun or amplicon dataset, or both. Only taxa that were determined in both shotgun and amplicon data are included.Full size imageWe determined a negative correlation between the average V9_PR2 reference sequence length (V9PR2AL) and the A:SG read counts ratio per taxon for all samples (rV9PR2AL,A:SG_1.2 = −0.27269, rV9PR2AL,A:SG_4.3 = −0.33233, rV9PR2AL,A:SG_7.3 = −0.28064, rV9PR2AL,A:SG_11.8 = −0.32559, rV9PR2AL,A:SG_16.4 = −0.30078). This means that shorter V9_PR2 reference sequences for our abundant taxa were associated with an overamplification of these taxa in the amplicon data (for average V9_PR2 reference sequence length of the abundant taxa and A:SG ratios see Supplementary Material Table 4).Eukaryota and Bacillariophyta sequence length and coverage post-V9_PR2 alignmentSequences assigned to Eukaryota in shotgun were on average 112 bp and in amplicon data 161 bp, i.e., shotgun reads were around ~50 bp shorter than amplicon reads (Table 2). Bases covered in shotgun were ~40 bp shorter than in amplicon data (Table 2). Similarly, sequences assigned to Bacillariophyta were on average 124 and 167 bp in shotgun and amplicon data, respectively, so showed an ~40 bp difference. For Eukaryota, there was a difference of ~23 bp and 29 bp between sequence length and coverage in shotgun and amplicon data, respectively. For Bacillariophyta, we found a ~36 and ~37 bp difference between sequence length and coverage in shotgun and amplicon data, respectively.Table 2 Lengths and coverage of sequences assigned to Eukaryota and Bacillariophyta in shotgun and amplicon data.Full size tableBacillariophyta read lengths and coverage were similar to those of Eukaryota, for both shotgun and amplicon data (Table 2). Variation in sequence lengths and coverage was much higher in shotgun than in amplicon data. We found no trend towards shorter (i.e., more fragmented) sequences with increasing subseafloor depth for either Eukaryota or Bacillariophyta in the shotgun data. Eukaryota shotgun read lengths were on average ~9 bp shorter (112 bp) than the average reference sequences in the V9_PR2 database (121 bp).Diatom composition detected via diat-rbcL and read length characteristicsA total of 60 (shotgun) and 80 674 (amplicon) reads were assigned to diatoms (Fig. 4). In total, 27 taxa were determined in the shotgun, and 140 in the amplicon dataset. When considering the “abundant” taxa (on average >0.1%), 27 and 49 diatoms were determined in the shotgun and amplicon data, respectively (Fig. 4). A total of 10 taxa were shared between the two datasets Bacillariophyta, Bacillariophycidae, Chaetoceros, C. cf. pseudobrevis 2 SEH-2013, Pseudo-nitzschia, P. fryxelliana, Thalassiosiraceae, Thalassiosirales, Thalassiosira and T. oceanica (Fig. 4C, Supplementary Material Fig. 2). Sequences assigned to diatoms via diat-rbcL were shorter (by ~16 bp) in the shotgun than in the amplicon data, with amplicon read lengths and coverage all 76 + 1 bases (Table 3).Fig. 4: Diatom composition in the Santa Barbara Basin sediment samples post-alignment with diat-rbcL database.Diatom composition is shown as relative abundance for (A) shotgun and (B) amplicon data. The surface sample should be considered with caution in both (A) and (B) due to the possibility of contamination (see “Methods”). C Venn diagram showing diatom taxa richness (species level) in the shotgun and amplicon data after alignment with the diat-rbcL database (diagram areas are proportional to the total number of taxa included, for a list of shared/non-shared taxa see Supplementary Material Fig. 2). Only taxa abundant on average >0.1% are included (in A, B, C).Full size imageTable 3 Bacillariophyta sequence lengths in shotgun and amplicon datasets.Full size tableNo diatoms were detected in the shotgun EBC, however, 45 taxa were determined in the amplicon EBC with most reads assigned to Chaetoceros spp. (especially, Chaetoceros debilis, C. socialis and C. radicans), several Thalassiosira and Pseudo-nitzschia species, as well as others (Supplementary Material Table 2).Comparison of V9_PR2 vs. diat-rbcL derived diatom compositionIn the shotgun data, 79 and 60 sequences were assigned to diatoms using V9_PR2 and diat-rbcL as the reference database, respectively, and composition differed considerably (Fig. 5). Using V9_PR2, diatoms were mostly assigned on relatively high taxonomic levels (e.g., Bacillariophyta) with few taxa being differentiated sporadically in the different samples (Fig. 5A, Supplementary Material Fig. 3). Using diat-rbcL, Chaetoceros, Thalassiosira and Pseudo-nitzschia were more prominent (Fig. 5B).Fig. 5: Comparison of diatom composition in Santa Barbara Basin sediment samples determined in shotgun and amplicon data using the V9_PR2 and diat-rbcL databases.Relative abundance of diatoms (genus level) in the shotgun data after aligning to (A) V9_PR2 and (B) diat-rbcL. Relative abundance of diatoms (genus level) in the amplicon data after aligning to (C) V9_PR2 and (D) diat-rbcL. The surface sample should be considered with caution in (A–D) due to the possibility of contamination (see “Methods”). Venn diagrams of shared and non-shared diatom taxa after alignment to the V9_PR2 (18S-V9) and diat-rbcL databases for the shotgun (E) and amplicon (F) data (species level, diagram areas are proportional to the total number of species included). For a complete species list and their read counts per sample see Supplementary Material Fig. 3, Supplementary Material Table 5.Full size imageIn the amplicon data, 329 sequences were assigned to diatoms using V9_PR2, and 80 674 using diat-rbcL. Using V9_PR2, few taxa were detected in the two top samples (Leptocylindrus and Fragilariaceae at 1.2 mbsf, Bacillariophycidae and Bacillariaceae at 4.3 mbsf) while the lowermost samples were more diverse (Fig. 5C). Using diat-rbcL, most reads were assigned to Thalassiosira, Chaetoceros, and Pseudo-nitzschia, with other taxa sporadically occurring at different depths (Fig. 5D). For a complete species list and their read counts see Supplementary Material Fig. 3, and Supplementary Material Table 5.We found large differences in the number of shared vs. non-shared taxa between shotgun and amplicon data, and V9_PR2 and diat-rbcL alignments (Fig. 5E, F). Database inspections showed that all taxa detected via V9_PR2 were also represented in the diat-rbcL database, except Rhizosoleniaceae. However, out of the 22 taxa exclusively detected via diat-rbcL in shotgun (Fig. 5E, F), 10 are only represented in the diat-rbcL database (Pseudo-nitzschia caciantha, P. dolorosa, Chaetoceros cf. contortus 1 SEH-2013, C. cf. lorenzianus 2 SEH-2013, C. cf. pseudobrevis 2 SEH-2013, Thalassiosirales, Thalassiosiraceae, Coscinodiscus wailesii, Arcocellulus mammifer, Meuniera membranacea, Supplementary Material Fig. 3). Similarly, out of the 134 taxa exclusively detected via diat-rbcl in amplicon, 84 were in this database only, noticeably including several species and strains of Chaetoceros, Pseudo-nitzschia, Thalassiosira and Cylindrotheca (eg., additions SHE-2013, BOF in species names), amongst others (see Supplementary Material Fig. 3, Supplementary Material Table 5). More

