Philippa Kaur

1.Chin, A. Urban transformation of river landscapes in a global context. Geomorphology 79, 460–487 (2006).ADS 
Article 

Google Scholar 
2.Thomas, W. L. Man’s role in changing the face of the earth. (The University of Chicago, 1956).3.Johnson, M. T. & Munshi-South, J. Evolution of life in urban environments. Science 358, eaam8327 (2017).PubMed 
Article 
CAS 

Google Scholar 
4.Dubois, J. & Cheptou, P.-O. Effects of fragmentation on plant adaptation to urban environments. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160038 (2017).Article 

Google Scholar 
5.Cavia, R., Cueto, G. R. & Suárez, O. V. Changes in rodent communities according to the landscape structure in an urban ecosystem. Landsc. Urban Plan. 90, 11–19 (2009).Article 

Google Scholar 
6.Jackson, J. A. Ivory-billed Woodpecker (Campephilus principalis): Hope, and the interfaces of science, conservation, and politics. Auk 123, 1–15 (2006).Article 

Google Scholar 
7.McIntyre, N. E. Ecology of urban arthropods: a review and a call to action. Ann. Entomol. Soc. Am. 93, 825–835 (2000).Article 

Google Scholar 
8.Crispo, E., Moore, J. S., Lee-Yaw, J. A., Gray, S. M. & Haller, B. C. Broken barriers: Human-induced changes to gene flow and introgression in animals: An examination of the ways in which humans increase genetic exchange among populations and species and the consequences for biodiversity. BioEssays 33, 508–518 (2011).PubMed 
Article 

Google Scholar 
9.Triteeraprapab, S. et al. Transmission of the nocturnal periodic strain of Wuchereria bancrofti by Culex quinquefasciatus: establishing the potential for urban filariasis in Thailand. Epidemiol. Infect. 125, 207–212 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Carrieri, M., Bacchi, M., Bellini, R. & Maini, S. On the competition occurring between Aedes albopictus and Culex pipiens (Diptera: Culicidae) in Italy. Environ. Entomol. 32, 1313–1321 (2003).Article 

Google Scholar 
11.Doby, J. & Mouchet, J. Écologie larvaire de quelques espèces de Culicidés dans la région de Yaoundé (Sud-Cameroun). Bulletin de la Société de Pathologie Exotique 50, 945–957 (1957).CAS 

Google Scholar 
12.Delatte, H. et al. Aedes albopictus, vecteur des virus du chikungunya et de la dengue à la Réunion: biologie et contrôle. Parasite 15, 3–13 (2008).CAS 
PubMed 
Article 

Google Scholar 
13.Vazeille, M., Moutailler, S., Pages, F., Jarjaval, F. & Failloux, A. B. Introduction of Aedes albopictus in Gabon: what consequences for dengue and chikungunya transmission?. Tropical Med. Int. Health 13, 1176–1179 (2008).Article 

Google Scholar 
14.United Nations, Department of Economic and Social Affairs, Population Division. World Urbanization Prospects: The 2018 Revision (ST/ESA/SER.A/420). (New York, United Nations, 2019).15.Trape, J. F.  L’impact de l’urbanisation sur le paludisme en Afrique centrale. Doctoral dissertation, Université Paris 11 (1986).16.Robert, V. et al. Malaria transmission in urban sub-Saharan Africa. Am. J. Trop. Med. Hyg. 68, 169–176 (2003).PubMed 
Article 

Google Scholar 
17.Hay, S. I., Guerra, C. A., Tatem, A. J., Noor, A. M. & Snow, R. W. The global distribution and population at risk of malaria: past, present, and future. Lancet. Infect. Dis 4, 327–336 (2004).PubMed 
PubMed Central 
Article 

Google Scholar 
18.Keiser, J. et al. Urbanization in sub-saharan Africa and implication for malaria control. Am. J. Trop. Med. Hyg. 71, 118–127 (2004).PubMed 
Article 

Google Scholar 
19.Hay, S. I., Guerra, C. A., Tatem, A. J., Atkinson, P. M. & Snow, R. W. Urbanization, malaria transmission and disease burden in Africa. Nat. Rev. Microbiol. 3, 81 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Mourou, J.-R. et al. Malaria transmission and insecticide resistance of Anopheles gambiae in Libreville and Port-Gentil, Gabon. Malaria J. 9, 1475–2875 (2010).Article 

Google Scholar 
21.Ndo, C., Menze-Djantio, B. & Antonio-Nkondjio, C. Awareness, attitudes and prevention of malaria in the cities of Douala and Yaoundé (Cameroon). Parasit. Vectors 4, 181 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
22.Kamdem, C. et al. Anthropogenic habitat disturbance and ecological divergence between incipient species of the malaria mosquito Anopheles gambiae. PloS One 7, e39453 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.Akogbeto, M., Chippaux, J.-P. & Coluzzi, M. . Le. paludisme urbain côtier à Cotonou (République du Bénin). Étude entomologique. Revue d’Epidémiologie Santé Publique 40, 233–239 (1992).CAS 

Google Scholar 
24.Awolola, T., Oduola, A., Obansa, J., Chukwurar, N. & Unyimadu, J. Anopheles gambiae ss breeding in polluted water bodies in urban Lagos, southwestern Nigeria. J. Vect. Borne Diseas. 44, 241 (2007).25.Ravoahangimalala, R., Randrianambinintsoa, E., Tchuinkam, T. & Robert, V. Malaria in the urban highland area of Antananarivo, Madagascar: bioecology of Anopheles arabiensis. Bull. Soc. Pathol. Exot. 101, 348 (2008).CAS 
PubMed 
Article 

Google Scholar 
26.Labbo, R. et al. Ecology of urban malaria vectors in Niamey, Republic of Niger. Malaria J. 15, 314 (2016).Article 

Google Scholar 
27.Klinkenberg, E. et al. Malaria and irrigated crops, Accra, Ghana. Emerg. Infect. Dis. 11, 1290 (2005).PubMed 
PubMed Central 
Article 

Google Scholar 
28.Baudon, D. & Spiegel, A. Paludisme urbain, paludisme de demain pour l’Afrique sub-saharienne. Bull. Soc. Pathol. Exot. 96, 3–155 (2003).
Google Scholar 
29.Carme, B. Reducing the risk of malaria acquisition by urban dwellers of sub-Saharan Africa during travel in malaria-endemic areas. J. Infect. Dis. 170, 257–258 (1994).CAS 
PubMed 
Article 

Google Scholar 
30.Silva, P. M. & Marshall, J. M. Factors contributing to urban malaria transmission in sub-Saharan Africa: a systematic review. J. Trop. Med. https://doi.org/10.1155/2012/819563 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Bouyou-Akotet, M. K. et al. Falciparum malaria as an emerging cause of fever in adults living in Gabon, Central Africa. BioMed. Res. Internat. (2014).32.Coetzee, M. et al. Anopheles coluzzii and Anopheles amharicus, new members of the Anopheles gambiae complex. Zootaxa 3619, 246–274 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
33.Fontaine, M. C. et al. Extensive introgression in a malaria vector species complex revealed by phylogenomics. Science 347, 1258524 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
34.Tene, F. B. et al. Habitat segregation and ecological character displacement in cryptic African malaria mosquitoes. Evolut. Appl. 7 (2015).35.Mourou, J.-R. et al. Malaria transmission in Libreville: results of a one year survey. Malar. J. 11, 40 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
36.Trape, J.-F. et al. Malaria morbidity among children exposed to low seasonal transmission in Dakar, Senegal and its implications for malaria control in tropical Africa. Am. J. Trop. Med. Hyg. 48, 748–756 (1993).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Dukeen, M. Y. & Omer, S. Ecology of the malaria vector Anopheles arabiensis Patton (Diptera: Culicidae) by the Nile in northern Sudan. Bull. Entomol. Res. 76, 451–467 (1986).Article 

Google Scholar 
38.Robert, V., Awono-Ambene, H. & Thioulouse, J. Ecology of larval mosquitoes, with special reference to Anopheles arabiensis (Diptera: Culcidae) in market-garden wells in urban Dakar, Senegal. J. Med. Entomol. 35, 948–955 (1998).CAS 
PubMed 
Article 

Google Scholar 
39.Takken, W. & Lindsay, S. Increased threat of urban malaria from Anopheles stephensi mosquitoes, Africa. Emerg. Infect. Dis. 25, 1431 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
40.Seyfarth, M., Khaireh, B. A., Abdi, A. A., Bouh, S. M. & Faulde, M. K. Five years following first detection of Anopheles stephensi (Diptera: Culicidae) in Djibouti, Horn of Africa: populations established—malaria emerging. Parasitol. Res. 118, 725–732 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
41.Sinka, M. et al. A new malaria vector in Africa: predicting the expansion range of Anopheles stephensi and identifying the urban populations at risk. Proceedi. Nat. Acad. Sci. 117 (2020).42.Tene, B. et al. Physiological correlates of ecological divergence along an urbanization gradient: differential tolerance to ammonia among molecular forms of the malaria mosquito Anopheles gambiae. BMC Ecol. 13, 1 (2013).Article 

Google Scholar 
43.Akpodiete, N. O. & Tripet, F. Laboratory and microcosm experiments reveal contrasted adaptive responses to ammonia and water mineralisation in aquatic stages of the sibling species Anopheles gambiae (sensu stricto) and Anopheles coluzzii. Parasit. Vectors 14, 1–19 (2021).Article 
CAS 

Google Scholar 
44.Udo , W., In Su, C. & Eva-Maria, D. (ed Environmental Protection Agency) (2009).45.Huff, L., Delos, C., Gallagher, K. & Beaman, J. Aquatic life ambient water quality criteria for ammonia-freshwater. Washington DC: US Environmental Protection Agency (2013).46.Antonio-Nkondjio, C. et al. Anopheles gambiae distribution and insecticide resistance in the cities of Douala and Yaounde(Cameroon): influence of urban agriculture and pollution. Malar. J. 10, 154–154 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
47.Djouaka, R. F. et al. Does the spillage of petroleum products in Anopheles breeding sites have an impact on the pyrethroid resistance?. Malar. J. 6, 159 (2007).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
48.Tene Fossog, B. et al. Water quality and Anopheles gambiae larval tolerance to pyrethroids in the cities of Douala and Yaounde (Cameroon). J. Trop. Med. 2012 (2012).49.Ossè, R., Bangana, S., Aïkpon, R., Kintonou, J. & Sagbohan, H. Adaptation of Anopheles coluzzii Larvae to polluted breeding Sites in Cotonou: a strengthening in Urban Malaria transmission in Benin. Vector Biology Journal 6, 2 (2019).
Google Scholar 
50.Cassone, B. J. et al. Gene expression divergence between malaria vector sibling species Anopheles gambiae and Anopheles coluzzii from rural and urban Yaounde Cameroon. Mol. Ecol. 23, 2242–2259 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Kamdem, C., Fouet, C., Gamez, S. & White, B. J. Pollutants and insecticides drive local adaptation in African malaria mosquitoes. Mol. Biol. Evol. 34, 1261–1275 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Kengne, P., Charmantier, G., Blondeau-Bidet, E., Costantini, C. & Ayala, D. Tolerance of disease-vector mosquitoes to brackish water and their osmoregulatory ability. Ecosphere 10, e02783. https://doi.org/10.1002/ecs2.2783 (2019).Article 

Google Scholar 
53.Peres-Neto, P. R., Jackson, D. A. & Somers, K. M. How many principal components? Stopping rules for determining the number of non-trivial axes revisited. Comput. Stat. Data Anal. 49, 974–997 (2005).MathSciNet 
MATH 
Article 

Google Scholar 
54.Panagopoulos, G., Lambrakis, N., Tsolis-Katagas, P. & Papoulis, D. Cation exchange processes and human activities in unconfined aquifers. Environ. Geol. 46, 542–552 (2004).CAS 
Article 

Google Scholar 
55.Elhatip, H., Afşin, M., Dirik, K., Kurmaç, Y. & Kavurmacı, M. Influences of human activities and agriculture on groundwater quality of Kayseri-Incesu-Dokuzpınar springs, central Anatolian part of Turkey. Environ. Geol. 44, 490–494 (2003).CAS 
Article 

Google Scholar 
56.Azedine, H., Lynda, C. & Younes, S. Wastewater discharge impact on groundwater quality of Béchar city, southwestern Algeria: an anthropogenic activities mapping approach. Procedia Eng. 33, 242–247 (2012).Article 
CAS 

Google Scholar 
57.Hamon, J., Burnett, G., Adam, J.-P., Rickenbach, A. & Grjébine, A. Culex pipiens fatigans Wiedemann, Wuchereria bancrofti Cobbold, et le développement économique de l’Afrique tropicale. Bull. World Health Organ. 37, 217 (1967).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Subra, R. Biology and control of Culex pipiens quinquefasciatus* Say, 1823 (Diptera, Culicidae) with special reference to Africa. Int. J. Trop. Insect Sci. 1, 319–338 (1981).CAS 
Article 

Google Scholar 
59.Brengues, J. Culex pipiens fatigans Wiedemann, en Afrique tropicale: son importance et son contrôle. Med. Trop. 38, 691–694 (1978).CAS 

Google Scholar 
60.Fanello, C., Santolamazza, F. & Della, T. A. Simultaneous identification of species and molecular forms of the Anopheles gambiae complex by PCR-RFLP. Med. Vet. Entomol. 16, 461–464 (2002).CAS 
PubMed 
Article 

Google Scholar 
61.Cangelosi, R. & Goriely, A. Component retention in principal component analysis with application to cDNA microarray data. Biol. Direct 2, 2 (2007).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
62.Edi, C. V. et al. CYP6 P450 enzymes and ACE-1 duplication produce extreme and multiple insecticide resistance in the malaria mosquito Anopheles gambiae. PloS Genet. 10, e1004236 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
63.Djouaka, R. F. et al. Expression of the cytochrome P450s, CYP6P3 and CYP6M2 are significantly elevated in multiple pyrethroid resistant populations of Anopheles gambiae ss from Southern Benin and Nigeria. BMC Genom. 9, 538 (2008).Article 
CAS 

Google Scholar 
64.Balabanidou, V. et al. Cytochrome P450 associated with insecticide resistance catalyzes cuticular hydrocarbon production in Anopheles gambiae. Proc. Natl. Acad. Sci. 113, 9268–9273 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Mueller, P. et al. Pyrethroid tolerance is associated with elevated expression of antioxidants and agricultural practice in Anopheles arabiensis sampled from an area of cotton fields in Northern Cameroon. Mol. Ecol. 17, 1145–1155 (2008).Article 
CAS 

Google Scholar 
66.Larsen, E. H. et al. Osmoregulation and excretion. Compr. Physiol. 4, 405–573 (2011).
Google Scholar 
67.Lin, L.-Y., Horng, J.-L., Kunkel, J. G. & Hwang, P.-P. Proton pump-rich cell secretes acid in skin of zebrafish larvae. Am. J. Physiol. Cell Physiol. 290, C371–C378 (2006).CAS 
PubMed 
Article 

Google Scholar 
68.Mantel, L. H. & Farmer, L. L. Osmotic and ionic regulation. Internal Anatomy Physiol. Regul. 5, 53–161 (1983).Article 

Google Scholar 
69.Furriel, R., McNamara, J. & Leone, F. Characterization of (Na+, K+)-ATPase in gill microsomes of the freshwater shrimp Macrobrachium olfersii. Comput. Biochem. Physiol. B: Biochem. Mol. Biol. 126, 303–315 (2000).CAS 
Article 

Google Scholar 
70.Chiu, T.-L., Wen, Z., Rupasinghe, S. G. & Schuler, M. A. Comparative molecular modeling of Anopheles gambiae CYP6Z1, a mosquito P450 capable of metabolizing DDT. Proc. Natl. Acad. Sci. 105, 8855–8860 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Antonio-Nkondjio, C. et al. Investigation of mechanisms of bendiocarb resistance in Anopheles gambiae populations from the city of Yaoundé, Cameroon. Malaria J. 15, 424 (2016).Article 
CAS 

Google Scholar 
72.David, J.-P., Ismail, H. M., Chandor-Proust, A. & Paine, M. J. I. Role of cytochrome P450s in insecticide resistance: impact on the control of mosquito-borne diseases and use of insecticides on Earth. Phil. Trans. R. Soc. B 368, 20120429 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
73.Müller, P. et al. Field-caught permethrin-resistant Anopheles gambiae overexpress CYP6P3, a P450 that metabolises pyrethroids. PloS Genet. 4, e1000286 (2008).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
74.Mitchell, S. N. et al. Identification and validation of a gene causing cross-resistance between insecticide classes in Anopheles gambiae from Ghana. Proc. Natl. Acad. Sci. 109, 6147–6152 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
75.Irving, H., Riveron, J., Ibrahim, S. S., Lobo, N. & Wondji, C. Positional cloning of rp2 QTL associates the P450 genes CYP6Z1, CYP6Z3 and CYP6M7 with pyrethroid resistance in the malaria vector Anopheles funestus. Heredity 109, 383 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Rongnoparut, P., Boonsuepsakul, S., Chareonviriyaphap, T. & Thanomsing, N. Cloning of cytochrome P450, CYP6P5, and CYP6AA2 from Anopheles minimus resistant to deltamethrin. J. Vector Ecol. 28, 150–158 (2003).PubMed 

