Ecology

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

Test surfacesWe used smooth and hairy, homogeneous and striped (with different widths of black and white stripes) test surfaces. In order to mimic the curved zebra back, all test surfaces had a convex cylindrical shape (length: 15 cm, radius: 7 cm). In the schlieren measurements (see subsection “Schlieren imaging”), the cylinder’s horizontal long axis was perpendicular (Supplementary Fig. S1A,B) or parallel (Supplementary Fig. S1C) to the collimated horizontal light beam illuminating the target area, while the stripes were perpendicular (Supplementary Fig. S1A) or parallel (Supplementary Fig. S1B) to the cylinder’s long axis. Supplementary Tables S1 and S2 list the patterns, colours and names of the 8 smooth and 10 hairy test surfaces. The smooth surfaces were composed of cardboard squares (15 cm × 15 cm) (Supplementary Fig. S2). The hairy surfaces were composed of cattle, horse and zebra hides glued by dextrin to a cylindrical (length: 15 cm, radius: 7 cm) gypsum base (Supplementary Fig. S3). Surfaces hsc1(7b7w)perp, hsc1(8b7w)par, hsc3(3b2w)perp and hsgc3(3s2L)perp were used to model that the black stripes of zebras could be separately erected, while the white remained flat7. No animals were killed, the horse and cattle hides were provided by Hungarian horse and cattle keepers, while the zebra hide was obtained from a Hungarian zoological garden.Thermography of lamplit test surfacesUsing a thermocamera (VarioCAM, Jenoptik Laser Optik Systeme GmbH, Jena, Germany, nominal precision of ± 1.5 K, with relative pixel-to-pixel precision  1 s. Since in the upper rectangular window of the filtered schlieren images the lifespan t of local minima of I(x) was very short, we performed statistics only for the lower rectangular window.For the statistical analysis of the characteristics of I(x) (Nmin, ΔI, dave) and air stream behaviour (t, dcovered, v, dmax, dse), we applied Principal Component Analysis (PCA) and fitted ellipses to component scores with 95% confidence interval. We applied Wilcoxon rank sum test with Bonferroni correction for the datasets. Because of the large sample size of the dataset Nmin, ΔI, dave—for smooth test surfaces 4475 observations per surface and for hairy test surfaces 5369 observations per surface—, the Wilcoxon rank sum test would result in highly significant differences for almost all comparisons15. Therefore, we used a Monte Carlo approach, where we randomly selected 250 (≈ 5% of the full dataset) samples, ran the Wilcoxon rank sum test with Bonferroni correction, recorded the results, repeated this 499 times (to have 500 runs) and finally the average of the results of the 500 runs was calculated. The data evaluation was made by our custom-written scripts in Python programming language. For statistical analyses we used the R statistical package 3.6.316.Disturbance caused by an artificial wind and a butterflyTo demonstrate the influence of very weak winds on the air streams above lamplit test surfaces, we blew air by a compressor with a press of 1.6 bar from 1.5 m above the following test surfaces: ss1(8b7w), hsz(8b7w)perp, hsh1(8b8w)perp and hhbc. The compressor tube ended in an air-blow gun. Before the measurements, the air pressure was 4 bar in the 3-litre compressor tank, and the regulator was set to 1.6 bar. The compressor was turned off prior to recording a given sequence and during the measurement the air-blow gun was used to generate horizontal “wind” until the pressure decreased to 1 bar (atmospheric pressure). The artificial wind speed created by the compressor was measured (with accuracy  More