The Wuikinuxv, Kitasoo/Xai’xais, Heiltsuk and Nuxalk First Nations hold Indigenous rights to their territories, where all data were collected. Scientific staff who are members of these Nations or who work directly for them had direct approvals from Indigenous rights holders and were exempt from other research permit requirements. Collaborating DFO scientists worked in partnership with the First Nations to collect data in their territories..Sampling targeted rocky reefs, the preferred habitat for most Sebastidae38, which we located through local Indigenous knowledge or a bathymetric model49. Data were collected by four fishery-independent methods—shallow diver transects, mid-depth video transects, deep video transects, and hook-and-line sampling—detailed in earlier publications32,33,34,35,50,51 and summarized in Table 1. Data had a spatial resolution of ≤ 130 m2 and each sampling location (N = 2936 for Sebastidae, 2654 for sponges, 2321 for corals) was ascribed to a 1-km2 planning unit within the standardized grid used to design the MPA network (N = 632 for Sebastidae, 525 for sponges, 529 for corals, 516 inclusive of surveys for all taxonomic groups).Table 1 Survey methods used for data collection.Full size tableAlthough sampling encompassed 11 years (2006–2007, 2013–2021: Table 1), 84% of 1-km2 planning units were sampled during only one year (Appendix S2). Analyses, therefore, focus on spatial variability in species distributions and do not address temporal variability within planning units. When all years and methods are combined, 1-km2 planning units had a median of 3 samples (range = 1 to 80, Q1 = 2, Q3 = 6) (i.e., sum of dive transects, video sub-transects, and hook-and-line sessions). Supplementary Data Set 1 reports sampling effort by 1-km2 planning unit, survey type, and year (see Data Availability for link to these data).For each 1-km2 planning unit, u, we calculated hotspot indices for Sebastidae (BSEB,u), structural corals (BCor,u), and large-bodied sponges (BSp,u). These indices did not consider cup corals, whip-like corals or encrusting corals or sponges.As detailed below (Eqs. 1–4), each species of Sebastidae or genera of corals contributed to BSEB,u or BCor,u, according to their abundance weighted by Wt: a conservation prioritization score based on taxon characteristics. For the 26 species of Sebastidae that we observed, Wt equaled the sum of scores for (1) fishery vulnerability, using intrinsic population growth rate, r, as a proxy variable52,53, (2) depletion level, using the ratio of recent biomass to unfished biomass as a proxy variable, (3) ecological role, with trophic level as proxy, and (4) evolutionary distinctiveness14 (Table 2; Appendix S3). Because several rockfishes are very long-lived (i.e., have low values for r) and depleted, maximum potential scores were twice as large for fishery vulnerability and depletion level than for ecological role and evolutionary distinctiveness. Data for depletion level and evolutionary distinctiveness were unavailable for some species, and score calculations (detailed in Table 2) account for missing values (Appendix S3).Table 2 Criteria and equations used to calculate the conservation prioritization score, Wt, for each species of Sebastidae and for each taxa of structural corals.Full size tableFor the 6 genera of structural corals analyzed (Appendix S4), Wt depended on mean height (estimated from video transect images: Table 1), which correlates positively with vulnerability to physical damage from bottom-contact fishing gear (including longer time to recovery)20,54,55 and with strength of ecological role (e.g., amount of biogenic habitat and carbon sequestration increases with height)44,56 (Table 2, Appendix S4). Wt for corals did not include depletion level due to lack of data.The hotspot index for large-bodied sponges, BSp,u did not differentiate between species characteristics (i.e., ({W}_{t}=1)) and we pooled the abundances of all observed species of Hexactinellidae (Aphrocallistes vastus, Farrea occa, Heterochone calyx, Rhabdocalyptus dawsoni, Staurocalyptus dowlingi) and Demospongiae (Mycale cf loveni). This approach is consistent with regional fishery bodies worldwide, which treat large-bodied sponges as a single functional group57.To derive hotspot indices for each taxonomic group (Sebastidae, structural corals, or large-bodied sponges), we first developed a set of candidate generalized linear mixed models (GLMM) to explain relative abundance data for rockfish, corals, and sponges. For each GLMM, we estimated ({lambda }_{t,i,l}), the expected counts (or expected percent cover) for taxa t obtained with survey method i at point location l. (Point locations are individual dive transects, video transect bins, or hook-and-line timed sessions: Table 1.) Specifically,$${lambda }_{t,i,l}=gleft(beta {X}_{t,i,l}right)$$
(1)
$${C}_{t,i,l}mathrm {, or ,} {D}_{t,i,l}sim fleft({lambda }_{t,i,l}right)$$
(2)
where g was the link function for the GLMM and f the distribution for the likelihood function modelling either the observed counts C (negative binomial) for Sebastidae and structural corals or a combination of counts (negative binomial) and percent cover D (beta distribution) for large-bodied sponges. We used multiple GLMMs to model large-bodied sponges because deep video transects recorded actual counts whereas dive or mid-depth video transects recorded percent cover categories (Table 1).For each taxonomic group, we estimated a set of coefficients (beta) for the vector of X covariates that best estimated counts or percent cover. Our hypothesized covariates included the 1-km2 planning unit (modelled as a random intercept to control for repeated measures within a given planning unit), survey method, depth (including both linear or a 2nd order polynomial), and taxa. Each GLMM controlled for sample effort as an offset—effort was measured either as area covered by dive transects or video bins, or the duration of