Ecology

1.Thieltges, D. W., Mouritsen, K. N. & Poulin, R. in Mudflat Ecology (ed Beninger, P.) (Springer International Publishing, 2018).2.Tyler-Walters, H. Cerastoderma edule Common cockle. Marine Life Information Network: Biology and Sensitivity Key Information Reviews (2007).3.Malham, S. K., Hutchinson, T. H. & Longshaw, M. A review of the biology of European cockles (Cerastoderma spp.). J. Mar. Biol. Assoc. U. K. 92, 1563–1577 (2012).4.Magalhaes, L., Freitas, R., Dairain, A. & De Montaudouin, X. Can host density attenuate parasitism?. J. Mar. Biol. Assoc. U. K. 97, 497–505 (2017).
Google Scholar 
5.Carss, D. N. et al. Ecosystem services provided by a non-cultured shellfish species: The common cockle Cerastoderma edule. Mar. Environ. Res. 158, 104931 (2020).CAS 
PubMed 

Google Scholar 
6.Lassalle, G., de Montaudouin, X., Soudant, P. & Paillard, C. Parasite co-infection of two sympatric bivalves, the Manila clam (Ruditapes philippinarum) and the cockle (Cerastoderma edule) along a latitudinal gradient. Aquat. Living Resour. 20, 33–42 (2007).
Google Scholar 
7.Hoberg, E. P. Faunal diversity among avian parasite assemblages: the interaction of history, ecology and biogeography in marine systems. Bull. Scand. Soc. Parasitol. 6, 65–89 (1996).
Google Scholar 
8.Muzaffar, S. B. & Jones, I. L. Parasites and diseases of auks (Alcidae) of the world and their ecology-A review. Mar. Ornithol. 32, 121–146 (2004).
Google Scholar 
9.Lafferty, K. D., Dobson, A. P. & Kuris, A. M. Parasites dominate food web links. Proc. Natl. Acad. Sci. U. S. A. 103, 11211–11216 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Lafferty, K. D. et al. Parasites in food webs: The ultimate missing links. Ecol. Lett. 11, 533–546 (2008).PubMed 
PubMed Central 

Google Scholar 
11.Johnson, P. T. J. et al. When parasites become prey: ecological and epidemiological significance of eating parasites. Trends Ecol. Evol. 25, 362–371 (2010).PubMed 

Google Scholar 
12.Zannella, C. et al. Microbial diseases of bivalve mollusks: Infections, immunology and antimicrobial defense. Mar. Drugs 15, 182 (2017).PubMed Central 

Google Scholar 
13.Fermer, J., Culloty, S. C., Kelly, T. C. & O’riordan, R. M. Parasitological survey of the edible cockle Cerastoderma edule (Bivalvia) on the south coast of Ireland. J. Mar. Biol. Assoc. U. K. 91, 923–928 (2011).
Google Scholar 
14.Longshaw, M. & Malham, S. K. A review of the infectious agents, parasites, pathogens and commensals of European cockles (Cerastoderma edule and C. glaucum) (vol 93, pg 227, 2013). J. Mar. Biol. Assoc. U. K. 93, 1141 (2013).15.Newman, S. H. et al. Aquatic bird disease and mortality as an indicator of changing ecosystem health. Mar. Ecol. Prog. Ser. 352, 299–309 (2007).ADS 

Google Scholar 
16.Vezzulli, L. et al. Climate influence on Vibrio and associated human diseases during the past half-century in the coastal North Atlantic. Proc. Natl. Acad. Sci. U. S. A. 113, E5062–E5071 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
17.Jesser, K. J. & Noble, R. T. Vibrio ecology in the Neuse River Estuary, North Carolina, characterized by next-generation amplicon sequencing of the gene encoding heat shock protein 60 (hsp60). Appl. Environ. Microbiol. 84, e00333-e418 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
18.Romalde, J. L., Dieguez, A. L., Lasa, A. & Balboa, S. New Vibrio species associated to molluscan microbiota: A review. Front. Microbiol. 4, 413 (2014).PubMed 
PubMed Central 

Google Scholar 
19.Allam, B., Paillard, C. & Ford, S. Pathogenicity of Vibrio tapetis, the etiological agent of brown ring disease in clams. Dis. Aquat. Org. 48, 221–231 (2002).
Google Scholar 
20.Waechter, M., Le Roux, F., Nicolas, J., Marissal, E. & Berthe, F. Characterisation of Crassostrea gigas spat pathogenic bacteria. C.R. Biol. 325, 231–238 (2002).CAS 
PubMed 

Google Scholar 
21.Gay, M., Renault, T., Pons, A. & Le Roux, F. Two Vibrio splendidus related strains collaborate to kill Crassostrea gigas: Taxonomy and host alterations. Dis. Aquat. Org. 62, 65–74 (2004).
Google Scholar 
22.Paillard, C., Le Roux, F. & Borrego, J. Bacterial disease in marine bivalves, a review of recent studies: Trends and evolution. Aquat. Living Resour. 17, 477–498 (2004).
Google Scholar 
23.Prado, S., Romalde, J., Montes, J. & Barja, J. Pathogenic bacteria isolated from disease outbreaks in shellfish hatcheries. First description of Vibrio neptunius as an oyster pathogen. Dis. Aquat. Org. 67, 209–215 (2005).CAS 

Google Scholar 
24.Garnier, M., Labreuche, Y. & Nicolas, J. Molecular and phenotypic characterization of Vibrio aestuarianus subsp francensis subsp nov., a pathogen of the oyster Crassostrea gigas. Syst. Appl. Microbiol. 31, 358–365 (2008).CAS 
PubMed 

Google Scholar 
25.Egerton, S., Culloty, S., Whooley, J., Stanton, C. & Ross, R. P. The gut microbiota of marine fish. Front. Microbiol. 9, 873 (2018).PubMed 
PubMed Central 

Google Scholar 
26.Vezzulli, L., Colwell, R. R. & Pruzzo, C. Ocean warming and spread of pathogenic vibrios in the aquatic environment. Microb. Ecol. 65, 817–825 (2013).PubMed 

Google Scholar 
27.Vezzulli, L. et al. Aquatic ecology of the oyster pathogens Vibrio splendidus and Vibrio aestuarianus. Environ. Microbiol. 17, 1065–1080 (2015).CAS 
PubMed 

Google Scholar 
28.Azandegbe, A. et al. Occurrence and seasonality of Vibrio aestuarianus in sediment and Crassostrea gigas haemolymph at two oyster farms in France. Dis. Aquat. Org. 91, 213–221 (2010).
Google Scholar 
29.Burreson, E. & Ford, S. A review of recent information on the Haplosporidia, with special reference to Haplosporidium nelsoni (MSX disease). Aquat. Living Resour. 17, 499–517 (2004).
Google Scholar 
30.Engelsma, M. Y. et al. Digenean trematodes and haplosporidian protozoans associated with summer mortality of cockles Cerastoderma edule in the Oosterschelde, The Netherlands. (European Association of Fish Pathologists Conference, Split, Croatia., 2011).31.Arzul, I. & Carnegie, R. B. New perspective on the haplosporidian parasites of molluscs. J. Invertebr. Pathol. 131, 32–42 (2015).PubMed 

Google Scholar 
32.Carnegie, R. B., Arzul, I. & Bushek, D. Managing marine mollusc diseases in the context of regional and international commerce: Policy issues and emerging concerns. Philos. Trans. R. Soc. B-Biol. Sci. 371, 20150215 (2016).
Google Scholar 
33.Ramilo, A., Abollo, E., Villalba, A. & Carballal, M. J. A Minchinia mercenariae-like parasite infects cockles Cerastoderma edule in Galicia (NW Spain). J. Fish Dis. 41, 41–48 (2018).CAS 
PubMed 

Google Scholar 
34.Lynch, S. A. et al. Detection of haplosporidian protistan parasites supports an increase to their known diversity, geographic range and bivalve host specificity. Parasitology 147, 584–592 (2020).CAS 
PubMed 

Google Scholar 
35.Albuixech-Marti, S., Lynch, S. A. & Culloty, S. C. Biotic and abiotic factors influencing haplosporidian species distribution in the cockle Cerastoderma edule in Ireland. J. Invertebr. Pathol. 174, 107425 (2020).CAS 
PubMed 

Google Scholar 
36.Azevedo, C., Conchas, R. & Montes, J. Description of Haplosporidium edule n. sp (Phylum Haplosporidia), a parasite of Cerastoderma edule (Mollusca, Bivalvia) with complex spore ornamentation. Eur. J. Protistol. 39, 161–167 (2003).
Google Scholar 
37.Carballal, M., Diaz, S. & Villalba, A. Urosporidium sp hyperparasite of the turbellarian Paravortex cardii in the cockle Cerastoderma edule. J. Invertebr. Pathol. 90, 104–107 (2005).PubMed 

Google Scholar 
38.Daoust, P., Conboy, G., McBurney, S. & Burgess, N. Interactive mortality factors in common loons from Maritime Canada. J. Wildl. Dis. 34, 524–531 (1998).CAS 
PubMed 

Google Scholar 
39.Converse, K. & Kidd, G. Duck plague epizootics in the United States, 1967–1995. J. Wildl. Dis. 37, 347–357 (2001).CAS 
PubMed 

Google Scholar 
40.Friend, M., McLean, R. & Dein, F. Disease emergence in birds: Challenges for the twenty-first century. Auk 118, 290–303 (2001).
Google Scholar 
41.Hubalek, Z. An annotated checklist of pathogenic microorganisms associated with migratory birds. J. Wildl. Dis. 40, 639–659 (2004).PubMed 

Google Scholar 
42.Quesada, R. J. et al. Detection and phylogenetic characterization of a novel herpesvirus from the trachea of two stranded common loons (Gavia immer). J. Wildl. Dis. 47, 233–239 (2011).PubMed 

Google Scholar 
43.Niemeyer, C. et al. Genetically diverse herpesviruses in South American Atlantic coast seabirds. PLoS ONE 12, e0178811 (2017).PubMed 
PubMed Central 

Google Scholar 
44.Bookelaar, B., Lynch, S. A. & Culloty, S. C. Host plasticity supports spread of an aquaculture introduced virus to an ecosystem engineer. Parasit. Vectors 13, 498 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Honjo, M. N., Minamoto, T. & Kawabata, Z. Reservoirs of Cyprinid herpesvirus 3 (CyHV-3) DNA in sediments of natural lakes and ponds. Vet. Microbiol. 155, 183–190 (2012).CAS 
PubMed 

Google Scholar 
46.Evans, O., Paul-Pont, I. & Whittington, R. J. Detection of ostreid herpesvirus 1 microvariant DNA in aquatic invertebrate species, sediment and other samples collected from the Georges River estuary, New South Wales, Australia. Dis. Aquat. Org. 122, 247–255 (2017).CAS 

Google Scholar 
47.Slodkowicz-Kowalska, A. et al. Microsporidian species known to infect humans are present in aquatic birds: Implications for transmission via water?. Appl. Environ. Microbiol. 72, 4540–4544 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Malcekova, B., Valencakova, A., Molnar, L. & Kocisova, A. First detection and genotyping of human-associated microsporidia in wild waterfowl of Slovakia. Acta Parasitol. 58, 13–17 (2013).CAS 
PubMed 

Google Scholar 
49.Fermer, J., Culloty, S. C., Kelly, T. C. & O’Riordan, R. M. Intrapopulational distribution of Meiogymnophallus minutus (Digenea, Gymnophallidae) infections in its first and second intermediate host. Parasitol. Res. 105, 1231–1238 (2009).PubMed 

Google Scholar 
50.Yun, Y. et al. Phylogenetic analysis of severe fever with thrombocytopenia syndrome virus in South Korea and migratory bird routes between China, South Korea, and Japan. Am. J. Trop. Med. Hyg. 93, 468–474 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Xu, Y., Gong, P., Wielstra, B. & Si, Y. Southward autumn migration of waterfowl facilitates cross-continental transmission of the highly pathogenic avian influenza H5N1 virus. Sci. Rep. 6, 30262 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
52.King, R. A., Read, D. S., Traugott, M. & Symondson, W. O. C. Molecular analysis of predation: A review of best practice for DNA-based approaches. Mol. Ecol. 17, 947–963 (2008).CAS 
PubMed 

Google Scholar 
53.Harper, G. et al. Rapid screening of invertebrate predators for multiple prey DNA targets. Mol. Ecol. 14, 819–827 (2005).CAS 
PubMed 

Google Scholar 
54.Martin, D. L., Ross, R. M., Quetin, L. B. & Murray, A. E. Molecular approach (PCR-DGGE) to diet analysis in young Antarctic krill Euphausia superba. Mar. Ecol. Prog. Ser. 319, 155–165 (2006).ADS 
CAS 

Google Scholar 
55.Read, D. S., Sheppard, S. K., Bruford, M. W., Glen, D. M. & Symondson, W. O. C. Molecular detection of predation by soil micro-arthropods on nematodes. Mol. Ecol. 15, 1963–1972 (2006).CAS 
PubMed 

Google Scholar 
56.Harwood, J. D. et al. Tracking the role of alternative prey in soybean aphid predation by Orius insidiosus: A molecular approach. Mol. Ecol. 16, 4390–4400 (2007).CAS 
PubMed 