Google Scholar 
77.Costantini, C. et al. Living at the edge: biogeographic patterns of habitat segregation conform to speciation by niche expansion in Anopheles gambiae. BMC Ecol. 9, 16 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
78.Simard, F. et al. Ecological niche partitioning between Anopheles gambiae molecular forms in Cameroon: the ecological side of speciation. BMC Ecol. 9, 17 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
79.Sattler, M. A. et al. Habitat characterization and spatial distribution of Anopheles sp. mosquito larvae in Dar es Salaam (Tanzania) during an extended dry period. Malaria J. 4, 4 (2005).Article 

Google Scholar 
80.Kudom, A. A. Larval ecology of Anopheles coluzzii in Cape Coast, Ghana: water quality, nature of habitat and implication for larval control. Malar. J. 14, 447 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
81.Etang, J. et al. Anopheles coluzzii larval habitat and insecticide resistance in the island area of Manoka, Cameroon. BMC Infect. Dis. 16, 217 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
82.Mbida, A. M. et al. Nouvel aperçu sur l’écologie larvaire d’Anopheles coluzzii Coetzee et Wilkerson, 2013 dans l’estuaire du Wouri, Littoral-Cameroun. Bulletin de la Société de pathologie exotique 110, 92–101 (2017).Article 

Google Scholar 
83.Gimonneau, G. et al. Larval habitat segregation between the molecular forms of the mosquito Anopheles gambiae in a rice field area of Burkina Faso, West Africa. Med. Vet. Entomol. 26, 9–17 (2012).CAS 
PubMed 
Article 

Google Scholar 
84.Djamouko-Djonkam, L. et al. Spatial distribution of Anopheles gambiae sensu lato larvae in the urban environment of Yaoundé, Cameroon. Infect. Dis. Poverty 8, 84 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
85.Dida, G. O. et al. Spatial distribution and habitat characterization of mosquito species during the dry season along the Mara River and its tributaries, in Kenya and Tanzania. Infect. Dis. Poverty 7, 2 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
86.King, S. A. et al. The Role of Detoxification Enzymes in the Adaptation of the Major Malaria Vector Anopheles gambiae (Giles; Diptera: Culicidae) to Polluted Water. J. Med. Entomol. 54, 1674–1683 (2017).CAS 
PubMed 
Article 

Google Scholar 
87.Donihue, C. M. & Lambert, M. R. Adaptive evolution in urban ecosystems. Ambio 44, 194–203 (2015).PubMed 
Article 

Google Scholar 
88.Coret, C., Zaugg, R. & Chouin, G. Les villes en Afrique avant 1900. Bilan historiographique et perspectives de recherche. Afriques. Débats, méthodes et terrains d’histoire (2020).89.Ndiath, M. O. et al. Composition and genetics of malaria vector populations in the Central African Republic. Malar. J. 15, 1–10 (2016).Article 

Google Scholar 
90.Mattah, P. A. D. et al. Diversity in breeding sites and distribution of Anopheles mosquitoes in selected urban areas of southern Ghana. Parasit. Vectors 10, 1–15 (2017).Article 

Google Scholar 
91.Diabaté, A. et al. Larval development of the molecular forms of Anopheles gambiae (Diptera: Culicidae) in different habitats: a transplantation experiment. J. Med. Entomol. 42, 548–553 (2005).PubMed 
Article 

Google Scholar 
92.Briones, M. J. I., Ineson, P. & Piearce, T. G. Effects of climate change on soil fauna; responses of enchytraeids, Diptera larvae and tardigrades in a transplant experiment. Appl. Soil. Ecol. 6, 117–134 (1997).Article 

Google Scholar 
93.Szulkin, M., Munshi-South, J. & Charmantier, A. Urban evolutionary biology (Oxford University Press, 2020).Book 

Google Scholar 
94.Bradley, T. Physiology of osmoregulation in mosquitoes. Annu. Rev. Entomol. 32, 439–462 (1987).CAS 
PubMed 
Article 

Google Scholar 
95.Raabe, W. & Lin, S. Pathophysiology of ammonia intoxication. Exp. Neurol. 87, 519–532 (1985).CAS 
PubMed 
Article 

Google Scholar 
96.Ip, Y., Chew, S. & Randall, D. Ammonia toxicity, tolerance, and excretion. Fish Physiol. 20, 109–148 (2001).CAS 
Article 

Google Scholar 
97.Randall, D. J. & Tsui, T. Ammonia toxicity in fish. Mar. Pollut. Bull. 45, 17–23 (2002).CAS 
PubMed 
Article 

Google Scholar 
98.Yadouléton, A. et al. The impact of the expansion of urban vegetable farming on malaria transmission in major cities of Benin. Parasit. Vectors 3, 118 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
99.Girod, R., Orlandi-Pradines, E., Rogier, C. & Pages, F. Malaria transmission and insecticide resistance of Anopheles gambiae (Diptera: Culicidae) in the French military camp of Port-Bouet, Abidjan (Cote d’Ivoire): implications for vector control. J. Med. Entomol. 43, 1082–1087 (2006).PubMed 

Google Scholar 
100.Cuamba, N., Choi, K. S. & Townson, H. Malaria vectors in Angola: distribution of species and molecular forms of the Anopheles gambiae complex, their pyrethroid insecticide knockdown resistance (kdr) status and Plasmodium falciparum sporozoite rates. Malar. J. 5, 2 (2006).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
101.Jones, C. M. et al. Insecticide resistance in Culex quinquefasciatus from Zanzibar: implications for vector control programmes. Parasit. Vectors 5, 1–9 (2012).Article 
CAS 

Google Scholar 
102.Pagès, F. et al. Malaria transmission in Dakar: a two-year survey. Malar. J. 7, 178 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
103.Corbel, V. et al. Multiple insecticide resistance mechanisms in Anopheles gambiae and Culex quinquefasciatus from Benin, West Africa. Acta Tropica 101, 207–216 (2007).CAS 
PubMed 
Article 

Google Scholar 
104.Nchoutpouen, E. et al. Culex species diversity, susceptibility to insecticides and role as potential vector of lymphatic filariasis in the city of Yaoundé, Cameroon. PLoS Negl. Trop. Dis. 13, e0007229 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
105.Antonio-Nkondjio, C. et al. High mosquito burden and malaria transmission in a district of the city of Douala, Cameroon. BMC Infect. Dis. 12, 275 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
106.Calhoun, L. M. et al. Combined sewage overflows (CSO) are major urban breeding sites for Culex quinquefasciatus in Atlanta, Georgia. Am. J. Trop. Med. Hyg. 77, 478–484 (2007).PubMed 
Article 

Google Scholar 
107.Lines, J., Harpham, T., Leake, C. & Schofield, C. Trends, priorities and policy directions in the control of vector-borne diseases in urban environments. Health Policy Plan. 9, 113–129 (1994).CAS 
PubMed 
Article 

Google Scholar 
108.Adje, D. D. et al. Étude de la pollution organique de la rivière Okedama dans la Commune de Parakou. Afrique Sci. 15, 299–305 (2019).
Google Scholar 
109.Mpakam, H. et al. Etude des facteurs de pollution des ressources en eau en milieu urbain: cas de Bafoussam (Ouest-Cameroun). Actes du colloque international sur le thème” changements climatiques et évaluation environnementale”, de Niamey (Niger) (2009).110.Adams, N. & Bealing, D. Organic pollution: biochemical oxygen demand and ammonia. Handbook of Ecotoxicology, 728–749 (1997).111.Mireji, P. O. et al. Heavy metals in mosquito larval habitats in urban Kisumu and Malindi, Kenya, and their impact. Ecotoxicol. Environ. Saf. 70, 147–153 (2008).CAS 
PubMed 
Article 

Google Scholar 
112.White, B. J. et al. Dose and developmental responses of Anopheles merus larvae to salinity. J. Exp. Biol. 216, 3433–3441 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
113.Zhao, G.-D. et al. Transcription profiling of eight cytochrome P450s potentially involved in xenobiotic metabolism in the silkworm, Bombyx mori. Pesticide Biochem. Physiol. 100, 251–255 (2011).CAS 
Article 

Google Scholar 
114.Oliver, S. V. & Brooke, B. D. The effect of metal pollution on the life history and insecticide resistance phenotype of the major malaria vector Anopheles arabiensis (Diptera: Culicidae). PloS One 13, e0192551 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
115.Nkya, T. E. et al. Impact of agriculture on the selection of insecticide resistance in the malaria vector Anopheles gambiae: a multigenerational study in controlled conditions. Parasit. Vectors 7, 1–12 (2014).Article 
CAS 

Google Scholar 
116.David, J.-P., Ismail, H. M., Chandor-Proust, A. & Paine, M. J. I. Role of cytochrome P450s in insecticide resistance: impact on the control of mosquito-borne diseases and use of insecticides on Earth. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120429 (2013).Article 
CAS 

Google Scholar 
117.Wondji, C. S. et al. Two duplicated P450 genes are associated with pyrethroid resistance in Anopheles funestus, a major malaria vector. Genome Res. 19, 452–459 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
118.Vontas, J., Katsavou, E. & Mavridis, K. Cytochrome P450-based metabolic insecticide resistance in Anopheles and Aedes mosquito vectors: Muddying the waters. Pesticide Biochem. Physiol. 69, 104666 (2020).Article 
CAS 

Google Scholar 
119.Patrick, M. L., Aimanova, K., Sanders, H. R. & Gill, S. S. P-type Na+/K+-ATPase and V-type H+-ATPase expression patterns in the osmoregulatory organs of larval and adult mosquito Aedes aegypti. J. Exp. Biol. 209, 4638–4651 (2006).CAS 
PubMed 
Article 

Google Scholar 
120.White, B. J., Collins, F. H. & Besansky, N. J. Evolution of Anopheles gambiae in Relation to Humans and Malaria. Annual Review of Ecology, Evolution, and Systematics 42 (2011).121.Dabiré, K. et al. Occurrence of natural Anopheles arabiensis swarms in an urban area of Bobo-Dioulasso city, Burkina Faso, West Africa. Acta Tropica 132, S35–S41 (2014).PubMed 
Article 

Google Scholar 
122.Service, M. W. Mosquito ecology field sampling methods. 2nd edn, (Elsevier Applied Science, 1993).123.Bass, C. et al. Detection of knockdown resistance (kdr) mutations in Anopheles gambiae: a comparison of two new high-throughput assays with existing methods. Malar. J. 6, 1–14 (2007).Article 
CAS 

Google Scholar 
124.Santolamazza, F. et al. Insertion polymorphisms of SINE200 retrotransposons within speciation islands of Anopheles gambiae molecular forms. Malar. J. 7, 163 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
125.Tene F. B. et al. Resistance to DDT in an urban setting: common mechanisms implicated in both M and S forms of Anopheles gambiae in the city of Yaoundé Cameroon. PloS one 8 (2013).126.Nsango, S. E. et al. AP-1/Fos-TGase2 axis mediates wounding-induced Plasmodium falciparum killing in Anopheles gambiae. J. Biolog. Chem. 288 (2013).127.Dray, S. & Dufour, A.-B. The ade4 package: implementing the duality diagram for ecologists. J. Statis. Software 22 (2007).128.Bates, D., Maechler, M., Bolker, B. & Walker, S. (2015).129.Bartlett, M. S. Properties of sufficiency and statistical tests. Proc. R. Soc. London. Ser. A Math. Phys. Sci. 160, 268–282 (1937).ADS 
MATH 

Google Scholar 
130.Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723 (1974).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
131.Burnham, K. P. & Anderson, D. R. Multimodel inference: understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304 (2004).MathSciNet 
Article 

Google Scholar 
132.Schloerke, B., Crowley, J., Cook, D., Briatte, F., Marbach, M., Thoen, E., Elberg, A., & Larmarange, J. GGally: extension to ‘ggplot2’. R package version 1.4. 0. R Foundation for Statistical Computing. (2018). More

1.Holden, C. Report warns of looming pollination crisis in North America. Science 314, 397 (2006).CAS 
PubMed 
Article 

Google Scholar 
2.Aizen, M. A. & Harder, L. D. The global stock of domesticated honey bees is growing slower than agricultural demand for pollination. Curr. Biol. 19, 915–918 (2009).CAS 
PubMed 
Article 

Google Scholar 
3.Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).PubMed 
Article 
CAS 

Google Scholar 
4.Woodcock, B. A. et al. Impacts of neonicotinoid use on long-term population changes in wild bees in England. Nat. Commun. 7, 12459 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Siviter, H., Brown, M. J. F. & Leadbeater, E. Sulfoxaflor exposure reduces bumblebee reproductive success. Nature 561, 109–112 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
6.Cameron, S. A. et al. Patterns of widespread decline in North American bumble bees. Proc. Natl Acad. Sci. USA 108, 662–667 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Powney, G. D. et al. Widespread losses of pollinating insects in Britain. Nat. Commun. 10, 1018 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
8.Vanbergen, A. J. & The Insect Pollinators Initiative. Threats to an ecosystem service: pressures on pollinators. Front. Ecol. Environ. 11, 251–259 (2013).Article 

Google Scholar 
9.EFSA. Bee health. https://www.efsa.europa.eu/en/topics/topic/bee-health (2019).10.Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
11.Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R. & Polasky, S. Agricultural sustainability and intensive production practices. Nature 418, 671–677 (2002).ADS 
CAS 
Article 

Google Scholar 
12.Potts, S. G. et al. Safeguarding pollinators and their values to human well-being. Nature 540, 220–229 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
13.Pettis, J. S. et al. Crop pollination exposes honey bees to pesticides which alters their susceptibility to the gut pathogen Nosema ceranae. PLoS ONE 8, e70182 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Siviter, H., Folly, A. J., Brown, M. J. F. & Leadbeater, E. Individual and combined impacts of sulfoxaflor and Nosema bombi on bumblebee (Bombus terrestris) larval growth. Proc. R. Soc. B 287, 20200935 (2020).CAS 
PubMed 
Article 

Google Scholar 
15.Retschnig, G. et al. Effects, but no interactions, of ubiquitous pesticide and parasite stressors on honey bee (Apis mellifera) lifespan and behaviour in a colony environment. Environ. Microbiol. 17, 4322–4331 (2015).CAS 
PubMed 
Article 

Google Scholar 
16.Doublet, V., Labarussias, M., de Miranda, J. R., Moritz, R. F. A. & Paxton, R. J. Bees under stress: sublethal doses of a neonicotinoid pesticide and pathogens interact to elevate honey bee mortality across the life cycle. Environ. Microbiol. 17, 969–983 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Folt, C. L., Chen, C. Y., Moore, M. V. & Burnaford, J. Synergism and antagonism among multiple stressors. Limnol. Oceanogr. 44, 864–877 (1999).ADS 
Article 

Google Scholar 
18.Di Prisco, G. et al. Neonicotinoid clothianidin adversely affects insect immunity and promotes replication of a viral pathogen in honey bees. Proc. Natl Acad. Sci. USA 110, 18466–18471 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Collison, E., Hird, H., Cresswell, J. & Tyler, C. Interactive effects of pesticide exposure and pathogen infection on bee health – a critical analysis. Biol. Rev. Camb. Philos. Soc. 91, 1006–1019 (2016).PubMed 
Article 

Google Scholar 
20.Tsvetkov, N. et al. Chronic exposure to neonicotinoids reduces honey bee health near corn crops. Science 356, 1395–1397 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
21.Carnesecchi, E. et al. Investigating combined toxicity of binary mixtures in bees: meta-analysis of laboratory tests, modelling, mechanistic basis and implications for risk assessment. Environ. Int. 133 (Pt B), 105256 (2019).CAS 
PubMed 
Article 

Google Scholar 
22.Jackson, M. C., Loewen, C. J. G., Vinebrooke, R. D. & Chimimba, C. T. Net effects of multiple stressors in freshwater ecosystems: a meta-analysis. Glob. Change Biol. 22, 180–189 (2016).ADS 
Article 

Google Scholar 
23.Piggott, J. J., Townsend, C. R. & Matthaei, C. D. Reconceptualizing synergism and antagonism among multiple stressors. Ecol. Evol. 5, 1538–1547 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
24.Ascher, J. S. & Pickering, J. Discover life: bee species guide and world checklist (Hymenoptera: Apoidea: Anthophila). https://www.discoverlife.org/mp/20q?guide=Apoidea_species&flags=HAS (2012).25.Gill, R. J., Ramos-Rodriguez, O. & Raine, N. E. Combined pesticide exposure severely affects individual- and colony-level traits in bees. Nature 491, 105–108 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Schmid-Hempel, P. Evolutionary Parasitology (Oxford Univ. Press, 2011).27.Sánchez-Bayo, F. et al. Are bee diseases linked to pesticides? — A brief review. Environ. Int. 89–90, 7–11 (2016).PubMed 
Article 
CAS 

Google Scholar 
28.Brandt, A., Gorenflo, A., Siede, R., Meixner, M. & Büchler, R. The neonicotinoids thiacloprid, imidacloprid, and clothianidin affect the immunocompetence of honey bees (Apis mellifera L.). J. Insect Physiol. 86, 40–47 (2016).CAS 
PubMed 
Article 

Google Scholar 
29.Vaudo, A. D., Patch, H. M., Mortensen, D. A., Tooker, J. F. & Grozinger, C. M. Macronutrient ratios in pollen shape bumble bee (Bombus impatiens) foraging strategies and floral preferences. Proc. Natl Acad. Sci. USA 113, E4035–E4042 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Fürst, M. A., McMahon, D. P., Osborne, J. L., Paxton, R. J. & Brown, M. J. F. Disease associations between honeybees and bumblebees as a threat to wild pollinators. Nature 506, 364–366 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
31.Cedergreen, N. Quantifying synergy: a systematic review of mixture toxicity studies within environmental toxicology. PLoS ONE 9, e96580 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
32.Carvell, C. et al. Declines in forage availability for bumblebees at a national scale. Biol. Conserv. 132, 481–489 (2006).Article 