Rearing methodologyEristalis flower flies were reared in laboratory conditions from egg clutches laid by wild-caught females in the summer of 2019 (see Supplementary Materials for detailed rearing methodology). Only flies that emerged on the same day were used in the experiments. An artificial diapause protocol (see Supplementary Materials for detailed protocol) was used to prolong the lifespan of lab-reared flower flies, as adult Eristalis flower flies in lab colonies have shorter lifespans than adult Eristalis flies in the wild48. Once the adult flies emerged, all siblings were placed in artificial diapause in a refrigerator and fed 10% sucrose ad libitum until the experiment began. These Eristalis flower fly rearing and artificial diapause protocols are a modification of previously published protocols48,49.Osmia lignaria (n = 50; Crown Bees, Woodinville, WA, USA) and Megachile rotundata (n = 50; Watts Solitary Bees, Bothell, WA, USA) were purchased and allowed to emerge in an incubator kept at 23 °C and 65% humidity. Bumble bees (Bombus impatiens) used as C. bombi source colonies or as uninfected sources of bees for the dose–response trials were purchased from Biobest (Biobest, Leamington, Ontario, Canada) and maintained in the lab by feeding sucrose and pollen from a mixture of honey bee-collected poly-floral pollen (Bee Pollen Granules, CC Pollen High Desert, Phoenix AZ, USA). To ensure the commercial colonies were free of parasites, we pulled 20 workers and screened them for parasites via microscopy. No parasites were found in any of the colonies used for the dose–response trials.Evaluating whether the European drone fly, Eristalis tenax, is a host of Crithidia bombi
After breaking artificial diapause, the E. tenax flower flies were allowed to groom, but not feed, for one hour. Each fly was then placed abdomen-first into a 1.5 mL microcentrifuge tube harness to collect defecation events (Supplementary Figure S5). The size of these tubes allowed the flies to feed comfortably, but the tubes were also tapered at the bottom, which prevented the flies from stepping in their feces. Holes were placed along the side of the tubes so the fly could respire. One large hole was placed on the lid of the tube so the fly could be inoculated directly with a pipette.Flies were randomly divided into treatment and control groups. E. tenax flies in a roughly 1:2 F:M sex ratio were used in both the treatment (n = 30) and control groups (n = 30), for a total of 60 replicates. The flies that emerged from the same egg clutch with this 1:2 F:M sex ratio were the only siblings that could accommodate the replicates needed for this experiment, which is why this sex ratio was used.The C. bombi inoculum was made fresh from infected B. impatens individuals the morning of the experiment using established protocols. Briefly, we dissected the gut of infected B. impatiens workers from a laboratory source colony that sustained a strain collected from wild B. impatiens workers from Massachusetts, USA (GPS coordinates: 42.363911 N, – 72.567747 W). We homogenized the bee guts in distilled water and diluted the mixture to 1280 C. bombi cells μL−1, which we then combined 1:1 with 30% sucrose solution for an inoculum of 640 cells μL−1, a standard inoculum concentration for infecting bumble bees with C. bombi35,50. Control groups were fed 5 μL of a 30% sucrose and blue dye (Butler Extract Co., Lancaster, PA, USA) that in pilot experiments was not found to influence host or parasite survival. Treatment groups were inoculated with 5 μL (3200 cells total) of C. bombi, 30% sucrose and blue dye solution. The number of cells used in the inoculum is similar to levels of C. bombi found in the feces of bumblebees with recently established infections37. Blue dye was used to better visualize when fecal events occurred and flies that did not drink the entire 5 μL inoculum were not used in the experiment.After feeding, the flies were monitored continuously until defecation occurred. As these flies recently emerged from artificial diapause and were starved pre-experiment, every hour post-inoculation the flies were fed a 30% sucrose and blue dye solution ad libitum to encourage defecation. Once a fly defecated, the feces were collected via pipette and diluted to a 10 μL solution with deionized water to observe and count parasites using Kova Glasstic slides. The fly was then placed in an individual 60 mL plastic portion cup with filter paper (Sigma–Aldrich, St Louis, MO, USA) and a 1.5 mL microcentrifuge tube feeder containing 500 μL of a 30% sucrose and blue dye solution for 10 days. Feeders and filter papers were replaced every 3 days to prevent mold growth. As C. bombi typically replicates in high numbers after 10 days in the guts of bumble bees51, both control and treatment flies were dissected and C. bombi gut counts were performed 10 days post-inoculation. Since actively swimming, and thus live, C. bombi is infective to susceptible bumble bee hosts35, only actively swimming C. bombi