Philippa Kaur

Brant, S. V. & Loker, E. S. Molecular systematics of the avian schistosome genus Trichobilharzia (Trematoda: Schistosomatidae) in North America. J. Parasitol. 95, 941–963 (2009).CAS 
PubMed 
Article 

Google Scholar 
Horák, P. et al. Avian schistosomes and outbreaks of cercarial dermatitis. Clin. Microbiol. Rev. 28, 165–190 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Brant, S. V. et al. An approach to revealing blood fluke life cycles, taxonomy, and diversity: Provision of key reference data including DNA sequence from single life cycle stages. J. Parasitol. 92, 77–88 (2006).CAS 
PubMed 
Article 

Google Scholar 
Brant, S. V. & Loker, E. S. Discovery-based studies of schistosome diversity stimulate new hypotheses about parasite biology. Trends Parasitol. 29, 449–459 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Lorenti, E., Brant, S. V, Gilardoni, C., Diaz, J. I. & Cremonte, F. Two new genera and species of avian schistosomes from Argentina with proposed recommendations and discussion of the polyphyletic genus Gigantobilharzia (Trematoda, Schistosomatidae). Parasitology. 149, 1–59 (2022).Article 

Google Scholar 
Khalil, L. F. Family Schistosomatidae Stiles & Hassall, 1898. Keys Trematoda 1, 419–432 (2002).Article 

Google Scholar 
Snyder, S. D. & Loker, E. S. Evolutionary relationships among the Schistosomatidae (Platyhelminthes: Digenea) and an Asian origin for Schistosoma. J. Parasitol. 86, 283–288 (2000).CAS 
PubMed 
Article 

Google Scholar 
Brant, S. V. et al. Cercarial dermatitis transmitted by exotic marine snail. Emerg. Infect. Dis. 16, 1357 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Leigh, W. H. The morphology of Gigantobilharzia huttoni (Leigh, 1953) an avian schistosome with marine dermatitis-producing larvae. J. Parasitol. 41, 262–269 (1955).CAS 
PubMed 
Article 

Google Scholar 
Ewers, W. H. A new intermediate host of schistosome trematodes from New South Wales. Nature 190, 283–284 (1961).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Rudolphi, K. A. Entozoorum synopsis cui accedunt mantissa duplex et indices locupletissimi. (Sumtibus A. Rücker, 1819).Odhner, T. Zum natürlichen System der digenen Trematoden. V. Zool. Anz. 41, 54–71 (1912).
Google Scholar 
Farley, J. A review of the family Schistosomatidae: Excluding the genus Schistosoma from mammals. J. Helminthol. 45, 289–320 (1971).CAS 
PubMed 
Article 

Google Scholar 
Penner, L. R. The biology of a marine dermatitis-producing schistosome cercaria from Batillaria minima (Gmelin). J. Parasitol. 39, 19–20 (1953).
Google Scholar 
Al-Kandari, W. Y., Al-Bustan, S. A., Isaac, A. M., George, B. A. & Chandy, B. S. Molecular identification of Austrobilharzia species parasitizing Cerithidea cingulata (Gastropoda: Potamididae) from Kuwait Bay. J. Helminthol. 86, 470 (2012).CAS 
PubMed 
Article 

Google Scholar 
Martin, W. E. An annotated key to the cercariae that develop in the snail Cerithidea californica. Bull South Calif. Acad. Sci. 71, 39–43 (1972).
Google Scholar 
Holliman, R. B. Larval trematodes from the Apalachee Bay area, Florida, with a checklist of known marine cercariae arranged in a key to their superfamilies. Tulane Stud. Zool. 9, 1–74 (1961).
Google Scholar 
Short, R. B. & Holliman, R. B. Austrobilharzia penneri, a new schistosome from marine snails. J. Parasitol. 47, 447–450 (1961).Article 

Google Scholar 
Lindberg, W. F. P. D. R. Phylogeny and Evolution of the Mollusca (Univ of California Press, 2008).
Google Scholar 
Chong-ti, T. Philophthalmid larval trematodes from Hong Kong and the coast of south China. In The Marine Flora and Fauna of Hong Kong and Southern China II: Proceedings of the Second International Marine Biological Workshop Hong Kong, 2–24 April 1986 Vol. 1, 213 (Hong Kong University Press, 1990).Taraschewski, H. Investigations on the prevalence of Heterophyes species in twelve populations of the first intermediate host in Egypt and Sudan. J. Trop. Med. Hyg. 88, 265–271 (1985).CAS 
PubMed 

Google Scholar 
Reid, D. G. & Ozawa, T. The genus Pirenella Gray, 1847 (= Cerithideopsilla Thiele, 1929) (Gastropoda: Potamididae) in the Indo-West Pacific region and Mediterranean Sea. Zootaxa 4076, 1–91 (2016).PubMed 
Article 

Google Scholar 
Vahidi, F., Fatemi, S. M. R., Danehkar, A., Mashinchian, A. & Nadushan, R. M. Benthic macrofaunal dispersion within different mangrove habitats in Hara Biosphere Reserve, Persian Gulf. Int. J. Environ. Sci. Technol. 17, 1295–1306 (2020).CAS 
Article 

Google Scholar 
Nazeer, Z. et al. Macrofaunal assemblage in the intertidal area of Saudi Arabian Gulf Coast. Reg. Stud. Mar. Sci. 47, 101954 (2021).
Google Scholar 
Snyder, S. D. Phylogeny and paraphyly among tetrapod blood flukes (Digenea: Schistosomatidae and Spirorchiidae). Int. J. Parasitol. 34, 1385–1392 (2004).CAS 
PubMed 
Article 

Google Scholar 
Al-Zaidan, A. S. Y., Kennedy, H., Jones, D. A. & Al-Mohanna, S. Y. Role of microbial mats in Sulaibikhat Bay (Kuwait) mudflat food webs: Evidence from δ13C analysis. Mar. Ecol. Prog. Ser. 308, 27–36 (2006).ADS 
CAS 
Article 

Google Scholar 
Bearup, A. J. A schistosomc larva from the marine snail Pyrazus australisas a cause of cercarial dermatitis in man. Med. J. Aust. 1, 955–960 (1955).Article 

Google Scholar 
Grodhaus, G. & Keh, B. The marine, dermatitis-producing cercaria of Austrobilharzia variglandis in California (Trematoda: Schistosomatidae). J. Parasitol. 44, 633–638 (1958).CAS 
PubMed 
Article 