Google Scholar 
33.Baude, M. et al. Historical nectar assessment reveals the fall and rise of floral resources in Britain. Nature 530, 85–88 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Ovaskainen, O. et al. Community-level phenological response to climate change. Proc. Natl Acad. Sci. USA 110, 13434–13439 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Carvell, C. et al. Bumblebee family lineage survival is enhanced in high-quality landscapes. Nature 543, 547–549 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
36.Siviter, H. & Muth, F. Do novel insecticides pose a threat to beneficial insects? Proc. R. Soc. B 287, 20201265 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
37.Topping, C. J., Aldrich, A. & Berny, P. Overhaul environmental risk assessment for pesticides. Science 367, 360–363 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
38.Sgolastra, F. et al. Bees and pesticide regulation: lessons from the neonicotinoid experience. Biol. Conserv. 241, 108356 (2020).Article 

Google Scholar 
39.Mullin, C. A. Effects of ‘inactive’ ingredients on bees. Curr. Opin. Insect Sci. 10, 194–200 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
40.Colin, T., Monchanin, C., Lihoreau, M. & Barron, A. B. Pesticide dosing must be guided by ecological principles. Nat. Ecol. Evol. 4, 1575–1577 (2020).PubMed 
Article 

Google Scholar 
41.Milner, A. M. & Boyd, I. L. Toward pesticidovigilance. Science 357, 1232–1234 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
42.Franklin, E. L. & Raine, N. E. Moving beyond honeybee-centric pesticide risk assessments to protect all pollinators. Nat. Ecol. Evol. 3, 1373–1375 (2019PubMed 
Article 

Google Scholar 
43.Brühl, C. A. & Zaller, J. G. Biodiversity decline as a consequence of an inappropriate environmental risk assessment of pesticides. Front. Environ. Sci. 7, 177 (2019).Article 

Google Scholar 
44.OECD. Test No. 245: Honey Bee (Apis Mellifera L.), Chronic Oral Toxicity Test (10-Day Feeding) (OECD, 2017).45.Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).Article 

Google Scholar 
46.Duval, S. & Tweedie, R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics 56, 455–463 (2000).CAS 
PubMed 
MATH 
Article 

Google Scholar 
47.Woodcock, B. A. et al. Meta-analysis reveals that pollinator functional diversity and abundance enhance crop pollination and yield. Nat. Commun. 10, 1481 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Siviter, H., Koricheva, J., Brown, M. J. F. & Leadbeater, E. Quantifying the impact of pesticides on learning and memory in bees. J. Appl. Ecol. 55, 2812–2821 (2018).PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Sexton, J. P., McIntyre, P. J., Angert, A. L. & Rice, K. J. Evolution and ecology of species range limits. Annu. Rev. Ecol. Evol. S. 40, 415–436. https://doi.org/10.1146/annurev.ecolsys.110308.120317 (2009).Article 

Google Scholar 
2.Peischl, S., Kirkpatrick, M. & Excoffier, L. Expansion load and the evolutionary dynamics of a species range. Am. Nat. 185, E81-93. https://doi.org/10.1086/680220 (2015).Article 
PubMed 

Google Scholar 
3.MacLean, S. A. & Beissinger, S. R. Species’ traits as predictors of range shifts under contemporary climate change: A review and meta-analysis. Glob. Chang. Biol 23, 4094–4105. https://doi.org/10.1111/gcb.13736 (2017).ADS 
Article 
PubMed 

Google Scholar 
4.Reid, N. European hare (Lepus europaeus) invasion ecology: Implication for the conservation of the endemic Irish hare (Lepus timidus hibernicus). Biol. Invas. 13, 559–569. https://doi.org/10.1007/s10530-010-9849-x (2011).Article 

Google Scholar 
5.Thulin, C.-G. The distribution of mountain hares (Lepus timidus, L. 1758) in Europe: A challenge from brown hares (L. europaeus, Pall 1778)?. Mammal Rev. 33, 29–42. https://doi.org/10.1046/j.1365-2907.2003.00008.x (2003).Article 

Google Scholar 
6.Levanen, R., Kunnasranta, M. & Pohjoismaki, J. Mitochondrial DNA introgression at the northern edge of the brown hare (Lepus europaeus) range. Ann. Zool. Fenn. 55, 15–24 (2018).Article 

Google Scholar 
7.Lönnberg, D. On hybrids between Lepus timidus L. and Lepus europeus Pall. from southern Sweden. Proc. Zool. Soc. Lond. 1, 278–287 (1905).
Google Scholar 
8.Thenius, E. Grundzüge der Faunen- und Verbreitungsgesichte der Säugetiere (Gustav Fisher Verlag, 1980).
Google Scholar 
9.Levanen, R., Thulin, C. G., Spong, G. & Pohjoismaki, J. L. O. Widespread introgression of mountain hare genes into Fennoscandian brown hare populations. PLoS ONE https://doi.org/10.1371/journal.pone.0191790 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
10.Angerbjorn, A. & Flux, J. E. C. Lepus timidus. Mammalian Sp. 495, 1–11 (1995).
Google Scholar 
11.Ferreira, M. S. et al. The transcriptional landscape of seasonal coat colour moult in the snowshoe hare. Mol. Ecol. 26, 4173–4185. https://doi.org/10.1111/mec.14177 (2017).Article 
PubMed 

Google Scholar 
12.Jones, M. R. et al. Adaptive introgression underlies polymorphic seasonal camouflage in snowshoe hares. Science 360, 1355–1358. https://doi.org/10.1126/science.aar5273 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
13.Cheng, E., Hodges, K. E., Melo-Ferreira, J., Alves, P. C. & Mills, L. S. Conservation implications of the evolutionary history and genetic diversity hotspots of the snowshoe hare. Mol. Ecol. 23, 2929–2942. https://doi.org/10.1111/mec.12790 (2014).CAS 
Article 
PubMed 

Google Scholar 
14.Jones, M. R., Mills, L. S., Jensen, J. D. & Good, J. M. Convergent evolution of seasonal camouflage in response to reduced snow cover across the snowshoe hare range. Evolution 74, 2033–2045. https://doi.org/10.1111/evo.13976 (2020).CAS 
Article 
PubMed 

Google Scholar 
15.Jones, M. R., Mills, L. S., Jensen, J. D. & Good, J. M. The origin and spread of locally adaptive seasonal camouflage in snowshoe hares. Am. Nat. 196, 316–332. https://doi.org/10.1086/710022 (2020).Article 
PubMed 

Google Scholar 
16.Ferreira, M. S. et al. Transcriptomic regulation of seasonal coat color change in hares. Ecol. Evol. 10, 1180–1192. https://doi.org/10.1002/ece3.5956 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
17.Ferreira, M. S. et al. The legacy of recurrent introgression during the radiation of hares. Syst. Biol. 70, 593–607. https://doi.org/10.1093/sysbio/syaa088 (2021).Article 
PubMed 

Google Scholar 
18.Giska, I. et al. Introgression drives repeated evolution of winter coat color polymorphism in hares. Proc. Natl. Acad. Sci. U.S.A. 116, 24150–24156. https://doi.org/10.1073/pnas.1910471116 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
19.Zimova, M., Mills, L. S. & Nowak, J. J. High fitness costs of climate change-induced camouflage mismatch. Ecol. Lett. 19, 299–307. https://doi.org/10.1111/ele.12568 (2016).Article 
PubMed 

Google Scholar 
20.Zimova, M., Mills, L. S., Lukacs, P. M. & Mitchell, M. S. Snowshoe hares display limited phenotypic plasticity to mismatch in seasonal camouflage. Proc. Biol. Sci. 281, 20140029. https://doi.org/10.1098/rspb.2014.0029 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
21.Zimova, M. et al. Lack of phenological shift leads to increased camouflage mismatch in mountain hares. Proc. Biol. Sci. 287, 20201786. https://doi.org/10.1098/rspb.2020.1786 (2020).Article 
PubMed 

Google Scholar 
22.Chouchani, E. T., Kazak, L. & Spiegelman, B. M. New advances in adaptive thermogenesis: UCP1 and beyond. Cell Metab. 29, 27–37. https://doi.org/10.1016/j.cmet.2018.11.002 (2019).CAS 
Article 
PubMed 

Google Scholar 
23.Nowack, J., Giroud, S., Arnold, W. & Ruf, T. Muscle non-shivering thermogenesis and its role in the evolution of endothermy. Front. Physiol. 8, 889. https://doi.org/10.3389/fphys.2017.00889 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
24.Hancock, A. M., Clark, V. J., Qian, Y. D. & Di Rienzo, A. Population genetic analysis of the uncoupling proteins supports a role for UCP3 in human cold resistance. Mol. Biol. Evol. 28, 601–614. https://doi.org/10.1093/molbev/msq228 (2011).CAS 
Article 
PubMed 

Google Scholar 
25.Piertney, S. B. & Oliver, M. K. The evolutionary ecology of the major histocompatibility complex. Heredity (Edinb) 96, 7–21. https://doi.org/10.1038/sj.hdy.6800724 (2006).CAS 
Article 

Google Scholar 
26.Sin, Y. W. et al. Pathogen burden, co-infection and major histocompatibility complex variability in the European badger (Meles meles). Mol. Ecol. 23, 5072–5088. https://doi.org/10.1111/mec.12917 (2014).CAS 
Article 
PubMed 

Google Scholar 
27.Borghans, J. A., Beltman, J. B. & De Boer, R. J. MHC polymorphism under host-pathogen coevolution. Immunogenetics 55, 732–739. https://doi.org/10.1007/s00251-003-0630-5 (2004).CAS 
Article 
PubMed 

Google Scholar 
28.Apanius, V., Penn, D., Slev, P. R., Ruff, L. R. & Potts, W. K. The nature of selection on the major histocompatibility complex. Crit. Rev. Immunol. 37, 75–120. https://doi.org/10.1615/CritRevImmunol.v37.i2-6.10 (2017).Article 
PubMed 

Google Scholar 
29.Manlik, O. et al. Is MHC diversity a better marker for conservation than neutral genetic diversity? A case study of two contrasting dolphin populations. Ecol. Evol. 9, 6986–6998. https://doi.org/10.1002/ece3.5265 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
30.Radwan, J., Biedrzycka, A. & Babik, W. Does reduced MHC diversity decrease viability of vertebrate populations?. Biol. Conserv. 143, 537–544. https://doi.org/10.1016/j.biocon.2009.07.026 (2010).Article 
PubMed 

Google Scholar 
31.Lan, H., Zhou, T., Wan, Q. H. & Fang, S. G. Genetic diversity and differentiation at structurally varying MHC haplotypes and microsatellites in bottlenecked populations of endangered crested ibis. Cells-Basel https://doi.org/10.3390/cells8040377 (2019).Article 

Google Scholar 
32.Cornetti, L., Hilfiker, D., Lemoine, M. & Tschirren, B. Small-scale spatial variation in infection risk shapes the evolution of a Borrelia resistance gene in wild rodents. Mol. Ecol. 27, 3515–3524. https://doi.org/10.1111/mec.14812 (2018).CAS 
Article 
PubMed 

Google Scholar 
33.Tschirren, B., Andersson, M., Scherman, K., Westerdahl, H. & Raberg, L. Contrasting patterns of diversity and population differentiation at the innate immunity gene toll-like receptor 2 (TLR2) in two sympatric rodent species. Evolution 66, 720–731. https://doi.org/10.1111/j.1558-5646.2011.01473.x (2012).CAS 
Article 
PubMed 

Google Scholar 
34.Mukherjee, S., Ganguli, D. & Majumder, P. P. Global footprints of purifying selection on Toll-like receptor genes primarily associated with response to bacterial infections in humans. Genome Biol. Evol. 6, 551–558. https://doi.org/10.1093/gbe/evu032 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Burton, R. S. & Barreto, F. S. A disproportionate role for mtDNA in Dobzhansky-Muller incompatibilities?. Mol. Ecol. 21, 4942–4957. https://doi.org/10.1111/mec.12006 (2012).CAS 
Article 
PubMed 

Google Scholar 
36.Nei, M. Analysis of gene diversity in subdivided populations. Proc. Natl. Acad. Sci. U.S.A. 70, 3321–3323 (1973).ADS 
CAS 
Article 

Google Scholar 
37.Klein, J., Sato, A. & Nikolaidis, N. MHC, TSP, and the origin of species: From immunogenetics to evolutionary genetics. Annu. Rev. Genet. 41, 281–304. https://doi.org/10.1146/annurev.genet.41.110306.130137 (2007).CAS 
Article 
PubMed 

Google Scholar 
38.Surridge, A. K. et al. Diversity and evolutionary history of the MHC DQA gene in leporids. Immunogenetics 60, 515–525. https://doi.org/10.1007/s00251-008-0309-z (2008).CAS 
Article 
PubMed 

Google Scholar 
39.Smith, S., de Bellocq, J. G., Suchentrunk, F. & Schaschl, H. Evolutionary genetics of MHC class II beta genes in the brown hare, Lepus europaeus. Immunogenetics 63, 743–751. https://doi.org/10.1007/s00251-011-0539-3 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
40.Excoffier, L., Hofer, T. & Foll, M. Detecting loci under selection in a hierarchically structured population. Heredity (Edinb) 103, 285–298. https://doi.org/10.1038/hdy.2009.74 (2009).CAS 
Article 

Google Scholar 
41.Thulin, C. G., Jaarola, M. & Tegelstrom, H. The occurrence of mountain hare mitochondrial DNA in wild brown hares. Mol. Ecol. 6, 463–467 (1997).CAS 
Article 

Google Scholar 
42.Marques, J. P. et al. Range expansion underlies historical introgressive hybridization in the Iberian hare. Sci. Rep. https://doi.org/10.1038/Srep40788 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
43.Melo-Ferreira, J., Boursot, P., Suchentrunk, F., Ferrand, N. & Alves, P. C. Invasion from the cold past: extensive introgression of mountain hare (Lepus timidus) mitochondrial DNA into three other hare species in northern Iberia. Mol. Ecol. 14, 2459–2464. https://doi.org/10.1111/j.1365-294X.2005.02599.x (2005).CAS 
Article 
PubMed 

Google Scholar 
44.Glover, K. A. et al. A comparison of SNP and STR loci for delineating population structure and performing individual genetic assignment. BMC Genet. 11, 2. https://doi.org/10.1186/1471-2156-11-2 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Kristensen, T. N., Hoffmann, A. A., Pertoldi, C. & Stronen, A. V. What can livestock breeders learn from conservation genetics and vice versa?. Front. Genet. https://doi.org/10.3389/Fgene.2015.00038 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
46.Nishimura, T., Katsumura, T., Motoi, M., Oota, H. & Watanuki, S. Experimental evidence reveals the UCP1 genotype changes the oxygen consumption attributed to non-shivering thermogenesis in humans. Sci. Rep. 7, 5570. https://doi.org/10.1038/s41598-017-05766-3 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
47.Glanville, E. J., Murray, S. A. & Seebacher, F. Thermal adaptation in endotherms: Climate and phylogeny interact to determine population-level responses in a wild rat. Funct. Ecol. 26, 390–398. https://doi.org/10.1111/j.1365-2435.2011.01933.x (2012).Article 

Google Scholar 
48.Leroy, G., Phocas, F., Hedan, B., Verrier, E. & Rognon, X. Inbreeding impact on litter size and survival in selected canine breeds. Vet. J. 203, 74–78. https://doi.org/10.1016/j.tvjl.2014.11.008 (2015).Article 
PubMed 

Google Scholar 
49.Zhang, P. et al. High polymorphism in MHC-DRB genes in golden snub-nosed monkeys reveals balancing selection in small, isolated populations. BMC Evol. Biol https://doi.org/10.1186/s12862-018-1148-7 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
50.Cortazar-Chinarro, M., Meyer-Lucht, Y., Laurila, A. & Hoglund, J. Signatures of historical selection on MHC reveal different selection patterns in the moor frog (Rana arvalis). Immunogenetics 70, 477–484. https://doi.org/10.1007/s00251-017-1051-1 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
51.Darfour-Oduro, K. A., Megens, H. J., Roca, A. L., Groenen, M. A. & Schook, L. B. Adaptive evolution of toll-like receptors (TLRs) in the Family Suidae. PLoS ONE 10, e0124069. https://doi.org/10.1371/journal.pone.0124069 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
52.Lotterhos, K. E. & Whitlock, M. C. Evaluation of demographic history and neutral parameterization on the performance of FST outlier tests. Mol. Ecol. 23, 2178–2192. https://doi.org/10.1111/mec.12725 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
53.Quesada-Lopez, T. et al. GPR120 controls neonatal brown adipose tissue thermogenic induction. Am. J. Physiol. Endocrinol. Metab. 317, E742–E750. https://doi.org/10.1152/ajpendo.00081.2019 (2019).CAS 
Article 
PubMed 

Google Scholar 
54.Luijten, I. H. N., Feldmann, H. M., von Essen, G., Cannon, B. & Nedergaard, J. In the absence of UCP1-mediated diet-induced thermogenesis, obesity is augmented even in the obesity-resistant 129S mouse strain. Am. J. Physiol. Endocrinol. Metab. 316, E729–E740. https://doi.org/10.1152/ajpendo.00020.2019 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
55.Klein, J., Sato, A., Nagl, S. & OhUigin, C. Molecular trans-species polymorphism. Annu. Rev. Ecol. Syst. 29, 1. https://doi.org/10.1146/annurev.ecolsys.29.1.1 (1998).Article 