were counted. The fecal volume, dilution factor and counts of C. bombi were quantified for each individual fly to calculate the exact amount of C. bombi in the individual’s first defecation event.Dose–response dataCrithidia bombi inoculum was made from infected B. impatiens individuals the morning of each trial using the protocols described above, with two exceptions. First, the C. bombi strain was collected from wild B. impatiens workers from New York, USA (GPS coordinates: 42.457350, − 76.426907). Second, a range of serially diluted doses were used to inoculate uninfected B. impatiens workers. The doses were: 25,000 cells, 12,500 cells, 6250 cells, 3125 cells, 1563 cells, 781 cells, 391 cells, 195 cells, 98 cells, 49 cells, 24 cells, and 12 cells. To obtain these doses, we homogenized bee guts in distilled water and diluted the mixture to 5000 C. bombi cells μL−1 with 30% sucrose solution. Serial dilutions were then conducted with a 10% sucrose solution to ensure the same osmolarity of each inoculum.We conducted four replicate dose–response trials over a period of four weeks. Each week, five uninfected workers per dose from each of two colonies were administered 5 μL of C. bombi inoculum. The ten highest doses were administered for the first 2 weeks, and two additional doses (24 cells, and 12 cells) were added for the final 2 weeks. Inoculated bees were kept individually in vials and fed 30% sucrose ad libitum for 7 days at 23 °C and 65% humidity. After 7 days, the bees were dissected and C. bombi loads were quantified using a hemocytometer as described above. In addition, the right forewing was removed from each bee and marginal cell length was measured as a proxy for size52. In total, 220 bees were inoculated (20 replicates for each of the ten highest doses, 10 replicates for the two lowest doses).Defecation patterns on a shared floral resourceAll pollinators (O. lignaria, M. rotundata, E. arbustorum and E. tenax) were placed in individual 60 mL plastic portion cups lined with filter paper. Each pollinator received a 1.5 mL microcentrifuge tube feeder containing 500 μL of fluorescent dye via 2.5 g of fluorescent powder (Stardust Micas) dissolved into 500 mL 30% sucrose feeders to visualize fecal deposition on flowers. After 24 hours, filter papers were collected (for analysis of fecal volume and defecation frequency, see below) and a total of five, randomly selected pollinators of the same species were placed in 12 × 12 × 12″ mesh cages (Bioquip Products, Rancho Dominguez, CA, USA) containing inflorescences of similar sized Solidago dansolitum ‘Little Lemon’ goldenrod each replicate trial. Goldenrod was used in this experiment because both bees and flower flies were observed foraging on this abundant floral resource. Only pollinators with filter papers containing fluorescing defecation events were released in the mesh cages.All E. arbustorum cages (n = 10) contained 2:3 F:M sex ratios, except one cage contained a 3:2 F:M sex ratio. All E. tenax cages (n = 20) contained 3:2 F:M sex ratios, except four cages contained 2:3 F:M sex ratios. All O. lignaria cages (n = 10) contained 4:1 F:M sex ratios, except one cage contained a 3:2 F:M sex ratio. For the two fly species, sample sizes and F:M sex ratios were determined by the greatest, same-day sibling emergence. For O. lignaria, sample sizes and F:M sex ratios were determined by emergence availability. M. rotundata floral deposition data was not collected, as the F:M emergence was heavily skewed to males that did not interact with, and therefore defecate on, the flowers.After 24-hours, the pollinators were removed and the defecation events on the goldenrod from all cages were counted under a blacklight. The location of the defecation events on the goldenrod was recorded. The plant parts were divided into six categories: ‘inside’ the flower (inside the corolla), ‘outside’ the flower (surface of the corolla), on the sepal, on the bract (the leaflike structure beneath the flower), on the stem or on a leaf.Defecation frequency and fecal volumesThe diameter of the smallest and largest defecation events per filter paper was measured by a digital caliper and an average diameter was calculated from these two values for all pollinators. The average diameter of the defecation events was converted to an average volume (in μL) using a standard curve (Supplementary Figure S6; R2 = 0.99 for the calibration data). The collected fecal volumes defecated by control flies from the E. tenax inoculation experiment (see above) were compared to the average fly fecal volumes calculated here. This was done to analyze whether flies in a confined environment, where they were inoculated with C. bombi, defecated similar volumes to flies allowed to move freely in an individual cup, which the average volumes were estimated from. In addition, the number of defecation events (frequency) over a 24-hour period on the collected E. arbustorum (n = 46) and E. tenax (n = 100) filter papers were counted for each fly.Statistical