Google Scholar 
57.Albuixech-Martí, S., Culloty, S. C. & Lynch, S. A. Co-occurrence of pathogen assemblages in a keystone species the common cockle Cerastoderma edule on the Irish coast. Parasitology, 1–15 (2021).58.Lewis, L. J. & Tierney, T. D. Low tide waterbird surveys: Survey methods and guidance notes. Irish Wildlife Manuals 80 (2014).59.Garcia, C. et al. Vibrio aestuarianus subsp. cardii subsp. nov., pathogenic to the edible cockles Cerastoderma edule in France, and establishment of Vibrio aestuarianus subsp. aestuarianus subsp. nov. and Vibrio aestuarianus subsp. francensis subsp. nov. Int. J. Syst. Evol. Microbiol. 71, 004654 (2021).60.Lacoste, A. et al. A Vibrio splendidus strain is associated with summer mortality of juvenile oysters Crassostrea gigas in the Bay of Morlaix (North Brittany, France). Dis. Aquat. Org. 46, 139–145 (2001).CAS 

Google Scholar 
61.Le Roux, F. et al. Comparative analysis of Vibrio splendidus-related strains isolated during Crassostrea gigas mortality events. Aquat. Living Resour. 15, 251–258 (2002).
Google Scholar 
62.Garnier, M., Labreuche, Y., Garcia, C., Robert, A. & Nicolas, J. Evidence for the involvement of pathogenic bacteria in summer mortalities of the Pacific oyster Crassostrea gigas. Microb. Ecol. 53, 187–196 (2007).CAS 
PubMed 

Google Scholar 
63.McCleary, S. & Henshilwood, K. Novel quantitative TaqMan (R) MGB real-time PCR for sensitive detection of Vibrio aestuarianus in Crassostrea gigas. Dis. Aquat. Org. 114, 239–248 (2015).CAS 

Google Scholar 
64.Halpern, M., Senderovich, Y. & Izhaki, I. Waterfowl-The missing link in epidemic and pandemic cholera dissemination?. PLoS Pathog. 4, e1000173 (2008).PubMed 
PubMed Central 

Google Scholar 
65.Rodríguez, J., López, P., Muñoz, J. & Rodríguez, N. Detection of Vibrio cholerae no toxigenico in migratory and resident birds (Charadriiformes) in a coastal lagoon from northeastern Venezuela. Saber 22, 122–126 (2010).
Google Scholar 
66.Fernandez-Delgado, M. et al. Prevalence and distribution of Vibrio spp. in wild aquatic birds of the Southern Caribbean Sea, Venezuela, 2011–12. J. Wildl. Dis. 52, 621–626 (2016).67.Laviad-Shitrit, S., Izhaki, I. & Halpern, M. Accumulating evidence suggests that some waterbird species are potential vectors of Vibrio cholerae. PLoS Pathog. 15, e1007814 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
68.Buck, J. D. Isolation of Candida-albicans and halophilic Vibrio spp. from aquatic birds in Connecticut and Florida. Appl. Environ. Microbiol. 56, 826–828 (1990).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
69.Miyasaka, J. et al. Isolation of Vibrio parahaemolyticus and Vibrio vulnificus from wild aquatic birds in Japan. Epidemiol. Infect. 134, 780–785 (2006).CAS 
PubMed 

Google Scholar 
70.Fu, S. et al. Long-distance transmission of pathogenic Vibrio species by migratory waterbirds: A potential threat to the public health. Sci. Rep. 9, 16303 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
71.Senderovich, Y., Izhaki, I. & Halpern, M. Fish as reservoirs and vectors of Vibrio cholerae. PLoS ONE 5, e8607 (2010).ADS 
PubMed 
PubMed Central 

Google Scholar 
72.Laviad-Shitrit, S. et al. Great cormorants (Phalacrocorax carbo) as potential vectors for the dispersal of Vibrio cholerae. Sci. Rep. 7, 7973 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
73.Hossain, Z. Z., Farhana, I., Tulsiani’, S. M., Beguml, A. & Jensen, P. K. M. Transmission and toxigenic potential of Vibrio cholerae in hilsha fish (Tenualosa ilisha) for human consumption in Bangladesh. Front. Microbiol. 9, 222 (2018).PubMed 
PubMed Central 

Google Scholar 
74.Bryant, D. M. Effects of prey density and site character on estuary usage by overwintering waders (Charadrii). Estuar. Coast. Mar. Sci. 9, 369–384 (1979).ADS 

Google Scholar 
75.Hicklin, P. W. & Smith, P. C. Selection of foraging sites and invertebrate prey by migrant semipalmated sandpipers, Calidris-pusilla (Pallas), in Minas Basin, Bay of Fundy. Can. J. Zool. 62, 2201–2210 (1984).
Google Scholar 
76.Colwell, M. A. & Landrum, S. L. Nonrandom shorebird distribution and fine-scale variation in prey abundance. Condor 95, 94–103 (1993).
Google Scholar 
77.Ben-Horin, T., Bidegain, G., Huey, L., Narvaez, D. A. & Bushek, D. Parasite transmission through suspension feeding. J. Invertebr. Pathol. 131, 155–176 (2015).PubMed 

Google Scholar 
78.Pruzzo, C., Vezzulli, L. & Colwell, R. R. Global impact of Vibrio cholerae interactions with chitin. Environ. Microbiol. 10, 1400–1410 (2008).CAS 
PubMed 

Google Scholar 
79.Vezzulli, L., Pruzzo, C., Huq, A. & Colwell, R. R. Environmental reservoirs of Vibrio cholerae and their role in cholera. Environ. Microbiol. Rep. 2, 27–33 (2010).PubMed 

Google Scholar 
80.Freitas, C., Glatter, T. & Ringgaard, S. The release of a distinct cell type from swarm colonies facilitates dissemination of Vibrio parahaemolyticus in the environment. ISME J. 14, 230–244 (2020).PubMed 

Google Scholar 
81.Vezzulli, L. et al. Benthic ecology of Vibrio spp. and pathogenic Vibrio species in a coastal Mediterranean environment (La Spezia Gulf, Italy). Microb. Ecol. 58, 808–818 (2009).CAS 
PubMed 

Google Scholar 
82.Piersma, T., Degoeij, P. & Tulp, I. An evaluation of intertidal feeding habitats from a shorebird perspective – Towards relevant comparisons between temperate and tropical mudflats. Neth. J. Sea Res. 31, 503–512 (1993).
Google Scholar 
83.Hervas, A., Tully, O., Hickey, J., O’Keefe, E. & Kelly, K. Assessment, monitoring and management of the Dundalk Bay and Waterford Cockle (Cerastoderma edule) Fisheries in 2007. BIM Fisheries Resource Series 7 (2008).84.Martins, R. C., Catry, T., Santos, C. D., Palmeirim, J. M. & Granadeiro, J. P. Seasonal variations in the diet and foraging behaviour of dunlins Calidris alpina in a South European Estuary: Improved feeding conditions for northward migrants. PLoS ONE 8, e81174 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar 
85.Walsh, P. S., Metzger, D. A. & Higuchi, R. Chelex-100 as a medium for simple extraction of DNA for PCR-based typing from forensic material. Biotechniques 10, 506–513 (1991).CAS 
PubMed 

Google Scholar 
86.Lynch, S. A., Mulcahy, M. F. & Culloty, S. C. Efficiency of diagnostic techniques for the parasite, Bonamia ostreae, in the flat oyster, Ostrea edulis. Aquaculture 281, 17–21 (2008).
Google Scholar 
87.Zeale, M. R. K., Butlin, R. K., Barker, G. L. A., Lees, D. C. & Jones, G. Taxon-specific PCR for DNA barcoding arthropod prey in bat faeces. Mol. Ecol. Resour. 11, 236–244 (2011).CAS 
PubMed 

Google Scholar 
88.Freire, R., Arias, A., Mendez, J. & Insua, A. Identification of European commercial cockles (Cerastoderma edule and C. glaucum) by species-specific PCR amplification of the ribosomal DNA ITS region. Eur. Food Res. Technol. 232, 83–86 (2011).89.Thompson, J. et al. Diversity and dynamics of a North Atlantic coastal Vibrio community. Appl. Environ. Microbiol. 70, 4103–4110 (2004).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
90.Vezzulli, L. et al. Long-term effects of ocean warming on the prokaryotic community: Evidence from the vibrios. ISME J. 6, 21–30 (2012).PubMed 

Google Scholar 
91.Renault, T. et al. Haplosporidiosis in the pacific oyster Crassostrea gigas from the French Atlantic coast. Dis. Aquat. Org. 42, 207–214 (2000).CAS 

Google Scholar 
92.Molloy, D. P., Giamberini, L., Stokes, N. A., Burreson, E. M. & Ovcharenko, M. A. Haplosporidium raabei n. sp (Haplosporidia): A parasite of zebra mussels, Dreissena polymorpha (Pallas, 1771). Parasitology 139, 463–477 (2012).93.Lynch, S. A., Dillane, E., Carlsson, J. & Culloty, S. C. Development and assessment of a sensitive and cost-effective polymerase chain reaction to detect ostreid herpesvirus 1 and variants. J. Shellfish Res. 32, 657–664 (2013).
Google Scholar  More

1.Intergovernmental Panel on Climate Change (IPCC). Climate Change 2014: Synthesis Report (Intergovernmental Panel on Climate Change, Geneva) p 52 (2014). https://www.ipcc.ch/report/ar5/wg2/2.Easterling, D. R., Meehl, G. A., Parmesan, C., Karl, T. R. & Mearns, L. O. Climate extremes: Observations, modelling and impacts. Science 5487, 2068–2074 (2000).ADS 

Google Scholar 
3.Intergovernmental Panel on Climate Change (IPCC). Climate Change 2007: Synthesis Report (Intergovernmental Panel on Climate Change, Geneva) p 52 (2007). https://www.ipcc.ch/report/ar5/syr/4.Ju, R. T., Zhu, H. Y., Gao, L., Zhu, X. H. & Li, B. Increase in both temperature means, and extremes likely facilitates invasive herbivore outbreaks. Sci. Rep. 5, 15715. https://doi.org/10.1038/srep15715 (2015).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
5.World Meteorological Organisation (WMO). State of the Climate in Africa. WMO-No. 1253. 2020. Available at: https://library.wmo.int/doc_num.php?explnum_id=10421. Accessed 12 September 2021.6.Dube, O. P. Impact of climate change vulnerability and adaptation options: Exploring the case for Botswana through Southern Africa: A review. Botswana Notes Rec. 35, 147–168 (2003).
Google Scholar 
7.Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. 105, 6668–6672 (2008).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
8.Perkins-Kirkpatrick, S. E. & Lewis, S. C. Increasing trends in regional heatwaves. Nat. Commun. 11, 3357. https://doi.org/10.1038/s41467-020-16970-7(2020).9.National Oceanic and Atmospheric Administration (NOAA). Astounding heat obliterates all-time records across the Pacific Northwest and Western Canada in June 2021. Climate. Gov. Science and Information for Climate smart Nation. Available at: https://www.climate.gov/news-features/event-tracker/astounding-heat-obliterates-all-time-records-across-pacific-northwest. Accessed 03 July, 2021.10.UK Met Office. Record breaking Heat wave, July 2019. (2020). Available at: https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/weather/learn-about/uk-past-events/interesting/2019/2019_007_july_heatwave.pdf. Accessed 10 June, 2021.11.Kendon, M. et al. State of the UK Climate 2019. Int. J. Climatol. 40, 1–69 (2020).
Google Scholar 
12.Head, L., Adams, M., McGregor, H. V. & Toole, S. Climate change and Australia. Wiley Interdiscipl. Rev. WIREs Clim. Change 5, 175–197 (2014).
Google Scholar 
13.Nangombe, S. et al. Record-breaking climate extremes in Africa under stabilized 1.5 °C and 2 °C Global warming scenarios. Nat. Clim. Change 8, 375–380 (2018).14.Gergis, J., Ashcroft, L. & Whetton, P. A. historical perspective on Australian temperature extremes. Clim. Dyn. 55, 843–868 (2020).
Google Scholar 
15.Carpaneto, G. M., Mazziotta, A. & Valerio, L. Inferring species decline from collection records: roller dung beetles in Italy (Coleoptera, Scarabaeidae). Divers. Distrib. 13, 903–919 (2007).
Google Scholar 
16.Walther, G. R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).CAS 
PubMed 
ADS 

Google Scholar 
17.Chown, S. L. & Nicolson, S. W. Insect Physiological Ecology: Mechanisms and Patterns (Oxford University Press, 2004).
Google Scholar 
18.Huey, R. B. & Kearney, M. R. Dynamics of death by heat. Science 369, 1163. https://doi.org/10.1126/science.abe0320 (2020).CAS 
Article 
PubMed 
ADS 

Google Scholar 
19.Jørgensen, L. B. et al. A unifying model to estimate thermal tolerance limits in ectotherms across static, dynamic and fluctuating exposures to thermal stress. Sci. Rep. 11, 12840. https://doi.org/10.1038/s41598-021-92004-6 (2021)20.Buyantuyev, A. & Wu, J. Urban heat islands and landscape heterogeneity: Linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns. Landsc. Ecol. 25, 17–33 (2010).
Google Scholar 
21.Aalto, J., Riihimäki, H., Meineri, E., Hylander, K. & Luoto, M. Revealing topoclimatic heterogeneity using meteorological station data. Int. J. Climatol. 37, 544–556 (2017).
Google Scholar 
22.Holley, J. M. & Andrew, N. R. Experimental warming alters the relative survival and emigration of two dung beetle species from an Australian dung pat community. Austral. Ecol. 44, 800–811 (2019).
Google Scholar 
23.Giannini, T. C. et al. Pollination services at risk: Bee habitats will decrease owing to climate change in Brazil. Ecol. Model. 244, 127–131 (2012).
Google Scholar 
24.Wu, X. W. & Sun, S. C. Artificial warming advances egg laying and decreases larval size in the dung beetle, Aphodius erractus (Coleoptera: Scarabaeidae) in a Tibetan alpine meadow. Ann. Zool. Fennici. 49, 174–181 (2012).
Google Scholar 
25.Mamantov, M. A. & Sheldon, K. S. Behavioural responses to warming differentially impact survival in introduced and native dung beetles. J. Anim. Ecol. 90, 273–281 (2021).PubMed 