Google Scholar 
Sindermann, C. J. Ecological studies of marine dermatitis-producing schistosome larvae in northern New England. Ecology 41, 678–684 (1960).Article 

Google Scholar 
Pinto, H. A., Pulido-Murillo, E. A., de Melo, A. L. & Brant, S. V. Putative new genera and species of avian schistosomes potentially involved in human cercarial dermatitis in the Americas, Europe and Africa. Acta Trop. 176, 415–420 (2017).PubMed 
Article 

Google Scholar 
Hechinger, R. F. & Lafferty, K. D. Host diversity begets parasite diversity: Bird final hosts and trematodes in snail intermediate hosts. Proc. R. Soc. B Biol. Sci. 272, 1059–1066 (2005).Article 

Google Scholar 
Aldhoun, J. A. & Horne, E. C. Schistosomes in South African penguins. Parasitol. Res. 114, 237–246 (2015).PubMed 
Article 

Google Scholar 
Vanstreels, R. E. T. et al. Schistosomes and microfilarial parasites in Magellanic penguins. J. Parasitol. 104, 322–328 (2018).CAS 
PubMed 
Article 

Google Scholar 
Brant, S. V. & Loker, E. S. Can specialized pathogens colonize distantly related hosts? Schistosome evolution as a case study. PLoS Pathog. 1, e38 (2005).PubMed Central 
Article 
CAS 

Google Scholar 
Blair, D., Davis, G. M. & Wu, B. Evolutionary relationships between trematodes and snails emphasizing schistosomes and paragonimids. Parasitology 123, 229–243 (2001).Article 

Google Scholar 
Miller, H. M. Jr. & Northup, F. E. The seasonal infestation of Nassa obsoleta (Say) with larval trematodes. Biol. Bull. 50, 490–508 (1926).Article 

Google Scholar 
Chu, G. & Cutress, C. E. Human dermatitis caused by marine organisms in Hawaii. In Proceedings of the Hawaiian Academy of Science. 29th Annual Meeting (1953–54) (1954).Szidat, L. Investigaciones sobre Cercaria chascomusi n. sp. Agente causal de una nueva enfermedad humana en la Argentina: La dermatitis de los bañistas de la laguna Chascomús. Bol Mus Argent Cienc Nat Bernardino Rivadavia 18, 1–16 (1958).
Google Scholar 
ITO, J. Studies on the morphology and life cycle of Pseudobilharziella corvi Yamaguti, 1941 (Trematoda: Schistosomatidae). Jpn. J. Med. Sci. Biol. 13, 53–58 (1960).Article 

Google Scholar 
Karamian, M. et al. Parasitological and molecular study of the furcocercariae from Melanoides tuberculata as a probable agent of cercarial dermatitis. Parasitol. Res. 108, 955–962 (2011).PubMed 
Article 

Google Scholar 
Leedom, W. S. & Short, R. B. Cercaria pomaceae sp. n., a dermatitis-producing schistosome cercaria from Pomacea paludosa, the Florida apple snail. J. Parasitol. 67, 257–261 (1981).Article 

Google Scholar 
Aldhoun, J. A., Faltýnková, A., Karvonen, A. & Horák, P. Schistosomes in the North: A unique finding from a prosobranch snail using molecular tools. Parasitol. Int. 58, 314–317 (2009).CAS 
PubMed 
Article 

Google Scholar 
Horák, P., Kolářová, L. & Adema, C. M. Biology of the schistosome genus Trichobilharzia. (2002).Martorelli, S. R. Sobre una cercaria de la familia Schistosomatidae (Digenea) parásita de Chilina gibbosa Sowerby, 1841 en el lago Pellegrini, Provincia de Río Negro, República Argentina. Neotrópica 30, 97–106 (1984).
Google Scholar 
Braun, M. Zur Revision der Trematoden der Vögel II. Zentralblatt fur Bakteriol. Abth I(29), 895–897 (1901).
Google Scholar 
Cheatum, E. L. Dendritobilharzia anatinarum n. sp., a blood fluke from the mallard. J. Parasitol. 27, 165–170 (1941).Article 

Google Scholar 
Leite, A. C. R., Costa, H. M. D. A. & Costa, J. O. Trichobilharzia jequitibaensis sp. n (Trematoda, Schistosomatidae) in Cairina moschata domestica (Anatidae). Rev. Bras. Biol. 38, 843–846 (1978).
Google Scholar 
McLeod, J. A. Two new schistosomid trematodes from water-birds. J. Parasitol. 23, 456–466 (1937).Article 

Google Scholar 
Ebbs, E. T. et al. Schistosomes with wings: How host phylogeny and ecology shape the global distribution of Trichobilharzia querquedulae (Schistosomatidae). Int. J. Parasitol. 46, 669–677 (2016).PubMed 
Article 

Google Scholar 
Flores, V., Viozzi, G., Casalins, L., Loker, E. S. & Brant, S. V. A new schistosome (Digenea: Schistosomatidae) from the nasal tissue of South America black-necked swans, Cygnus melancoryphus (Anatidae) and the endemic pulmonate snail Chilina gibbosa. Zootaxa 4948, zootaxa-4948 (2021).Article 

Google Scholar 
Kolářová, L., Horák, P., Skírnisson, K., Marečková, H. & Doenhoff, M. Cercarial dermatitis, a neglected allergic disease. Clin. Rev. Allergy Immunol. 45, 63–74 (2013).PubMed 
Article 
CAS 

Google Scholar 
QGIS.org, QGIS 3.4. QGIS Geographic Information System. QGIS Association. http://www.qgis.org (2019).Tkach, V., Grabda-Kazubska, B., Pawlowski, J. & Swiderski, Z. Molecular and morphological evidence for close phylogenetic affinities of the genera Macrodera, Leptophallus, Metaleptophallus and Paralepoderma [Digenea, Plagiorchiata]. Acta Parasitol. 44, 3 (1999).
Google Scholar 
Tkach, V. V., Littlewood, D. T. J., Olson, P. D., Kinsella, J. M. & Swiderski, Z. Molecular phylogenetic analysis of the Microphalloidea Ward, 1901 (Trematoda: Digenea). Syst. Parasitol. 56, 1–15 (2003).PubMed 
Article 

Google Scholar 
Littlewood, D. T. J., Curini-Galletti, M. & Herniou, E. A. The interrelationships of Proseriata (Platyhelminthes: Seriata) tested with molecules and morphology. Mol. Phylogenet. Evol. 16, 449–466 (2000).CAS 
PubMed 
Article 