analysesFor the E. tenax inoculation experiment, we evaluated the amount of C. bombi cells in the first defecation event using a negative binomial generalized linear model (GLM), with fly sex as predictor. We chose negative binomial over Poisson to account for overdispersion, which we evaluated using Pearson residuals. Significance of sex was evaluated using a likelihood ratio test (LRT).Data from the B. impatiens inoculation experiment were used to fit two dose–response curves, the first for infection probability, and the second for infection intensity among infected bees. Infection intensity was defined using the loads estimated from the hemocytometer. A bee was considered infected if the counts were nonzero. We first tested whether the dose ingested, wing length (as a proxy for body size) and the colony the bee came from affected its response. For infection probability, this was done using a GLM with log10(dose), colony, wing length and their interactions as predictors, and infection status as the Bernoulli response. For infection intensity, this was done using a linear model (LM) with the same predictors, and log10(intensity) as response, using only infected bees. Doses were log-transformed in accordance to how the experimental doses were varied, while intensities were log-transformed to achieve normality of the residuals. Significance of predictors were tested in accordance with the principle of marginality.While we found that wing length and colony were significant predictors, in practice the colony-specific response of a wild bee is unknown (since it would not have come from any of the experimental colonies), while the dependence on wing length is only useful in a size-based epidemiological model. Hence, we generated dose–response curves by marginalizing across colony and wing length. Finally, we tested whether linear relationships between the link function and log10(dose), assumed in LMs and GLMs, were sufficient to capture the shape of the dose–response curves, by fitting the data to shape-constrained additive models and then comparing AIC values53. SCAMs are generalized additive models (GAMs) on which additional constraints such as monotonicity have been imposed; being more flexible, they can better capture the shapes of the dose–response curves should the linear relationships be inadequate.We evaluated whether fecal volume depended on pollinator species and sex with a linear model (LM), fitted using weighted least squares to account for unequal variances between group (detected using Levene’s test). Since the transformation from diameter (of feces on filter paper) to volume introduced a noticeable skew to the distribution, we transformed the volume back to diameter and further performed a Box-Cox transformation to achieve normality54, which we verified using the Shapiro–Wilk and D’Agostino’s K2 test. The transformed volume was used as the response in the abovementioned linear model.For E. tenax, fecal volume was also manually collected from the 1.5 microcentrifuge tubes during the inoculations experiment. We compared the fecal volume from the two methods using a LM with method and fly sex as well as their interaction as predictors. Volumes were log-transformed to achieve normality, while the linear model was fitted using ordinary least squares since Levene’s test indicated no significant deviation from the assumption of equal variance.We evaluated whether defecation frequency depended on pollinator species and sex with a LM, again fitted using weighted least squares to account for unequal variances between groups. While two of the groups showed deviation from normality using the Shapiro–Wilk test, the deviations were only marginally significant and hence not expected to qualitatively affect the results55.Finally, we evaluated defecation patterns on goldenrod using a negative binomial GLM, with feces counts as the response, and pollinator species, plant location and their interaction as predictors. We did not use a mixed model with cage number as a random effect since there was only one count value per cage per location, so pseudo-replication was not an issue. Significance of predictors were evaluated using LRT in accordance to the principle of marginality56 (i.e., main effects were tested only when their interactions were insignificant and hence dropped). Post-hoc tests of pairwise contrasts with Tukey corrections were performed for predictors that were significant. We recognize that the principle sex ratio and its interactions with other predictors could also be included among the predictors; however, since each species had cages with predominantly one sex ratio (E. tenax 3F:2M; E. arbustorum 2F:3M; O. lignaria mix of 4F:1M and 5F:0M), this meant that species and sex ratios were highly correlated, making it impossible to separate their effects. Nonetheless, since female Eristalis flies do not provision their brood, the differences between sexes (e.g., time spent foraging on plants) may be less pronounced than in bees. More