Google Scholar 
26.Clusella-Trullas, S., Blackburn, T. N. & Chown, S. L. Climate predictors of temperature performance curves parameters in ectotherms. Am. Nat. 177, 738–751 (2011).PubMed 

Google Scholar 
27.Ma, G., Rudolf, V. H. & Ma, C. S. Extreme temperature events alter demographic rates, relative fitness and community structure. Glob. Change Biol. 21, 1794–1808 (2014).ADS 

Google Scholar 
28.Gunderson, A. R. & Stillman, J. H. Plasticity in thermal tolerance has limited potential to buffer ectotherms from global warming. Proc. R. Soc. B 282, 20150401. https://doi.org/10.1098/rspb.2015.0401 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
29.van Heerwaarden, B., Kellermann, V. & Sgrò, C. M. Limited scope for plasticity to increase upper thermal limits. Funct. Ecol. 30, 1947–1956 (2016).
Google Scholar 
30.Nyamukondiwa, C., Terblamche, J. S., Marshall, K. E. & Sinclair, B. K. Basal cold but not heat tolerance constrains plasticity among Drosophila species (Diptera: Drosophilidae). J. Evol. Biol. 24, 1927–1938 (2011).CAS 
PubMed 

Google Scholar 
31.Blackburn, S., van Heerwaarden, B., Kellermann, V. & Sgró, C. M. Evolutionary capacity of upper thermal limits: Beyond single trait assessments. J. Exp. Biol. 217, 1918–1924 (2014).PubMed 

Google Scholar 
32.Bowler, K. & Terblanche, J. S. Insect thermal tolerance: What is the role of ontogeny, ageing and senescence?. Biol. Rev. Camb. Philos. Soc. 83, 339–355 (2008).PubMed 

Google Scholar 
33.Barley, J. M., Cheng, B. S., Sasaki, M., Gignoux-Wolfsohn, S., Hays, C. G., Putnam, A. B., Sheth, S., Villeneuve, A. R. & Kelly, M. Limited plasticity in thermally tolerant ectotherm populations: Evidence for a trade-off. Proc. R. Soc. B (2021). https://doi.org/10.1098/rspb.2021.0765.34.Sgrò, C. M., Terblanche, J. S. & Hoffmann, A. A. What can plasticity contribute to insect responses to climate change?. Annu. Rev. Entomol. 61, 433–451 (2016).PubMed 

Google Scholar 
35.Pincebourde, S. & Woods, H. A. There is plenty of room at the bottom: Microclimates drive insect vulnerability to climate change. Curr. Opin. Insect Sci. 41, 63–70 (2020).PubMed 

Google Scholar 
36.Woods, A., Pincebourde, S., Dillon, M. E. & Terblanche, J. S. Extended phenotypes: Buffers or amplifiers of climate change?. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2021.05.010 (2021).Article 
PubMed 

Google Scholar 
37.Gunderson, A. R., Dillon, M. E. & Stillman, J. H. Estimating the benefits of plasticity in ectotherm heat tolerance under natural thermal variability. Funct. Ecol. 31, 1529–1539 (2017).
Google Scholar 
38.Esperk, T., Kjaersgaard, A., Walters, R. J., Berger, D. & Blanckenhorn, W. U. Plastic and evolutionary responses to heat stress in a temperate dung fly: Negative correlation between basal and induced heat tolerance?. J. Evol. Biol. 29, 900–915 (2016).CAS 
PubMed 

Google Scholar 
39.Calosi, P., Bilton, D. T. & Spicer, J. I. Thermal tolerance, acclimatory capacity and vulnerability to global climate change. Biol. Lett. 4, 99–102 (2008).PubMed 

Google Scholar 
40.van Heerwaarden, B. & Kellermann, V. Does plasticity trade off with basal heat tolerance?. Trends Ecol. Evol. 35, 874–885 (2020).PubMed 

Google Scholar 
41.Malhi, Y., Franklin, J., Seddon, N., Solan, M., Turner, M. G., Field, C. B. & Knowlton, N. Climate change and ecosystems: threats, opportunities and solutions. Philos. Trans. R. Soc. B 375, 20190104. https://doi.org/10.1098/rstb.2019.0104 (2020).42.Stillman, J. H. Heat waves, the new normal: Summertime temperature extremes will impact animals, ecosystems, and human communities. Physiology 34, 86–100 (2019).CAS 
PubMed 

Google Scholar 
43.Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Ecology—Putting the heat on tropical animals. Science 320, 1296–1297 (2008).CAS 
PubMed 

Google Scholar 
44.Kelley, A. M. The role thermal physiology plays in species invasion. Conserv. Physiol. 10, 2. https://doi.org/10.1093/conphys/cou045 (2014).CAS 
Article 

Google Scholar 
45.Mitchell, K. A., Sgró, C. M. & Hoffmann, A. A. Phenotypic plasticity in upper thermal limits is weakly related to Drosophila species distributions. Funct. Ecol. 25, 661–670 (2011).
Google Scholar 
46.Allen, J. L., Chown, S. L., Janion-Scheepers, C. & Clusella-Trullas, S. Interactions between rates of temperature change and acclimation affect latitudinal patterns of warming tolerance. Conserv. Physiol. 4, 1–14 (2020).
Google Scholar 
47.Edwards, P. B. & Aschenborn, H. H. Patterns of nesting and dung burial in onitis dung beetles: Implications for pasture productivity and fly control. J. Appl. Ecol. 24, 837–851 (1987).
Google Scholar 
48.Bertone, M. A., Green, J. T., Washburn, S. P., Poore, M. H. & Watson, D. W. The contribution of tunneling dung beetles to pasture soil nutrition. Forage Grazinglands https://doi.org/10.1094/FG-2006-0711-02-RS (2006).Article 

Google Scholar 
49.Yamada, D., Imura, O., Shi, K. & Shibuya, T. Effect of tunneler dung beetles on cattle dung decomposition, soil nutrients and herbage growth. Grassl. Sci. 53, 121–129 (2007).
Google Scholar 
50.Slade, E. M. & Roslin, T. Dung beetle species interactions and multifunctionality are affected by an experimentally warmed climate. Oikos 125, 1607–1616 (2016).
Google Scholar 
51.Yoshihara, Y. & Sato, S. The relationship between dung beetle species richness and ecosystem functioning. Appl. Soil Ecol. 88, 21–25 (2015).
Google Scholar 
52.Manning, P., Slade, E. M., Beynon, S. A. & Lewis, O. T. Functionally rich dung beetle assemblages are required to provide multiple ecosystem services. Agric. Ecosyst. Environ. 218, 87–94 (2016).
Google Scholar 
53.Milotić, T. et al. Functionally richer communities improve ecosystem functioning: Dung removal and secondary seed dispersal by dung beetles in the Western Palaearctic. J. Biogeogr. 46, 70–82 (2019).
Google Scholar 
54.Slade, E. M., Riutta, T., Roslin, T. & Tuomisto, H. L. The role of dung beetles in reducing greenhouse gas emissions from cattle farming. Sci. Rep. 6, 18140. https://doi.org/10.1038/srep1814 (2016).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
55.Penttilä, A. et al. Quantifying beetle-mediated effects on gas fluxes from dung pats. PLoS ONE 8, e71454. https://doi.org/10.1371/journal.pone.0071454 (2013).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
56.Spector, S. Scarabaeine dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae): An invertebrate focal taxon for biodiversity research and conservation. Coleopt. Bull. 5, 71–83 (2006).
Google Scholar 
57.Osberg, D. C., Hanrahan, S. A. & Doube, B.M. The spatial distribution of Allogymnopleurus thalassinus Klug and A. consocius (Pringuey) (Coleoptera: Scarabaeidae) in an area of mixed soil types in South Africa. J. Entomol. Soc. S. Afr. 55, 85–92 (1992).58.Global Biodiversity Information Facility (GBIF). Allogymnopleurus thalassinus (Klug. 1855) (2020) Available at: https://www.gbif.org/species/1093939. Online Database. Accessed 29 December, 2020.59.Doube, B. M. Dung beetles of Southern Africa. (In: Hanski, I & Cambefort, Y. eds, Chapter 8). In Dung beetle ecology 133–155 (Princeton University Press, Princeton, 2014).60.Janssens, A. Monographie des Gymnopleurides. Verhandelingen Koninklijk Natuurhistorisch Museum Belgie. Brussel, 2, 1–74 (1940).61.Gotcha, N., Machekano, H., Cuthbert, R. N. & Nyamukondiwa, C. Low-temperature tolerance in coprophagic beetle species (Coleoptera: Scarabaeidae): Implications for ecological services. Ecol. Entomol. https://doi.org/10.1111/een.13054 (2021).62.Gittings, T., Giller, P. S. & Stakelum, G. Dung decomposition in contrasting temperate pastures in relation to dung beetle and earthworm activity. Pedobiologia, 38, 455–474 (1994).63.Rosenlew, H. & Roslin, T. Habitat fragmentation and the functional efficiency of temperate dung beetles. Oikos 117, 1659–1666 (2008).
Google Scholar 
64.Mitchell, K. A. & Hoffmann, A. A. Thermal ramping rate influences evolutionary potential and species differences for upper thermal limits in Drosophila. Funct. Ecol. 24, 694–700 (2010).
Google Scholar 
65.Terblanche, J. S., Nyamukondiwa, C. & Kleynhans, E. Thermal variability alters climatic stress resistance and plastic responses in a globally invasive pest, the Mediterranean fruit fly (Ceratitis capitata). Entomol. Exp. Appl. 137, 304–315 (2010).
Google Scholar 
66.Janzen, D. H. Why mountain passes are higher in tropics. Am. Nat. 101, 233–249 (1967).
Google Scholar 
67.Somero, G. N. The physiology of climate change: How potentials for acclimatization and genetic adaptation will determine ‘winners’ and ‘losers’. J. Exp. Biol. 213, 912–920 (2010).CAS 
PubMed 

Google Scholar 
68.Overgaard, J., Kristensen, T. N., Mitchell, K. A. & Hoffmann, A. A. Thermal tolerance in widespread and tropical Drosophila species: Does phenotypic plasticity increase with latitude?. Am. Nat. 178, S80–S96 (2011).PubMed 

Google Scholar 
69.Terblanche, J. S. et al. Ecologically relevant measures of tolerance to potentially lethal temperatures. J. Exp. Biol. 214, 3713–3725 (2011).PubMed 

Google Scholar 
70.Giménez Gómez, V.C., Verdú, J. R. & Zurita, G. A. Thermal niche helps to explain the ability of dung beetles to exploit disturbed habitats. Sci. Rep. 10, 13364. https://doi.org/10.1038/s41598-020-70284-8 (2020).71.Gotcha, N., Machekano, H., Cuthbert, R. N. & Nyamukondiwa, C. Heat tolerance may determine activity time in coprophagic beetle species (Coleoptera: Scarabaeidae). Insect Sci. https://doi.org/10.1111/1744-7917.12844 (2020).Article 
PubMed 

Google Scholar 
72.Nyamukondiwa, C., Chidawanyika, F., Machekano, H., Mutamiswa, R., Sands, B., Mdigiswa, N. & Wall, R. Climate variability differentially impacts thermal fitness traits in three coprophagic beetle species. PLOS One 13(6), e0198610. https://doi.org/10.1371/journal.pone.0198610 (2018).73.Jumbam, K., Jackson, S., Terblanche, J. S., McGeoch, M. A. & Chown, S. Acclimation effects on critical and lethal thermal limits of workers of the Argentine ant, Linepithema humile. J. Insect Physiol. 54, 1008–1014 (2008).CAS 
PubMed 

Google Scholar 
74.Dallas, H. F. & Rivers-Moore, N. A. Critical thermal maxima of aquatic macroinvertebrates: Towards identifying bioindicators of thermal alteration. Hydrobiologia 679, 61–76 (2012).
Google Scholar 
75.Gallego, B., Verdú, J. R. & Lobo, J. M. Comparative thermoregulation between different species of dung beetles (Coleoptera: Geotrupinae). J. Thermal Biol. 74, 84–91 (2018).
Google Scholar 
76.Qari, S. A. Thermal tolerance of the marine crab, Portunus pelagicus (Brachyura, Portunidae). Crustaceana 87, 827–833 (2014).
Google Scholar 
77.Azra, M. N., Mohamad, A., Hidir, A., Taufik, M., Abol-Munafi, A. B. & Ikhwanuddin, M. Critical thermal maxima of two species of intertidal crabs, Scylla olivacea and Thalamita crenata at different acclimation temperatures. Aquacul. Rep. 17, 100301. https://doi.org/10.1016/j.aqrep.2020.100301 (2020)78.Lutterschmidt, W. I. & Hutchison, V. H. The critical thermal maximum: History and critique. Can. J. Zool. 75, 1561–1574 (1997).
Google Scholar 
79.Käfer, H. et al. Insects 11, 197. https://doi.org/10.3390/insects11030197 (2020).Article 
PubMed Central 