Google Scholar 
Olson, P. D., Cribb, T. H., Tkach, V. V., Bray, R. A. & Littlewood, D. T. J. Phylogeny and classification of the Digenea (Platyhelminthes: Trematoda). Int. J. Parasitol. 33, 733–755 (2003).CAS 
PubMed 
Article 

Google Scholar 
Bowles, J. & McManus, D. P. Rapid discrimination of Echinococcus species and strains using a polymerase chain reaction-based RFLP method. Mol. Biochem. Parasitol. 57, 231–239 (1993).CAS 
PubMed 
Article 

Google Scholar 
Miura, O. et al. Molecular-genetic analyses reveal cryptic species of trematodes in the intertidal gastropod, Batillaria cumingi (Crosse). Int. J. Parasitol. 35, 793–801 (2005).CAS 
PubMed 
Article 

Google Scholar 
Kuraku, S., Zmasek, C. M., Nishimura, O. & Katoh, K. aLeaves facilitates on-demand exploration of metazoan gene family trees on MAFFT sequence alignment server with enhanced interactivity. Nucleic Acids Res. 41, W22–W28 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Katoh, K., Rozewicki, J. & Yamada, K. D. MAFFT online service: Multiple sequence alignment, interactive sequence choice and visualization. Brief. Bioinform. 20, 1160–1166 (2019).CAS 
PubMed 
Article 

Google Scholar 
Telford, M. J., Herniou, E. A., Russell, R. B. & Littlewood, D. T. J. Changes in mitochondrial genetic codes as phylogenetic characters: Two examples from the flatworms. Proc. Natl. Acad. Sci. 97, 11359–11364 (2000).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: More models, new heuristics and parallel computing. Nat. Methods 9, 772 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Miller, M. A., Pfeiffer, W. & Schwartz, T. The CIPRES science gateway: a community resource for phylogenetic analyses. In Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery 1–8 (2011).Rambaut, A. & Drummond, A. J. Tracer v1. 5 http://beast.bio.ed.ac.uk/Tracer (2009).Huelsenbeck, J. P., Ronquist, F., Nielsen, R. & Bollback, J. P. Bayesian inference of phylogeny and its impact on evolutionary biology. Science 294, 2310–2314 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Rambaut, A. & Drummond, A. J. FigTree v1. 4. 2012. (2012).Lockyer, A. E. et al. The phylogeny of the Schistosomatidae based on three genes with emphasis on the interrelationships of Schistosoma Weinland, 1858. Parasitology 126, 203 (2003).CAS 
PubMed 
Article 

Google Scholar 
Walker, J. C. Austrobilharzia terrigalensis: A schistosome dominant in interspecific interactions in the molluscan host. Int. J. Parasitol. 9, 137–140 (1979).Article 

Google Scholar 
Appleton, C. C. Studies on austrobilharzia terrigalensis (trematoda: schistosomatidae) in the swan estuary, Western Australia: Observations on the biology of the cercaria. Int. J. Parasitol. 13, 239–247 (1983).CAS 
PubMed 
Article 

Google Scholar 
Appleton, C. C. Studies on Austrobilharzia terrigalensis (Trematoda: Schistosomatidae) in the Swan Estuary, Western Australia: Frequency of infection in the intermediate host population. Int. J. Parasitol. 13, 51–60 (1983).CAS 
PubMed 
Article 

Google Scholar 
Johnston, S. H. On the Trematodes of Australian Birds. (1916).Appleton, C. C. Observations on the histology of Austrobilharzia terrigalensis (Trematoda: Schistosomatidae) infection in the silver gull, Larus novaehollandiae. Int. J. Parasitol. 14, 23–28 (1984).Article 

Google Scholar 
Bearup, A. J. Life cycle of Austrobilharzia terrigalensis Johnston, 1917. Parasitology 46, 470–479 (1956).CAS 
PubMed 
Article 

Google Scholar 
CAMismoN, G. M., Bacha Jr, W. J. & Stempen, H. The circumoval precipitate and cercarienhiillen reaktion of Austrobilharzia variglandis. In Proc. Helminthol. Soc. Wash Vol. 48, 202–208 (1981).Zibulewsky, J., Fried, B. & Bacha Jr, W. J. Skin surface lipids of the domestic chicken, and neutral lipid standards as stimuli for the penetration response of Austrobilharzia variglandis cercariae. J. Parasitol. 68, 905–908 (1982).CAS 
PubMed 
Article 

Google Scholar 
Bacha, W. J., Roush, R. & Icardi, S. Infection of the gerbil by the avian schistosome Austrobilharzia variglandis (Miller and Northup 1926; Penner 1953). J. Parasitol. 68, 505–507 (1982).CAS 
Article 

Google Scholar 
Wood, L. M. & Bacha Jr, W. J. Distribution of eggs and the host response in chickens infected with Austrobilharzia variglandis (Trematoda). J. Parasitol. 69, 682–688 (1983).CAS 
PubMed 
Article 

Google Scholar 
Sindermann, C. J. The ecology of marine dermatitis-producing schistosomes. I. Seasonal variation in infection of mud snails (Nassa obsoleta) with larvae of Austrobilharzia variglandis. J. Parasitol. 42, 27 (1956).
Google Scholar 
Cutress, C. E. Austrobilharzia variglandis (Miller and Northup, 1926) Penner, 1953,(Trematoda: Schistosomatidae) in Hawaii with notes on its biology. J. Parasitol. 40, 515–524 (1954).PubMed 
Article 

Google Scholar 
Rohde, K. The bird schistosome Austrobilharzia terrigalensis from the Great Barrier Reef, Australia. Zeitschrift für Parasitenkd. 52, 39–51 (1977).CAS 
Article 

Google Scholar 
Price, E. W. A synopsis of the trematode family Schistosomidae, with descriptions of new genera and species. Proc. United States Natl. Museum (1929).McLeod, J. A. Studies on cercarial dermatitis and the trematode family Schistosomatidae in Manitoba. Can. J. Res. 18, 1–28 (1940).Article 

Google Scholar 
Keppner, E. J. Some internal parasites of the California gull Larus californicus Lawrence, in Wyoming. Trans. Am. Microsc. Soc. 92, 288–291 (1973).CAS 
PubMed 
Article 