hook-and-line sessions. We also tested for possible covariate’s effects on the dispersion parameter (for the negative binomial GLMMs) and zero-inflation terms (for both the negative binomial and beta GLMMs). The best set of covariates to predict counts or percent cover were then chosen based on AIC model selection criteria. All models were fitted using ‘glmmTMB’58 in R version 4.0.259, and simulated residuals and diagnostic tests performed for each best-fit model using the package ‘DHARMa’60. For example, our best model for Sebastidae counts predicted 2% fewer zero counts than were observed.We applied depth and survey method selectivity criteria to reduce excessive zeroes in the count data that may be biologically unjustified (Appendix S5). For all taxon, if i detected t, then the method was valid for that taxon. If i did not detect t and t is a Sebastidae, then the method was valid (i.e., count = 0) only if the overall 10th and 90th percentiles of depths sampled by that method encompassed the expected depth range of t (Appendix S5). If i did not detect t and t is a coral or sponge (which are rarer than Sebastidae), then the method is valid only if the depth of the sampling event exceeded or equaled the minimum expected depth of t. Also, hook-and-line gear cannot systematically sample sessile benthic organisms or planktivores and this method was valid only for non-planktivorous Sebastidae (Appendix S5).Using the best-fit models from above, we calculated the expected count (or percent cover) per unit of effort, (mu), for taxa t observed with method i at each planning unit u:$${mu }_{t,i,u}=frac{{sum }_{l=1}^{{n}_{i,u}}left({lambda }_{t,i,l}right)}{{sum }_{l=1}^{{n}_{i,u}}left({mathrm{E}}_{t,i,l}right)}$$
(3)
where ({n}_{i,u}) was the total number of point locations sampled by that method within the planning unit and effort was either the cumulative area covered by dive or video surveys or the cumulative duration of hook-and-line sampling sessions within the planning unit. Because survey methods differed in their maximum values and potential biases (e.g., field of view is greater for divers than for video cameras; hook-and-line gear samples one fish at a time while visual methods can observe multiple fish simultaneously),({mu }_{t,i,u}) was rescaled as a min–max normalization,({mu }_{t,i,u}^{^{prime}}) (i.e., difference between the observed value and the minimum value across all u, divided by the range of values across all u).The hotspot index for each of Sebastidae, structural corals, and large-bodied sponges (denoted as taxonomic group g) was then calculated for each planning unit as:$${B}_{g,u }={sum }_{t=1}^{{n}_{s,g}}{sum }_{i=1}^{{n}_{m,g}}{mu }_{t,i,u}^{^{prime}}{W}_{t}$$
(4)
where Wt was the taxon-specific weighing factor (Table 2, Appendices S3, S4), ({n}_{s,g}) was the number of species in taxonomic group g, and ({n}_{m,g}) was the number of valid methods to sample group g.For each 1-km2 planning unit where all taxonomic groups were surveyed (N = 518), we then calculated the overall hotspot index:$${B}_{o,u }=H{sum }_{g=1}^{G}{B}_{g,u}.$$
(5)
where H is Shannon’s evenness index, with proportional abundance of each taxonomic group represented by BSEB,u, BCor,u, and BSp,u.Hotspot index values were normalized as the proportion of the maximum value and converted to decile ranks. Relationships between decile ranks and index values were nonlinear (Appendix S6).For Sebastidae, large-bodied sponges, and the overall hotspot index, we defined hotspots as planning units containing decile ranks 9 or 10: criterion which we deemed appropriate for the small spatial scales of conservation planning being used for the central portion of the Northern Shelf Bioregion (16-km2 planning units in Fig. 2). We are aware that other studies define hotspots based on a narrower range of values (e.g., top 10%26; top 2.5%28) but their context is generally one in which conservation planning is done at a much greater scale (e.g., ≈50,000-km2 grid cells26;1° latitude × 1° longitude grid cells28). For structural corals, which had near-zero index values in all but the top-ranking planning units (Appendix S6), we defined hotspots as planning units containing decile rank 10.Maximum depths sampled within planning units were deepest in the Mainland Fjord and shallowest in the Aristazabal Banks Upwelling Upper Ocean Subregion (Appendix S7). Accordingly, we used multiple logistic regression implemented with the ‘glm’ function in R to estimate the probabilities hotspot occurrence within 1-km2 planning units in relation to maximum depth sampled (including a 2nd-order polynomial) and Upper Ocean Subregion. Competing models were compared with AIC model selection procedures.Following the directive of Central Coast First Nations, decile rank distributions were mapped as 16-km2 planning units, u16 (N = 283 for Sebastidae, 264 for sponges, 263 for corals, 260 inclusive of surveys for all taxonomic groups), thereby protecting sensitive locations that would be revealed at smaller scales. To do so, we took the average between the maximum index value and the mean of the remainder of index values among the 1-km2 planning units, u, contained within each u16, and converted these values into decile ranks. This approach balances conservation prioritization among u16 that may have good average index values for multiple u, and u16 with a single high-ranking u among multiple low-scoring u. Relationships between decile ranks and hotspot index values also were nonlinear at this scale (Appendix S6). The same hotspot definitions developed for u apply to u16.Eighty one percent of 16-km2 planning units were sampled during only one or two years (Appendix S2). When all years and methods are combined, 16-km2 planning units had a median of 6 samples (range = 1 to 110, Q1 = 3, Q3 = 13). Supplementary Data Set 2 reports sampling effort by 16-km2 planning unit, survey type, and year (see Data Availability for link to these data). More