Google Scholar 
80.Gehring, W. J. & Wehner, R. Heat shock protein synthesis and thermotolerance in Cataglyphis, an ant from the Sahara desert. Proc Natl. Acad. Sci. 92, 2994–2998 (1995).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
81.Bishop, T. R., Robertson, M. P., Van Rensburg, B. & Parr, C. L. Coping with the cold: Minimum temperatures and thermal tolerances dominate the ecology of mountain ants. Ecol. Entomol. 42, 105–114 (2017).
Google Scholar 
82.Smolka, J. et al. Dung beetles use their dung ball as a mobile thermal refuge. Curr. Biol. 20, R863–R864. https://doi.org/10.1016/j.cub.2012.08.057 (2012).CAS 
Article 

Google Scholar 
83.Terblanche, J. S., Deere, J. A., Clusella-Trullas, S., Janion, C. & Chown, S. L. Critical thermal limits depend on methodological context. Proc. R. Soc. B: Biol. Sci. 274, 2935–2943 (2007).
Google Scholar 
84.Chown, S. L., Jumbam, K. R., Sørensen, J. G. & Terblanche, J. S. Phenotypic variance, plasticity and heritability estimates of critical thermal limits depend on methodological context. Funct. Ecol. 23, 133–140 (2009).
Google Scholar 
85.Hoffmann, A. A., Sørensen, J. G. & Loeschcke, V. Adaptation of Drosophila to temperature extremes: bringing together quantitative and molecular approaches. J. Thermal Biol. 28, 175–216 (2003).
Google Scholar 
86.Pelster, B. & Burggren, W. W. Responses to environmental stressors in developing animals: Costs and benefits of phenotypic plasticity. In Development and environment (eds Burggren, W. & Dubansky, B.) (Springer, Cham, 2018).
Google Scholar 
87.Kristensen, T. N., Kjeldal, H., Schou, M. F. & Nielsen, J. L. Proteomic data reveal a physiological basis for costs and benefits associated with thermal acclimation. J. Exp. Biol. 219, 969–976 (2016).PubMed 

Google Scholar 
88.Chanthy, P., Martin, R. J., Gunning, R. V., & Andrew, N. R. The effects of thermal acclimation on lethal temperatures and critical thermal limits in the green vegetable bug, Nezara viridula (L.) (Hemiptera: Pentatomidae). Front. Physiol. 3, 465 https://doi.org/10.3389/fphys.2012.00465 (2012).89.Anthony, S. E., Buddle, C. M., Høye, T. T., Hein, N. & Sinclair, B. J. Thermal acclimation has limited effect on thermal tolerance of summer collected Arctic and sub-Arctic wolf spiders. Comp. Biochem. Physiol. Part A, Mol. Integr. Physiol. 257, 110974 (2021).90.Hofmann, G. & Somero, G. Evidence for protein damage at environmental temperatures: seasonal changes in levels of ubiquitin conjugates and hsp70 in the intertidal mussel Mytilus trossulus. J. Exp. Biol. 198, 1509–1518 (1995).CAS 
PubMed 

Google Scholar 
91.Munang, R., Thiaw, I., Alverson, K., Liu, J. & Han, Z. The role of ecosystem services in climate change adaptation and disaster risk reduction. Curr. Opin. Environ. Sustain. 5, 47–52 (2013).
Google Scholar 
92.Department of Wildlife and National Parks (DWNP). Aerial census of animals in Botswana 2012 dry season. Gaborone, Republic of Botswana (2012).93.Braga, R. F., Korasaki, V., Andresen, E. & Louzada, J. Dung beetle community and functions along a habitat-disturbance gradient in the amazon: A rapid assessment of ecological functions associated to biodiversity. PLoS ONE 8, e57786. https://doi.org/10.1371/journal.pone.0057786 (2013).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
94.Niino, M. et al. Diel flight activity and habitat preference of dung beetles (Coleoptera: Scarabaeidae) in Peninsular Malaysia. Raffles Bull. Zool. 62, 795–804 (2014).
Google Scholar 
95.Beetles of Africa. The Website for the Beetle Collector. Online database available at: http://www.beetlesofafrica.com (2021). Accessed 22 April, 2020.96.Mathur, V. & Schmidt, P. S. Adaptive patterns of phenotypic plasticity in laboratory and field environments in Drosophila melanogaster. Evol. 71, 465–474 (2017).
Google Scholar 
97.Chidawanyika, F., Nyamukondiwa, C., Strathie, L., Fischer, K. Effects of thermal regimes, starvation and age on heat tolerance of the Parthenium Beetle Zygogramma bicolorata (Coleoptera: Chrysomelidae) following dynamic and static protocols. PLoS ONE 12(1), e0169371. https://doi.org/10.1371/journal.pone.0169371 (2017).98.Moretti, M. et al. Handbook of protocols for standardized measurement of terrestrial invertebrate functional traits. Funct. Ecol. 31, 558–567 (2017).
Google Scholar 
99.El-Saadi, M. I., Ritchie, M. W., Davis, H. E. & MacMillan, H. A. Warm periods in repeated cold stresses protect Drosophila against iono-regulatory collapse, chilling injury, and reproductive deficits. J. Insect Physiol. 123, 104055. https://doi.org/10.1016/j.jinsphys.2020.104055 (2020).CAS 
Article 
PubMed 

Google Scholar 
100.Morley, S. A., Peck, L. S., Sunday, J. M., Heiser, S. & Bates, A. E. Physiological acclimation and persistence of ectothermic species under extreme heat events. Glob. Ecol. Biogeogr. 28, 1018–1037 (2019).
Google Scholar 
101.Nyamukondiwa, C. & Terblanche, J. S. Within-generation variation of critical thermal limits in adult Mediterranean and Natal fruit flies Ceratitis capitata and Ceratitis rosa: Thermal history affects short-term responses to temperature. Physiol. Entomol. 35, 255–264 (2010).
Google Scholar 
102.Weldon, C. W., Terblanche, J. S. & Chown, S. L. Time-course for attainment and reversal of acclimation to constant temperature in two Ceratitis species. J. Thermal Biol. 36, 479–485 (2011).
Google Scholar 
103.Sullivan, J. T., Ozman-Sullivan, S. K., Lumaret, J. P., Zalucki, M. P. & Baxter, G. Does one size suit all? Dung pad size and ball production by Scarabaeus sacer (Coleoptera: Scarabaeidae: Scarabaeinae). Eur. J. Entomol. 113, 70–75 (2016).
Google Scholar 
104.Nervo, B., Tocco, C., Caprio, C., Palestrini, C. & Rolando, A. Effects of body mass on dung removal efficiency in dung beetles. PLoS ONE 9, e107699. https://doi.org/10.1371/journal.pone.0107699 (2014).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
105.Slade, E. M., Mann, D. J., Villanueva, J. M. & Lewis, O. T. Experimental evidence for the effects of dung beetle functional group richness and composition on ecosystem function in a tropical forest. J. Anim. Ecol. 76, 1094–1104 (2007).PubMed 

Google Scholar 
106.R Core Team. R: A Language and environment for Statistical computing. R Foundation for Statistical computing, Vienna, Austria. 2021. Available at: https://www.R-project.org/.107.Wobbrock, J. O., Findlater, L., Gergle, D. & Higgins, J. J. The aligned rank transform for nonparametric factorial analyses using only ANOVA procedures, in Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI ’11). Vancouver, British Columbia (May 7–12, 2011) 143–146 (ACM Press, New York, 2011).108.Elkin, L. A., Kay, M., Higgins, J. & Wobbrock, J. O. An aligned rank transform procedure for multifactor contrast tests, in Proceedings of the ACM Symposium on User Interface Software and Technology (UIST ’21). Virtual Event (October 10–13, 2021) (ACM Press, New York, NY, 2021). More

1.Madsen, P. T. & Wahlberg, M. Recording and quantification of ultrasonic echolocation clicks from free-ranging toothed whales. Deep. Res. Part I(54), 1421–1444 (2007).
Google Scholar 
2.Au, W. W. L. Sonar of Dolphins (Springer, 1993).
Google Scholar 
3.Reeves, R. R. et al. Distribution of endemic cetaceans in relation to hydrocarbon development and commercial shipping in a warming Arctic. Mar. Policy 44, 375–389 (2014).
Google Scholar 
4.Hauser, D. D. W. et al. Habitat selection by two beluga whale populations in the Chukchi and Beaufort seas. PLoS One 12, e0172755 (2017).PubMed 
PubMed Central 

Google Scholar 
5.Vacquié-Garcia, J., Lydersen, C., Ims, R. A. & Kovacs, K. M. Habitats and movement patterns of white whales Delphinapterus leucas in Svalbard, Norway in a changing climate. Mov. Ecol. 6, 21 (2018).PubMed 
PubMed Central 

Google Scholar 
6.Lydersen, C., Martin, A. R., Kovacs, K. M. & Gjertz, I. Summer and autumn movements of white whales Delphinapterus leucas in Svalbard, Norway. Mar. Ecol. Prog. Ser. 219, 265–274 (2001).ADS 

Google Scholar 
7.Innes, S. et al. Surveys of belugas and narwhals in the Canadian High Arctic in 1996. NAMMCO Sci. Publ. 4, 169–190 (2002).
Google Scholar 
8.Smith, T. G. & Martin, A. R. Distribution and movements of belugas, Delphinapterus leucas, in the Canadian High Arctic. Can. J. Fish. Aquat. Sci. 51, 1653–1663 (1994).
Google Scholar 
9.Hobbs, R. et al. Global review of the conservation status of Monodontid stocks. Mar. Fish. Rev. 81, 1–53 (2019).ADS 

Google Scholar 
10.Frost, K. J. & Lowry, L. F. Distribution, abundance, and movements of beluga whales, Delphinapterus leucas, in coastal waters of western Alaska. In Advances in Research on the Beluga Whale, Delphinapterus leucas Vol. 224 (eds Smith, T. G. et al.) 39–57 (Canadian Bulletin of Fisheries and Aquatic Sciences, 1990).
Google Scholar 
11.Lewis, A. E., Hammill, M. O., Power, M., Doidge, D. W. & Lesage, V. Movement and aggregation of eastern Hudson Bay beluga whales (Delphinapterus leucas): A comparison of patterns found through satellite telemetry and Nunavik Traditional Ecological Knowledge. Arctic 62, 13–24 (2009).
Google Scholar 
12.Ahonen, H., Stafford, K. M., Lydersen, C., Steur, L. D. & Kovacs, K. M. A multi-year study of narwhal occurrence in the western Fram Strait—detected via passive acoustic monitoring. Polar Res. 38, 1–14 (2019).
Google Scholar 
13.Heide-Jørgensen, M. P. et al. The migratory behaviour of narwhals (Monodon monoceros). Can. J. Zool. 81, 1298–1305 (2003).
Google Scholar 
14.Richard, P. R. et al. Baffin Bay narwhal population distribution and numbers: Aerial surveys in the Canadian High Arctic, 2002–04. Arctic 63, 85–99 (2010).
Google Scholar 
15.Dietz, R., Heide-Jørgensen, M. P., Richard, P. R. & Acquarone, M. Summer and fall movements of narwhals (Monodon monoceros) from northeastern Baffin Island towards northern Davis Strait. Arctic 54, 244–261 (2001).
Google Scholar 
16.Castellote, M. et al. Monitoring white whales (Delphinapterus leucas) with echolocation loggers. Polar Biol. 36, 493–509 (2013).
Google Scholar 
17.Frouin-Mouy, H., Kowarski, K., Martin, B. & Bröker, K. Seasonal trends in acoustic detection of marine mammals in Baffin Bay and Melville Bay, Northwest Greenland. Arctic 70, 59–76 (2017).
Google Scholar 
18.Sousa-Lima, R. S., Norris, T. F., Oswald, J. N. & Fernandes, D. P. A review and inventory of fixed autonomous recorders for passive acoustic monitoring of marine mammals. Aquat. Mamm. 39, 23–53 (2013).
Google Scholar 
19.Zhong, M. et al. Beluga whale acoustic signal classification using deep learning neural network models. J. Acoust. Soc. Am. 147, 1834–1841 (2020).ADS 
PubMed 

Google Scholar 
20.Castellote, M. et al. Seasonal distribution and foraging occurrence of Cook Inlet beluga whales based on passive acoustic monitoring. Endanger. Species Res. 41, 225–243 (2020).
Google Scholar 
21.Sjare, B. L. & Smith, T. G. The vocal repertoire of white whales, Delphinapterus leucas, summering in Cunningham Inlet, Northwest Territories. Can. J. Zool. 64, 407–415 (1986).
Google Scholar 
22.Chmelnitsky, E. G. & Ferguson, S. H. Beluga whale, Delphinapterus leucas, vocalizations from the Churchill River, Manitoba, Canada. J. Acoust. Soc. Am. 131, 4821–4835 (2012).ADS 
PubMed 

Google Scholar 
23.Marcoux, M., Auger-Méthé, M. & Humphries, M. M. Variability and context specificity of narwhal (Monodon monoceros) whistles and pulsed calls. Mar. Mammal Sci. 28, 649–665 (2012).
Google Scholar 
24.Garland, E. C., Castellote, M. & Berchok, C. L. Beluga whale (Delphinapterus leucas) vocalizations and call classification from the eastern Beaufort Sea population. J. Acoust. Soc. Am. 137, 3054–3067 (2015).ADS 
PubMed 