Google Scholar 
56.Gouy Bellocq, J., Suchentrunk, F., Baird, S. J. & Schaschl, H. Evolutionary history of an MHC gene in two leporid species: Characterisation of Mhc-DQA in the European brown hare and comparison with the European rabbit. Immunogenetics 61, 131–144. https://doi.org/10.1007/s00251-008-0349-4 (2009).Article 

Google Scholar 
57.Arbogast, B. S., Edwards, S. V., Wakeley, J., Beerli, P. & Slowinski, J. B. Estimating divergence times from molecular data on phylogenetic and population genetic timescales. Annu. Rev. Ecol. Syst. 33, 707–740. https://doi.org/10.1146/annurev.ecolsys.33.010802.150500 (2002).Article 

Google Scholar 
58.Lenz, T. L. Adaptive value of novel MHC immune gene variants. Proc. Natl. Acad. Sci. U.S.A. 115, 1414–1416. https://doi.org/10.1073/pnas.1722600115 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
59.Fijarczyk, A., Dudek, K., Niedzicka, M. & Babik, W. Balancing selection and introgression of newt immune-response genes. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2018.0819 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
60.Grossen, C., Keller, L., Biebach, I., Croll, D. & Consortium, I. G. G. Introgression from domestic goat generated variation at the major histocompatibility complex of alpine Ibex. PLoS Genet. https://doi.org/10.1371/journal.pgen.1004438 (2014).Article 

Google Scholar 
61.Nadachowska-Brzyska, K., Zielinski, P., Radwan, J. & Babik, W. Interspecific hybridization increases MHC class II diversity in two sister species of newts. Mol. Ecol. 21, 887–906. https://doi.org/10.1111/j.1365-294X.2011.05347.x (2012).CAS 
Article 
PubMed 

Google Scholar 
62.Wegner, K. M. & Eizaguirre, C. New(t)s and views from hybridizing MHC genes: Introgression rather than trans-species polymorphism may shape allelic repertoires. Mol. Ecol. 21, 779–781. https://doi.org/10.1111/j.1365-294X.2011.05401.x (2012).CAS 
Article 
PubMed 

Google Scholar 
63.Thulin, C. G. The distribution of mountain hares Lepus timidus in Europe: A challenge from brown hares L-europaeus ?. Mammal. Rev. 33, 29–42. https://doi.org/10.1046/j.1365-2907.2003.00008.x (2003).Article 

Google Scholar 
64.Jansson, G. & Pehrson, A. The recent expansion of the brown hare (Lepus europaeus) in Sweden with possible implications to the mountain hare (L-timidus). Eur. J. Wildlife Res. 53, 125–130. https://doi.org/10.1007/s10344-007-0086-2 (2007).Article 

Google Scholar 
65.Smith, S. et al. Nonreceding hare lines: Genetic continuity since the Late Pleistocene in European mountain hares (Lepus timidus). Biol. J. Linn Soc. 120, 891–908 (2017).Article 

Google Scholar 
66.Levanen, R., Pohjoismaki, J. L. O. & Kunnasranta, M. Home ranges of semi-urban brown hares (Lepus europaeus) and mountain hares (Lepus timidus) at northern latitudes. Ann. Zool. Fenn. 56, 107–120 (2019).Article 

Google Scholar 
67.Palo, J. U., Ulmanen, I., Lukka, M., Ellonen, P. & Sajantila, A. Genetic markers and population history: Finland revisited. Eur. J. Hum. Genet. EJHG 17, 1336–1346. https://doi.org/10.1038/ejhg.2009.53 (2009).Article 
PubMed 

Google Scholar 
68.RCoreTeam. R: A language and environment for statistical computing., Vol. https://www.R-project.org/. ( R Foundation for Statistical Computing, 2020).69.Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Vol. https://ggplot2.tidyverse.org (Springer-Verlag, 2016).70.Biedrzycka, A., Sebastian, A., Migalska, M., Westerdahl, H. & Radwan, J. Testing genotyping strategies for ultra-deep sequencing of a co-amplifying gene family: MHC class I in a passerine bird. Mol. Ecol. Resour. 17, 642–655. https://doi.org/10.1111/1755-0998.12612 (2017).CAS 
Article 
PubMed 

Google Scholar 
71.Sebastian, A., Migalska, M. & Biedrzycka, A. AmpliSAS and AmpliHLA: Web server tools for MHC typing of non-model species and human using NGS data. Methods Mol. Biol. 249–273, 2018. https://doi.org/10.1007/978-1-4939-8546-3_18 (1802).CAS 
Article 

Google Scholar 
72.Larkin, M. A. et al. Clustal W and Clustal X version 2.0. Bioinformatics 23, 2947–2948. https://doi.org/10.1093/bioinformatics/btm404 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
73.Ronquist, F. et al. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542. https://doi.org/10.1093/sysbio/sys029 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
74.Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214. https://doi.org/10.1186/1471-2148-7-214 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
75.Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973. https://doi.org/10.1093/molbev/mss075 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
76.Rambaut, A. FigTree. 1.4.3. Graphical viewer of phylogenetic trees. (http://tree.bio.ed.ac.uk/software/figtree/), (2018).77.Excoffier, L. & Lischer, H. E. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resourc. 10, 564–567. https://doi.org/10.1111/j.1755-0998.2010.02847.x (2010).Article 

Google Scholar 
78.Smith, S. et al. Homozygosity at a class II MHC locus depresses female reproductive ability in European brown hares. Mol. Ecol. 19, 4131–4143. https://doi.org/10.1111/j.1365-294X.2010.04765.x (2010).Article 
PubMed 

Google Scholar 
79.Melo-Ferreira, J., Seixas, F. A., Cheng, E., Mills, L. S. & Alves, P. C. The hidden history of the snowshoe hare, Lepus americanus: extensive mitochondrial DNA introgression inferred from multilocus genetic variation. Mol. Ecol. 23, 4617–4630. https://doi.org/10.1111/mec.12886 (2014).CAS 
Article 
PubMed 

Google Scholar 
80.Matthee, C. A., van Vuuren, B. J., Bell, D. & Robinson, T. J. A molecular supermatrix of the rabbits and hares (Leporidae) allows for the identification of five intercontinental exchanges during the Miocene. Syst. Biol. 53, 433–447. https://doi.org/10.1080/10635150490445715 (2004).Article 
PubMed 

Google Scholar 
81.Humphreys, A. M. & Barraclough, T. G. The evolutionary reality of higher taxa in mammals. Proc. Biol. Sci. 281, 20132750. https://doi.org/10.1098/rspb.2013.2750 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
82.Ge, D. et al. Evolutionary history of lagomorphs in response to global environmental change. PLoS ONE 8, e59668. https://doi.org/10.1371/journal.pone.0059668 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
83.Soria-Carrasco, V. & Castresana, J. Diversification rates and the latitudinal gradient of diversity in mammals. Proc. Biol. Sci. 279, 4148–4155. https://doi.org/10.1098/rspb.2012.1393 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
84.Peakall, R. & Smouse, P. E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research–an update. Bioinformatics 28, 2537–2539. https://doi.org/10.1093/bioinformatics/bts460 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
85.Bouckaert, R. et al. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 15, e1006650. https://doi.org/10.1371/journal.pcbi.1006650 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
86.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
Article 

Google Scholar  More

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In June 2018, 180 cars fanned out across Denmark and parts of Germany on a grand insect hunt. Armed with white, funnel-shaped nets mounted on their car roofs, enthusiastic citizen naturalists roamed through cities, farmlands, grasslands, wetlands and forests. The drivers sent the haul from their ‘InsectMobiles’ to scientists at the National History Museum of Denmark in Copenhagen and the German Centre for Integrative Biodiversity Research in Leipzig.The researchers dried and weighed the collections to determine the total mass of flying insects in each landscape. They expected some bad news. The previous year, scientists in Germany had found that the flying-insect biomass in their nature reserves had plunged by 76% over 27 years1. Similar studies had led to news headlines that screamed of an ongoing “insectageddon” and “insect apocalypse”. British columnist George Monbiot wrote in The Guardian: “Insectageddon: farming is more catastrophic than climate breakdown”.But when the researchers tallied the InsectMobile results2, they didn’t see evidence of declines everywhere. Insect biomass totals were higher than expected in agricultural fields, and indeed in all places except cities in their study, which is yet to be peer reviewed2. Aletta Bonn, an entomologist at the Leipzig centre and a co-author of the study, says this could be because the fertilizers that farmers use are leading to lush plant life, which is reverberating through the ecosystem. That said, she cautions, not every insect species in the study area might be doing well; some could be thriving, others not so much.“We do need to understand better what kind of insects are affected and to which degree,” Bonn says. “I think the generalization that all agriculture is bad — I wouldn’t say so.”The findings resonate with what biologist Mark Vellend and his colleagues have seen in their studies of trees at the edge of boreal forests in eastern Canada. They’ve found that spruce, eastern white cedar, eastern hemlock and American beech have been struggling to maintain their roothold since European and American settlers began clearing land more than a century ago. But poplar, paper birch, maple and balsam fir are thriving3. Vellend, who teaches at the University of Sherbrooke in Quebec, Canada, poses a question to his students every year: if they were to count the plant species in the province, would the number have gone up or down since Europeans arrived?Most students so far have got it wrong. “Many of them are surprised to learn that there’s 25% more [species] than there were 500 years ago, before people of European origin laid a foot here,” Vellend says.
Humans are driving one million species to extinction
Something odd is going on in biodiversity studies. Scientists have long warned that animal and plant species are disappearing at an alarming rate. In 2019, an international group of hundreds of researchers produced the most comprehensive report on biodiversity ever assembled, and they concluded that some one million animals and plant species are facing extinction. On top of that, humans have cleared landscapes and chopped down nearly one-third of the world’s forests since the Industrial Revolution — all of which bodes poorly for protecting species.So, scientists naturally assumed that they would find precipitous declines in biodiversity nearly everywhere they looked. But they haven’t. And a consensus is emerging that, even though species are disappearing globally at alarming rates, scientists cannot always detect the declines at the local level. Some species, populations and ecosystems are indeed crashing, but others are ebbing more slowly, holding steady or even thriving. This is not necessarily good news. In most places, new species are moving in when older ones leave or blink out, changing the character of the communities. And that has important implications, because biodiversity at the small scale has outsize importance; it provides food, fresh water, fuel, pollination and many ecosystem services that humans and other organisms depend on.“Ecosystems don’t work at the global scale,” says Maria Dornelas, an ecologist at the University of St Andrews, UK. “I’m interested in what is happening to biodiversity at the local scale, because that’s the scale that we experience.”Scientists say it’s clear that there’s a biodiversity crisis, but there are many questions about the details. Which species will lose? Will new communities be healthy and desirable? Will the rapidly changing ecosystems be able to deal with climate change? And where should conservation actions be targeted?To find answers, scientists need better data from field sites around the world, collected at regular intervals over long periods of time. Such data don’t exist for much of the world, but scientists are trying to fill the gaps in Europe. They are planning a comprehensive network, called EuropaBON, that will combine research plots, citizen scientists, satellite sensors, models and other methods to generate a continuous stream of biodiversity data for the continent. The effort will inform European policymakers, who are pushing for a strong and verifiable global biodiversity agreement when nations next meet to renew the United Nations’ Convention on Biological Diversity (CBD) — an international pact to halt and reverse biodiversity loss.How to measure biodiversityBiological diversity is a shape-shifting term that has been used in many ways. The CBD takes a broad approach, defining it as “the variability among living organisms from all sources”. This includes, it says, “diversity within species, between species and of ecosystems”.“Everybody could sign up to such a definition,” says Chris Thomas, an ecologist at the University of York, UK. “It means that different people can pick on different aspects that are all included within that all-encompassing definition, and find almost whatever trend they want.”Scientists measure biodiversity through many metrics, but the most common is species richness: a simple count of the number of species in the area. They also check the relative abundance of different organisms — a metric called species evenness — and track the identity of species to learn the ‘community composition’. Further complicating matters, scientists sometimes tally biomass instead of species richness, especially when it comes to insects.Using such measures, the clearest signal that the world is losing biodiversity comes from the bookkeeper of species, the International Union for Conservation of Nature. It has found that 26% of all mammals, 14% of birds and 41% of amphibians are currently threatened globally. Insufficient data are available for other groups, such as most plants and fungi. Extinction rates in the past few centuries are much higher than they had been before humans started to transform the planet; some estimates suggest current rates are 1,000 times the background level. One calculation estimates that, if high rates continue, then within 14,000 years, we could enter the sixth mass extinction — an event similar to the one that wiped out about three-quarters of the planet’s species, including dinosaurs, 65 million years ago4. For the most critically endangered species, the death knell could come within decades.

Lionfish have invaded the Red Sea, one example of species changes seen in many places.Credit: Alexis Rosenfeld/Getty

More bad news comes from the United Nations-backed Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) — the organization behind the 2019 report warning that about one million species were threatened by extinction. The report also found that the abundance of native species in local terrestrial ecosystems has dropped by an average of around 20% as a result of human activities.Another biodiversity report that draws considerable attention comes from the conservation organization WWF and the Zoological Society of London, among other groups. Every year, they produce the Living Planet Index, which has amassed data for 27,695 populations of 4,806 vertebrate species. Last year, the report stated that population sizes of birds, mammals, fish, amphibians and reptiles declined, on average, by 68% between 1970 and 2016.Some researchers worry that such averaged figures can hide a lot of nuance, because many people might assume incorrectly that the average applies to most species. Dornelas likes to illustrate the danger by pointing out that the ‘average human’ has one breast and one testicle, and doesn’t exist.
Why deforestation and extinctions make pandemics more likely
Last year, Brian Leung, a biologist at McGill University in Montreal, and his colleagues re-analysed the Living Planet Index data from 2018 and found that a handful of populations are declining catastrophically, strongly pulling down the average. If these outliers are dropped from the computation, 98.6% of the populations on the index are holding steady or increasing or declining more slowly5. “We’re not saying there are not problems,” says Leung, who stresses that declines are still bad. “But there should be some caution about using these really broad-based global metrics, even though they are pretty powerful statements. But they can mask a whole lot of variation and be driven by extreme outliers.”When scientists talk about the world entering a sixth mass extinction, what sometimes gets lost is the timescale. Extinction rates for past periods of Earth’s history are calculated per one million years, and at present, researchers are seeing vertebrate species disappear at a rate of about 1% every century, and most of that has happened on islands.It’s clear there is a biodiversity crisis right now, although the pace is uncertain, says Henrique Pereira, a conservation biologist at the German Centre for Integrative Biodiversity Research , and a co-chair of an IPBES expert group. “It doesn’t mean that there is no decline. It means that if there is a decline, it’s much smaller than what maybe we thought.”So is the sixth mass extinction happening? “Well, not yet, if you want my scientific assessment of it. But is it going to be starting? Yes, maybe starting,” says Pereira.Difficult messageIn 2012, Vellend and his colleagues decided to see what’s happening with plant biodiversity by looking at a collection of individual field sites around the world. They compiled more than 16,000 studies in which scientists had monitored plants for at least 5 years, and found that only 8% of the studies noted a strong decline in the total number of species. Most plots showed either no change, smaller declines or even an increase in biodiversity6.The study was rejected by Nature, and one reviewer worried that journalists would garble the results and give the false impression that there were no problems with biodiversity. A Nature spokesperson says the peer-review process is confidential and that editorial decisions are not driven by considerations of potential media coverage. (Nature’s news team is editorially independent of its journal team.)

An experiment to trap and identify moth species in the Netherlands.Credit: Edwin Giesbers/Nature Picture Library

Vellend eventually published the study in the Proceedings of the National Academy of Sciences in 20136.His conclusions were soon backed up by Dornelas and her colleague Anne Magurran, an ecologist at the University of St Andrews, who have been compiling a database of biodiversity field studies, called BioTIME, since 2010. The database now has more than 12 million records for about 50,000 species from 600,000 locations around the world.In a study of 100 field sites worldwide, Dornelas and her colleagues had expected to find declines in species richness and abundance, but the data showed otherwise. Many sites were declining in biodiversity, but an equal proportion were improving. And about 20% showed no change over time. Overall, there wasn’t a clear trend7.At first, the researchers didn’t believe the results, so they reanalysed the data several ways and finally published the findings in 2014.“It was this tremendous shock. What’s going on?” says Pereira, who wasn’t involved in the study.Dornelas says reactions were mixed. Some people worried that the results could be misconstrued to suggest that everything’s fine with biodiversity. Others went even further. “Some people questioned our integrity, which is something that I take offence at, because being an ethical scientist is at the core of what I do,” she says. “But other people reached out to us and said, ‘Oh, interesting, that sort of matches my experience.’”Since then, many studies looking at biodiversity in the oceans, rivers, among insects — almost any grouping or biome one can think of — have found that there is no clear trend of decline. But that doesn’t mean the ecosystems are remaining static. Dornelas and her colleagues have continued to mine the BioTime database and have found that the mix of species in local communities is changing rapidly almost everywhere on Earth8 (see ‘Life on the go’). As some inhabitants disappear, colonizers move in and add to species richness, so the ‘average ecosystem’ shows no change or even an increase in the number of species, she says, with her usual cautions about averages9.