1.BirdLife International. Neophron percnopterus, Egyptian vulture. http://www.iucnredlist.org/details/22695180/0 (2017) https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T22695180A118600142.en2.Gradev, G., Garcia, V., Ivanov, I., Zhelev, P. & Kmetova, E. Data from Egyptian vultures (Neophron percnopterus) tagged with GPS/GSM transmitters in Bulgaria. Acta Zool. Bulg. 64, 141–146 (2012).
Google Scholar 
3.Green, R. E. et al. Diclofenac poisoning as a cause of vulture population declines across the Indian subcontinent. J. Appl. Ecol. 41, 793–800 (2004).CAS 
Article 

Google Scholar 
4.Arkumarev, V., Dobrev, V., Abebe, Y. D., Popgeorgiev, G. & Nikolov, S. C. Congregations of wintering Egyptian Vultures Neophron percnopterus in Afar, Ethiopia: Present status and implications for conservation. Ostrich 85, 139–145 (2014).Article 

Google Scholar 
5.Grubač, B., Velevski, M. & Avukatov, V. Long-term population decrease and recent breeding performance of the Egyptian vulture Neophron percnopterus in Macedonia. North. West. J. Zool. 10, 25–35 (2014).
Google Scholar 
6.Angelov, I., Hashim, I. & Oppel, S. Persistent electrocution mortality of Egyptian vultures neophron percnopterus over 28 years in East Africa. Bird Conserv. Int. 23, 1–6 (2013).Article 

Google Scholar 
7.Zuberogoitia, I., Zabala, J., Martínez, J. A., Martínez, J. E. & Azkona, A. Effect of human activities on Egyptian vulture breeding success. Anim. Conserv. 11, 313–320 (2008).Article 

Google Scholar 
8.Sen, B., Avares, J. P. & Bilgin, C. C. Nest site selection patterns of a local Egyptian Vulture Neophron percnopterus population in Turkey. Bird Conserv. Int. 27, 568–581 (2017).Article 

Google Scholar 
9.Ceballos, O. & Donázar, J. A. Factors influencing the breeding density and nest-site selection of the Egyptian vulture (Neophron percnopterus). J. Ornithol. 130, 353–359 (1989).Article 

Google Scholar 
10.Sarà, M. & Vittorio, M. Factors influencing the distribution, abundance and nest-site selection of an endangered Egyptian vulture (Neophron percnopterus) population in Sicily. Anim. Conserv. 6, 317–328 (2003).Article 

Google Scholar 
11.KC, K. B. et al. Factors influencing the presence of the endangered Egyptian vulture Neophron percnopterus in Rukum, Nepal. Glob. Ecol. Conserv. 20, e00727 (2019).Article 

Google Scholar 
12.Mateo-Tomás, P. & Olea, P. P. Livestock-driven land use change to model species distributions: Egyptian vulture as a case study. Ecol. Indic. 57, 331–340 (2015).Article 

Google Scholar 
13.García-RIPOLLÉS, C., López-LÓPEZ, P. & Urios, V. First description of migration and wintering of adult Egyptian vultures neophron percnopterus tracked by GPS satellite telemetry. Bird Study 57, 261–265 (2010).Article 

Google Scholar 
14.Oppel, S. et al. Landscape factors affecting territory occupancy and breeding success of Egyptian vultures on the Balkan Peninsula. J. Ornithol. 158, 443–457 (2017).Article 

Google Scholar 
15.Bhusal, K. Population status and breeding success of Himalayan Griffon, Egyption vulture and Lammergeier in Gherabhir, Arghakhanchi, Nepal. (MSc thesis. Institute of Science and Technology, Tribuvan University, Kritipur, Nepal, 2011). https://doi.org/10.13140/RG.2.2.18494.69447.16.López-lópez, A. P. et al. Food predictability determines space use of endangered vultures: Implications for management of supplementary feeding. Ecol. Appl. 24, 938–949 (2014).PubMed 
Article 

Google Scholar 
17.Cortés-avizanda, A., Ceballos, O. & Donázar, J. Long-term trends in population size and breeding success in the Egyptian Vulture (Neophron percnopterus) in Northern Spain. J. Raptor Res. 43, 43–49 (2009).Article 

Google Scholar 
18.Rosenblatt, E. Neophron percnopterus Egyptian vulture. Animal Diversity Web https://animaldiversity.org/accounts/Neophron_percnopterus/ (2007).19.ESRI. ArcGIS Desktop: Release 10.5. Environmental systems research Redlands, California, USA https://www.arcgis.com/features/index.html (2017).20.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
21.USGS/EarthExplorer. Data Sets. United States Geological Survey https://earthexplorer.usgs.gov/ (2017).22.JAXA EORC. Global PALSAR-2/PALSAR/JERS-1 Mosaic and Forest/Non-forest Map. Earth Observation Research Center https://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/data/index.htm (2017).23.CIESIN. Gridded population of the world (GPW), v4. http://sedac.ciesin.columbia.edu/data/collection/gpw-v4 (2000).24.Robinson, T. P. et al. Mapping the global distribution of livestock. PLoS ONE 9, e96084 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
25.FAO/GeoNetwork. Global land cover share database. http://www.fao.org/geonetwork/srv/en/main.home (2014).26.Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography (Cop.) 29, 129–151 (2006).Article 

Google Scholar 
27.Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modelling of species geographic distributions. Ecol. Modell. 190, 231–259 (2006).Article 

Google Scholar 
28.Lobo, J. M., Jiménez-valverde, A. & Real, R. AUC: a misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17, 145–151 (2008).Article 

Google Scholar 
29.Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models : Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).Article 

Google Scholar 
30.Pearce, J. & Ferrier, S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Modell. 133, 225–245 (2000).Article 

Google Scholar 
31.Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many?. Methods Ecol. Evol. 3, 327–338 (2012).Article 

Google Scholar 
32.Liu, C., White, M. & Newell, G. Selecting thresholds for the prediction of species occurrence with presence-only data. J. Biogeogr. 40, 778–789 (2013).Article 

Google Scholar 
33.Cortés-Avizanda, A., Martín-López, B., Ceballos, O. & Pereira, H. M. Stakeholders perceptions of the endangered Egyptian vulture: Insights for conservation. Biol. Conserv. 218, 173–180 (2018).Article 

Google Scholar 
34.Hernández, M. & Margalida, A. Poison-related mortality effects in the endangered Egyptian vulture (Neophron percnopterus) population in Spain. Eur. J. Wildl. Res. 55, 415–423 (2009).Article 