Google Scholar 
25.Rasmussen, M. H., Koblitz, J. C. & Laidre, K. L. Buzzes and high-frequency clicks recorded from narwhals (Monodon monoceros) at their wintering ground. Aquat. Mamm. 41, 256–264 (2015).
Google Scholar 
26.McCullough, J. L. K., Simonis, A. E., Sakai, T. & Oleson, E. M. Acoustic classification of false killer whales in the Hawaiian islands based on comprehensive vocal repertoire. JASA Express Lett. 1, 071201 (2021).
Google Scholar 
27.Ford, J. K. B. & Fisher, H. D. Underwater acoustic signals of the narwhal (Monodon monoceros). Can. J. Zool. 56, 552–560 (1978).
Google Scholar 
28.Rankin, S. et al. Acoustic classification of dolphins in the California Current using whistles, echolocation clicks, and burst pulses. Mar. Mammal Sci. 33, 520–540 (2017).
Google Scholar 
29.Walmsley, S. F., Rendell, L., Hussey, N. E. & Marcoux, M. Vocal sequences in narwhals (Monodon monoceros). J. Acoust. Soc. Am. 147, 1078–1091 (2020).ADS 
PubMed 

Google Scholar 
30.Shapiro, A. D. Preliminary evidence for signature vocalizations among free-ranging narwhals (Monodon monceros). J. Acoust. Soc. Am. 120, 1695–1705 (2006).ADS 
PubMed 

Google Scholar 
31.Simões Amorim, T. O. et al. Integrative bioacoustics discrimination of eight delphinid species in the western South Atlantic Ocean. PLoS One 14, e0217977 (2019).PubMed 
PubMed Central 

Google Scholar 
32.Stafford, K. M., Laidre, K. L. & Heide-Jørgensen, M. P. First acoustic recordings of narwhals (Monodon monoceros) in winter. Mar. Mammal Sci. 28, 197–207 (2012).
Google Scholar 
33.Castellote, M. et al. Dual instrument passive acoustic monitoring of belugas in Cook Inlet, Alaska. J. Acoust. Soc. Am. 139, 2697–2707 (2016).ADS 
PubMed 

Google Scholar 
34.Lammers, M. O. et al. Passive acoustic monitoring of Cook Inlet beluga whales (Delphinapterus leucas). J. Acoust. Soc. Am. 134, 2497–2504 (2013).ADS 
PubMed 

Google Scholar 
35.Roch, M. A., Stinner-Sloan, J., Baumann-Pickering, S. & Wiggins, S. M. Compensating for the effects of site and equipment variation on delphinid species identification from their echolocation clicks. J. Acoust. Soc. Am. 137, 22–29 (2015).ADS 
PubMed 

Google Scholar 
36.Au, W. W., Penner, R. H., Carder, D. A. & Scronce, B. Demonstration of adaptation in beluga whale echolocation signals. J. Acoust. Soc. Am. 77, 726–730 (1985).ADS 
CAS 
PubMed 

Google Scholar 
37.Au, W. W. L., Penner, R. H. & Turl, C. W. Propagation of beluga echolocation signals. J. Acoust. Soc. Am. 82, 807–813 (1987).ADS 
CAS 
PubMed 

Google Scholar 
38.Roy, N., Simard, Y., Gervaise, C. & Dtn, E. 3D tracking of foraging belugas from their clicks: Experiment from a coastal hydrophone array. Appl. Acoust. 71, 1050–1056 (2010).
Google Scholar 
39.Zahn, M. J., Laidre, K. L., Stilz, P., Rasmussen, M. H. & Koblitz, J. C. Vertical sonar beam width of wild belugas (Delphinapterus leucas) in West Greenland. PLoS One 16, e0257054 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
40.Rutenko, A. N. & Vishnyakov, A. A. Time sequences of sonar signals generated by a beluga whale when locating underwater objects. Acoust. Phys. 52, 314–323 (2006).ADS 

Google Scholar 
41.Koblitz, J. C., Stilz, P., Rasmussen, M. H. & Laidre, K. L. Highly directional sonar beam of narwhals (Monodon monoceros) measured with a vertical 16 hydrophone array. PLoS One 11, e0162069 (2016).PubMed 
PubMed Central 

Google Scholar 
42.Podolskiy, E. A. & Sugiyama, S. Soundscape of a narwhal summering ground in a glacier fjord (Inglefield Bredning, Greenland). J. Geophys. Res. Ocean. 125, e2020JC016116 (2020).ADS 

Google Scholar 
43.Miller, L. A., Pristed, J., Mohl, B. & Surlykke, A. The click-sounds of narwhals (Monodon monoceros) in Inglefield Bay, Northwest Greenland. Mar. Mammal Sci. 11, 491–502 (1995).
Google Scholar 
44.Marcoux, M., Auger-Methe, M., Chmelnitsky, E., Ferguson, S. H. & Humphries, M. M. Local passive acoustic monitoring of narwhal presence in the Canadian Arctic: A pilot project. Arctic 64, 307–316 (2011).
Google Scholar 
45.Overland, J. et al. The urgency of Arctic change. Polar Sci. 21, 6–13 (2019).ADS 

Google Scholar 
46.Comiso, J. C. & Hall, D. K. Climate trends in the Arctic as observed from space. WIREs Clim. Change 5, 389–409 (2014).
Google Scholar 
47.Kwok, R. Arctic sea ice thickness, volume, and multiyear ice coverage: Losses and coupled variability (1958–2018). Environ. Res. Lett. 13, 105005 (2018).
Google Scholar 
48.Overland, J. E. & Wang, M. When will the summer Arctic be nearly sea ice free?. Geophys. Res. Lett. 40, 2097–2101 (2013).ADS 

Google Scholar 
49.Smith, L. C. & Stephenson, S. R. New Trans-Arctic shipping routes navigable by midcentury. Proc. Natl. Acad. Sci. U.S.A. 110, E1191–E1195 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Hauser, D. D. W., Laidre, K. L. & Stern, H. L. Vulnerability of Arctic marine mammals to vessel traffic in the increasingly ice-free Northwest Passage and Northern Sea Route. Proc. Natl. Acad. Sci. U.S.A. 115, 7617–7622 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Halliday, W. D., Pine, M. K. & Insley, S. J. Underwater noise and Arctic marine mammals: Review and policy recommendations. Environ. Rev. 28, 438–448 (2020).
Google Scholar 
52.Halliday, W. D. et al. Underwater sound levels in the Canadian Arctic, 2014–2019. Mar. Pollut. Bull. 168, 112437 (2021).CAS 
PubMed 

Google Scholar 
53.Kochanowicz, Z. et al. Using western science and Inuit knowledge to model ship-source noise exposure for cetaceans (marine mammals) in Tallurutiup Imanga (Lancaster Sound), Nunavut, Canada. Mar. Policy 130, 104557 (2021).
Google Scholar 
54.Stewart, R. E. A., Lesage, V., Lawson, J. W., Cleator, H. & Martin, K. A. Science technical review of the draft Environmental Impact Statement (EIS) for Baffinland’s Mary River Project (Canadian Science Advisory Secretariat, Fisheries and Oceans Canada, 2011).
Google Scholar 
55.Heide-Jørgensen, M. P., Hansen, R. G., Westdal, K., Reeves, R. R. & Mosbech, A. Narwhals and seismic exploration: Is seismic noise increasing the risk of ice entrapments?. Biol. Conserv. 158, 50–54 (2013).
Google Scholar 
56.Blackwell, S. B., Greene, C. R. & Richardson, W. J. Drilling and operational sounds from an oil production island in the ice-covered Beaufort Sea. J. Acoust. Soc. Am. 116, 3199–3211 (2004).ADS 
PubMed 

Google Scholar 
57.Yang, W. et al. Anthropogenic sound exposure-induced stress in captive dolphins and implications for cetacean health. Front. Mar. Sci. 8, 606736 (2021).
Google Scholar 
58.Erbe, C. & Farmer, D. M. Zones of impact around icebreakers affecting beluga whales in the Beaufort Sea. J. Acoust. Soc. Am. 108, 1332–1340 (2000).ADS 
CAS 
PubMed 

Google Scholar 
59.Heide-Jørgensen, M. P. et al. Behavioral response study on seismic airgun and vessel exposures in narwhals. Front. Mar. Sci. 8, 658173 (2021).
Google Scholar 
60.Gillespie, D., Mellinger, D. K., Gordon, J. & Al, E. PAMGUARD: Semiautomated, open source software for real-time acoustic detection and localization of cetaceans. Proc. Inst. Acoust. 30, 54–62 (2008).
Google Scholar 
61.Sakai, T. PAMpal: Load and process passive acoustic data. R package version 0.12.6. http://cran.r-project.org/package=PAMpal (2021).62.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing http://www.r-project.org/ (2021).63.Griffiths, E. T. et al. Detection and classification of narrow-band high frequency echolocation clicks from drifting recorders. J. Acoust. Soc. Am. 147, 3511–3522 (2020).ADS 
PubMed 

Google Scholar 
64.Baumann-Pickering, S., Wiggins, S. M., Hildebrand, J. A., Roch, M. A. & Schnitzler, H. Discriminating features of echolocation clicks of melon-headed whales (Peponocephala electra), bottlenose dolphins (Tursiops truncatus), and Gray’s spinner dolphins (Stenella longirostris longirostris). J. Acoust. Soc. Am. 128, 2212–2224 (2010).ADS 
PubMed 

Google Scholar 
65.Sakai, T. PAMpal standardClickCalcs. https://taikisan21.github.io/PAMpal/StandardCalcs.html (2021).66.Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2001).
Google Scholar 
67.Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).MathSciNet 
PubMed 
MATH 

Google Scholar 
68.Anderson, M. J. Permutational Multivariate Analysis of Variance (PERMANOVA). Wiley StatsRef Stat. Ref. Online https://doi.org/10.1002/9781118445112.stat07841 (2017).Article 

Google Scholar 
69.Pearson, K. On lines and planes of closest fit to systems of points in space. Philos. Mag. 2, 559–572 (1901).MATH 

Google Scholar 
70.Lever, J., Krzywinski, M. & Altman, N. Principal component analysis. Nat. Methods 14, 641–642 (2017).CAS 

Google Scholar 
71.Jackson, D. A. Stopping rules in principal components analysis: A comparison of heuristical and statistical approaches. Ecology 74, 2204–2214 (1993).
Google Scholar 
72.Oksanen, J. et al. Vegan: Community ecology package. R package version 2.5-7. https://cran.r-project.org/package=vegan (2020).73.Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).MATH 

Google Scholar 
74.Yang, L. et al. Description and classification of echolocation clicks of Indian Ocean humpback (Sousa plumbea) and Indo-Pacific bottlenose (Tursiops aduncus) dolphins from Menai Bay, Zanzibar, East Africa. PLoS One 15, e0230319 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Archer, F. I., Rankin, S., Stafford, K. M., Castellote, M. & Delarue, J. Quantifying spatial and temporal variation of North Pacific fin whale (Balaenoptera physalus) acoustic behavior. Mar. Mammal Sci. 36, 224–245 (2020).
Google Scholar 
76.Ross, J. C. & Allen, P. E. Random Forest for improved analysis efficiency in passive acoustic monitoring. Ecol. Inform. 21, 34–39 (2014).
Google Scholar 
77.Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Google Scholar 
78.Archer, E. rfPermute: Estimate permutation p-values for Random Forest importance metrics. R package version 2.5. https://github.com/EricArcher/rfPermute (2021).79.Gurevich, V. S. & Evans, W. E. Echolocation discrimination of complex planar targets by the Beluga whale (Delphinapterus leucas). J. Acoust. Soc. Am. 60, S5 (1976).ADS 

Google Scholar 
80.Soldevilla, M. S. et al. Classification of Risso’s and Pacific white-sided dolphins using spectral properties of echolocation clicks. J. Acoust. Soc. Am. 124, 609–624 (2008).ADS 
PubMed 

Google Scholar 
81.Morisaka, T., Yoshida, Y., Akune, Y., Mishima, H. & Nishimoto, S. Exchange of ‘signature’ calls in captive belugas (Delphinapterus leucas). J. Ethol. 31, 141–149 (2013).
Google Scholar 
82.Vergara, V., Michaud, R. & Barrett-Lennard, L. G. What can captive whales tell us about their wild counterparts? Identification, usage, and ontogeny of contact calls in belugas (Delphinapterus leucas). Int. J. Comp. Psychol. 23, 278–309 (2010).
Google Scholar 
83.Vergara, V. & Mikus, M. A. Contact call diversity in natural beluga entrapments in an Arctic estuary: Preliminary evidence of vocal signatures in wild belugas. Mar. Mammal Sci. 35, 434–465 (2019).
Google Scholar 
84.Panova, E. M. et al. Intraspecific variability in the ‘vowel’-like sounds of beluga whales (Delphinapterus leucas): Intra- and interpopulation comparisons. Mar. Mammal Sci. 32, 452–465 (2016).
Google Scholar 
85.Ames, A. E., Blackwell, S. B., Tervo, O. M. & Heide-Jørgensen, M. P. Evidence of stereotyped contact call use in narwhal (Monodon monoceros) mother-calf communication. PLoS One 16, e0254393 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
86.Baumann-Pickering, S. et al. False killer whale and short-finned pilot whale acoustic identification. Endanger. Species Res. 28, 97–108 (2015).
Google Scholar 
87.Halliday, W. D. et al. Potential exposure of beluga and bowhead whales to underwater noise from ship traffic in the Beaufort and Chukchi Seas. Ocean Coast. Manag. 204, 105473 (2021).
Google Scholar 
88.Laidre, K. L., Jørgensen, O. A. & Treble, M. A. Deep-ocean predation by a high Arctic cetacean. ICES J. Mar. Sci. 61, 430–440 (2004).
Google Scholar 
89.Laidre, K. L., Heide-Jørgensen, M. P., Dietz, R., Hobbs, R. C. & Jørgensen, O. A. Deep-diving by narwhals Monodon monoceros: Differences in foraging behavior between wintering areas?. Mar. Ecol. Prog. Ser. 261, 269–281 (2003).ADS 