Source: Ref. 8

“Species are at the moment playing musical chairs,” says Dornelas.This can be seen most clearly on isolated islands, where 95% of the world’s extinctions have happened. Take New Zealand, where there were no mammalian predators before humans first settled there, less than 800 years ago. Since then, nearly half of New Zealand’s endemic birds have gone extinct.But despite the extinctions, biodiversity, measured by species richness, has improved over time in New Zealand, Vellend says. Continental birds have replaced the lost endemics. Plant biodiversity is doing well; fewer than 10 native species have gone extinct, and there are now 4,000 plant species on the islands, up from 2,000 before human settlers. And there are more than two dozen new land mammals.The lesson is that species richness or abundance figures might not tell the whole story, says Dornelas. Rather, scientists need to know the identity of all the species in a community, and track their relative abundances. This will allow them to learn which species are declining and which could be targeted for conservation.The story is similar on the continents, except with fewer complete extinctions. In Denmark over the past 140 years, 50 plant species have declined in abundance and range, but 236 have expanded their habitats. The large majority are holding steady10. Scientists looking at Europe’s birds since 1980 have found that 175 species are declining while 203 are increasing11. Rare birds are doing better than more common species, such as the house sparrow (Passer domesticus). A study of vertebrates in North America and Europe by Leung and his colleagues found that, whereas amphibians are declining across the board, other taxa have winners and losers in roughly equal measure12.Even corals seems to show the same pattern: between 1981 and 2013, 26 genera in the Caribbean and Indo-Pacific became more abundant, while 31 declined13.With studies piling up, it’s become increasingly acceptable for scientists to say that biodiversity isn’t declining everywhere and for all taxa, says Dan Greenberg, an ecologist at University of California, San Diego. “The tide is turning,” he says, “but the field is grappling with how to translate that to a public audience, or what does that mean in terms of social consequences.”That doesn’t mean there’s no biodiversity crisis, stresses Helmut Hillebrand, an ecologist at the University of Oldenburg in Germany. Some scientists worry that unusually high turnover, together with signals of instability in some populations, could itself portend ecological collapse. Humans are carrying species into new environs, leading to colonization. Whereas climate change is spurring warm-loving species to expand into new zones, cold-adapted species are losing out. Plus, generalist species that are fast-growing and less particular about where they live are thriving in human-modified landscapes.Specialists that need highly specific environments or that disperse poorly get easily isolated, which increases their extinction risk, says Greenberg. Case in point: amphibians. “If something changes in that environment, you can’t really hop over to another site very easily,” he says.
The battle for the soul of biodiversity
Turnover could lead to distant communities that increasingly resemble each other — a process called homogenization that has been documented in particular regions and taxa. In 2015, César Capinha, a biogeographer at the University of Lisbon, and his colleagues found that snail populations in temperate regions as far flung as Virginia, New Zealand and South Africa had species in common, thanks to human travel and trade14. Similarly, in the plant study in Denmark, scientists found that plant communities are increasingly looking like each other and are dominated by generalists. Scientists worry that such landscapes might not be resilient to environmental change.Dornelas urges caution in interpreting the changes seen so far. There hasn’t yet been a robust global study of homogenization to know the extent to which this is happening. And there is also increased habitat fragmentation, which can counter this process. “We don’t often talk about both of those at the same time,” Dornelas says. “I’ve now learned not to assume I know what’s going on until I’ve seen what the data show.”Scientists have also observed cases in which a colonizer mixes with a resident to rapidly form a new hybrid species, especially in plants, says Thomas. But it’s unclear how long these hybrids will persist, and most other groups usually take one million years or so to form new species. Many of the beasts of today could go extinct before that process can catch up, says Dolph Schluter, an evolutionary biologist at the University of British Columbia in Vancouver, Canada. “We are going to lose a lot of the ancients. And no amount of evolution in the short term is going to replace those,” Schluter says.Keeping tabs on lifeGlobal studies of biodiversity have important biases owing to data gaps. Most of the records of species come from Europe and North America; there are very few data from the tropics, where rainforests house half of all species in just 7% of the Earth’s surface. And even in the most richly monitored places on Earth, such as Europe, the data are patchy. “We are trying to read the book, but we have only a few letters,” says Pereira.Pereira and his colleagues are designing a top-down monitoring network in Europe called EuropaBON that can add in more letters, and maybe even sentences. The project has received 3 million (US$3.5 million) from the European Commission, and was launched last December. Pereira and Jessica Junker, the scientific coordinator of EuropaBON and a conservationist at Martin Luther University Halle-Wittenberg in Germany, have assembled a 350-strong community of national conservation authorities, non-governmental organizations, scientists and government officials. Among the first goals is to create a map that identifies data gaps as well as a list of metrics to be tracked, Pereira says. At the end of the initial three-year stage, EuropaBON aims to set up a coordinating centre for the monitoring network.It’d have to be affordable to be replicable and maintained over time. Lack of funds has hampered a global version of this project, called GEO BON, on which EuropaBON is based, says Dornelas. To contain costs, EuropaBON intends to use existing long-term monitoring sites. Where there are data gaps, the scientists would launch new tracking efforts using technology such as sensors, weather radar and drones, or citizen volunteers, who already do 80% of the biodiversity monitoring in Europe.EuropaBON would also use satellite data of land cover, vegetation growth and other indicators of local biodiversity. The data streams would be combined with modelling to generate seamless biodiversity data over time and across Europe. The plan is that data from the project will help the European Commission to decide what research to fund on the continent’s biodiversity, says Pereira. In a stakeholder meeting in May for EuropaBON, Humberto Delgado Rosa, the director for natural capital at the European Commission, said that the European Union wants to make “huge leaps internationally in biodiversity, as it has done with climate in Paris”. EuropaBON should help Europe to meet its international commitments to report on its biodiversity, Rosa said.“This new global biodiversity framework needs quantification, measurability,” he said. “In a nutshell, we need knowledge.”Dornelas, who is also part of EuropaBON, says she would like to expand this initiative across the world. Canada is exploring a national version, called CanBON. But for now, monitoring remains sparse in the poorer parts of the world, where most of the planet’s biodiversity remains.“Europe is one of the best monitored parts of the planet, and where we’re really, really missing data is from other parts of the world,” she says. “But I guess we got to start somewhere.”

Nature 596, 22-25 (2021)
doi: https://doi.org/10.1038/d41586-021-02088-3

References1.Hallmann, C. A. et al. PLoS ONE 12, e0185809 (2017).PubMed 
Article 

Google Scholar 
2.Svenningsen, C. S. et al. Preprint at bioRxiv https://doi.org/10.1101/2020.09.16.299404 (2020).3.Danneyrolles, V. et al. J. Ecol. 109, 273–283 (2021).Article 

Google Scholar 
4.McGill, B. J., Dornelas, M., Gotelli, N. J. & Magurran, A. E. Trends Ecol. Evol. 30, 104–113 (2015).PubMed 
Article 

Google Scholar 
5.Leung, B. et al. Nature 588, 267–271 (2020).PubMed 
Article 

Google Scholar 
6.Vellend, M. et al. Proc. Natl Acad. Sci. USA 110, 19456–19459 (2013).PubMed 
Article 

Google Scholar 
7.Dornelas, M. et al. Science 344, 296–299 (2014).PubMed 
Article 

Google Scholar 
8.Blowes, S. A. et al. Science 366, 339–345 (2019).PubMed 
Article 

Google Scholar 
9.Dornelas, M. et al. Ecol. Lett. 22, 847–854 (2019).PubMed 
Article 

Google Scholar 
10.Nielsen, T. F., Sand-Jensen, K., Dornelas, M. & Bruun, H. H. Ecol. Lett. 22, 1650–1657 (2019).PubMed 
Article 

Google Scholar 
11.Burns, F. et al. Preprint at Authorea https://doi.org/10.22541/au.162557488.83915072 (2021).12.Leung, B., Greenberg, D. A. & Green, D. M. Divers. Distrib. 23, 1372–1380 (2017).Article 

Google Scholar 
13.Edmunds, P. J. et al. PLoS ONE 9, e107525 (2014).PubMed 
Article 

Google Scholar 
14.Capinha, C., Essl, F., Seebens, H., Moser, D. & Pereira, H. M. Science 348, 1248–1251 (2015).PubMed 
Article 

Google Scholar 
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1.Orams, M. B. Feeding wildlife as a tourism attraction: A review of issues and impacts. Tour. Manag. 23, 281–293 (2002).Article 

Google Scholar 
2.Balmford, A. et al. Walk on the wild side: Estimating the global magnitude of visits to protected areas. PLoS Biol. 13, e1002074 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
3.Knight, J. Making wildlife viewable: Habituation and attraction. Soc. Anim. 17, 167–184 (2009).Article 

Google Scholar 
4.Carter, N. H. et al. Coupled human and natural systems approach to wildlife research and conservation. Ecol. Soc. 19, 43 (2014).Article 

Google Scholar 
5.Balasubramaniam, K. N. et al. Addressing the challenges of research on human–wildlife interactions using the concept of coupled natural & human systems. Biol. Conserv. 257, 109095 (2021).Article 

Google Scholar 
6.Knight, J. The ready-to-view wild monkey: The convenience principle in Japanese wildlife tourism. Ann. Tour. Res. 37, 744–762 (2010).Article 

Google Scholar 
7.Okello, M. M., Manka, S. G. & D’Amour, D. E. The relative importance of large mammal species for tourism in Amboseli National Park, Kenya. Tour. Manag. 29, 751–760 (2008).Article 

Google Scholar 
8.Penteriani, V. et al. Consequences of brown bear viewing tourism: A review. Biol. Conserv. 206, 169–180 (2017).Article 

Google Scholar 
9.Ewen, J. G., Walker, L., Canessa, S. & Groombridge, J. J. Improving supplementary feeding in species conservation. Conserv. Biol. 29, 341–349 (2015).PubMed 
Article 

Google Scholar 
10.Jones, C. G. et al. The restoration of the Mauritius Kestrel Falco punctatus population. Ibis 137, S173–S180 (1995).Article 

Google Scholar 
11.Murray, M. H., Becker, D. J., Hall, R. J. & Hernandez, S. M. Wildlife health and supplemental feeding: A review and management recommendations. Biol. Conserv. 204, 163–174 (2016).Article 

Google Scholar 
12.Oro, D., Genovart, M., Tavecchia, G., Fowler, M. S. & Martínez-Abraín, A. Ecological and evolutionary implications of food subsidies from humans. Ecol. Lett. 16, 1501–1514 (2013).PubMed 
Article 

Google Scholar 
13.Civitello, D. J., Allman, B. E., Morozumi, C. & Rohr, J. R. Assessing the direct and indirect effects of food provisioning and nutrient enrichment on wildlife infectious disease dynamics. Philos. Trans. R. Soc. B Biol. Sci. 373, 20170101 (2018).Article 

Google Scholar 
14.Lappan, S., Malaivijitnond, S., Radhakrishna, S., Riley, E. P. & Ruppert, N. The human–primate interface in the new normal: Challenges and opportunities for primatologists in the COVID-19 era and beyond. Am. J. Primatol. 82, e23176 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Gibb, R. et al. Zoonotic host diversity increases in human-dominated ecosystems. Nature 584, 398–402 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
16.Dunay, E., Apakupakul, K., Leard, S., Palmer, J. L. & Deem, S. L. Pathogen transmission from humans to great apes is a growing threat to primate conservation. EcoHealth 15, 148–162 (2018).PubMed 
Article 

Google Scholar 
17.Fuentes, A., Shaw, E. & Cortes, J. Qualitative assessment of macaque tourist sites in Padangtegal, Bali, Indonesia, and the Upper Rock Nature Reserve, Gibraltar. Int. J. Primatol. 28, 1143–1158 (2007).Article 

Google Scholar 
18.Dellatore, D. F., Waitt, C. D. & Foitovà, I. The impact of tourism on the behavior of rehabilitated orangutans (Pongo abelii) in Bukit Lawang, North Sumatra, Indonesia. In Primate Tourism: A Tool for Conservation (eds Russon, A. E. & Wallis, J.) 98–120 (Cambridge University Press, 2014).Chapter 

Google Scholar 
19.Berman, C. M., Matheson, M. D., Ogawa, H. & Ionica, C. S. Tourism, infant mortality and stress indicators among Tibetan macaques at Huangshan, China. In Primate Tourism: A Tool for Conservation (eds Russon, A. E. & Wallis, J.) 21–43 (Cambridge University Press, 2014).Chapter 

Google Scholar 
20.Kurita, C. M. Provisioning and tourism infree-ranging Japanese macaques. In Primate Tourism: A Tool for Conservation (eds Russon, A. E. & Wallis, J.) 44–55 (Cambridge University Press, 2014).Chapter 

Google Scholar 
21.Long, Y., Bleisch, W. & Richardson, M. Rhinopithecus bieti. The IUCN Red List of Threatened Species 2020: e.T19597A8986243. https://doi.org/10.2305/IUCN.UK.2008.RLTS.T19597A8986243.en. IUCN Red List of Threatened Species https://www.iucnredlist.org/en (2020).22.Li, B., Pan, R. & Oxnard, C. E. Extinction of snub-nosed monkeys in China during the past 400 years. Int. J. Primatol. 23, 1227–1244 (2002).Article 

Google Scholar 
23.Wong, M. H. G., Li, R., Xu, M. & Long, Y. An integrative approach to assessing the potential impacts of climate change on the Yunnan snub-nosed monkey. Biol. Conserv. 158, 401–409 (2013).Article 

Google Scholar 
24.Li, L., Xue, Y., Wu, G., Li, D. & Giraudoux, P. Potential habitat corridors and restoration areas for the black-and-white snub-nosed monkey Rhinopithecus bieti in Yunnan, China. Oryx 49, 719–726 (2015).Article 

Google Scholar 
25.Long, Y., Kirkpatrick, C. R., Zhongtai, & Xiaolin,. Report on the distribution, population, and ecology of the Yunnan snub-nosed monkey (Rhinopithecus bieti). Primates 35, 241–250 (1994).Article 

Google Scholar 
26.Afonso, E. et al. Creating small food-habituated groups might alter genetic diversity in the endangered Yunnan snub-nosed monkey. Glob. Ecol. Conserv. 26, e01422 (2021).Article 

Google Scholar 
27.Cui, Z., Li, J., Chen, Y. & Zhang, L. Molecular epidemiology, evolution, and phylogeny of Entamoeba spp. Infect. Genet. Evol. 75, 104018 (2019).PubMed 
Article 

Google Scholar 
28.Jacob, A. S., Busby, E. J., Levy, A. D., Komm, N. & Clark, C. G. Expanding the Entamoeba universe: New hosts yield novel ribosomal lineages. J. Eukaryot. Microbiol. 63, 69–78 (2016).CAS 
PubMed 
Article 

Google Scholar 
29.Verweij, J. J. et al. Entamoeba histolytica infections in captive primates. Parasitol. Res. 90, 100–103 (2003).PubMed 
Article 

Google Scholar 
30.Tachibana, H. et al. Isolation and characterization of a potentially virulent species Entamoeba nuttalli from captive Japanese macaques. Parasitology 136, 1169–1177 (2009).CAS 
PubMed 
Article 

Google Scholar 
31.Levecke, B. et al. Molecular identification of Entamoeba spp. in captive nonhuman primates. J. Clin. Microbiol. 48, 2988–2990 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Levecke, B. et al. Transmission of Entamoeba nuttalli and Trichuris trichiura from nonhuman primates to humans. Emerg. Infect. Dis. 21, 1871–1872 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
33.Rivera, W. L., Yason, J. A. D. L. & Adao, D. E. V. Entamoeba histolytica and E. dispar infections in captive macaques (Macaca fascicularis) in the Philippines. Primates 51, 69 (2009).PubMed 
Article 

Google Scholar 
34.Regan, C. S., Yon, L., Hossain, M. & Elsheikha, H. M. Prevalence of Entamoeba species in captive primates in zoological gardens in the UK. PeerJ 2, e492 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Elsheikha, H. M., Regan, C. S. & Clark, C. G. Novel Entamoeba findings in nonhuman primates. Trends Parasitol. 34, 283–294 (2018).PubMed 
Article 

Google Scholar 
36.Tuda, J. et al. Identification of Entamoeba polecki with unique 18S rRNA gene sequences from celebes crested macaques and pigs in Tangkoko Nature Reserve, North Sulawesi, Indonesia. J. Eukaryot. Microbiol. 63, 572–577 (2016).CAS 
PubMed 
Article 

Google Scholar 
37.Nolan, M. J. et al. Molecular characterisation of protist parasites in human-habituated mountain gorillas (Gorilla beringei beringei), humans and livestock, from Bwindi Impenetrable National Park, Uganda. Parasit. Vectors 10, 340 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
38.Ruiz-López, M. J., Monello, R. J., Gompper, M. E. & Eggert, L. S. The effect and relative importance of neutral genetic diversity for predicting parasitism varies across parasite taxa. PLoS ONE 7, e45404 (2012).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
39.Acevedo-Whitehouse, K. et al. Contrasting effects of heterozygosity on survival and hookworm resistance in California sea lion pups. Mol. Ecol. 15, 1973–1982 (2006).CAS 
PubMed 
Article 

Google Scholar 
40.Grueter, C. C. et al. Ranging of Rhinopithecus bieti in the Samage Forest, China. I. Characteristics of range use. Int. J. Primatol. 29, 1121–1145 (2008).Article 

Google Scholar 
41.Li, D. et al. Ranging of Rhinopithecus bieti in the Samage Forest, China. II. Use of land cover types and altitudes. Int. J. Primatol. 29, 1147 (2008).Article 

Google Scholar 
42.Xue, Y. et al. Analysis of habitat connectivity of the Yunnan snub-nosed monkeys (Rhinopithecus bieti) using landscape genetics. Shengtai Xuebao Acta Ecol. Sin. 31, 5886–5893 (2011).ADS 