Google Scholar 
Johnston, S. J. On the trematodes of Australian birds. J. R. Soc. New South Wales 50, 187–261 (1917).
Google Scholar 
Appleton, C. C. Studies on Austrobilharzia terrigalensis (Trematoda: Schistosomatidae) in the Swan Estuary, Western Australia: Infection in the definitive host, Larus novaehollandiae. Int. J. Parasitol. 13, 249–259 (1983).CAS 
PubMed 
Article 

Google Scholar 
Penner, L. R. The red-breasted merganser as a natural avian host of the causative agent of clam diggers’ itch. J. Parasitol. 39, 20 (1953).
Google Scholar 
Johnston, T. H. Bather’s itch (schistosome dermatitis) in the Murray Swamps, South Australia. Trans. R. Soc. South Aust. 65, 276–284 (1941).
Google Scholar 
Witenberg, G. & Lengy, J. Redescription of Ornithobilharzia canaliculata (Rud.) Odhner, with notes on classification of the genus Ornithobilharzia and the subfamily Schistosomatinae (Trematoda). Isr. J. Zool. 16, 193–204 (1967).CAS 
PubMed 

Google Scholar 
Curtis, L. A. Ilyanassa obsoleta (Gastropoda) as a host for trematodes in Delaware estuaries. J. Parasitol. 83, 793–803 (1997).CAS 
PubMed 
Article 

Google Scholar 
Curtis, L. A. & Tanner, N. L. Trematode accumulation by the estuarine gastropod Ilyanassa obsoleta. J. Parasitol. 85, 419–425 (1999).CAS 
PubMed 
Article 

Google Scholar 
Barber, K. E. & Caira, J. N. Investigation of the life cycle and adult morphology of the avian blood fluke Austrobilharzia variglandis (Trematoda: Schistosomatidae) from Connecticut. J. Parasitol. 81, 584–592 (1995).CAS 
PubMed 
Article 

Google Scholar 
Leighton, B. J. et al. Schistosome dermatitis at Crescent Beach, preliminary report. Environ. Heal. Rev. 48, 5–13 (2004).
Google Scholar 
Ferris, M. & Bacha Jr, W. J. Response of leukocytes in chickens infected with the avian schistosome Austrobilharzia variglandis (Trematoda). Avian Dis. 30, 683–686 (1986).CAS 
PubMed 
Article 

Google Scholar 
Stunkard, H. W. & Hinchliffe, M. C. The life-cycle of Microbilharzia variglandis (== Cercaría varíglandis Miller and Northup, 1926), an avian schistosome whose larvae produce’swimmer’s itch’of ocean beaches. Anat. Rec. 3, 529–530 (1951).
Google Scholar 
Stunkard, H. W. & Hinchliffe, M. C. The morphology and life-history of Microbilharzia variglandis (Miller and Northup, 1926) Stunkard and Hinchliffe, 1951, avian blood-flukes whose larvae cause” swimmer’s itch” of ocean beaches. J. Parasitol. 38, 248–265 (1952).CAS 
PubMed 
Article 

Google Scholar 
Penner, L. R. Experimental infections of avian hosts with Cercaria littorinalinae Penner, 1950. J. Parasitol. 39, 20 (1953).

Google Scholar 
Faust, E. C. Notes on Ornithobilharzia odhneri n. sp. from the Asiatic Curlew. J. Parasitol. 11, 50–54 (1924).Article 

Google Scholar 
Sousa, W. P. Interspecific antagonism and species coexistence in a diverse guild of larval trematode parasites. Ecol. Monogr. 63, 103–128 (1993).Article 

Google Scholar 
Chu, G. W. T. C. First report of the presence of a dermatitis-producing marine larval schistosome in Hawaii. Science 115, 151–153 (1952).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Canestri-Trotti, G., Fioravanti, M. L. & Pampiglione, S. Cercarial dermatitis in Italy. Helminthologia 38, 245 (2001).
Google Scholar 
Penner, L. R. Cercaria littorinalinae sp. nov., a dermatitis-producing schistosome larva from the marine snail, Littorina planaxis Philippi. J. Parasitol. 36, 466–472 (1950).CAS 
PubMed 
Article 

Google Scholar 
Abdul-Salam, J. & Sreelatha, B. S. Description and surface topography of the cercaria of Austrobilharzia sp. (Digenea: Schistosomatidae). Parasitol. Int. 53, 11–21 (2004).CAS 
PubMed 
Article 

Google Scholar 
Kinsella, J. M. & Forrester, D. J. Parasitic helminths of the common loon, Gavia immer, on its wintering grounds in Florida. Helminthol. Soc. Washingt. 66, 1–6 (1999).
Google Scholar 
Appleton, C. C. The eggs of some blood-flukes (Trematoda: Schistosomatidae) from South African birds. Afr. Zool. 17, 147–150 (1982).
Google Scholar 
Appleton, C. C. Occurrence of avian Schistosomatidae (Trematoda) in South African birds as determined by a faecal survey. Afr. Zool. 21, 60–67 (1986).
Google Scholar 
Courtney, C. H. & Forrester, D. J. Helminth parasites of the brown pelican in Florida and Louisiana. (1973).Morales, G. A., Helmboldt, C. F. & Penner, L. R. Pathology of experimentally induced schistosome dermatitis in chickens: the role of Ornithobilharzia canaliculata (Rudolphi, 1819) Odhner 1912 (Trematoda: Schistosomatidae). Avian Dis. 262–276 (1971).
Travassos, L., Freitas, J. F. & Kohn, A. Trematódeos do Brazil. Mem. Inst. Oswaldo Cruz 67, 1–886 (1969).CAS 
PubMed 

Google Scholar 
Saidov, Y. S. Gel’mintofauna ryb i ryboyadnykh ptits Dagestana (Helminthofauna of Fish and Ichthyophagous Birds of Dagestan). Candidate Thesis, VIGIS (1953).Bykhovskaya-Pavlovskaya, I. E. et al. Key to parasites of freshwater fishes of the USSR, Academy of Science of the USSR. Zool. Inc (1962).Leonov, V. A. New trematodes of ichthyophagus birds. Uchenye Zapiski Gorkovskogo Gosudarstvennogo Peda-gogicheskogo Instituta 19, 43–52 (1957).
Google Scholar 
Macro, J. K. Revision of Ornithobilharzia canaliculata (Rudolphi, 1819) (Trematoda: Schistosomatidae). Helminthologia 4, 303–311 (1963).
Google Scholar 
Bykhovskaya-Pavlovskaya, I. E. Trematode fauna of birds of Leningrad region. In Contrib. to Helminthol. Publ. to Commem. 75th Birthd. KI Skryabin.] Izd. Akad. Nauk SSSR, Moscov 85–92 (1953).Santoro, M. et al. Helminth community structure of the Mediterranean gull (Ichthyaetus melanocephalus) in Southern Italy. J. Parasitol. 97, 364–366 (2011).CAS 
PubMed 
Article 