Google Scholar 
90.Lydersen, C. & Kovacs, K. M. A review of the ecology and status of white whales (Delphinapterus leucas) in Svalbard, Norway. Polar Res. 40, 5509 (2021).
Google Scholar 
91.Hauser, D. D. W. et al. Regional diving behavior of Pacific Arctic beluga whales Delphinapterus leucas and possible associations with prey. Mar. Ecol. Prog. Ser. 541, 245–264 (2015).ADS 

Google Scholar 
92.Ragen, T. J., Huntington, H. P. & Hovelsrud, G. K. Conservation of Arctic marine mammals faced with climate change. Ecol. Appl. 18, S166–S174 (2008).PubMed 

Google Scholar 
93.Laidre, K. L. et al. Quantifying the sensitivity of Arctic marine mammals to climate-induced habitat change. Ecol. Appl. 18, S97–S125 (2008).PubMed 

Google Scholar 
94.Heide-Jørgensen, M. P., Dietz, R., Laidre, K. L. & Richard, P. Autumn movements, home ranges, and winter density of narwhals (Monodon monoceros) tagged in Tremblay Sound, Baffin Island. Polar Biol. 25, 331–341 (2002).
Google Scholar 
95.Hauser, D. D. W., Laidre, K. L., Suydam, R. S. & Richard, P. R. Population-specific home ranges and migration timing of Pacific Arctic beluga whales (Delphinapterus leucas). Polar Biol. 37, 1171–1183 (2014).
Google Scholar 
96.Huntington, H. P. A preliminary assessment of threats to Arctic marine mammals and their conservation in the coming decades. Mar. Policy 33, 77–82 (2009).
Google Scholar 
97.Gregersen, U., Hopper, J. R. & Knutz, P. C. Basin seismic stratigraphy and aspects of prospectivity in the NE Baffin Bay, Northwest Greenland. Mar. Pet. Geol. 46, 1–18 (2013).
Google Scholar 
98.McCauley, R. D. et al. Widely used marine seismic survey air gun operations negatively impact zooplankton. Nat. Ecol. Evol. 1, 0195 (2017).
Google Scholar  More

1.Helm, B. et al. Annual rhythms that underlie phenology: Biological time-keeping meets environmental change. Proc. R. Soc. B Biol. Sci. 280, 20130016 (2013).
Google Scholar 
2.Bradshaw, W. E. & Holzapfel, C. M. Evolution of animal photoperiodism. Annu. Rev. Ecol. Evol. Syst. 38, 1–25 (2007).
Google Scholar 
3.Dawson, A. Control of the annual cycle in birds: Endocrine constraints and plasticity in response to ecological variability. Philos. Trans. R. Soc. B Biol. Sci. 363, 1621–1633 (2008).
Google Scholar 
4.Dawson, A., King, V. M., Bentley, G. E. & Ball, G. F. Photoperiodic control of seasonality in birds. J. Biol. Rhythms 16, 365–380 (2001).CAS 
PubMed 

Google Scholar 
5.Wingfield, J. C. & Kenagy, G. J. Natural regulation of reproductive cycles. Vertebr. Endocrinol. Fundam. Biomed. Implic. 4, 181–241 (1991).
Google Scholar 
6.Hahn, T. P., Pereyra, M. E., Sharbaugh, S. M. & Bentley, G. E. Physiological responses to photoperiod in three cardueline finch species. Gen. Comp. Endocrinol. 137, 99–108 (2004).CAS 
PubMed 

Google Scholar 
7.Perfito, N., Meddle, S. L., Tramontin, A. D., Sharp, P. J. & Wingfield, J. C. Seasonal gonadal recrudescence in song sparrows: Response to temperature cues. Gen. Comp. Endocrinol. 143, 121–128 (2005).CAS 
PubMed 

Google Scholar 
8.Shutt, J. D. et al. The environmental predictors of spatio-temporal variation in the breeding phenology of a passerine bird. Proc. R. Soc. B Biol. Sci. 286, 20190952 (2019).
Google Scholar 
9.Drake, A. & Martin, K. Rainfall and nest site competition delay mountain bluebird and tree swallow breeding but do not impact productivity. Auk 137, 1–18 (2020).
Google Scholar 
10.Bison, M. et al. Best environmental predictors of breeding phenology differ with elevation in a common woodland bird species. Ecol. Evolut. https://doi.org/10.1002/ece3.6684 (2020).Article 

Google Scholar 
11.McNamara, J. M., Barta, Z., Klaassen, M. & Bauer, S. Cues and the optimal timing of activities under environmental changes. Ecol. Lett. 14, 1183–1190 (2011).PubMed 
PubMed Central 

Google Scholar 
12.Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).ADS 
CAS 
PubMed 

Google Scholar 
13.Moussus, J.-P., Clavel, J., Jiguet, F. & Julliard, R. Which are the phenologically flexible species? A case study with common passerine birds. Oikos 120, 991–998 (2011).
Google Scholar 
14.Chamberlain, D. et al. The altitudinal frontier in avian climate impact research. Ibis 154, 205–209 (2012).
Google Scholar 
15.Wipf, S., Stoeckli, V. & Bebi, P. Winter climate change in alpine tundra: Plant responses to changes in snow depth and snowmelt timing. Clim. Change 94, 105–121 (2009).ADS 

Google Scholar 
16.Jonas, T., Rixen, C., Sturm, M. & Stoeckli, V. How alpine plant growth is linked to snow cover and climate variability. J. Geophys. Res. Biogeosci. 113, G03013 (2008).ADS 

Google Scholar 
17.Kudo, G. & Hirao, A. S. Habitat-specific responses in the flowering phenology and seed set of alpine plants to climate variation: Implications for global-change impacts. Popul. Ecol. 48, 49–58 (2006).
Google Scholar 
18.Trant, A., Higgs, E. & Starzomski, B. M. A century of high elevation ecosystem change in the Canadian Rocky Mountains. Sci. Rep. 10, 9698 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Ceppi, P., Scherrer, S. C., Fischer, A. M. & Appenzeller, C. Revisiting Swiss temperature trends 1959–2008. Int. J. Climatol. 32, 203–213 (2012).
Google Scholar 
20.Pepin, N. et al. Elevation-dependent warming in mountain regions of the world. Nat. Clim. Chang. 5, 424–430 (2015).ADS 

Google Scholar 
21.Rosenzweig, C. et al. Attributing physical and biological impacts to anthropogenic climate change. Nature 453, 353–357 (2008).ADS 
CAS 
PubMed 

Google Scholar 
22.Brunetti, M. et al. Precipitation variability and changes in the greater Alpine region over the 1800–2003 period. J. Geophys. Res. Atmos. 111, D11107 (2006).ADS 

Google Scholar 
23.Napoli, A., Crespi, A., Ragone, F., Maugeri, M. & Pasquero, C. Variability of orographic enhancement of precipitation in the Alpine region. Sci. Rep. 9, 13352 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
24.Diffenbaugh, N. S., Scherer, M. & Ashfaq, M. Response of snow-dependent hydrologic extremes to continued global warming. Nat. Clim. Chang. 3, 379–384 (2013).ADS 
PubMed 

Google Scholar 
25.Beniston, M., Keller, F. & Goyette, S. Snow pack in the Swiss Alps under changing climatic conditions: An empirical approach for climate impacts studies. Theoret. Appl. Climatol. 74, 19–31 (2003).ADS 

Google Scholar 
26.Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).
Google Scholar 
27.Saalfeld, S. T. et al. Phenological mismatch in Arctic-breeding shorebirds: Impact of snowmelt and unpredictable weather conditions on food availability and chick growth. Ecol. Evol. 9, 6693–6707 (2019).PubMed 
PubMed Central 

Google Scholar 
28.Tulp, I. & Schekkerman, H. Has prey availability for arctic birds advanced with climate change? Hindcasting the abundance of tundra arthropods using weather and seasonal variation. Arctic 61, 48–60 (2008).
Google Scholar 
29.Leung, M.C.-Y. et al. Phenology of hatching and food in low Arctic passerines and shorebirds: Is there a mismatch?. Arctic Sci. 4, 538–556 (2018).
Google Scholar 
30.Grabowski, M. M., Doyle, F. I., Reid, D. G., Mossop, D. & Talarico, D. Do Arctic-nesting birds respond to earlier snowmelt? A multi-species study in north Yukon, Canada. Polar Biol. 36, 1097–1105 (2013).
Google Scholar 
31.Liebezeit, J. R., Gurney, K. E. B., Budde, M., Zack, S. & Ward, D. Phenological advancement in arctic bird species: Relative importance of snow melt and ecological factors. Polar Biol. 37, 1309–1320 (2014).
Google Scholar 
32.Hendricks, P. Spring snow conditions, laying date, and clutch size in an alpine population of American Pipits. J. Field Ornithol. 74, 423–429 (2003).
Google Scholar 
33.Pereyra, M. E. Effects of snow-related environmental variation on breeding schedules and productivity of a high-altitude population of dusky flycatchers (Empidonax oberholseri). Auk 128, 746–758 (2011).
Google Scholar 
34.Resano-Mayor, J. et al. Snow cover phenology is the main driver of foraging habitat selection for a high-alpine passerine during breeding: implications for species persistence in the face of climate change. Biodivers. Conserv. 28, 2669–2685 (2019).
Google Scholar 
35.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).MATH 

Google Scholar 
36.Bears, H., Martin, K. & White, G. C. Breeding in high-elevation habitat results in shift to slower life-history strategy within a single species. J. Anim. Ecol. 78, 365–375 (2009).CAS 
PubMed 

Google Scholar 
37.García-González, R., Aldezabal, A., Laskurain, N. A., Margalida, A. & Novoa, C. Influence of snowmelt timing on the diet quality of pyrenean rock ptarmigan (Lagopus muta pyrenaica): Implications for reproductive success. PLoS ONE 11, e0148632 (2016).PubMed 
PubMed Central 

Google Scholar 
38.Antor, R. J. Arthropod fallout on high alpine snow patches of the Central Pyrenees, northeastern Spain. Arct. Alp. Res. 26, 72–76 (1994).
Google Scholar 
39.Brambilla, M. et al. Foraging habitat selection by alpine white-winged snowfinches Montifringilla nivalis during the nestling rearing period. J. Ornithol. 158, 277–286 (2017).
Google Scholar 
40.Heiniger, P. H. Anpassungsstrategien des Schneefinken (Montifringilla nivalis) an die extremen Umweltbedingungen des Hochgebirges. Der Ornithol. Beobachter 88, 193–207 (1991).
Google Scholar 
41.MacDonald, E. C., Camfield, A. F., Jankowski, J. E. & Martin, K. An alpine-breeding songbird can adjust dawn incubation rhythms to annual thermal regimes. Auk 131, 495–506 (2014).
Google Scholar 
42.Mortensen, L. O., Schmidt, N. M., Høye, T. T., Damgaard, C. & Forchhammer, M. C. Analysis of trophic interactions reveals highly plastic response to climate change in a tri-trophic high-arctic ecosystem. Polar Biol. 39, 1467–1478 (2016).
Google Scholar 
43.Grangé, J. L. Biologie de la reproduction de la Niverolle alpine Montifringilla nivalis dans les Pyrénnées occidentales françaises. Nos Oiseaux 55, 67–82 (2008).
Google Scholar 
44.Strinella, E., Vianale, P., Pirrello, S. & Artese, C. Biologia riproduttiva del Fringuello Alpino Montifringilla nivalis a Campo Imperatore nel Parco Nazionale del Gran Sasso e Monti della Laga (AQ). Alula 18, 95–100 (2011).
Google Scholar 
45.Visser, M. E. et al. Variable responses to large-scale climate change in European Parus populations. Proc. R. Soc. Lond. Ser. B Biol. Sci. 270, 367–372 (2003).
Google Scholar 
46.Knaus, P. et al. Schweizer Brutvogelatlas 2013–2016. Verbreitung und Bestandsentwicklung der Vögel in der Schweiz und im Fürstentum Liechtenstein. (Schweizerische Vogelwarte, 2018).47.Basist, A., Bell, G. D. & Meentemeyer, V. Statistical relationships between topography and precipitation patterns. J. Clim. 7, 1305–1315 (1994).ADS 

Google Scholar 
48.Hock, R. et al. High mountain areas. in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds. Pörtner, H. O. et al.). 131–202. (IPCC-Intergovernmental Panel on Climate Change, 2019).49.Schmidt, N. M., Reneerkens, J., Christensen, J. H., Olesen, M. & Roslin, T. An ecosystem-wide reproductive failure with more snow in the Arctic. PLOS Biol. 17, e3000392 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Martin, K. & Wiebe, K. L. Coping mechanisms of alpine and arctic breeding birds: extreme weather and limitations to reproductive resilience. Integr. Comp. Biol. 44, 177–185 (2004).PubMed 