1.Somveille, M., Rodrigues, A. S. L. & Manica, A. Why do birds migrate? A macroecological perspective. Glob. Ecol. Biogeogr. 24, 664–674 (2015).Article 

Google Scholar 
2.Van Der Graaf, S., Stahl, J., Klimkowska, A., Bakker, J. P. & Drent, R. H. Surfing on a green wave—How plant growth drives spring migration in the Barnacle Goose Branta leucopsis. Ardea 94, 567–577 (2006).
Google Scholar 
3.Shariatinajafabadi, M. et al. Migratory herbivorous waterfowl track satellite-derived green wave index. PLoS ONE 9, 1–11 (2014).Article 
CAS 

Google Scholar 
4.Bennetts, R. E. & Kitchens, W. M. Factors influencing movement probabilities of a nomadic food specialist: Proximate foraging benefits or ultimate gains from exploration?. Oikos 91, 459–467 (2000).Article 

Google Scholar 
5.Trierweiler, C. et al. A Palaearctic migratory raptor species tracks shifting prey availability within its wintering range in the Sahel. J. Anim. Ecol. 82, 107–120 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Ma, Z., Cai, Y., Li, B. & Chen, J. Managing wetland habitats for waterbirds: An international perspective. Wetlands 30, 15–27 (2010).CAS 
Article 

Google Scholar 
7.Smit, I. P. J. Resources driving landscape-scale distribution patterns of grazers in an African savanna. Ecography (Cop.) 34, 67–74 (2011).Article 

Google Scholar 
8.Donnelly, J. P. et al. Synchronizing conservation to seasonal wetland hydrology and waterbird migration in semi-arid landscapes. Ecosphere 10, 1–12 (2019).Article 

Google Scholar 
9.Bennitt, E., Bonyongo, M. C. & Harris, S. Habitat selection by African buffalo (Syncerus caffer) in response to landscape-level fluctuations in water availability on two temporal scales. PLoS ONE 9, 1–14 (2014).Article 

Google Scholar 
10.Kleyheeg, E. et al. Movement patterns of a keystone waterbird species are highly predictable from landscape configuration. Mov. Ecol. 5, 1–14 (2017).Article 

Google Scholar 
11.Roshier, D. A., Doerr, V. A. J. & Doerr, E. D. Animal movement in dynamic landscapes: Interaction between behavioural strategies and resource distributions. Oecologia 156, 465–477 (2008).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Henry, D. A. W., Ament, J. M. & Cumming, G. S. Exploring the environmental drivers of waterfowl movement in arid landscapes using first-passage time analysis. Mov. Ecol. 4, 1–18 (2016).Article 

Google Scholar 
13.Cook, M. I., Call, E. M., Kobza, R., Mac Hill, S. D. & Saunders, C. J. Seasonal movements of crayfish in a fluctuating wetland: Implications for restoring wading bird populations. Freshw. Biol. 59, 1608–1621 (2014).Article 

Google Scholar 
14.Weimerskirch, H. et al. Lifetime foraging patterns of the wandering albatross: Life on the move!. J. Exp. Mar. Bio. Ecol. 450, 68–78 (2014).Article 

Google Scholar 
15.Krüger, S., Reid, T. & Amar, A. Differential range use between age classes of southern African bearded vultures Gypaetus barbatus. PLoS ONE 9, e114920 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
16.Wolfson, D. W., Fieberg, J. R. & Andersen, D. E. Juvenile Sandhill Cranes exhibit wider ranging and more exploratory movements than adults during the breeding season. Ibis 162, 556–562 (2019).Article 

Google Scholar 
17.Péron, C. & Grémillet, D. Tracking through life stages: Adult, immature and juvenile Autumn migration in a long-lived seabird. PLoS ONE 8, e72713 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
18.Hake, M., Kjellén, N. & Alerstam, T. Age-dependent migration strategy in honey buzzards Pernis apivorus tracked by satellite. Oikos 103, 385–396 (2003).Article 