Google Scholar 
Sanmartín, M. L., Cordeiro, J. A., Alvarez, M. F. & Leiro, J. Helminth fauna of the yellow-legged gull Larus cachinnans in Galicia, north-west Spain. J. Helminthol. 79, 361–371 (2005).PubMed 
Article 

Google Scholar 
Panova, L. G. On the trematode fauna of sea-gulls of the Don district. Trudy Leningrad. Gosudarstv. Vet. Inst. 1(1), 52–62 (1927) (in Russian).
Google Scholar 
Travassos, L. Contribucoes ao conhecimento dos Schistosomatidae. Sobre (Rudolphi, 1819). Rev. Bras. Biol. 2, 473–476 (1942).
Google Scholar 
Rind, S. The blood fluke Ornithobilharzia canaliculata (Rudolphi, 1819) (Trematoda: Schistosomatidae) from the gull Larus dominicanus at Lyttelton, New Zealand. (1984).Szidat, L. Vergleichende helminthologische Untersuchungen an den argentinischen Grossmowen Larus marinus dominicanus Lichtenstein und Larus ridibundus maculipennis Lichtenstein neuen Beobachtungen uber die Artbildung bei Parasiten. Zeitschrift für Parasitenkd. 24, 351–414 (1964).CAS 

Google Scholar 
Parona, C. & Ariola, V. Bilharzìa kowalewskii n. sp. nel Larus melanocephalus [Nota preventiva]. Atti. Soc. Ligust. Sc. Nat. e Georg 7, 114–116 (1896).
Google Scholar 
Jothikumar, N. et al. Real-time PCR and sequencing assays for rapid detection and identification of avian schistosomes in environmental samples. Appl. Environ. Microbiol. 81, 4207–4215 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shigin A.A. The helminth fauna of the Rybinsk Reservoir. Author’s abstract of dissertation, (1954).Witenberg, G. Studies on the trematode—family Heterophyidae. Ann. Trop. Med. Parasitol. 23, 131–239 (1929).Article 

Google Scholar 
Bush, A. O. & Forrester, D. J. Helminths of the white ibis in Florida. Proc. Helminthol. Soc. Wash. 43, 17–23 (1976).
Google Scholar 
Mamaev, Y. L. Helminth fauna of Galliformes and Charadriiformes in Eastern Siberia. Tr. Gelmintol. Lab. Akad. Nauk SSSR (1959). More

Study areaThere is a long history of diversified cropping patterns due to the climatic and topographic complexity in China4. Cropping intensity increases from north to south, and multiple cropping dominates in regions south of 400N4. For example, multiple cropping systems of double rice and winter wheat plus maize are popular in the Middle-lower Yangtze river plain and the Huang-Huai-Hai plain, respectively (Fig. 1)22. Three staple crops, maize, paddy rice, and wheat, are widely distributed across the country (Figure S1). These three major crops contributed to more than half (57.08%) of the total sown area in China in 2020 (http://www.stats.gov.cn/english/).Fig. 1The distribution map of cropping patterns in 2021, 9 agricultural regions and validation sites in China. Notes: A to I represented nine agricultural regions in China. (A) Middle-lower Yangtze River Plain; (B) Huang-Huai-Hai plain; (C) Northeast China; (D) Inner Mongolia and along the Great Wall; (E) Loess plateau; (F) Southwest China; (G) Southern China; (H) Gansu-Xinjiang region; (I) Qinghai-Tibet region.Full size imageMODIS images and pre-processingWe used the 500 m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance products (MOD09A1) from 2015 to 2021. Three spectral indices were calculated: the 2-band Enhanced Vegetation Index (EVI2)23, LSWI16, and Normalized Multi-band Drought Index (NMDI)24 (Fig. 2). The functions of EVI2, LSWI, and NMDI are provided in Eqs. 1–3 as follows.$${rm{EVI2}}=2.5times left({rho }_{NIR}-{rho }_{{rm{Red}}}right)/left({rho }_{NIR}+2.4times {rho }_{{rm{Red}}}+1right)$$
(1)
$${rm{LSWI}}=left({rho }_{NIR}-{rho }_{SWIR6}right)/left({rho }_{NIR}+{rho }_{SWIR6}right)$$
(2)
$$NMDI=frac{{rho }_{NIR}-left({rho }_{SWIR6}-{rho }_{SWIR7}right)}{{rho }_{NIR}+left({rho }_{SWIR6}-{rho }_{SWIR7}right)}$$
(3)
where, ρNIR, ρRed, ρSWIR6 and ρSWIR7 represented the surface reflectance values from the red (620–670 nm), Near-infrared (841–875 nm), short wave infrared band centered at 1640 nm (1628–1652 nm) and 2130 nm (2105–2155 nm), respectively.Fig. 2The workflow of the methodology: Data preprocessing, deriving cropping intensity, mapping three staple crops and obtaining annual maps of cropping patterns in China.Full size imageFor each spectral index (EVI2, LSWI, and NMDI), a daily continuous time series was developed based on the cloud-free observations using the Whittaker Smoother (WS)25. The WS smoother performed well in multiple cropping regions and therefore was applied here26.Validation data and other datasetsThe validation data in this study included the ground truth reference data and agricultural census data. The ground truth reference data were collected in major agricultural regions with GPS receivers and digital cameras during the study period (2015–2021) (Fig. 1, Table S1). For each sampling site, the geographic location and crop types were recorded. The reliability of ground survey data was improved through visual confirmation based on high-resolution images in Google Earth. Some reference sites with small field sizes were removed to considering the mixed-pixel problems of MODIS images. Finally, we obtained a total of 18,379 ground samples collected during 2015–2021 (Table S1). All the ground truth reference data were used to validate the cropping pattern data in its corresponding year. Agricultural census data were obtained from the National Statistical Bureau of China (NSBC) (http://www.stats.gov.cn/english/), which was collected through sampling statistics. The crop calendar data from agro-meteorological stations recorded the sowing, seedling, tillering, heading, and harvesting dates of winter wheat (210 sites) or spring wheat (90 sites). The calendar data were applied to establish the trend surfaces of key phenological stages of winter wheat and spring wheat, respectively. The crop calendar data were provided by the National Meteorological Information Center, China Meteorological Administration.The cropland distribution data were derived from the 30 m GlobeLand30 global land cover data in 202027. The total accuracy of GlobeLand30 in 2020 is 85.72%, and the Kappa coefficient is 0.82 (www.globallandcover.com). To correspond to MODIS images, the 30 m cropland pixels from GlobeLand30 data were spatially aggregated to a 500 m cropland fraction map. For simplification, we classified pixel purity of MODIS pixels into three groups: cropland percentages of >90%, 50–90%, and More