Google Scholar 
51.Williams, C. T. et al. Seasonal reproductive tactics: Annual timing and the capital-to-income breeder continuum. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160250 (2017).
Google Scholar 
52.Barlow, K. E. et al. Citizen science reveals trends in bat populations: The National Bat Monitoring Programme in Great Britain. Biol. Cons. 182, 14–26 (2015).
Google Scholar 
53.Strebel, N., Kéry, M., Schaub, M. & Schmid, H. Studying phenology by flexible modelling of seasonal detectability peaks. Methods Ecol. Evol. 5, 483–490 (2014).
Google Scholar 
54.Maggini, R. et al. Are Swiss birds tracking climate change?: Detecting elevational shifts using response curve shapes. Ecol. Model. 222, 21–32 (2011).
Google Scholar 
55.Gilg, O. et al. Climate change and the ecology and evolution of Arctic vertebrates. Ann. N. Y. Acad. Sci. 1249, 166–190 (2012).ADS 
PubMed 

Google Scholar 
56.Gossmann, T. I. et al. Ice-age climate adaptations trap the alpine marmot in a state of low genetic diversity. Curr. Biol. 29, 1712–1720 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Charmantier, A. & Gienapp, P. Climate change and timing of avian breeding and migration: Evolutionary versus plastic changes. Evol. Appl. 7, 15–28 (2014).PubMed 

Google Scholar 
58.Klein, G., Vitasse, Y., Rixen, C., Marty, C. & Rebetez, M. Shorter snow cover duration since 1970 in the Swiss Alps due to earlier snowmelt more than to later snow onset. Clim. Change 139, 637–649 (2016).
Google Scholar 
59.Scridel, D. et al. A review and meta-analysis of the effects of climate change on Holarctic mountain and upland bird populations. Ibis 160, 489–515 (2018).
Google Scholar 
60.Strinella, E., Scridel, D., Brambilla, M., Schano, C. & Korner-Nievergelt, F. Potential sex-dependent effects of weather on apparent survival of a high-elevation specialist. Sci. Rep. 10, 8386 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
61.Gottfried, M. et al. Continent-wide response of mountain vegetation to climate change. Nat. Clim. Chang. 2, 111–115 (2012).ADS 

Google Scholar 
62.Kharouba, H. M. & Wolkovich, E. M. Disconnects between ecological theory and data in phenological mismatch research. Nat. Clim. Chang. 10, 406–415 (2020).ADS 

Google Scholar 
63.Summers-Smith, J. Handbook of the Birds of the World, Volume 14: Bush-Shrikes to Old World Sparrows. (2009).64.Glutz von Blotzheim, U., Bauer, K. & Bezzel, E. I: Passeridae. in Handbuch der Vögel Mitteleuropas. Vol. 12 (Akademische Verlagsgesellschaft, 1997).65.Antor, R. J. The importance of arthropod fallout on snow patches for the foraging of high-alpine birds. J. Avian Biol. 26, 81–85 (1995).
Google Scholar 
66.Gonseth, Y., Wohlgemuth, T., Sansonnens, B. & Buttler, A. Die Biogeographischen Regionen der Schweiz. Erläuterungen und Einteilungsstandard. Umwelt Materialien. Vol. 137 (2001).67.Thornton, P. E., Running, S. W. & White, M. A. Generating surfaces of daily meteorological variables over large regions of complex terrain. J. Hydrol. 190, 214–251 (1997).ADS 

Google Scholar 
68.Magnusson, J., Gustafsson, D., Hüsler, F. & Jonas, T. Assimilation of point SWE data into a distributed snow cover model comparing two contrasting methods. Water Resour. Res. 50, 7816–7835 (2014).ADS 

Google Scholar 
69.Helbig, N., van Herwijnen, A., Magnusson, J. & Jonas, T. Fractional snow-covered area parameterization over complex topography. Hydrol. Earth Syst. Sci. 19, 1339–1351 (2015).ADS 

Google Scholar 
70.Begert, M. & Frei, C. Long-term area-mean temperature series for Switzerland—Combining homogenized station data and high resolution grid data. Int. J. Climatol. 38, 2792–2807 (2018).
Google Scholar 
71.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. ArXiv e-prints 1406 (2015).72.R Core Team. R: A Language and Environment for Statistical Computing. (2020).73.Gelman, A. & Su, Y.-S. Arm: Data analysis using regression and multilevel/hierarchical models. (2020).74.Carpenter, B. et al. Stan: A probabilistic programming language. J. Stat. Softw. 76 (2017).75.Stan Development Team. RStan: The R interface to Stan. (2020).76.Gabry, J. shinystan: Interactive Visual and Numerical Diagnostics and Posterior Analysis for Bayesian Models. (2018).77.Pebesma, E. J. Multivariable geostatistics in S: The gstat package. Comput. Geosci. 30, 683–691 (2004).ADS 

Google Scholar 
78.Pebesma, E. & Bivand, R. S. S classes and methods for spatial data: the sp package. R News 5, 9–13 (2005).
Google Scholar 
79.Gelman, A. & Greenland, S. Are confidence intervals better termed “uncertainty intervals”?. BMJ 366, I5381 (2019).
Google Scholar  More

Study area and samplingSmall mammals (murid rodents and shrews) were captured using mouse-type snap traps in Tatarstan, Russian Federation (Fig. 1, Table S1). Area type (urban or rural), vegetation (forest or field) and distance from trapping points to the nearest human settlement were recorded. The distinction between forest and field was made based on the UN Food and Agriculture Organization’s criteria23,24. Each administrative division in the Tatarstan was defined to be urban or rural by the Federal Service of State Statistics of Russian Federation25. Based on these criteria, Kazan city and Naberezhnye Chelny city were classified as urban districts and Vysokogorsky district, Yelabuzhsky district, Laishevsky district, Mamadyshsky district, Nizhnekamsky district, Pestrechinsky district and Tukayevsky district were classified as rural districts. Small mammals were captured during the spring and fall periods of 2016 and 2017. Fifty traps were placed in a line every 5 m in one place. Traps were baited and left for one night. Animal suffering was minimized as snap traps cause rapid death in murid rodents and shrews. Each captured small mammal’s species, age, and sex were morphologically identified using a reference guide26, and the animals were then stored at − 20 °C until their brains were isolated.EthicsAll experiments were performed in compliance with relevant Russian and Japanese and institutional laws and guidelines and were approved by the Ministry of Health of the Russian Federation and the Animal Research Committee of Gifu University (Permit Nos. MU 3.1.1029-01, and 17060, respectively). Study was carried out in compliance with the ARRIVE guidelines (https://arriveguidelines.org).DNA extraction and PCRBrain tissue samples were prepared as described previously12. Brain samples stored at − 20 °C were transferred to a − 86 °C deep freezer. Each deep-frozen whole brain sample was homogenized in 1 ml of a 0.9% saline solution. Total DNA was extracted from the brain tissues of each small mammal using a Genomic DNA Purification Kit (Promega, Madison, WI, USA), following the manufacturer’s instructions. Nested PCR was performed with the Takara PCR Amplification Kit (Takara Bio Inc., Foster City, California, USA) according to the manufacturer’s instructions. The primer sets and PCR conditions used to detect the B1 gene from T. gondii were those described previously12.MappingSpatial referencing of the sampling sites was conducted using global positioning system navigation with a Garmin eTrex 10 device. Visualization of cartographic data and measurements of the distances from the trapping points to the nearest human settlements were performed using QGIS 3.12 software27. Geodetic coordinates were projected into planar rectangular coordinates in the Universal Transverse Mercator projection on the WGS-84 ellipsoid (Universal Transverse Mercator, zone 39N). The overview map of the European part of Russia was made in the Lambert Conformal Conic Projection. Map coordinates are represented as geodetic coordinates (WGS-84, degrees and minutes north latitude and east longitude). To visualize thematic objects (administrative boundaries, forests, agricultural lands, and water bodies), a set of vector data layers, NextGIS (Russia), was purchased from OpenStreetMap and contributors, 2021 (https://data.nextgis.com). Data license: ODbL.Dataset and statistical analysesMultivariate logistic regression was performed using the R statistical software package (version 3.6.3)28 to assess the trapping point area (urban or rural), vegetation (forest or field), small mammal species type (alien or non-alien species), age (0–2 months-old juveniles, 3–6 months-old adults or ≧ 6 months old), sex (male or female) and distance from trapping points to the nearest human settlements as risk factors for PCR positivity. According to previous reports2,13,16,17,18, four species, Mi. arvalis, A. flavicollis, A. agrarius, A. uralensis, and three species, My. glareolus, S. araneus and D. nitedula are considered alien and non-alien species, respectively. Quantitative data were replaced with 0 or 1 dummy variables, and age data were replaced by 0, 1 and 2 for juveniles, adults and elders, respectively. Multicollinearity of the explanatory variables was evaluated using Spearman’s coefficient29 calculated using dplyr, FSA and psych packages30,31,32. None of the Spearman’s coefficients were  > 0.6. To find the best fit model, a forward selection procedure was used. Predictive performance and model fitting were assessed using the area under the receiver operating characteristic (ROC) curve, area under the curve (AUC) and corrected Akaike’s information criterion (AICc) with Akaike weight (Wi). AICc and Wi were calculated using the MuMIN package33, and the AUC was calculated using the R pROC package34. P-values of  More

The micro-CT results from Arthrorhynchus agree perfectly with the previously known light microscope and transmission electron microscope images2. This emphasizes that microtomography is a good technique to visualize the type of fungal attachment to the host and especially the penetration of the cuticle, apart from the study of thallus in amber fossils17. As Jensen et al. (2019) demonstrated the presence of a haustorium in Arthrorhynchus using scanning electron microscopy, we are confident that the lack of penetration and haustorium in Rickia found by micro-CT is real. This is also in agreement with results from the scanning electron microscopical investigation of the attachment sites of R. gigas, which exhibits no indication of penetration and are very similar to those of R. wasmannii previously shown18.Despite the absence of a haustorium, and hence without any obvious means of obtaining nutrition, Rickia gigas is quite a successful fungus, being often abundant on several species of Afrotropical millipedes of the family Spirostreptidae10. It was originally described from Archispirostreptus gigas, and Tropostreptus (= ‘Spirostreptus’) hamatus20, and was subsequently reported from several other Tropostreptus species19.A further challenge for Laboulbeniales growing on millipedes is that infected millipedes, in some species even adults, may moult, shedding the exuviae with the fungus, as has been observed by us on an undescribed Rickia species on a millipede of the genus Spirobolus (family Spirobolidae).The question of how non-haustoriate Laboulbeniales obtain nutrients has been discussed by several authors18, including staining experiments using fungi of the non-haustoriate genus Laboulbenia on various beetles21. Whereas the surface of the main thallus was almost impenetrable to the dye applied (Nile Blue), the smaller appendages could sometimes be penetrated21. The dye injection into the beetle elytra upon which the fungi were sitting, actually spread from the elytron into the fungus, thus indicating that in spite of the lack of a haustorium, the fungus is able to extract nutrients from the interior of its host21.Such experiments have not been performed on Rickia species, but the possibility that nutrients may pass from the host into the basis of the fungus cannot be excluded. For this genus, or at least R. gigas, there may, however, be an alternative way to obtain nutrients: the small opening in the circular wall by which the thallus is attached to the host may allow nutrients from the surface of the millipede or from the environment to seep into the foot of the fungus. However, further experiments are needed in order to evaluate this hypothesis. Moreover, we should not exclude a potential role of primary and secondary appendages in Laboulbeniales nutrition, as we still do not understand exactly their functional role on the fungus life cycle11.The predominant position of the Laboulbeniales on the host might be related to the absence or presence of a haustorium. Thus, the haustoriate species of the genus Arthrorhynchus are most frequently encountered in large numbers on the arthrodial membranes of the host’s abdomen, although some thalli are found on legs2,22. At the arthrodial membranes the cuticle is more flexible and therefore might be easier to penetrate by a parasite. Furthermore, most tissues providing/storing nutrition (e.g., fat body) are located within the abdomen. In contrast, non-haustoriate fungi as are often located on more stiff and sclerotized body-parts like the genus Rickia on the legs or body-rings of millipedes7,20,23 or the genus Laboulbenia on the elytra of beetles21,24. A reason for this might be that the non-haustoriate forms, which are only superficially attached to the host need a more or less smooth surface for adherence and can easily become detached from a flexible surface, which is movable in itself, like the arthrodial membrane, while the haustoriate forms are firmly anchored within the hosts’ cuticle.Whereas the vast majority of the more than 2000 described species of Laboulbeniales show no sign of host penetration, haustoria have been reported from some other genera18, including Trenomyces parasitizing bird lice25,26, Hesperomyces growing on coccinellid beetles and Herpomyces on cockroaches (formerly a Laboulbeniales and now in the order Herpomycetales10), with pernicious consequences on the hosts’ fitness18,27. Micro-CT studies on these genera could help to understand the host penetration. In order to fully understand how Laboulbeniales obtain nourishment, although other approaches are, also needed—for the time being it remains a mystery how the non-haustoriate Laboulbeniales sustain themselves. More