Google Scholar 
43.Fu, R., Li, L., Yu, Z., Afonso, E. & Giraudoux, P. Spatial and temporal distribution of Yunnan snub-nosed monkey, Rhinopithecus bieti, indices. Mammalia 83, 103 (2018).Article 

Google Scholar 
44.Vlčková, K. et al. Diversity of Entamoeba spp. in African great apes and humans: An insight from Illumina MiSeq high-throughput sequencing. Int. J. Parasitol. 48, 519–530 (2018).PubMed 
Article 
CAS 

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

Google Scholar 
46.Paradis, E. & Schliep, K. ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).CAS 
PubMed 
Article 

Google Scholar 
47.Pagès, H., Aboyoun, P., Gentleman, R. & DebRoy, S. Biostrings: Efficient manipulation of biological strings. (2017).48.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Wright, E. S., Yilmaz, L. S. & Noguera, D. R. DECIPHER, a search-based approach to chimera identification for 16S rRNA sequences. Appl. Environ. Microbiol. 78, 717–725 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Schliep, K. P. phangorn: Phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).CAS 
PubMed 
Article 

Google Scholar 
51.McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Morgan, M. et al. ShortRead: A bioconductor package for input, quality assessment and exploration of high-throughput sequence data. Bioinformatics 25, 2607–2608 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Galan, M. et al. 16S rRNA Amplicon sequencing for epidemiological surveys of bacteria in wildlife. mSystems 1, e00032 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
54.Smith, D. P. & Peay, K. G. Sequence depth, not PCR replication, improves ecological inference from next generation DNA sequencing. PLoS ONE 9, e90234 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
55.Stensvold, C. R. et al. Increased sampling reveals novel lineages of Entamoeba: Consequences of genetic diversity and host specificity for taxonomy and molecular detection. Protist 162, 525–541 (2011).PubMed 
Article 

Google Scholar 
56.Burnham, K. P. & Anderson, D. R. Data-based selection of an appropriate biological model: The key to modern data analysis. In Wildlife 2001: Populations (eds McCullough, D. R. & Barrett, R. H.) 16–30 (Springer, 2001). https://doi.org/10.1007/978-94-011-2868-1_3.Chapter 

Google Scholar 
57.Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-6. (2019).58.Matsubayashi, M. et al. First detection and molecular identification of Entamoeba bovis from Japanese cattle. Parasitol. Res. 117, 339–342 (2018).PubMed 
Article 

Google Scholar 
59.Balloux, F., Amos, W. & Coulson, T. Does heterozygosity estimate inbreeding in real populations?. Mol. Ecol. 13, 3021–3031 (2004).CAS 
PubMed 
Article 

Google Scholar 
60.Szulkin, M., Bierne, N. & David, P. Heterozygosity-fitness correlations: A time for reappraisal. Evolution 64, 1202–1217 (2010).PubMed 

Google Scholar 
61.Feng, M. et al. Prevalence and genetic diversity of Entamoeba species infecting macaques in southwest China. Parasitol. Res. 112, 1529–1536 (2013).PubMed 
Article 

Google Scholar 
62.Guan, Y. et al. Comparative analysis of genotypic diversity in Entamoeba nuttalli isolates from Tibetan macaques and rhesus macaques in China. Infect. Genet. Evol. 38, 126–131 (2016).CAS 
PubMed 
Article 

Google Scholar 
63.Ponce Gordo, F., Martı́nez Dı́az, R. A. & Herrera, S. Entamoeba struthionis n.sp. (Sarcomastigophora: Endamoebidae) from ostriches (Struthio camelus). Vet. Parasitol. 119, 327–335 (2004).CAS 
PubMed 
Article 

Google Scholar 
64.Ai, S. et al. The first survey and molecular identification of Entamoeba spp. in farm animals on Qinghai-Tibetan Plateau of China. Comp. Immunol. Microbiol. Infect. Dis. 75, 101607 (2021).PubMed 
Article 

Google Scholar 
65.Stensvold, C. R., Lebbad, M. & Clark, C. G. Genetic characterisation of uninucleated cyst-producing Entamoeba spp. from ruminants. Int. J. Parasitol. 40, 775–778 (2010).CAS 
PubMed 
Article 

Google Scholar 
66.Fadrosh, D. W. et al. An improved dual-indexing approach for multiplexed 16S rRNA gene sequencing on the Illumina MiSeq platform. Microbiome 2, 6 (2014).PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Hutchinson, G. E. Cold Spring Harbor Symposia on Quantitative Biology. Concluding Remarks 22 415–427 (1957).2.Smith, E. P. Niche breadth, resource availability, and inference. Ecology 63, 1675–1681. https://doi.org/10.2307/1940109 (1982).Article 

Google Scholar 
3.Leibold, M. A. The niche concept revisited: Mechanistic models and community context. Ecology 76, 1371–1382. https://doi.org/10.2307/1938141 (1995).Article 

Google Scholar 
4.Sexton, J. P., Montiel, J., Shay, J. E., Stephens, M. R. & Slatyer, R. A. Evolution of ecological niche breadth. Annu. Rev. Ecol. Evol. Syst. 48, 183–206. https://doi.org/10.1146/annurev-ecolsys-110316-023003 (2017).Article 

Google Scholar 
5.Jaenike, J. Host specialization in phytophagous insects. Annu. Rev. Ecol. Syst. 21, 243–273. https://doi.org/10.1146/annurev.es.21.110190.001331 (1990).Article 

Google Scholar 
6.Thompson, J. N. The Coevolutionary Process (University of Chicago Press, 1994).Book 

Google Scholar 
7.Krasnov, B. R., Mouillot, D., Shenbrot, G. I., Khokhlova, I. S. & Poulin, R. Geographical variation in host specificity of fleas (Siphonaptera) parasitic on small mammals: The influence of phylogeny and local environmental conditions. Ecography 27, 787–797. https://doi.org/10.1111/j.0906-7590.2004.04015.x (2004).Article 

Google Scholar 
8.Poullain, V., Gandon, S., Brockhurst, M. A., Buckling, A. & Hochberg, M. E. The evolution of specificity in evolving and coevolving antagonistic interactions between bacteria and its phage. Evolution 62, 1–11. https://doi.org/10.1111/j.1558-5646.2007.00260.x (2008).Article 
PubMed 

Google Scholar 
9.Whitlock, M. C. The Red Queen beats the Jack-Of-All-Trades: The limitations on the evolution of phenotypic plasticity and niche breadth. Am. Nat. 148, S65–S77. https://doi.org/10.1086/285902 (1996).Article 

Google Scholar 
10.Gandon, S. Local adaptation and the geometry of host–parasite coevolution. Ecol. Lett. 5, 246–256. https://doi.org/10.1046/j.1461-0248.2002.00305.x (2002).Article 

Google Scholar 
11.Alizon, S. & Michalakis, Y. Adaptive virulence evolution: The good old fitness-based approach. Trends Ecol. Evol. 30, 248–254. https://doi.org/10.1016/j.tree.2015.02.009 (2015).Article 
PubMed 

Google Scholar 
12.Frank, S. A. & Schmid-Hempel, P. Mechanisms of pathogenesis and the evolution of parasite virulence. J. Evol. Biol. 21, 396–404. https://doi.org/10.1111/j.1420-9101.2007.01480.x (2008).CAS 
Article 
PubMed 

Google Scholar 
13.Beadell, J. S. et al. Global phylogeographic limits of Hawaii’s avian malaria. Proc. R. Soc. B: Biol. Sci. 273, 2935–2944. https://doi.org/10.1098/rspb.2006.3671 (2006).Article 

Google Scholar 
14.Krasnov, B. R. Functional and Evolutionary Ecology of Fleas: A Model for Ecological Parasitology (Cambridge University Press, 2008).Book 

Google Scholar 
15.Poulin, R. Evolutionary Ecology of Parasites (Princeton University Press, 2011).Book 

Google Scholar 
16.Välimäki, P. et al. Geographical variation in host use of a blood-feeding ectoparasitic fly: Implications for population invasiveness. Oecologia 166, 985–995. https://doi.org/10.1007/s00442-011-1951-y (2011).ADS 
Article 
PubMed 

Google Scholar 
17.Theodosopoulos, A. N., Hund, A. K. & Taylor, S. A. Parasites and host species barriers in animal hybrid zones. Trends Ecol. Evol. 34, 19–30. https://doi.org/10.1016/j.tree.2018.09.011 (2019).Article 
PubMed 

Google Scholar 
18.Mackenzie, A. A trade-off for host plant utilization in the black bean aphid, Aphis fabae. Evolution 50, 155–162. https://doi.org/10.1111/j.1558-5646.1996.tb04482.x (1996).Article 
PubMed 

Google Scholar 
19.Harrington, L. C., Edman, J. D. & Scott, T. W. Why do female Aedes aegypti (Diptera: Culicidae) feed preferentially and frequently on human blood?. J. Med. Entomol. 38, 411–422. https://doi.org/10.1603/0022-2585-38.3.411 (2001).CAS 
Article 
PubMed 

Google Scholar 
20.Dick, C. W. & Patterson, B. D. Against all odds: Explaining high host specificity in dispersal-prone parasites. Int. J. Parasitol. 37, 871–876. https://doi.org/10.1016/j.ijpara.2007.02.004 (2007).Article 
PubMed 

Google Scholar 
21.Torchin, M. E. & Mitchell, C. E. Parasites, pathogens, and invasions by plants and animals. Front. Ecol. Environ. 2, 183–190. https://doi.org/10.1890/1540-9295(2004)002[0183:PPAIBP]2.0.CO;2 (2004).Article 

Google Scholar 
22.Clark, N. J. & Clegg, S. M. The influence of vagrant hosts and weather patterns on the colonization and persistence of blood parasites in an island bird. J. Biogeogr. 42, 641–651. https://doi.org/10.1111/jbi.12454 (2015).Article 

Google Scholar 
23.Kawecki, T. J. Red Queen meets Santa Rosalia: Arms races and the evolution of host specialization in organisms with parasitic lifestyles. Am. Nat. 152, 635–651. https://doi.org/10.1086/286195 (1998).CAS 
Article 
PubMed 

Google Scholar 
24.Egas, M., Dieckmann, U. & Sabelis, M. W. Evolution restricts the coexistence of specialists and generalists: The role of trade-off structure. Am. Nat. 163, 518–531. https://doi.org/10.1086/382599 (2004).Article 
PubMed 

Google Scholar 
25.Poulin, R. & Keeney, D. B. Host specificity under molecular and experimental scrutiny. Trends Parasitol. 24, 24–28. https://doi.org/10.1016/j.pt.2007.10.002 (2008).CAS 
Article 
PubMed 

Google Scholar 
26.Lyimo, I. N. & Ferguson, H. M. Ecological and evolutionary determinants of host species choice in mosquito vectors. Trends Parasitol. 25, 189–196. https://doi.org/10.1016/j.pt.2009.01.005 (2009).Article 
PubMed 

Google Scholar 
27.Visher, E. & Boots, M. The problem of mediocre generalists: Population genetics and eco-evolutionary perspectives on host breadth evolution in pathogens. Proc. R. Soc. B: Biol. Sci. 287, 20201230. https://doi.org/10.1098/rspb.2020.1230 (2020).Article 

Google Scholar 
28.Sarfati, M. et al. Energy costs of blood digestion in a host-specific haematophagous parasite. J. Exp. Biol. 208, 2489. https://doi.org/10.1242/jeb.01676 (2005).Article 
PubMed 

Google Scholar 
29.Fry, J. D. The evolution of host specialization: Are trade-offs overrated?. Am. Nat. 148, S84–S107. https://doi.org/10.1086/285904 (1996).Article 

Google Scholar 
30.Fessl, B. et al. Galápagos landbirds (passerines, cuckoos, and doves): Status, threats, and knowledge gaps. Galápagos Rep. 2016, 149 (2015).
Google Scholar 
31.Fessl, B., Heimpel, G. E. & Causton, C. E. Invasion of an avian nest parasite, Philornis downsi, to the Galapagos Islands: colonization history, adaptations to novel ecosystems, and conservation challenges. In Disease Ecology: Galapagos Birds and their Parasites (ed Patricia G. Parker) 213–266 (Springer International Publishing, 2018).32.Frankham, R. Do island populations have less genetic variation than mainland populations?. Heredity 78, 311–327. https://doi.org/10.1038/hdy.1997.46 (1997).Article 
PubMed 

Google Scholar 
33.Reichard, M. et al. The bitterling–mussel coevolutionary relationship in areas of recent and ancient sympatry. Evolution 64, 3047–3056. https://doi.org/10.1111/j.1558-5646.2010.01032.x (2010).Article 
PubMed 

Google Scholar 
34.Wiedenfeld, D. A., Jiménez, G. U., Fessl, B., Kleindorfer, S. & Carlos Valarezo, J. Distribution of the introduced parasitic fly Philornis downsi (Diptera, Muscidae) in the Galápagos Islands. Pacific Conserv. Biol. 13, 14–19. https://doi.org/10.1071/PC070014 (2007).Article 

Google Scholar 
35.Fessl, B., Sinclair, B. J. & Kleindorfer, S. The life-cycle of Philornis downsi (Diptera: Muscidae) parasitizing Darwin’s finches and its impacts on nestling survival. Parasitology 133, 739–747. https://doi.org/10.1017/S0031182006001089 (2006).CAS 
Article 
PubMed 

Google Scholar 
36.Kleindorfer, S. & Dudaniec, R. Y. Host-parasite ecology, behavior and genetics: A review of the introduced fly parasite Philornis downsi and its Darwin’s finch hosts. BMC Zool. 1, 1. https://doi.org/10.1186/s40850-016-0003-9 (2016).Article 

Google Scholar 
37.Galligan, T. H. & Kleindorfer, S. Naris and beak malformation caused by the parasitic fly, Philornis downsi (Diptera: Muscidae), in Darwin’s small ground finch, Geospiza fuliginosa (Passeriformes: Emberizidae). Biol. J. Lin. Soc. 98, 577–585. https://doi.org/10.1111/j.1095-8312.2009.01309.x (2009).Article 

Google Scholar 
38.Kleindorfer, S., Custance, G., Peters Katharina, J. & Sulloway Frank, J. Introduced parasite changes host phenotype, mating signal and hybridization risk: Philornis downsi effects on Darwin’s finch song. Proc. R. Soc. B: Biol. Sci. 286, 20190461. https://doi.org/10.1098/rspb.2019.0461 (2019).Article 

Google Scholar 
39.Kleindorfer, S., Peters, K. J., Custance, G., Dudaniec, R. Y. & O’Connor, J. A. Changes in Philornis infestation behavior threaten Darwin’s finch survival. Curr. Zool. 60, 542–550. https://doi.org/10.1093/czoolo/60.4.542 (2014).Article 

Google Scholar 
40.O’Connor, J. A., Sulloway, F. J., Robertson, J. & Kleindorfer, S. Philornis downsi parasitism is the primary cause of nestling mortality in the critically endangered Darwin’s medium tree finch (Camarhynchus pauper). Biodivers. Conserv. 19, 853–866. https://doi.org/10.1007/s10531-009-9740-1 (2010).Article 

Google Scholar 
41.Knutie, S. A. et al. Galápagos mockingbirds tolerate introduced parasites that affect Darwin’s finches. Ecology https://doi.org/10.1890/15-0119 (2016).Article 
PubMed 

Google Scholar 
42.Peters, K. J., Evans, C., Aguirre, J. D. & Kleindorfer, S. Genetic admixture predicts parasite intensity: Evidence for increased hybrid performance in Darwin’s tree finches. R. Soc. Open Sci. 6, 181616. https://doi.org/10.1098/rsos.181616 (2019).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
43.Kleindorfer, S. The ecology of clutch size variation in Darwin’s Small Ground Finch Geospiza fuliginosa: Comparison between lowland and highland habitats. Ibis 149, 730–741. https://doi.org/10.1111/j.1474-919X.2007.00694.x (2007).Article 

Google Scholar 
44.Fessl, B. & Tebbich, S. Philornis downsi– a recently discovered parasite on the Galápagos archipelago: A threat for Darwin’s finches?. Ibis 144, 445–451. https://doi.org/10.1046/j.1474-919X.2002.00076.x (2002).Article 

Google Scholar 
45.Dudaniec, R. Y., Fessl, B. & Kleindorfer, S. Interannual and interspecific variation in intensity of the parasitic fly, Philornis downsi, Darwin’s finches. Biol. Cons. 139, 325–332. https://doi.org/10.1016/j.biocon.2007.07.006 (2007).Article 

Google Scholar 
46.Cimadom, A. et al. Invasive parasites, habitat change and heavy rainfall reduce breeding success in Darwin’s Finches. PLoS ONE 9, e107518. https://doi.org/10.1371/journal.pone.0107518 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
47.Cimadom, A. et al. Weed management increases the detrimental effect of an invasive parasite on arboreal Darwin’s finches. Biol. Cons. 233, 93–101. https://doi.org/10.1016/j.biocon.2019.02.025 (2019).Article 

Google Scholar 
48.Kleindorfer, S. & Dudaniec, R. Y. Love thy neighbour? Social nesting pattern, host mass and nest size affect ectoparasite intensity in Darwin’s tree finches. Behav. Ecol. Sociobiol. 63, 731–739. https://doi.org/10.1007/s00265-008-0706-1 (2009).Article 