Google Scholar 
19.Gschweng, M., Kalko, E. K. V., Querner, U., Fiedler, W. & Berthold, P. All across Africa: Highly individual migration routes of Eleonora’s falcon. Proc. R. Soc. B Biol. Sci. 275, 2887–2896 (2008).Article 

Google Scholar 
20.Miller, T. A. et al. Limitations and mechanisms influencing the migratory performance of soaring birds. Ibis 158, 116–134 (2016).Article 

Google Scholar 
21.Rotics, S. et al. The challenges of the first migration: movement and behaviour of juvenile vs. adult white storks with insights regarding juvenile mortality. J. Anim. Ecol. 85, 938–947 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Sergio, F. et al. Individual improvements and selective mortality shape lifelong migratory performance. Nature 515, 410–413 (2014).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Thorup, K. et al. Resource tracking within and across continents in long-distance bird migrants. Sci. Adv. 3, e1601360 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Howison, R. A., Piersma, T., Kentie, R., Hooijmeijer, J. C. E. W. & Olff, H. Quantifying landscape-level land-use intensity patterns through radar-based remote sensing. J. Appl. Ecol. 55, 1276–1287 (2018).Article 

Google Scholar 
25.Wang, X. et al. Stochastic simulations reveal few green wave surfing populations among spring migrating herbivorous waterfowl. Nat. Commun. 10, 2187 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
26.McFeeters, S. K. The use of the normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 17, 1425–1432 (1996).ADS 
Article 

Google Scholar 
27.Mcfeeters, S. K. Using the normalized difference water index (NDWI) within a geographic information system to detect swimming pools for mosquito abatement: A practical approach. Remote Sens. 5, 3544–3561 (2013).ADS 
Article 

Google Scholar 
28.Yang, X., Zhao, S., Qin, X., Zhao, N. & Liang, L. Mapping of urban surface water bodies from Sentinel-2 MSI Imagery at 10m resolution via NDWI-based image sharpening. Remote Sens. 9, 1–19 (2017).ADS 

Google Scholar 
29.Choi, C. Y. et al. Where to draw the line? Using movement data to inform protected area design and conserve mobile species. Biol. Conserv. 234, 64–71 (2019).Article 

Google Scholar 
30.Guillet, A. Distribution and conservation of the shoebill (Balaeniceps rex) in the southern Sudan. Biol. Conserv. 13, 39–49 (1978).Article 

Google Scholar 
31.BirdLife International. Species factsheet: Balaeniceps rex. https://www.birdlife.org. Accessed on Apr 14, 2020 (2020).32.Dodman, T. International single species plan for the conservation of the Shoebill Balaeniceps rex. AEWA Technical Series 51 (2013).33.Guillet, A. Aspects of the foraging behaviour of the shoebill. Ostritch J. Afr. Ornithol. 50, 252–255 (1979).Article 

Google Scholar 
34.Mullers, R. H. E. & Amar, A. Shoebill Balaeniceps rex foraging behaviour in the Bangweulu Wetlands, Zambia. Ostritch J. Afr. Ornithol. 86, 113–118 (2015).Article 

Google Scholar 
35.Roxburgh, L. & Buchanan, G. M. Revising estimates of the Shoebill (Balaeniceps rex) population size in the Bangweulu Swamp, Zambia, through a combination of aerial surveys and habitat suitability modelling. Ostrich J. Afr. Ornithol. 81, 25–30 (2010).Article 

Google Scholar 
36.John, J. R. M., Nahonyo, C. L., Lee, W. S. & Msuya, C. A. Observations on nesting of Shoebill Balaeniceps rex and Wattled Crane Bugeranus carunculatus in Malagari wetlands, western Tanzania. Afr. J. Ecol. 51, 184–187 (2013).Article 

Google Scholar 
37.Mullers, R. H. E. & Amar, A. Parental nesting behavior, chick growth and breeding success of Shoebills (Balaeniceps rex) in the Bangweulu Wetlands, Zambia. Waterbirds 38, 1–9 (2015).Article 