Google Scholar 
35.Mateo-Tomás, P., Olea, P. P. & Fombellida, I. Status of the Endangered Egyptian vulture Neophron percnopterus in the Cantabrian Mountains, Spain, and assessment of threats. Oryx 44, 434–440 (2010).Article 

Google Scholar 
36.Carrete, M. et al. Habitat, human pressure, and social behavior : Partialling out factors affecting large-scale territory extinction in an endangered vulture. Biol. Conserv. https://doi.org/10.1016/j.biocon.2006.11.025 (2007).Article 

Google Scholar 
37.Zuberogoitia, I., Zabala, J., Martínez, J. E., González-Oreja, J. A. & López-López, P. Effective conservation measures to mitigate the impact of human disturbances on the endangered Egyptian vulture. Anim. Conserv. 17, 410–418 (2014).Article 

Google Scholar 
38.Garcia-Ripolles, C. & Lopez-Lopez, P. Population size and breeding performance of Egyptian vultures (Neophron percnopterus) in eastern Iberian Peninsula. J. Raptor Res. 40, 217–221 (2006).Article 

Google Scholar 
39.Velevski, M., Grubac, B. & Tomovic, L. Population viability analysis of the Egyptian vulture Neophron percnopterus in Macedonia and Implications for Its Conservation. Acta Zool. Bulg. 66, 43–58 (2014).
Google Scholar 
40.Arkumarev, V. et al. Breeding performance and population trend of the Egyptian vulture Neophron percnopterus in Bulgaria conservation implications. Ornis Fenn. 95, 115–127 (2018).
Google Scholar 
41.Dobrev, V. et al. Habitat of the Egyptian vulture (Neophron percnopterus) in Bulgaria and Greece (2003–2014). (2016).42.Milchev, B., Spassov, N. & Popov, V. Diet of the Egyptian vulture (Neophron percnopterus) after livestock reduction in Eastern Bulgaria. N. West. J. Zool. 8, 315–323 (2012).
Google Scholar 
43.Milchev, B. & Georgiev, V. Extinction of the globally endangered Egyptian vulture Neophron percnopterus breeding in SE Bulgaria. N. West. J. Zool. 10, 266–272 (2014).
Google Scholar 
44.Poirazidis, K., Goutner, V., Skartsi, T. & Stamou, G. Modelling nesting habitat as a conservation tool for the Eurasian black vulture (Aegypius monachus) in Dadia Nature Reserve, northeastern Greece. Biol. Conserv. 118, 235–248 (2004).Article 

Google Scholar 
45.Sanchis Serra, A. et al. Towards the identification of a new taphonomic agent: An analysis of bone accumulations obtained from modern Egyptian vulture (Neophron percnopterus) nests. Quat. Int. 330, 136–149 (2014).Article 

Google Scholar 
46.Vittorio, M. D., Lopez-Lopez, P., Cortone, G. & Luiselli, L. The diet of the Egyptian vulture (Neophron percnopterus) in Sicily: Temporal variation and conservation implications. Vie Milieu Life Environ. 67, 1–8 (2017).
Google Scholar 
47.Di Vittorio, M. et al. Successful fostering of a captive-born Egyptian Vulture (Neophron Percnopterus) in Sicily. J. Raptor Res. 40, 247–248 (2006).Article 

Google Scholar 
48.Sarà, M., Grenci, S. & Vittorio, M. D. Status of Egyptian vulture (Neophron percnopterus) in Sicily. J. Raptor Res. 43, 66–69 (2009).Article 

Google Scholar 
49.Vittorio, M. D. et al. Dispersal of Egyptian vultures Neophron percnopterus: the first case of long-distance relocation of an individual from France to Sicily. Ringing Migr. 31, 111–114 (2016).Article 

Google Scholar 
50.García-Heras, M. S., Cortés-Avizanda, A. & Donázar, J. A. Who are we feeding? Asymmetric individual use of surplus food resources in an insular population of the endangered Egyptian vulture Neophron percnopterus. PLoS ONE 8, e80523 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
51.Gangoso, L. et al. Susceptibility to infection and immune response in insular and continental populations of Egyptian vulture: Implications for conservation. PLoS ONE 4, e6333 (2009).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
52.Donazar, J. A. et al. Conservation status and limiting factors in the endangered population of Egyptian vulture (Neophron percnopterus) in the Canary Islands Conservation status and limiting factors in the endangered population of Egyptian vulture ( Neophron percnopterus ) in. Biol. Conserv. 107, 89–97 (2002).Article 

Google Scholar 
53.Rodríguez, B., Rodríguez, A., Siverio, F. & Siverio, M. Factors affecting the spatial distribution and breeding habitat of an insular cliff-nesting raptor community. Curr. Zool. 64, 173–181 (2018).PubMed 
Article 

Google Scholar 
54.Kret, E. et al. First documented case of the killing of an egyptian vulture (Neophron Percnopterus) for belief-based practices in Western Africa. Life Environ. 68, 45–50 (2018).
Google Scholar 
55.Thouless, C. R., Fanshawe, J. H. & Bertram, B. C. R. Egyptian vultures Neophron percnopterus and Ostrich Struthio camelus eggs: the origins of stone-throwing behaviour. Ibis (Lond.) 131, 9–15 (1989).Article 

Google Scholar 
56.Cuthbert, R. et al. Rapid population declines of Egyptian vulture (Neophron percnopterus) and red-headed vulture (Sarcogyps calvus) in India. Anim. Conserv. 9, 349–354 (2006).Article 

Google Scholar 
57.Samson, A. & Ramakarishnan, B. Observation of a population of Egyptian Vultures Neophron percnopterus in Ramanagaram Hills, Karnataka, southern India. Vulture News 71, 36–49 (2016).Article 

Google Scholar 
58.Farashi, A. & Alizadeh-Noughani, M. Niche modelling of the potential distribution of the Egyptian Vulture Neophron percnopterus during summer and winter in Iran, to identify gaps in protected area coverage. Bird Conserv. Int. 29, 423–436 (2019).Article 

Google Scholar 
59.Tauler-Ametller, H., Hernández-Matías, A., Pretus, J. L. L. & Real, J. Landfills determine the distribution of an expanding breeding population of the endangered Egyptian vulture Neophron percnopterus. Ibis (Lond). 159, 757–768 (2017).Article 