Google Scholar 
49.Common, L. K., Dudaniec, R. Y., Colombelli-Négrel, D. & Kleindorfer, S. Taxonomic shifts in Philornis larval behaviour and rapid changes in Philornis downsi Dodge & Aitken (Diptera: Muscidae): An invasive avian parasite on the Galápagos Islands. in Life Cycle and Development of Diptera (ed Muhammad Sarwar) (IntechOpen, 2019).50.McNew, S. M. et al. Annual environmental variation influences host tolerance to parasites. Proc. R. Soc. B: Biol. Sci. 286, 20190049. https://doi.org/10.1098/rspb.2019.0049 (2019).CAS 
Article 

Google Scholar 
51.McNew, S. M. & Clayton, D. H. Alien invasion: Biology of Philornis flies highlighting Philornis downsi, an introduced parasite of Galápagos birds. Annu. Rev. Entomol. 63, 369–387. https://doi.org/10.1146/annurev-ento-020117-043103 (2018).CAS 
Article 
PubMed 

Google Scholar 
52.Kleindorfer, S. & Dudaniec, R. Y. Hybridization fluctuates with rainfall in Darwin’s tree finches. Biol. J. Lin. Soc. 130, 79–88. https://doi.org/10.1093/biolinnean/blaa029 (2020).Article 

Google Scholar 
53.Peters, K. J., Myers, S. A., Dudaniec, R. Y., O’Connor, J. A. & Kleindorfer, S. Females drive asymmetrical introgression from rare to common species in Darwin’s tree finches. J. Evol. Biol. 30, 1940–1952. https://doi.org/10.1111/jeb.13167 (2017).CAS 
Article 
PubMed 

Google Scholar 
54.Kleindorfer, S. et al. Species collapse via hybridization in Darwin’s Tree Finches. Am. Nat. 183, 325–341. https://doi.org/10.1086/674899 (2014).Article 
PubMed 

Google Scholar 
55.Loo, W. T., Dudaniec, R. Y., Kleindorfer, S. & Cavanaugh, C. M. An inter-island comparison of Darwin’s finches reveals the impact of habitat, host phylogeny, and island on the gut microbiome. PLoS ONE 14, e0226432. https://doi.org/10.1371/journal.pone.0226432 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.Galapagos Conservancy. Galapagos Vital Signs: A satellite-based environmental monitoring system for the Galapagos Archipelago, https://galapagosvitalsigns.org (2021).57.Couri, M. Considerações sobre as relações ecológicas das larvas de Philornis Meinert, 1890 (Diptera, Muscidae) com aves. Revista Brasileira de Entomologia 29, 17–20. https://doi.org/10.1017/S0031182006001089 (1985).Article 

Google Scholar 
58.Skidmore, P. The Biology of the Muscidae of the World Vol. 29 (Springer, 1985).
Google Scholar 
59.O’Connor, J. A., Robertson, J. & Kleindorfer, S. Video analysis of host–parasite interactions in nests of Darwin’s finches. Oryx 44, 588–594. https://doi.org/10.1017/S0030605310000086 (2010).Article 

Google Scholar 
60.O’Connor, J. A., Robertson, J. & Kleindorfer, S. Darwin’s finch begging intensity does not honestly signal need in parasitised nests. Ethology 120, 228–237. https://doi.org/10.1111/eth.12196 (2014).Article 

Google Scholar 
61.Kleindorfer, S. & Sulloway, F. J. Naris deformation in Darwin’s finches: Experimental and historical evidence for a post-1960s arrival of the parasite Philornis downsi. Glob. Ecol. Conserv. 7, 122–131. https://doi.org/10.1016/j.gecco.2016.05.006 (2016).Article 

Google Scholar 
62.Lahuatte, P. F., Lincango, M. P., Heimpel, G. E. & Causton, C. E. Rearing larvae of the avian nest parasite, Philornis downsi (Diptera: Muscidae), on chicken blood-based diets. J. Insect Sci. https://doi.org/10.1093/jisesa/iew064 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
63.Kleindorfer, S. Nesting success in Darwin’s small tree finch, Camarhynchus parvulus: Evidence of female preference for older males and more concealed nests. Anim. Behav. 74, 795–804. https://doi.org/10.1016/j.anbehav.2007.01.020 (2007).Article 

Google Scholar 
64.Nijhout, H. F. & Callier, V. Developmental mechanisms of body size and wing-body scaling in insects. Annu. Rev. Entomol. 60, 141–156. https://doi.org/10.1146/annurev-ento-010814-020841 (2015).CAS 
Article 
PubMed 

Google Scholar 
65.Singh, D. & Bala, M. The effect of starvation on the larval behavior of two forensically important species of blow flies (Diptera: Calliphoridae). For. Sci. Int. 193, 118–121. https://doi.org/10.1016/j.forsciint.2009.09.022 (2009).Article 

Google Scholar 
66.Coulson, S. J. & Bale, J. S. Characterisation and limitations of the rapid cold-hardening response in the housefly Musca domestica (Diptera: Muscidae). J. Insect Physiol. 36, 207–211. https://doi.org/10.1016/0022-1910(90)90124-X (1990).Article 

Google Scholar 
67.R Core Team. R: A language and environment for statistical computing. R version 4.0.3 (R Foundation for Statistical Computing, Vienna, Austria, 2020).68.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
69.Venables, B. & Ripley, B. Modern Applied Statistics with S-PLUS (Springer Science & Business Media, 2002).70.Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage publications, 2011).
Google Scholar 
71.Sarkar, D. Lattice: Multivariate Data Visualization with R (Springer, 2008).Book 

Google Scholar 
72.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).Book 

Google Scholar 
73.Fox, J. Effect displays in R for generalised linear models. J. Stat. Softw. https://doi.org/10.18637/jss.v008.i15 (2003).Article 

Google Scholar 
74.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodal Inference: A Practical Information-Theoretic Approach (eds Kenneth P. Burnham & David R. Anderson) 75–117 (Springer New York, 1998).75.Grueber, C. E., Nakagawa, S., Laws, R. J. & Jamieson, I. G. Multimodel inference in ecology and evolution: Challenges and solutions. J. Evol. Biol. 24, 699–711. https://doi.org/10.1111/j.1420-9101.2010.02210.x (2011).CAS 
Article 
PubMed 

Google Scholar 
76.Haaland, T. R., Wright, J. & Ratikainen, I. I. Generalists versus specialists in fluctuating environments: A bet-hedging perspective. Oikos 129, 879–890. https://doi.org/10.1111/oik.07109 (2020).Article 

Google Scholar 
77.Davies, N. Cuckoos, Cowbirds and Other Cheats (Bloomsbury Publishing, 2010).
Google Scholar 
78.Dudaniec, R. Y., Gardner, M. G. & Kleindorfer, S. Offspring genetic structure reveals mating and nest infestation behaviour of an invasive parasitic fly (Philornis downsi) of Galápagos birds. Biol. Invas. 12, 581–592. https://doi.org/10.1007/s10530-009-9464-x (2010).Article 

Google Scholar 
79.Fredensborg, B. L. & Poulin, R. Larval helminths in intermediate hosts: Does competition early in life determine the fitness of adult parasites?. Int. J. Parasitol. 35, 1061–1070. https://doi.org/10.1016/j.ijpara.2005.05.005 (2005).CAS 
Article 
PubMed 

Google Scholar 
80.Begon, M., Harper, J. L. & Townsend, C. R. Ecology: Individuals, Populations and Communities (Blackwell Scientific Publications, 1986).
Google Scholar 
81.Fraik, A. K. et al. Disease swamps molecular signatures of genetic-environmental associations to abiotic factors in Tasmanian devil (Sarcophilus harrisii) populations. Evolution 74, 1392–1408. https://doi.org/10.1111/evo.14023 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
82.Dvorak, M. et al. Conservation status of landbirds on Floreana: The smallest inhabited Galápagos Island. J. Field Ornithol. 88, 132–145. https://doi.org/10.1111/jofo.12197 (2017).Article 

Google Scholar 
83.Hedrick, P. W., Kim, T. J. & Parker, K. M. Parasite resistance and genetic variation in the endangered Gila topminnow. Anim. Conserv. 4, 103–109. https://doi.org/10.1017/S1367943001001135 (2001).Article 

Google Scholar 
84.Lewontin, R. C. & Birch, L. C. Hybridization as a source of variation for adaptation to new environments. Evolution 20, 315–336. https://doi.org/10.2307/2406633 (1966).CAS 
Article 
PubMed 

Google Scholar 
85.Wolinska, J., Lively, C. M. & Spaak, P. Parasites in hybridizing communities: The Red Queen again?. Trends Parasitol. 24, 121–126. https://doi.org/10.1016/j.pt.2007.11.010 (2008).Article 
PubMed 

Google Scholar 
86.Floate, K. D. & Whitham, T. G. The, “Hybrid Bridge” Hypothesis: Host shifting via plant hybrid swarms. Am. Nat. 141, 651–662. https://doi.org/10.1086/285497 (1993).CAS 
Article 
PubMed 

Google Scholar 
87.Le Brun, N., Renaud, F., Berrebi, P. & Lambert, A. Hybrid zones and host-parasite relationships: Effect on the evolution of parasitic specificity. Evolution 46, 56–61. https://doi.org/10.1111/j.1558-5646.1992.tb01984.x (1992).Article 
PubMed 

Google Scholar 
88.Fritz, R. S., Moulia, C. & Newcombe, G. Resistance of hybrid plants and animals to herbivores, pathogens, and parasites. Annu. Rev. Ecol. Syst. 30, 565–591. https://doi.org/10.1146/annurev.ecolsys.30.1.565 (1999).Article 

Google Scholar 
89.Moulia, C., Brun, N. L., Loubes, C., Marin, R. & Renaud, F. Hybrid vigour against parasites in interspecific crosses between two mice species. Heredity 74, 48–52. https://doi.org/10.1038/hdy.1995.6 (1995).Article 
PubMed 

Google Scholar 
90.Gibson, A. K., Refrégier, G., Hood, M. E. & Giraud, T. Performance of a hybrid fungal pathogen on pure-species and hybrid host plants. Int. J. Plant Sci. 175, 724–730. https://doi.org/10.1086/676621 (2014).Article 

Google Scholar 
91.Arnold, M. L. & Martin, N. H. Hybrid fitness across time and habitats. Trends Ecol. Evol. 25, 530–536. https://doi.org/10.1016/j.tree.2010.06.005 (2010).Article 
PubMed 

Google Scholar 
92.Ben-Yosef, M. et al. Host-specific associations affect the microbiome of Philornis downsi, an introduced parasite to the Galápagos Islands. Mol. Ecol. 26, 4644–4656. https://doi.org/10.1111/mec.14219 (2017).Article 
PubMed 

Google Scholar 
93.Loo, W. T., García-Loor, J., Dudaniec, R. Y., Kleindorfer, S. & Cavanaugh, C. M. Host phylogeny, diet, and habitat differentiate the gut microbiomes of Darwin’s finches on Santa Cruz Island. Sci. Rep. 9, 18781. https://doi.org/10.1038/s41598-019-54869-6 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
94.Knutie, S. A. Relationships among introduced parasites, host defenses, and gut microbiota of Galapagos birds. Ecosphere 9, e02286. https://doi.org/10.1002/ecs2.2286 (2018).Article 

Google Scholar 
95.Knutie, S. A., Chaves, J. A. & Gotanda, K. M. Human activity can influence the gut microbiota of Darwin’s finches in the Galapagos Islands. Mol. Ecol. 28, 2441–2450. https://doi.org/10.1111/mec.15088 (2019).Article 
PubMed 

Google Scholar  More

1.Barringer, L. E., Donovall, L. R., Spichiger, S. E., Lynch, D. & Henry, D. The first New World record of Lycorma delicatula (Insecta: Hemiptera: Fulgoridae). Entomol. News 125, 20–23 (2015).Article 

Google Scholar 
2.Dara, S. K., Barringer, L. & Arthurs, S. P. Lycorma delicatula (Hemiptera: Fulgoridae): A new invasive pest in the United States. J. Integr. Pest Manag. 6, 20 (2015).Article 

Google Scholar 
3.Jung, J. M., Jung, S., Byeon, D. & Lee, W. Model-based prediction of potential distribution of the invasive insect pest, spotted lanternfly Lycorma delicatula (Hemiptera: Fulgoridae), by using CLIMEX. J. Asia-Pac. Biodivers. 10, 532–538 (2017).Article 

Google Scholar 
4.Lee, D.-H., Park, Y.-L. & Leskey, T. C. A review of biology and management of Lycorma delicatula (Hemiptera: Fulgoridae), an emerging global invasive species. J. Asia-Pac. Entomol. 22, 589–596 (2019).Article 

Google Scholar 
5.(NYSIPM) New York State Integrated Pest Management. 2020. Spotted lanternfly. https://nysipm.cornell.edu/environment/invasive-species-exotic-pests/spotted-lanternfly/. Accessed 18 January 2021.6.Park, M., Kim, S. M. & Lee, J. H. Genetic structure of Lycorma delicatula (Hemiptera: Fulgoridae) populations in Korea: implication for invasion processes in heterogeneous landscapes. Bull. Entomol. Res. 103, 414–424 (2013).CAS 
Article 

Google Scholar 
7.Keller, J. A. et al. Dispersal of Lycorma delicatula (Hemiptera: Fulgoridae) nymphs through contiguous, deciduous forest. Environ. Entomol. 49, 1012–1018 (2020).Article 

Google Scholar 
8.Smyers, E. C. et al. Spatio-temporal model for predicting spring hatch of the spotted lanternfly (Hemiptera: Fulgoridae). Environ. Entomol. 50, 126–137 (2020).Article 

Google Scholar 
9.Wakie, T. T., Neven, L. G., Yee, W. L. & Lu, Z. The establishment risk of Lycorma delicatula (Hemiptera: Fulgoridae) in the United States and globally. J. Econ. Entomol. 113, 306–314 (2019).
Google Scholar 
10.Urban, J. M. Perspective: shedding light on spotted lanternfly impacts in the USA. Pest Manag. Sci. 76, 10–17 (2020).CAS 
Article 

Google Scholar 
11.Harper, J. K., Stone, W., Kelsey, T. W. & Kime, L. F. Potential Economic Impact of the Spotted Lanternfly on Agriculture and Forestry in Pennsylvania (The Center for Rural Pennsylvania, 2019).
Google Scholar 
12.Song, M. K. Damage by Lycorma delicatula and chemical control in vineyards. M.S. thesis. Chunbuk National University, Korea (2010).13.Tedders, W. L. & Smith, J. S. Shading effect on pecan by sooty mold growth. J. Econ. Entomol. 69, 551–553 (1976).Article 

Google Scholar 
14.Lemos-Filho, J. P. D. & Paiva, É. A. S. The effects of sooty mold on photosynthesis and mesophyll structure of mahogany (Swietenia macrophylla King., Meliaceae). Bragantia 65, 11–17 (2006).Article 

Google Scholar 
15.Han, J. M. et al. Lycorma delicatula (Hemiptera: Auchenorrhyncha: Fulgoridae: Aphaeninae) finally, but suddenly arrived in Korea. Entomol. Res. 38, 281–286 (2008).Article 

Google Scholar 
16.Park, J. D. et al. Biological characteristics of Lycorma delicatula and the control effects of some insecticides. Korean J. Appl. Entomol. 48, 53–57 (2009).Article 

Google Scholar 
17.Liu, H. Oviposition substrate selection, egg mass characteristics, host preference, and life history of the spotted lanternfly (Hemiptera: Fulgoridae) in North America. Environ. Entomol. 48, 1452–1468 (2019).Article 

Google Scholar 
18.Barringer, L. E. & Ciafré, C. M. Worldwide feeding host plants of spotted lanternfly, with significant additions from North America. Environ. Entomol. 49, 999–1011 (2020).Article 

Google Scholar 
19.Uyi, O. et al. Spotted lanternfly (Hemiptera: Fulgoridae) can complete development and reproduce without access to the preferred host, Ailanthus altissima. Environ. Entomol. 49, 1185–1190 (2020).Article 

Google Scholar 
20.Murman, K. Distribution, survival, and development of spotted lanternfly on host plants found in north America. Environ. Entomol. 49, 1270–1281 (2020).Article 

Google Scholar 
21.Magnusson, A. glmmTMB: Generalized linear mixed models using template model builder. R package v. 0.1.3. https://github.com/glmmTMB (2017).22.R Development Core Team. R: A Language and Environment for Statistical Computing Computer Program, Version 3.6.3 (R Development Core Team, 2020).
Google Scholar 
23.Kariyat, R. R. & Portman, S. L. Plant–herbivore interactions: Thinking beyond larval growth and mortality. Am. J. Bot. 103, 1–3 (2016).Article 

Google Scholar 
24.Fordyce, J. A. & Shapiro, A. M. Another perspective on the slow-growth/high-mortality hypothesis: chilling effects on swallowtail larvae. Ecology 84, 263–268 (2003).Article 

Google Scholar 
25.Uesugi, A. The slow-growth high-mortality hypothesis: direct experimental support in a leaf mining fly. Ecol. Entomol. 40, 221–228 (2015).Article 

Google Scholar 
26.Song, S., Kim, S., Kwon, S. W., Lee, S.-I. & Jablonski, P. G. Defense sequestration associated with narrowing of diet and ontogenetic change to aposematic colours in the spotted lanternfly. Sci. Rep. 8, 16831 (2018).ADS 
Article 

Google Scholar 
27.Domingue, M. J. & Baker, T. C. Orientation of flight for physically disturbed spotted lanternflies, Lycorma delicatula, (Hemiptera, Fulgoridae). J. Asia Pac. Entomol. 22, 117–120 (2019).Article 