A white truffle (Tuber magnatum Pico) in the laboratory of Robin Pépinières, a nursery in Saint Laurent-du-Cros, France.Philippe Desmazes/AFP via Getty Images

On 10 October 2019, a dog began pawing excitedly at the ground beneath a young oak tree in western France. Its owner eased it out of the way and pulled an Italian white truffle (Tuber magnatum Pico) from the earth. Knobbly, covered in soil and about the size of a hen’s egg, it was not much to look at, but the fungal discovery nonetheless generated ripples of excitement among researchers, chefs and truffle growers worldwide.That’s not just because T. magnatum is the most expensive truffle species, for which wealthy gastronomes are willing to pay up to US$11,000 per kilogram. Although more than 90% of the also highly sought-after black Périgord truffles (Tuber melanosporum) served in restaurants today are farmed, previous attempts to cultivate their more elusive white counterparts had failed.That changed three years ago, when the Lagotto Romagnolo, the Italian dog breed commonly used as a truffle hunter, unearthed the first Italian white truffle confirmed to have been cultivated outside its natural range. The dog made the find at its owner’s plantation in the Nouvelle Aquitaine region of France, but the precise location is being kept secret to deter thieves.Scientists at a laboratory run jointly by France’s National Research Institute for Agriculture, Food and the Environment (INRAE) and the University of Lorraine in Nancy reported1 that since that first T. magnatum truffle was unearthed, two more were found at the site in 2019 and four in 2020. In an article published last month in Le Trufficulteur, the magazine of the French Federation of Truffle Growers, the researchers report the cultivation of 26 truffles last year2.“I was very happy to hear these results,” says Alessandra Zambonelli, a mycologist at the University of Bologna, Italy, who has studied Italian white truffles for more than 40 years, and whose own attempts to cultivate them in the 1980s failed. “I was sure it was possible to cultivate T. magnatum, but only now do we have the scientific proof.”The INRAE project is helping growers to better understand the optimal conditions for cultivating Italian white truffles. Some scientists think the breakthrough could help to reverse falls in harvests of wild truffles that have been linked to climate change. Researchers also hope the work will help them to answer outstanding questions about the life cycle of the species and understand why it is so much harder to farm than are other truffles.Farming failureTuber magnatum’s natural range is more limited than those of other sought-after truffles, growing as it does in parts of Italy, southeastern France, the Balkans and Switzerland. It is highly prized for its intense, some say intoxicating, aroma and flavour, variously described as reminiscent of garlic, fermented cheese and methanethiol — the additive that gives domestic gas its smell. Prices fluctuate in line with supply, which varies according to climatic conditions. These hit an all-time high in 2021, when US prices were more than triple what they were in 2019.Most land plants form symbiotic relationships with fungi to access extra water and mineral nutrients. In return, the plants provide their fungal partners, which grow around and into their root tips, with carbon-rich nutrients. These associations are known as mycorrhizae. What most people call truffles are, in fact, just the spore-containing fruiting bodies of the fungus.In the 1970s, French scientists successfully induced Périgord truffles to form mycorrhizal associations with tree seedlings by inoculating the seedlings with their spores. The same technique was used at the time to produce trees with T. magnatum mycorrhizae. More than 500,000 of these were planted in Italy. But when researchers later began using the polymerase chain reaction (PCR) technique to accurately identify truffle mycorrhizae, fruiting bodies and the root-like mycelia, it became clear that this species’ physical characteristics had been poorly described, and that, as a result, many of the trees had in fact partnered with less sought-after truffle species.Some sites in Italy did produce T. magnatum truffles 15–20 years after planting, but only in areas where the species occurs naturally. “It is likely that those found so long after being planted came from chance colonization of host plants by native T. magnatum strains in the environment,” says Claudia Riccioni, a plant and fungal biologist at Italy’s Institute of Biosciences and BioResources in Perugia.After the Italian white and Périgord truffles, the next most sought-after species is the summer truffle (Tuber aestivum), which grows in many European countries and sells for much less than its more highly regarded cousins. Plantations of T. aestivum have been established in France, Italy, Scandinavia, Germany and elsewhere.Buried treasuresIn 1999, INRAE researchers joined forces with Robin Pépinières, a nursery based in Saint-Laurent-du-Cros, southern France. Genetic analysis confirmed that the nursery had produced trees that partnered with T. magnatum, leading, from 2008, to the establishment of plantations in France1. In 2018, the INRAE group selected five of these, all outside the part of southeastern France where T. magnatum grows naturally, to see whether it had become established and to record the conditions under which any truffle fruiting bodies were produced.PCR tests confirmed the fungus’s mycelia were present in soil samples taken from near the trees at four of the locations. The first three truffles, found in Nouvelle Aquitaine, were discovered four-and-a-half years after the inoculated trees had been planted. Further PCR tests confirmed they were T. magnatum. The 26 truffles found in 2021 were unearthed beneath 11 different trees, with 5 under one of them. The largest weighed 150g.Mycologists Claude Murat and Cyrille Bach, both members of the INRAE–University of Lorraine lab, were present when one of the four fruiting bodies produced in 2020 was discovered. Asked how sure he was that the truffle grew in the plantation and hadn’t originated elsewhere, Murat said: “I’m 100% sure. We could see the soil had not been disturbed and that grasses were growing there.”Mycorrhizal mysteryPrevious attempts to cultivate Italian white truffles failed in part because their life cycle remains poorly understood. Twenty years ago, it was widely assumed that truffles, including T. magnatum, were self-fertile. However, research then showed they have one of two ‘mating type’ genes, and that the mycelia of individuals of different mating types must meet for reproduction to occur3.A remaining unresolved puzzle is why researchers have found T. magnatum mycorrhizae much harder to locate than those of other truffles. Mycologist Paul Thomas works to establish joint ventures with truffle growers through Mycorrhizal Systems, his UK-based company. He inoculated host trees with T. magnatum, and generated mycorrhizae at the company’s greenhouses in Preston, but these did not last long, so the trials were abandoned.“When you find fruiting bodies, you quite often can’t find mycorrhizae,” says Thomas, “and sometimes you get mycorrhizae but no fruiting bodies. Perhaps, in the case of T. magnatum we’ve become too focused on linking truffle production to mycorrhizae.”When Zambonelli’s group analysed soil from four Italian white-truffle sites over three years, they found a correlation between production of fruiting bodies and a location’s concentration of DNA from T. magnatum mycelia4. Some researchers began to suspect that the host–fungus relationship might not be as important as previously thought, and that T. magnatum might be saprotrophic, meaning that it digests dead or decaying organic matter.However, a 2018 comparison5 of the genomes of truffle species with those of several saprotrophic fungi showed this to be unlikely. “T. magnatum has very few plant-wall-degrading enzymes, which does not support the saprotrophic hypothesis,” says Riccioni, one of the study’s authors. Other researchers have tried to explain the elusiveness of T. magnatum mycorrhizae by pointing out that other truffles can form endophytic relationships with plants in which they which live throughout them, not just at their roots.Murat wonders whether he and others have just been looking in the wrong place. “We look on the roots down to 20 centimetres, never looked at 50 centimetres, even though we know other mycorrhizae can be found at those depths,” he says. “Or perhaps they produce mycorrhizae just for a very short time; we just don’t know.”A growing body of research shows that microorganisms have important roles in truffle life cycles. A 2015 review found that bacteria in T. magnatum fruiting bodies help to create the truffles’ odours6. Zambonelli and her colleagues found that bacteria in T. magnatum fruiting bodies can fix nitrogen for nutritional purposes7. Another Italian team found that microbes commonly associated with white truffles are involved in fruiting-body maturation8. “Some bacteria could also help T. magnatum become established at tree roots and fruiting-body formation,” says Zambonelli.A changing climateGathering accurate statistics on truffle yields before cultivation is difficult, although it is widely accepted that these fell significantly during the twentieth century. One study reports that Périgord truffle harvests in France collapsed from 500–1,000 tonnes annually in the 1900s to 10–50 tonnes by the 2000s. Yields in Italy declined, too, but not by as much, and mostly in the first half of the twentieth century9.The reasons for falls in truffle harvests are complex and vary by location, but researchers have blamed depopulation, loss of knowledge about truffle hunting and deforestation. Some of the older men who featured in the highly rated 2020 documentary The Truffle Hunters, set in Piedmont, northern Italy, say they will take what they know about truffles to the grave rather than pass it on to younger generations because of the greed they see in the industry.