Google Scholar 
38.Elliott, A., Garcia, E. F. J. & Boesman, P. Shoebill (Balaeniceps rex). in Handbook of the Birds of the World (eds. del Hoyo, J., Elliott, A., Sargatal, J., Christie, D. A. & de Juana, E.) (Lynx Edicions, 2020).39.African Parks. African Parks: Unlocking the value of protected areas. African Parks Annual Report 2018. (2018).40.Möller, W. Beobachtungen zum Nahrungserwerb des Schuhschnabels (Balaeniceps rex). J. Ornithol. 123, 19–28 (1982).Article 

Google Scholar 
41.Christensen, K. D., Falk, K., Jensen, F. P. & Petersen, B. S. Abdim’s Stork Ciconia abdimii in Niger: Population size, breeding ecology and home range. Ostritch J. Afr. Ornithol. 79, 177–185 (2008).Article 

Google Scholar 
42.McCann, K. I. & Benn, G. A. Land use patterns within Wattled Crane (Bugeranus carunculatus) home ranges in an agricultural landscape in KwaZulu-Natal, South Africa. Ostritch J. Afr. Ornithol. 77, 186–194 (2006).Article 

Google Scholar 
43.El-Hacen, E.-H.M., Overdijk, O., Lok, T., Olff, H. & Piersma, T. Home Range, habitat selection, and foraging rhythm in Mauritanian Spoonbills (Platalea leucorodia balsaci): A satellite tracking study. Waterbirds 36, 277–286 (2013).Article 

Google Scholar 
44.King, D. T. et al. Winter and summer home ranges of American White Pelicans (Pelecanus erythrorhynchos) captured at loafing sites in the Southeastern United States. Waterbirds 39, 287–294 (2016).Article 

Google Scholar 
45.Shaw, A. K. Drivers of animal migration and implications in changing environments. Evol. Ecol. 30, 991–1007 (2016).Article 

Google Scholar 
46.Folmer, E. O., Olff, H. & Piersma, T. The spatial distribution of flocking foragers: Disentangling the effects of food availability, interference and conspecific attraction by means of spatial autoregressive modeling. Oikos 121, 551–561 (2012).Article 

Google Scholar 
47.Folmer, E. O. & Piersma, T. The contributions of resource availability and social forces to foraging distributions: A spatial lag modelling approach. Anim. Behav. 84, 1371–1380 (2012).Article 

Google Scholar 
48.Mendez, L. & Weimerskirch, H. Ontogeny of foraging behaviour in juvenile red-footed boobies (Sula sula). Sci. Rep. 7, 13886 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
49.Patrick, S. C. & Weimerskirch, H. Consistency pays: Sex differences and fitness consequences of behavioural specialization in a wide-ranging seabird. Biol. Lett. 10, 20140630 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
50.Patrick, S. C. & Weimerskirch, H. Personality, foraging and fitness consequences in a long lived seabird. PLoS ONE 9, e87269 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
51.Doherty, T. S. & Driscoll, D. A. Coupling movement and landscape ecology for animal conservation in production landscapes. Proc. R. Soc. B Biol. Sci. 285, 20172272 (2018).Article 

Google Scholar 
52.Riotte-lambert, L. & Weimerskirch, H. Do naive juvenile seabirds forage differently from adults?. Proc. R. Soc. B Biol. Sci. 280, 20131434 (2013).Article 

Google Scholar 
53.Buxton, L., Slater, J. & Brown, L. The breeding behaviour of the shoebill or whale-headed stork Balaeniceps rex in the Bangweulu Swamps, Zambia. Afr. J. Ecol. 16, 201–220 (1978).Article 

Google Scholar 
54.Roshier, D. A., Robertson, A. I. & Kingsford, R. T. Responses of waterbirds to flooding in an arid region of Australia and implications for conservation. Biol. Conserv. 106, 399–411 (2002).Article 

Google Scholar 
55.Chevallier, D. et al. Human activity and the drying up of rivers determine abundance and spatial distribution of Black Storks Ciconia nigra on their wintering grounds determine abundance and spatial distribution of Black Storks Ciconia nigra on their wintering grounds. Bird Study 3657, 369–380 (2010).Article 