Collection and domestication of the wild populationsThe academic permission for collections and research on medicinal plants was obtained from the Head of Biotechnology Department, Research Institute of Modern Biological Techniques, University of Zanjan, Zanjan, Iran. The study complies with all relevant guidelines. Some populations of wild spinaches were harvested during spring season 2013 from the mountain habitat of this wild plant in the Tarom region of Zanjan province from an altitude of 2500–3000 m and were transferred to the greenhouses conditions. The domestication and cultivation experiments were conducted at Research Institute of Modern Biological Techniques, University of Zanjan, 1579° m above sea level, with 48° 28′ longitude and 36° 40′ latitude, from April 2013 to August 2020. The resulted seeds were cultured on pots to produce adequate seeds. The seedlings were transferred to the field with rows spaced 50 cm apart and also 50 cm between plants within the rows. Two seeds per hill were planted in an area of approximately 50 m2. Based on the organic conditions, no fertilization was performed. Thinning was done 25 days after emergence, leaving one plant per hill. The other cultural practices were those normally adopted for cultivation in the region.Mass selection of populationsIn the first year, phenotypic studies were performed during the growing season and weak, diseased and underdeveloped plants were removed from the field before the flowering stage. Then plants with the same phenotype and the desired traits were selected and after harvesting, their seeds were mixed. This election cycle was repeated for 5 years. In the final year, the new mass selected population was compared in a pilot project with cultivated spinach in traits such as yield, resistance to wilt, cold and pests, diseases, and mineral contents. This variety before the certification in the related national organization is a candida cultivar. It is a developed population that will be evaluated in the session of the Iranian variety of introduction committee.The seeds of cultivated spinach (Spinacia oleracea L. |Varamin 88|) were prepared from the Research Institute of Modern Biological Techniques, University of Zanjan, Zanjan, Iran.Performing tests of stability, uniformity and differentiationTo assess morphologically and differentiate advanced uniformity in the studied population (Candida cultivar), the population was managed as a randomized complete block design with three replications over 2 years according to the instructions for spinach differentiation, uniformity, and stability (DUS Testing) of the International Union New Plant Cultivation (UPOV) and some morphological traits on plants or parts of plants. The studied traits included: cotyledon length, presence or absence of anthocyanin in petiole and veins, green color intensity, shrinkage, presence of lobes in the petiole, petiole state, petiole length, foil shape, foil edge shape, tip shape, and part of the length of the petiole, the time of flowering and the color of the seeds.Mineral analysesTo compare the mineral content of mass-selected population-medicinal spinach (MSP) with cultivated spinach (Spinacia oleracea L. var. Varamin 88), both plants were planted in pots and fields on similar conditions. In five leaves stage, plant samples were taken from both leaf and crown sections. The sampling method was such that after removing half a meter from the beginning and end of each plot (to remove the marginal effect) and also removing the two sidelines, five plants were harvested randomly for plant mineral analysis. Atomic absorption spectroscopy was used to determine the mineral content including iron (Fe), zinc (Z), manganese (Mn), and copper (Cu).The dried samples of root-crown and leave were stored, and later grounded and analyzed for iron (Fe), zinc (Z), manganese (Mn), and copper (Cu) in mass-selected variety (MSP) and cultivated spinach (CSP). Studied minerals were measured using atomic absorption spectrometry in the model of GBC AVANTA (GBC scientific equipment Ltd., Melbourne, Vic., Australia).Calibration of AAS was done using the working standard prepared from commercially available metal/mineral standard solutions (1000 μg/mL, Merck, Germany). The most appropriate wavelength, hollow cathode lamp current, gas mixture flow rate, slit width, and other AAS instrument parameters for metals/minerals were selected as given in the instrument user’s manual, and background correction was used during the determination of metals/minerals. Measurements were made within the linear range of working standards used for calibration15,16.The concentrations of all the minerals were expressed as mg/1000 g (ppm) dry weight of the sample. Each value is the mean of three replicate determination ± standard deviation.Scanning electron microscopy (SEM)For SEM studies, the seeds enveloping were removed and were acetolyzed in a 1:9 sulfuric acid-acetic anhydride solution. The seeds were vigorously shaken for 5 min. Then, they were left for 24–48 h in the solution. After this time, seeds were again shaken for 5 min and then washed.in distilled water by shaking for a further 5 min. The seeds were dried overnight and then were mounted on stubs and covered with Au–Pd by sputter coater model SC 7620. After coating, coated seeds were photographed with an LEO 1450 VP Scanning Electron Microscope. All photographs were taken in the Taban laboratory (Tehran, Iran).Statistical analysisThe statistical evaluation including: data transformation, analysis of variance and comparison of means were performed (SPSS software, Version 11.0). The experiment was structured following a randomized complete block design (RCBD) with three replications. Means comparisons were conducted using an ANOVA protected the least significant difference (LSD) test, with the ANOVA confidence levels of 0.95. Data were presented with their standard deviations (SD). More

Dispersal modelsIn this study, the dispersal model consists of two parts, namely, kernel and observation model (Fig. 1). The main purpose of the kernel was employed to estimate the proportion of pollen dispersed from location s′ to location s and calculate the expected number of CP grains. The observation model used the expected number of CP grains as a parameter and described the number of CP grains at location s (Ys) by a specific distribution in the following:$${Y}_{s}sim fleft(left.{y}_{s}right|{{varvec{theta}}}_{s}right),$$
(1)
where f indicates the probability density function (PDF) of the specific distribution. The θs is the parameter vector of the distribution. This study constructed eight different dispersal models combined with two observation models, two kernels, and two conditions of the field border (FB) effect (Table 1). The details of the kernels and observation models were described in the following subsections.Figure 1Graphical summary of the establishment of the dispersal model using ZIP distribution observation model as an example.Full size imageTable 1 List of dispersal models constructed in this study.Full size tableKernelsThe kernel indicates the probability when the pollen emitted at location s′ and would fall down at location s. It can be expressed as γ(s, s′), where s′ is the source location closest to location s. Numerous kernels have been used to describe various dispersal phenomena24. The output of the kernel represents the donor pollen density of location s. In order to calculate the expected number of CP grains, the donor pollen density is multiplied by the average total grain number described as follows:$${lambda }_{s}=Ktimes gamma left(s,{s}^{^{prime}}right),$$
(2)
where λs and K indicate the expected number of CP grains at location s and the average number of grains per cob, respectively. The effect of the FB was introduced into the kernel to suit to the small-scale farming system in Asia. This study assumed that the relation between the pollen density at the first recipient row and the width of the FB displayed an exponential decrease25,26. To evaluate the improvement of the kernel with the FB effect, the kernels without the FB effect were also established in this study.The compound exponential kernel (γExpo) has been used in the previous pollen dispersal study27. Our study introduced the FB effect into this kernel. Therefore, the form of the compound exponential kernel can be expressed as follows:$$gamma_{{{text{Expo}}}} left( {s,s^{prime}} right) = left{ {begin{array}{*{20}l} {K_{e} exp left( { – a_{1} d^{*} left( {s,s^{prime}} right)} right)exp left( { – ksqrt {FB} } right),} \ {K_{e} exp left( { – a_{1} D – a_{2} left( {d^{*} left( {s,s^{prime}} right) – D} right)} right)exp left( { – ksqrt {FB} } right),} \ end{array} } right.begin{array}{*{20}l} {{text{if}},, d^{*} left( {s,s^{prime}} right) le D} \ {{text{if}} ,,d^{*} left( {s,s^{prime}} right) > D,} \ end{array}$$
(3)
where Ke, a1, a2, k, D are the parameters of the kernel. d*(s, s′) indicates the shortest distance between locations s′ and s in which the width of the FB has been subtracted. In the compound exponential kernel without the FB effect, the exponential term of the FB effect was removed and the d*(s, s′) was replaced directly by the shortest distance between s′ and s.The second kernel applied in this study was the modified Cauchy kernel (γCauchy) which was based on the PDF of the Cauchy distribution and the concept of compound distribution. The modified Cauchy kernel is represented as follows:$$gamma_{Cauchy} left( {s,s^{prime}} right) = left{ {begin{array}{*{20}l} {frac{2beta }{{pi left[ {beta^{2} + d^{*} left( {s,s^{prime}} right)^{2} } right]}}{text{exp}}left( { – ksqrt {FB} } right),} \ {frac{2beta }{{pi left[ {beta^{2} + D^{2} + c_{1} left( {d^{*} left( {s,s^{prime}} right) – D} right)^{2} } right]}}{text{exp}}left( { – ksqrt {FB} } right),} \ end{array} } right.begin{array}{*{20}l} {{text{if}} ,,d^{*} left( {s,s^{prime}} right) le D} \ {{text{if}} ,,d^{*} left( {s,s^{prime}} right) > D,} \ end{array}$$
(4)
where the β indicates the decline rate of the curve. Parameters of k and D are same as the compound exponential kernel. c1 indicates the relative slow decrease of pollen density at further distances. Similarly, in the modified Cauchy kernel without the FB effect, the term of the FB effect was removed and the d*(s, s′) was replaced directly by the shortest distance between s′ and s in which the row spacing (0.75 m) had been subtracted.Observation modelsBecause of the high proportions of zero value observations, the present study assumed that the CP grain count followed the zero-inflated Poisson (ZIP) distribution to account for zero-excess condition28. The ZIP distribution was first proposed by Lambert29, and several studies had applied the ZIP distribution to deal with the CP data27,30. The ZIP distribution consists of a Dirac distribution in zero and a Poisson distribution. Therefore, the distribution of CP grain count at location s (Ys) can be expressed as follows:$${Y}_{s}sim mathrm{ZIP}left(1-{q}_{s},{uplambda }_{s}right),$$
(5)
where qs indicates the probability of an observation following a Poisson distribution, and λs is the parameter of Poisson distribution calculated by Eq. (2). Furthermore, the parameter qs can be assumed to depend on the shortest distance between the recipient and donor plants. The border effect is also included in the estimation of qs because it is related to the distance effect. The relationship among distance, border, and the qs can be described using the following logistic function:$${q}_{s}=frac{1}{1+mathrm{exp}({b}_{1}-{b}_{2}{d}^{*}left(s,{s}^{^{prime}}right))},$$
(6)
where b1 and b2 are the parameters of the logistic function. The d*(s, s′) was the shortest distance between s′ and s in the version of dispersal models without the FB effect. The Poisson distribution was also used as an observation model for comparison with the ZIP observation model.Experimental and meteorological data collectionThe pollen dispersal data were collected from experiments performed in 2009 and 2010 at the geographic coordinates 23° 47′ N, 120° 26′ E, and an altitude of 20 m. These experiments were coded as 2009-1, 2009-2, and 2010-1, respectively. The experiment 2009-2 was divided into 2009-2A (without the FB) and 2009-2B (with the FB) based on the presence of the FB. The different layouts of the field experiments were designed to investigate the effect of the FB. Two commercial glutinous maize varieties, black pearl (purple grain) and Tainan No. 23 (white grain), were selected as the pollen donor and pollen recipient, respectively. The distance between the plants in a row was 25 cm, whereas the distance between the rows was 75 cm. The recipient plots consisted of 82 and 91 rows in 2009 and 2010 experiments, respectively.The CP rate was determined based on the differences in grain color on recipient cobs as a result of the xenia effect31. In the sampling framework, the whole field was divided into many grids and corn samples were collected from each grid in the whole field. The CP rate of each grid was calculated using the method presented in a previous study32 and defined as:$$mathrm{CP}left(%right)=left[sum_{i=1}^{n}{Cob}_{i}/left(ntimes Kright)right],$$
(7)

where Cobi and n indicate ith cob and total number of cobs in the grid, respectively. K is the average grain number per cob. Meteorological data were collected from the meteorological station at geographic coordinates 23° 35′ N, 120° 27′ E, and an altitude of 20 m. The detailed experimental setup was described in our previous study33. The study complies with relevant institutional, national, and international guidelines and legislation.Statistical analysesAll statistical analyses were performed using SAS (Statistical Analysis System, version 9.4). The dispersal model parameters were estimated by two methods. First, the nonlinear model estimation was conducted by PROC NLMIXED to evaluate the fitting and predictive abilities of dispersal models. Then the dispersal models with the observation model performed better fitting ability were re-estimated using the Bayesian estimation method to assess the uncertainty by PROC MCMC. In the Bayesian method, the noninformative prior distribution was used to estimate all parameters (Supplementary Table S1). The iteration of Markov Chain was 500,000 times and the burn-in was set to 450,000 iterations. In order to reduce the autocorrelations in the chain, the thinned value was set to 25.The validation method used in this study was the threefold cross-validation for the results of both estimation methods. The data from three experiments were combined and randomly partitioned into three sub-datasets. To avoid the heterogeneity of the different field designs and distances among sub-datasets, the observations from the same field design and same distance were considered as a group, and then partitioned into three parts. Each sub-dataset contained one part of all groups. At each validation run, two sub-datasets were selected as the training set, and the remaining one was used for validation.The fitting ability of the dispersal models was evaluated based on two criteria, namely, Akaike information criterion (AIC), Deviance, and coefficient of determination (R2). The smaller values of AIC or deviance indicate a better fitting. The higher R2 value represents a better fitting performance. The correlation coefficient (r) between the predicted and actual CP rates was used to assess the predictive ability. The deviance information criterion (DIC) was used to evaluate the performance of dispersal model fitting for the Bayesian estimation. The criterion values calculated from three training and validation sets were averaged to assess the overall results. The uncertainty of the model parameter was quantified by the standard deviation (SD) of parameter posterior distribution. The 95% credible intervals of posterior predictive distribution constructed by the 2.5th and 97.5th percentiles of 200,000 samples generated from the posterior predictive distribution were used to assess the predictive uncertainty. Furthermore, to assess the zero-excess condition, the percentage of observed zero CP grain events was compared with the Poisson probability of the zero CP grain event. A zero-excess condition occurred if the observed percentage was higher than the Poisson probability34. More