A canine forager and his owner who feature in the 2020 documentary The Truffle Hunters, set in northern Italy.BFA/Alamy

More recently, some researchers have highlighted climate change as another cause of declining yields. Truffle gastronomy and tourism are economically and culturally important in places where truffles occur naturally. That’s certainly true in parts of Croatia, where, from 2003 to 2013, reported annual harvests were 1–3 tonnes for Italian white truffles and 1–6 tonnes for Périgords, except for the years 2009, 2010 and 2013, when they fell to 0.1–0.5 tonnes.Field mycologist Željko Žgrablić at the Ruđer Bošković Institute in Zagreb says truffles have become harder to find on the Istria peninsula, where he grew up, in part because of increasingly frequent and severe droughts. Yields have also been affected by big increases in wild-boar populations as a result of warmer winters. The animals forage for the truffles and reduce human harvests, and, according to Žgrablić, also damage the fungus’s mycelia. “The climate has become unpredictable, with more extremes,” says Žgrablić. “It’s hard to prove it, but I think we have fewer white truffles as a result.”In a 2019 study, Thomas analysed annual Périgord truffle yields in the Mediterranean region over a 36-year period10. He concluded that decreased summer rain and increased summer temperatures significantly reduced subsequent winter harvests. He forecast declines of 78–100% in harvests between 2071 and 2100 as a result of further predicted warming. “White truffles need relatively moist soil, so in its natural range it might be okay in mountainous areas but particularly vulnerable in areas where falls in rainfall are predicted,” says Thomas.Future farmingBeyond producing the first confirmed cultivated white truffles, the INRAE project is also generating data on the optimal conditions for production. The soil temperature at the site that yielded the truffles was around 20 °C in the summer, and Murat says that the team’s tests suggest white truffles need more water than do Périgords.So could the increasing knowledge of how best to get Italian white truffles to grow be adopted more widely to help reverse declining yields? Fruiting bodies have been confirmed at only one site, so other growers are waiting to see whether this success will be repeated elsewhere. Murat is in the process of trying to confirm recent claims from two other owners that they, too, have cultivated T. magnatum truffles.Thomas is downbeat about the future of Italian white-truffle cultivation. “In parts of Spain, more and more orchards can no longer irrigate because of water shortages. Already, in France, it is hard to get permission to extract water from rivers for irrigation, and that’s only going to get worse.”Oak trees inoculated with Périgord- and summer-truffle spores are due to be planted later this year in Croatia as part of a collaboration run by the state-owned Croatian Forests. If successful, the group could try white truffles. Žgrablić, who is part of the project, is also advising an enthusiast who planted 650 seedlings inoculated with T. magnatum, also in Croatia, earlier this year. “We’re seeing increasing interest from private investors in cultivating Italian white truffles,” he says. “There is certainly a lot of potential, but what the results will be, I can’t tell.”Alongside his research work, Murat acts as a scientific consultant for WeTruf, a company he co-founded in Nancy that provides advice and monitoring services for truffle farmers. He is cautious about the potential for white-truffle cultivation, if optimistically so. “We are careful when people tell us they want to start big white-truffle plantations,” says Murat. “I tell them ‘we are only at the beginning, we don’t know if it will succeed or not’. But I think there will be more and more plantations, and, if they apply good management practices, I hope, more and more truffles.” More

Britto, D. T., Siddiqi, M. Y., Glass, A. D. M. & Kronzucker, H. J. Futile transmembrane NH4+ cycling: A cellular hypothesis to explain ammonium toxicity in plants. PNAS 98(7), 4255–4258 (2001).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Britto, D. T. & Konzucker, H. J. NH4 + toxicity in higher plants: a critical review. J. Plant Physiol. 159, 567–584 (2002).CAS 
Article 