Google Scholar 
28.Lee, J. E. et al. Feeding behavior of Lycorma delicatula (Hemiptera: Fulgoridae) and response on feeding stimulants of some plants. Korean J. Appl. Entomol. 48, 467–477 (2009).Article 

Google Scholar 
29.Liu, H. Seasonal development, cumulative growing degree-days, and population density of spotted lanternfly (Hemiptera: Fulgoridae) on selected hosts and substrates. Environ. Entomol. 49, 1171–1184 (2020).Article 

Google Scholar 
30.Jaenike, J. On optimal oviposition behavior in phytophagous insects. Theor. Popul. Biol. 14, 350–356 (1978).CAS 
Article 

Google Scholar 
31.Gripenberg, S., Mayhew, P. J., Parnell, M. & Roslin, T. A meta-analysis of preference–performance relationships in phytophagous insects. Ecol. Lett. 13, 383–393 (2010).Article 

Google Scholar 
32.Fujiyama, N., Torii, C., Akabane, M. & Katakura, H. Oviposition site selection by herbivorous beetles: a comparison of two thistle feeders: Cassida rubiginosa and Henosepilachnniponica. Entomol. Exp. Appl. 128, 41–48 (2008).Article 

Google Scholar 
33.Wolfin, M. S., Myrick, A. J. & Baker, T. C. Flight duration capabilities of dispersing adult spotted lanternflies, Lycorma delicatula. J. Insect Behav. 33, 125–137 (2020).Article 

Google Scholar 
34.Faraji, F., Janssen, A. & Sabelis, M. W. Oviposition patterns in a predatory mite reduce the risk of egg predation caused by prey. Ecol. Entomol. 27, 660–664 (2002).Article 

Google Scholar 
35.Behmer, S. T. & Joern, A. Coexisting generalist herbivores occupy unique nutritional feeding niches. Proc. Natl. Acad. Sci. USA 105, 1977–1982 (2008).ADS 
CAS 
Article 

Google Scholar 
36.Behmer, S. T. Insect herbivore nutrient regulation. Annu. Rev. Entomol. 54, 165–187 (2009).CAS 
Article 

Google Scholar  More

Acoustic measurementsA set of acoustic echo sounder data was used to analyze fish density. This was collected within the Mycto-3D-MAP program using split-beam echo sounders at 38 and 120 kHz. The Mycto-3D-MAP program included multiple large-scale oceanographic surveys during 2 years and a dedicated cruise in the Kerguelen area. The dataset was collected during 4 large-scale surveys in 2013 and 2014, both in summer (including both northward and southward transects) and in winter, corresponding to 6 acoustic transects of 2860 linear kilometers (see Table 1 for more details). Note that all legs except summer 2014 (MYCTO-3D-Map cruise) were logistic operations, during which environmental in situ data (such as temperature or salinity profiles) could not be collected. The data were then treated with a bi-frequency algorithm, applied to the 38 and 120 kHz frequencies (details of data collection and processing are provided in37). This provides a quantitative estimation of the concentration of gas-bearing organisms, mostly attributed to fish containing a gas-filled swimbladder in the water column38. Most mesopelagic fish present swimbladders and several works pointed out that myctophids are the dominant mesopelagic fish in the region39. Therefore, we considered the acoustic signal as mainly representative of myctophids concentration. Data were organized in acoustic units, averaging acoustic data over 1.1 km along the ship trajectory on average. Myctophid school length is in the order of tens of meters40. For this reason, acoustic units were considered as not autocorrelated. Every acoustic unit is composed of 30 layers, ranging from 10 to 300 meters (30 layers in total).The dataset was used to infer the Acoustic Fish Concentration (AFC) in the water column. We considered as AFC of the point ((x_i), (y_i)) of the ship trajectory the average of the bifrequency acoustic backscattering of 29 out of 30 layers (the first layer, 0-10 m, was excluded due to surface noise) AFC quantity is dimensionless.As the previous measurements were performed through acoustic measurements, a non-invasive technique, fishes were not handled for this study.Table 1 Details of the acoustic transects analyzed.Full size tableRegional data selectionThe geographic area of interest of the present study is the Southern Ocean. To select the ship transects belonging to this region, we used the ecopartition of41. Only points falling in the Antarctic Southern Ocean region were considered. We highlight that this choice is consistent with the ecopartition of42 (group 5), which is specifically designed for myctophids, the reference fish family (Myctophidae) of this study. Furthermore, this choice allowed us to exclude large scale fronts (i.e., fronts that are visible on monthly or yearly averaged maps) which have been the subject of past research works43,44. In this way our analysis is focused specifically on fine-scale fronts.Day-night data separationSeveral species of myctophids present a diel vertical migration. They live at great depths during the day (between 500 and 1000 m), ascending at dusk in the upper euphotic layer, where they spend the night. Since the maximal depth reached by the echo sounder we used is 300 m, in the analysis reported in Figs. 2 and 3 we excluded data collected during the day. However, their analysis is reported in SI.1. A restriction of our acoustic analyses to the ocean upper layer is also consistent with the fact that the fine-scale features we computed are derived in this work by satellite altimetry, thus representative of the upper part of the water column ((sim 50) m). Finally, we note that the choice of considering the echo sounder data in the first 300 m of the water columns is coherent with the fact that LCS may extend almost vertically in depth even at 600 m depth45,46 and with the fact that altimetry-derived velocity fields are consistent with subsurface currents in our region of interest down to 500 m20.Satellite dataVelocity current data and Finite-Size Lyapunov Exponent (FSLE) processing. Velocity currents are obtained from Sea Surface Height (SSH), which is measured by satellite altimetry, through geostrophic approximation. Data, which were downloaded from E.U. Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/), were obtained from DUACS (Data Unification and Altimeter Combination System) delayed-time multi-mission altimeter, and displaced over a regular grid with spatial resolution of (frac{1}{4}times frac{1}{4}^circ) and a temporal resolution of 1 day.Trajectories were computed with a Runge-Kutta scheme of the 4th order with an integration time of 6 hours. Finite-size Lyapunov Exponents (FSLE) were computed following the methods in14, with initial and final separation of (0.04^circ) and (0.4^circ) respectively. This Lagrangian diagnostic is commonly used to identify Lagrangian Coherent Structures. This method determines the location of barriers to transport, and it is usually associated with oceanic fronts9. Details on the Lagrangian techniques applied to altimetry data, including a discussion of its limitation, can be found in10.Temperature field and gradient computation The Sea Surface Temperature (SST) field was produced from the OSTIA global foundation Sea Surface Temperature (product id: SST_GLO_ SST_L4_NRT_OBSERVATIONS_010_001) from both infrared and microwave radiometers, and downloaded from CMEMS website. The data are represented over a regular grid with spatial resolution of (0.05times 0.05^circ) and daily-mean maps. The SST gradient was obtained from:$$begin{aligned} Grad SST=sqrt{g_x^2+g_y^2} end{aligned}$$where (g_x) and (g_y) are the gradients along the west-east and the north-south direction, respectively. To compute (g_x), the following expression was used:$$begin{aligned} g_x=frac{1}{2 dx}cdot (SST_{i+1}-SST_{i-1}) end{aligned}$$where the SST values of the adjacent grid cells (along the west-east direction: cells (i+1) and (i-1)) were employed. dx identifies the kilometric distance between two grid points along the longitude (which varies with latitude). The definition is analog for (g_y), considering this time the north-south direction and (dysimeq 5) km (0.05(^circ)).Chlorophyll field Chlorophyll estimations were obtained from the Global Ocean Color product (OCEANCOLOUR_ GLO_CHL_L4_REP_OBSERVATIONS_009_082-TDS) produced by ACRI-ST. This was downloaded from CMEMS website. Daily observations were used, in order to match the temporal resolution of the acoustic and satellite observations. The spatial resolution of the product is 1/24(^{circ }).Estimation of satellite data along ship trajectory For each point ((x_i), (y_i)) of the ship trajectory, we extracted a local value of FSLE, SST gradient, and chlorophyll concentration. These were obtained by considering the respective average value in a circular around of radius (sigma) of each point ((x_i), (y_i)) . Different (sigma) were tested (ranging from 0.1(^circ) to 0.6(^circ)), and the best results were obtained with (sigma =0.2^circ), reference value reported in the present work. This value is consistent with the resolution of the altimetry data.Statistical processingFront identification FSLE and SST gradient were used as diagnostics to detect frontal features, since they are typically associated with front intensity and Lagrangian Coherent Structures10. Note that the two diagnostics provide similar but not identical information. FSLEs are used to analyze the horizontal transport and to identify material lines along which a confluence of waters with different origins occur. These lines typically display an enhanced SST gradient because water masses of different origin have often contrasted SST signature. However, this is not a necessary condition. FSLE ridges may be invisible in SST maps if transport occurs in a region of homogeneous SST. Conversely, SST gradient unveils structures separating waters of different temperatures, whose contrast is often – but not always – associated with horizontal transport. Therefore, even if they usually detect the same structures, these two metrics are complementary. Frontal features were identified by considering a local FSLE (or SST gradient, respectively) value larger than a given threshold. The threshold values have been chosen heuristically but consistently with the ones found in previous works. For the FSLEs, we used 0.08 days(^{-1}), a threshold value in the range of the ones chosen in18 and47. For the SST gradient, we considered representative of thermal front values greater than 0.009({^circ })C/km, which is about half the value chosen in47. However, in that work, the SST gradient was obtained from the advection of the SST field with satellite-derived currents for the previous 3 days, a procedure which is known to enhance tracer gradients.Bootstrap method The threshold value splits the AFC into two groups: AFC co-located with FSLE or SST gradient values over the threshold are considered as measured in proximity of a front (i.e., statistically associated with a front), while AFC values below the threshold are considered as not associated with a frontal structure. The statistical independence of the two groups was tested using a Mann-Whitney or U test. Finally, bootstrap analysis is applied following the methodologies used in47. This allows estimating the probability that the difference in the mean AFC values, over and under the threshold, is significant, and not the result of statistical fluctuations. Other diagnostics tested are reported in SI.1.Linear quantile regression Linear quantile regression method48 was employed as no significant correlation was found between the explanatory and the response variables. This can be due to the fact that multiple factors (such as prey or predator distributions) influence the fish distribution other than the frontal activity considered. The presence of these other factors can shadow the relationship of the explanatory variables (in this case, the FSLE and the SST gradient) with the mean value of the response variable (the AFC). A common method to address this problem is the use of the quantile regression48,49, that explores the influence of the explanatory variables on other parts of the response variable distribution. Previous studies, adopting this methodology, revealed the limiting role played by the explanatory variables in the processes considered50. The percentiles values used are 75th, 90th, 95th, and 99th. The analysis is performed using the statistical package QUANTREG in R (https://CRAN.R-project.org/package=quantreg, v.5.3848,51), using the default settings.Chlorophyll-rich waters selection The AFC observations were considered in chlorophyll-rich waters if the corresponding chlorophyll concentration was higher than a given threshold. All the other AFC measurements were excluded, and a linear regression performed between the remaining AFC and FSLE (or SST gradient) values. The corresponding thresholds (one for FSLE and one for SST gradient case) were selected though a sensitivity test reported in SI.1. These resulted in 0.22 and 0.17 mg/m(^3) for FSLE and for SST gradient, respectively. These values are consistent among them and, in addition, they are in coherence with previous estimates of chlorophyll concentration used to characterise productive waters in the Southern Ocean (0.26mg/m(^3)52).Gradient climbing modelAn individual-based mechanistic model is built to describe how fish could move along frontal features. We assume that the direction of fish movement along a frontal gradient is influenced by the noise of the prey field (SI. 2). Specifically, we consider a Markovian process along the (one dimensional) cross-front direction. Time is discretized in timesteps of length (varDelta tau). We presuppose that, at each timestep, the fish chooses between swimming in one of the two opposite cross-front directions (“left” and “right”). This decision depends on the comparison between the quantity of a tracer (a cue) present at its actual position and the one perceived at a distance (p_R) from it, where (p_R) is the perceptual range of the fish. We consider the fish immersed in a tracer whose concentration is described by the function T(x).An expression for the average velocity of the fish, (U_F(x)), can now be derived. We assume that the fish is able to observe simultaneously the tracer to its right and its left. Fish sensorial capacities are discussed in SI.2. The tracer observed is affected by noise. Noise distribution is considered uniform, with (-xi _{MAX}{tilde{T}}(x_0-varDelta x)), the fish will move to the right, and, vice versa, to the left. We hypothesize that the observational range is small enough to consider the tracer variation as linear, as illustrated in Fig. S7 (SI. 3). In this way:$$begin{aligned}&{tilde{T}}(x_0+varDelta x)=T(x_0)+ p_R,frac{partial T}{partial x}+xi _1 \&{tilde{T}}(x_0-varDelta x)=T(x_0)- p_R,frac{partial T}{partial x}+xi _2 ;. end{aligned}$$In case of absence of noise, or with (xi _{MAX}p_R,frac{partial T}{partial x}). If (T(x_0+varDelta x) >T(x_0-varDelta x)) (as in Fig. S7), and the fish will swim leftward if$$begin{aligned} xi _1-xi _2 >2p_R,frac{partial T}{partial x}; . end{aligned}$$Since (xi _1) and (xi _2) range both between (-xi _{MAX}) and (xi _{MAX}), we can obtain the probability of leftward moving P(L). This will be the probability that the difference between (xi _1) and (xi _2) is greater than (2p_R,frac{partial T}{partial x})$$begin{aligned} P(L)&=frac{1}{8xi _{MAX}^2} bigg (2 xi _{MAX} – 2 p_R,frac{partial T}{partial x}bigg )^2\&=frac{1}{2} bigg (1-frac{p_R}{xi _{MAX}},frac{partial T}{partial x}bigg )^2 end{aligned}$$.The probability of moving right will be$$begin{aligned} P(R)&=1-P(L) end{aligned}$$and their difference gives the frequency of rightward moving$$begin{aligned} P(R)-P(L)&=1-2P(L)=1-bigg (1-frac{p_R}{xi _{MAX}},frac{partial T}{partial x}bigg )^2\&=frac{p_R}{xi _{MAX}}frac{partial T}{partial x}bigg (2-frac{p_R}{xi _{MAX}}bigg |frac{partial T}{partial x}bigg |bigg ); , end{aligned}$$where the absolute value of (frac{partial T}{partial x}) has been added to preserve the correct climbing direction in case of negative gradient. The above expression leads to:$$begin{aligned} U_F(x)=frac{V p_R}{xi _{MAX}}frac{partial T}{partial x}bigg (2-frac{p_R}{xi _{MAX}}bigg |frac{partial T}{partial x}bigg |bigg );. end{aligned}$$
(1)
We then assume that, over a certain value of tracer gradient (frac{partial T}{partial x}_{MAX}), the fish are able to climb the gradient without being affected by the noise. This assumption, from a biological perspective, means that the fish does not live in a completely noisy environment, but that under favorable circumstances it is able to correctly identify the swimming direction that leads to higher prey availability. This means that$$begin{aligned} p_R*frac{partial T}{partial x}_{MAX}=xi _{MAX},. end{aligned}$$
(2)
Substituting then (2) into (1) gives:$$begin{aligned} U_F(x)=V frac{frac{partial T}{partial x}}{frac{partial T}{partial x}_{MAX}}bigg (2-frac{big |frac{partial T}{partial x}big |}{frac{partial T}{partial x}_{MAX}}bigg );. end{aligned}$$
(3)
Finally, we can include an eventual effect of transport by the ocean currents, considering that the tracer is advected passively by them, simply adding the current speed (U_C) to Expr. (3).The representations provided are individual based, with each individual representing a single fish. However, we note that all the considerations done are also valid if we consider an individual representing a fish school. (U_F) will then simply represent the average velocity of the fish schools. This aspect should be stressed since many fish species live and feed in groups, especially myctophids (see SI.2 for further details).Continuity equation in one dimension The solution of this model will now be discussed. The continuity equation is first considered in one dimension. The starting scenario is simply an initially homogeneous distribution of fish, that are moving in a one dimensional space with a velocity given by (U_{F}(x)).We assume that in the time scales considered (few days to some weeks), the fish biomass is conserved, so for instance fishing mortality or growing rates are neglected. In that case, we can express the evolution of the concentration of the fish (rho) by the continuity equation$$begin{aligned} frac{partial rho }{partial t}+nabla cdot mathbf{j },=,0 end{aligned}$$
(4)
in which (mathbf{j }=rho ;U_{F}(x)), so that Eq. (4) becomes$$begin{aligned} frac{partial rho }{partial t}+nabla cdot big (rho ;U_{F}(x)big ),=,0;. end{aligned}$$
(5)
In one dimension, the divergence is simply the partial derivate along the x-axis. Eq. (5) becomes$$begin{aligned} frac{partial rho }{partial t}=-frac{partial }{partial x} bigg (rho ;U_{F}bigg ) end{aligned}$$
(6)
Now, we decompose the fish concentration (rho) in two parts, a constant one and a variable one (rho ,=,rho _0+{tilde{rho }}). Eq. (6) will then become$$begin{aligned} frac{partial rho }{partial t}=-U_Ffrac{partial {tilde{rho }}}{partial x}-rho frac{partial U_F}{partial x};. end{aligned}$$
(7)
Using Expr. (3), Eq. (7) is numerically simulated with the Lax method. In Expr. (3) we impose that (U_F(x)=V) when (U_F(x) >V). This biological assumption means that fish maximal velocity is limited by a physiological constraint rather than by gradient steepness. Details of the physical and biological parameters are provided in SI.6. More