Google Scholar 
60.Mateo-Tomás, P. & Olea, P. P. Diagnosing the causes of territory abandonment by the Endangered Egyptian vulture Neophron percnopterus: The importance of traditional pastoralism and regional conservation. Oryx 44, 424–433 (2010).Article 

Google Scholar 
61.Galligan, T. H. et al. Have population declines in Egyptian vulture and Red-headed vulture in India slowed since the 2006 ban on veterinary diclofenac?. Bird Conserv. Int. 24, 272–281 (2014).Article 

Google Scholar 
62.Lieury, N., Gallardo, M., Ponchon, C., Besnard, A. & Millon, A. Relative contribution of local demography and immigration in the recovery of a geographically-isolated population of the endangered Egyptian vulture. Biol. Conserv. 191, 349–356 (2015).Article 

Google Scholar 
63.Porter, R. F. & Suleiman, A. S. the Egyptian Vulture Neophron percnopterus on Socotra, Yemen: Population, ecology, conservation and ethno-ornithology. Sandgrouse 34, 44–62 (2012).
Google Scholar  More

1.Conradt, L. & Roper, T. J. Group decision-making in animals. Nature 421, 155–158 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.King, A. J. & Cowlishaw, G. Leaders, followers and group decision-making. Commun. Integr. Biol. 2, 147–150 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Couzin, I. D. & Franks, N. R. Self-organized lane formation and optimized traffic flow in army ants. Proc. R. Soc. B Biol. Sci. 270, 139–146 (2003).CAS 
Article 

Google Scholar 
4.Ballerini, M. et al. Empirical investigation of starling flocks: a benchmark study in collective animal behaviour. Anim. Behav. 76, 201–215 (2008).Article 

Google Scholar 
5.Couzin, I. D., Krause, J., James, R., Ruxton, G. D. & Franks, N. R. Collective memory and spatial sorting in animal groups. J. Theor. Biol. 218, 1–11 (2002).ADS 
MathSciNet 
PubMed 
Article 

Google Scholar 
6.Dyer, J. R. G., Johansson, A., Helbing, D., Couzin, I. D. & Krause, J. Leadership, consensus decision making and collective behaviour in humans. Philos. Trans. R. Soc. B Biol. Sci. 364, 781–789 (2009).7.Brent, L. J. N. et al. Ecological knowledge, leadership, and the evolution of menopause in killer whales. Curr. Biol. 25, 746–750 (2015).CAS 
PubMed 
Article 

Google Scholar 
8.Lee, H. C. & Teichroeb, J. A. Partially shared consensus decision making and distributed leadership in vervet monkeys: older females lead the group to forage. Am. J. Phys. Anthropol. 161, 580–590 (2016).PubMed 
Article 

Google Scholar 
9.Smith, J. E. et al. Collective movements, leadership and consensus costs at reunions in spotted hyaenas. Anim. Behav. 105, 187–200 (2015).Article 

Google Scholar 
10.Fischhoff, I. R. et al. Social relationships and reproductive state influence leadership roles in movements of plains zebra Equus burchellii. Anim. Behav. 73, 825–831 (2007).Article 

Google Scholar 
11.Conradt, L. & Roper, T. J. Consensus decision making in animals. Trends Ecol. Evol. 20, 449–456 (2005).PubMed 
Article 
PubMed Central 

Google Scholar 
12.Stueckle, S. & Zinner, D. To follow or not to follow: decision making and leadership during the morning departure in chacma baboons. Anim. Behav. 75, 1995–2004 (2008).Article 

Google Scholar 
13.Sueur, C. & Petit, O. Shared or unshared consensus decision in macaques?. Behav. Processes 78, 84–92 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Strandburg, P, Eshkin, A., Farine, D. R., Couzin, I. D. & Crofoot, M. C. Shared decision-making drives collective movement in wild baboons. Science 348, 1358–1361 (2015).15.Fischer, J. & Zinner, D. Communication and cognition in primate group movement. Int. J. Primatol. 32, 1279–1295 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
16.Pyritz, L. W., King, A. J., Sueur, C. & Fichtel, C. Reaching a consensus: terminology and concepts used in coordination and decision-making research. Int. J. Primatol. 32, 1268–1278 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
17.Raveling, D. G. Preflight and flight behavior of Canada geese. Auk 86, 671–681 (1969).Article 

Google Scholar 
18.Byrne, R. W., Whiten, A. & Henzi, S. P. Social relationships of mountain baboons: leadership and affiliation in a non-female-bonded monkey. Am. J. Primatol. 20, 313–329 (1990).CAS 
PubMed 
Article 

Google Scholar 
19.Boinski, S. & Garber, P. A. On the move: how and why animals travel in groups: on the move: how and why animals travel in groups. Am. Anthropol. 104, 669–670 (2002).Article 

Google Scholar 
20.Ramseyer, A., Thierry, B., Boissy, A. & Dumont, B. Decision-making processes in group departures of cattle. Ethology 115, 948–957 (2009).Article 

Google Scholar 
21.Petit, O. & Bon, R. Decision-making processes: the case of collective movements. Behav. Processes 84, 635–647 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
22.King, A. J., Johnson, D. D. P. & Van Vugt, M. The origins and evolution of leadership. Curr. Biol. 19, R911–R916 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Krause, J., Hoare, D., Krause, S., Hemelrijk, C. K. & Rubenstein, D. I. Leadership in fish shoals. Fish Fish. 1, 82–89 (2000).Article 

Google Scholar 
24.Allen, C. R. B., Brent, L. J. N., Motsentwa, T., Weiss, M. N. & Croft, D. P. Importance of old bulls: leaders and followers in collective movements of all-male groups in African savannah elephants (Loxodonta africana). Sci. Rep. 10, 1–9 (2020).Article 
CAS 