Google Scholar 
56.Ng’onga, M., Kalaba, F. K., Mwitwa, J. & Nyimbiri, B. The interactive effects of rainfall, temperature and water level on fish yield in Lake Bangweulu fishery, Zambia. J. Therm. Biol. 84, 45–52 (2019).57.Grissac, S. D., Bartumeus, F., Cox, S. L. & Weimerskirch, H. Early-life foraging: Behavioral responses of newly fledged albatrosses to environmental conditions. Ecol. Evol. 7, 6766–6778 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
58.Bolduc, F. & Afton, A. D. Relationships between wintering waterbirds and invertebrates, sediments and hydrology of coastal marsh ponds. Waterbirds 27, 333–341 (2004).Article 

Google Scholar 
59.Ratcliffe, C. The fishery of the Lower Shire River area. Malawy Fisheries Bulletin No. 3. Fisheries Department, Malawi (1972).60.Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 27, 3025–3033 (2006).ADS 
Article 

Google Scholar 
61.Tian, S., Zhang, X., Tian, J. & Sun, Q. Random forest classification of wetland landcovers from multi-sensor data in the arid region of Xinjuang, China. Remote Sens. 8, 1–14 (2016).CAS 

Google Scholar 
62.Kamweneshe, B. M. Ecology, Conservation and Management of the Black Lechwe (Kobus leche smithemani) in the Bangweulu Basin, Zambia. University of Pretoria (2000).63.BirdLife International. Important Bird Areas Factsheet: Bangweulu Swamps. https://www.birdlife.org. Accessed on Oct 14, 2020 (2020).64.Thurlow, J., Zhu, T. & Diao, X. The impact of climate variability and change on economic growth and poverty in Zambia. International Food Policy Research Institute (2009).65.Evans, D. W. Lake Bangweulu: A study of the complex and fishery. Fisheries Service Reports, Zambia (1978).66.Kolding, J. & van Zwieten, P. A. M. Relative lake level fluctuations and their influence on productivity and resilience in tropical lakes and reservoirs. Fish. Res. 115–116, 99–109 (2012).Article 

Google Scholar 
67.Howard, G. W. & Aspinwall, D. R. Aerial censuses of Shoebills, Saddlebilled Storks and Wattled Cranes at the Bangweulu Swamps and Kafue Flats, Zambia. Ostrich J. Afr. Ornithol. 55, 207–212 (1984).Article 

Google Scholar 
68.Microwave Telemetry. Microwave Telemetry Solar Argos/GPS 70g PTT. https://www.microwavetelemetry.com/. Accessed on Oct 14, 2020 (2020).69.Calenge, C. The package “adehabitat” for the R software: A tool for the analysis of space and habitat use by animals. Ecol. Model. 197, 516–519 (2006).Article 

Google Scholar 
70.Hijmans, R. J. geosphere: Spherical trignometry. https://cran.r-project.org/package=geosphere (2019).71.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.r-project.org/ (2019).72.Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. https://cran.r-project.org/package=sp (2005).73.Bivand, R. S., Pebesma, E. J. & Gomez-Rubio, V. Applied Spatial Data Analysis with R. (Springer, 2013).74.E. Vermote. MOD09A1 MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD09A1.006 (2015).75.Hijmans, R. J. raster: Geographic data analysis and modeling. https://cran.r-project.org/package=raster (2019).76.Bivand, R. S., Keitt, T. & Rowlingson, B. rgdal: Bindings for the ‘geospatial’ abstraction library. https://cran.r-project.org/package=rgdal (2019).77.Bivand, R. S. & Rundel, C. rgeos: Interface to geometry engine – Open source (GEOS). https://cran.r-project.org/package=rgeos (2019).78.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
79.Barton, K. MuMIn: Multi-Model Inference. https://cran.r-project.org/package=MuMIn (2019). More

Residual HMs in the flushed soil and their mobilityOne of the aims of soil remediation is a permanent and substantial reduction in the amount, toxicity or mobility of pollutants. In this study, many factors affected HM removal, such as the type of WA, the flow rate of the WA and the type of HM. In general, the residual HM contents in soil flushed at a flow rate of 1.0 ml/min. were significantly lower than those in soil flushed at 0.5 ml/min (p  More