Google Scholar 
25.Pettit, B., Ákos, Z., Vicsek, T. & Biro, D. Speed determines leadership and leadership determines learning during pigeon flocking. Curr. Biol. 25, 3132–3137 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Mutinda, H., Poole, J. H. & Moss, C. F. Decision making and leadership in using the ecosystem. in The Amboseli Elephants: A Long-Term Perspective on a Long-Lived Mammal (Chicago Scholarship, 2011).27.Kummer, H. Social Organization of Hamadryas Baboons: A Field Study. Bibliotheca Primatologica (University of Chicago Press, 1968).28.Holekamp, K. E., Boydston, E. E., & Smale, L. Group travel in social carnivores. in On the move: How and why animals travel in groups 587–627 (University of Chicago Press, 2000).29.Pyritz, L. W., Kappeler, P. M. & Fichtel, C. Coordination of group movements in wild red-fronted lemurs (Eulemur rufifrons): processes and influence of ecological and reproductive seasonality. Int. J. Primatol. 32, 1325–1347 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
30.Jacobs, A., Maumy, M. & Petit, O. The influence of social organisation on leadership in brown lemurs (Eulemur fulvus fulvus) in a controlled environment. Behav. Processes 79, 111–113 (2008).CAS 
PubMed 
Article 

Google Scholar 
31.Farine, D. R., Strandburg-Peshkin, A., Couzin, I. D., Berger-Wolf, T. Y. & Crofoot, M. C. Individual variation in local interaction rules can explain emergent patterns of spatial organization in wild baboons. Proc. R. Soc. B Biol. Sci. 284, 25–29 (2017).
Google Scholar 
32.Kappeler, P. M. A framework for studying social complexity. Behav. Ecol. Sociobiol. 73, 13 (2019).Article 

Google Scholar 
33.Papageorgiou, D. & Farine, D. R. Shared decision-making allows subordinates to lead when dominants monopolize resources. Sci. Adv. 6, 1–8 (2020).Article 

Google Scholar 
34.Conradt, L., Krause, J., Couzin, I. D. & Roper, T. J. ‘Leading according to need’ in self-organizing groups. Am. Nat. 173, 304–312 (2009).CAS 
PubMed 
Article 

Google Scholar 
35.Rodriguez-Santiago, M. et al. Behavioral traits that define social dominance are the same that reduce social influence in a consensus task. Proc. Natl. Acad. Sci. U. S. A. 117, 18566–18573 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Grueter, C. C. et al. Multilevel organisation of animal sociality. Trends Ecol. Evol. 35, 834–847 (2020).PubMed 
Article 

Google Scholar 
37.Kummer, H. In Quest of the Sacred Baboon: a Scientist’s Journey. (Princeton University Press, 1995).38.Fischer, J. et al. Charting the neglected West: The social system of Guinea baboons. Am. J. Phys. Anthropol. 162, 15–31 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Whitehead, H. et al. Multilevel societies of female sperm whales (Physeter macrocephalus) in the Atlantic and Pacific: Why Are they so different?. Int. J. Primatol. 33, 1142–1164 (2012).Article 

Google Scholar 
40.Kummer, H. Two variations in the social organization of baboons. in Primates: studies in adaptation and variability 293–312 (Holt, Rinehart & Winston, 1968).41.Fischer, J. et al. The Natural History of Model Organisms: Insights into the evolution of social systems and species from baboon studies. Elife 8, e50989 (2019).42.Swedell, L. African Papionins: Diversity of social organization and ecological flexibility. in Primates in perspective 241–277 (Oxford University Press, 2011).43.Anandam, M., Bennett, E. & Davenport, T. Species accounts of Cercopithecidae. in Handbook ofthe mammals of the world Vol. 3 primates 628–753 (Lynx Edicions, 2013).44.Barrett, L. & Henzi, S. P. Baboons. Curr. Biol. 18, 404–406 (2008).Article 
CAS 

Google Scholar 
45.Ransom, T. W. Beach troop of the Gombe. (Bucknell University Press, 1981).46.Norton, G. Leadership: decision processes of group movement in yellow baboons. in Primate ecology and conservation. 145–156 (Cambridge University Press, 1986).47.Stoltz, L. & Saayman, G. S. Ecology and behaviour of baboons in the northern transvaal. Nature 26, 99–142 (1970).
Google Scholar 
48.Buskirk, W. H., Buskirk, R. E. & Hamilton, W. J. Troop-mobilizing behavior of adult male chacma baboons. Folia Primatol. 22, 9–18 (1974).CAS 
Article 

Google Scholar 
49.Collins, D. A. Spatial pattern in a troop of yellow baboons (Papio cynocephalus) in Tanzania. Anim. Behav. 32, 536–553 (1984).Article 

Google Scholar 
50.Rhine, R. J., Hendy, H. M., Stillwell-Barnes, R., Westlund, B. J. & Westlund, H. D. Movement Patterns of YeIIow Baboons (Papio cynocephaius): Central Positioning of Walking Infants. Am. J. Phys. Anthropol. 53, 159–167 (1980).Article 

Google Scholar 
51.Rhine, R. J. & Owens, N. W. The order of movement of adult male and black infant baboons (Papio anubis) entering and leaving a potentially dangerous clearing. Folia Primatol. 18, 276–283 (1972).CAS 
Article 

Google Scholar 
52.Rhine, R. J. & Westlund, B. J. Adult Male positioning in baboon progressions: order and chaos revisited. Folia Primatol. 35, 77–116 (1981).CAS 
Article 

Google Scholar 
53.Rhine, R. J., Bioland, P. & Lodwick, L. Progressions of adult male chacma baboons (Papio ursinus) in the moremi wildlife reserve. Int. J. Primatol. 6, 115–122 (1985).Article 

Google Scholar 
54.Rowell, T. Long-term changes in a population of ugandan baboons. Folia Primatol. 11, 241–254 (1969).CAS 
Article 

Google Scholar 
55.Sigg, H. & Stolba, A. Home range and daily march in a Hamadryas baboon troop. Folia Primatol. (Basel) 36, 40–75 (1981).CAS 
Article 

Google Scholar 
56.Schweitzer, C., Gaillard, T., Guerbois, C., Fritz, H. & Petit, O. Participant profiling and pattern of crop-foraging in chacma baboons (Papio hamadryas ursinus) in Zimbabwe: Why Does Investigating Age-Sex Classes Matter?. Int. J. Primatol. 38, 207–223 (2017).Article 

Google Scholar 
57.Stolba, A. Entscheidungstindung in verbanden von papio hamadryas. (University of Zurich, 1979).58.Strandburg-Peshkin, A., Papageorgiou, D., Crofoot, M. C. & Farine, D. R. Inferring influence and leadership in moving animal groups. Philos. Trans. R. Soc. B Biol. Sci. 373, (2018).59.Harding, R. S. O. Patterns of movement in open country baboons. Am. J. Phys. Anthropol. 47, 349–353 (1977).Article 

Google Scholar 
60.DeVore, I. & Washburn, S. L. Baboon Ecology and Human Evolution. in African Ecology and Human Evolution 335–367 (Routledge, 2017).61.Altmann, S. A. Baboon progressions: Order or chaos? A study of one-dimensional group geometry. Anim. Behav. 27, 46–80 (1979).Article 

Google Scholar 
62.Goffe, A. S., Zinner, D. & Fischer, J. Sex and friendship in a multilevel society : behavioural patterns and associations between female and male Guinea baboons. Behav. Ecol. Sociobiol. 70, 323–336 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
63.Patzelt, A. et al. Male tolerance and male – male bonds in a multilevel primate society. PNAS 111, 14740–14745 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Pines, M., Saunders, J. & Swedell, L. Alternative routes to the leader male role in a multi-level society: Follower vs. solitary male strategies and outcomes in hamadryas baboons. Am. J. Primatol. 73, 679–691 (2011).65.Schreier, A. L. & Swedell, L. The fourth level of social structure in a multi-level society: Ecological and social functions of clans in Hamadryas Baboons. Am. J. Primatol. 71, 948–955 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Dal Pesco, F., Trede, F., Zinner, D. & Fischer, J. Kin bias and male pair-bond status shape male-male relationships in a multilevel primate society. Behav. Ecol. Sociobiol. 75, 1–14 (2021).Article 

Google Scholar 
67.Strandburg-peshkin, A., Farine, D. R., Couzin, I. D. & Crofoot, M. C. Shared decision-making drives collective movement in wild baboons. Science 348, 1358–1361 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Leca, J. B., Gunst, N., Thierry, B. & Petit, O. Distributed leadership in semifree-ranging white-faced capuchin monkeys. Anim. Behav. 66, 1045–1052 (2003).Article 

Google Scholar 
69.Rhine, R. J. The order of movement of yellow baboons. Folia Primatol 23, 72–104 (1975).CAS 
Article 

Google Scholar 
70.Rhine, R. J. & Tilson, R. Reactions to fear as a proximate factor in the sociospatial organization of baboon progressions. Am. J. Primatol. 13, 119–128 (1987).PubMed 
Article 

Google Scholar 
71.Bonnell, T. R., Clarke, P. M., Henzi, S. P. & Barrett, L. Individual-level movement bias leads to the formation of higher-order social structure in a mobile group of baboons. R. Soc. Open Sci. 4, (2017).72.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, (2017).73.King, A. J., Douglas, C. M. S., Huchard, E., Isaac, N. J. B. & Cowlishaw, G. Dominance and affiliation mediate despotism in a social primate. Curr. Biol. 18, 1833–1838 (2008).CAS 
PubMed 
Article 

Google Scholar 
74.King, A. J., Sueur, C., Huchard, E. & Cowlishaw, G. A rule-of-thumb based on social affiliation explains collective movements in desert baboons. Anim. Behav. 82, 1337–1345 (2011).Article 

Google Scholar 
75.Harel, R., Loftus, C. J. & Crofoot, M. C. Locomotor compromises maintain group cohesion in baboon troops on the move. bioRxiv (2020).76.Wang, C. et al. Decision-making process during collective movement initiation in golden snub-nosed monkeys (Rhinopithecus roxellana). Sci. Rep. 10, 1–10 (2020).Article 
CAS 

Google Scholar 
77.Whitehead, H. Consensus movements by groups of sperm whales. Mar. Mammal Sci. 32, 1402–1415 (2016).Article 

Google Scholar 
78.Crook, J. H. Gelada baboon herd structure and movement a comparative report. Symp. Zool. Soc. London 18, 237–258 (1966).
Google Scholar 
79.Grueter, C. C., Li, D., Ren, B., Wei, F. & Li, M. Deciphering the social organization and structure of wild yunnan snub-nosed monkeys (Rhinopithecus bieti). Folia Primatol. 88, 358–383 (2017).Article 

Google Scholar 
80.Zinner, D. et al. Comparative ecology of Guinea baboons (Papio papio). Primate Biol. 8, 19–35 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
81.Altmann, J. Observational study of behavior: sampling methods. Behaviour 49, 227–266 (1974).CAS 
PubMed 
Article 

Google Scholar 
82.Sueur, C. & Petit, O. Organization of group members at departure is driven by social structure in Macaca. Int. J. Primatol. 29, 1085–1098 (2008).Article 

Google Scholar 
83.Seltmann, A., Majolo, B., Schülke, O. & Ostner, J. The Organization of Collective Group Movements in Wild Barbary Macaques (Macaca sylvanus): Social Structure Drives Processes of Group Coordination in Macaques. PLoS One 8, (2013).84.Core Team, R. R: A Language and Environment for Statistical Computing. (2018).85.Baayen, R. H. Analyzing linguistic data: A practical introduction to statistics using R. Anal. Linguist. Data A Pract. Introd. to Stat. Using R 1–353 (2008).86.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Software 67, 1–48 (2015).Article 

Google Scholar 
87.Dobson, A. An introduction to generalized linear models. (CRC Press, 2002).88.Forstmeier, W. & Schielzeth, H. Cryptic multiple hypotheses testing in linear models: Overestimated effect sizes and the winner’s curse. Behav. Ecol. Sociobiol. 65, 47–55 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
89.Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
90.Barr, D. J., Levy, R., Scheepers, C. & Tily, H. J. Random effects structure for confirmatory hypothesis testing: Keep it maximal. J. Mem. Lang. 68, 255–278 (2013).Article 

Google Scholar 
91.Fahrmeir, L., Kneib, T., Lang, S. & Marx, B. Regression Modesl (Springer, 2013).MATH 
Book 

Google Scholar 
92.Hadfield, J. D. MCMCglmm: MCMC Methods for Multi-Response GLMMs in R. J. Stat. Softw. 33, 1–22 (2010).Article 

Google Scholar  More