Google Scholar 
Müller, T., Walter, B., Wirtz, A. & Burkovski, A. Ammonium toxicity in bacteria. Curr. Microbiol. 52, 400–406 (2006).PubMed 
Article 
CAS 

Google Scholar 
Mayes, M. A., Alexander, H. C., Hopkins, D. L. & Latvaitis, P. B. Acute and chronic toxicity of ammonia to freshwater fish: a site-specific study. Environ. Toxicol. Chem. 5, 437–442 (1986).CAS 
PubMed 
Article 

Google Scholar 
Archer, M. C. Hazards of nitrate, nitrite, and n-nitroso compounds in human nutrition. Nutr. Toxicol. 1, 329–381 (2012).
Google Scholar 
Brione, E., Martin, G. & Morvan, J. Non-destructive technique for elimination of nutrients from pig manure, 33–37. In Horan, N. J., Lowe, P. & Stentiford, E. I. (ed.), Nutrient removal from wastewaters. Techonomic Publishing Co. (Lancaster 1994).Butler, D., Friedler, E. & Gatt, K. Characterising the quantity and quality of domestic wastewater inflows. Wal. Sci. Tech. 31(7), 13–24 (1995).CAS 
Article 

Google Scholar 
Mahne, I., Prinčič, A. & Megušar, F. Nitrification/denitrification in nitrogen high-strength liquid wastes. Water Res. 30, 2107–2111 (1996).CAS 
Article 

Google Scholar 
Arp, D. J., Sayavedra-Soto, L. A. & Hommes, N. G. Molecular biology and biochemistry of ammonia oxidation by Nitrosomonas Europaea. Arch. Microbiol. 178, 250–255. https://doi.org/10.1007/s00203-002-0452-0 (2002).CAS 
Article 
PubMed 

Google Scholar 
Dalton, H. Ammonia oxidation by the methane oxidizing bacterium Methylococcuscapsulatus strain bath. Arch. Microbiol. 114(3), 273–279 (1977).CAS 
Article 

Google Scholar 
Daum, M. et al. Physiological and molecular biological characterization of ammonia oxidation of the heterotrophic nitrifier pseudomonas putida. Curr Microbiol 37, 281–288 (1998).CAS 
PubMed 
Article 

Google Scholar 
Do, H. et al. Simultaneous effect of temperature, cyanide and ammonia-oxidizing bacteria concentrations on ammonia oxidation. J. Ind. Microbiol. Biotechnol. 35, 1331–1338. https://doi.org/10.1007/s10295-008-0415-9 (2008).MathSciNet 
CAS 
Article 
PubMed 

Google Scholar 
Lin, Y. et al. Physiological and molecular biological characteristics of heterotrophic ammonia oxidation by Bacillus sp. LY. World J. Microbiol. Biotechnol. 26, 1605–1612 (2010).ADS 
CAS 
Article 

Google Scholar 
Snider, M. J. & Wolfenden, R. The rate of spontaneous decarboxylation of amino acids. J. Am. Chem. Soc. 122(46), 11507–11508 (2000).CAS 
Article 

Google Scholar 
Zamora, R., León, M. M. & Hidalgo, F. J. Oxidative versus non-oxidative decarboxylation of amino acids: conditions for the preferential formation of either strecker aldehydes or amines in amino acid/lipid-derived reactive carbonyl model systems. J. Agric. Food Chem. 63(36), 8037–8043 (2015).CAS 
PubMed 
Article 

Google Scholar 
Perez, M. et al. The relationship among tyrosine decarboxylase and agmatine deiminase pathways in enterococcus faecalis. Front. Microbiol. https://doi.org/10.3389/fmicb.2017.02107/full (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Chen, B. Y., Lin, K. W., Wang, Y. M. & Yen, C. Y. Revealing interactive toxicity of aromatic amines to azo dye decolorizer Aeromonas hydrophila. J. Hazard Mater. 166(1), 187–194 (2009).CAS 
PubMed 
Article 

Google Scholar 
Greim, H., Bury, D., Klimisch, H. J., Oeben-Negele, M. & Ziegler-Skylakakis, K. Toxicity of aliphatic amines: structure-activity relationship. Chemosphere 36(2), 271–295. https://doi.org/10.1016/s0045-6535(97)00365-2 (1998).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Newsome, L. D., Johnson, D. E., Lipnick, R. L., Broderius, S. J. & Russom, C. L. A QSAR study of the toxicity of amines to the fathead minnow. Sci. Total Environ. 109–110, 537–551 (1991).ADS 
PubMed 
Article 

Google Scholar 
Pinheiro, H. M., Touraud, E. & Thomas, O. Aromatic amines from azo dye reduction: status review with emphasis on direct UV spectrophotometric detection in textile industry wastewaters. Dyes Pigm. 61, 121–139 (2004).CAS 
Article 

Google Scholar 
Poste, A. E., Grung, M. & Wright, R. F. Wright Amines and amine-related compounds in surface waters: A review of sources, concentrations and aquatic toxicity. Sci. Total Environ. 481, 274–279 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ramos, E. U., Vaal, M. A. & Hermens, J. L. M. Interspecies sensitivity in the aquatic toxicity of aromatic amines. Environ. Toxicol. Pharmacol. 11(3–4), 149–158 (2002).CAS 
PubMed 
Article 

Google Scholar 
Nicholas, G. A., Peter, J. B., Milton, W. G. & Stan, V. G. Microbial decomposition of wood in streams: distribution of microflora and factors affecting [14C] lignocellulose mineralization. Appl. Environ. Microbiol. 46(6), 1409–1416 (1983).Article 

Google Scholar 
Okabe, S., Kindaichi, T. & Ito, T. Fate of 14C-labeled microbial products derived from nitrifying bacteria in autotrophic nitrifying biofilms. Appl. Environ. Microbiol. 71(7), 3987–3994 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Qiao, Z. et al. Microbial Heterotrophic Nitrification-Aerobic Denitrification Dominates Simultaneous Removal of Aniline and Ammonium in Aquatic Ecosystems. Water Air Soil Pollut. https://doi.org/10.1007/s11270-020-04476-3 (2020).Article 

Google Scholar 
Celik, A. Oxytetracycline and paracetamol biodegradation performance in the same enriched feed medium with aerobic nitrification/anaerobic denitrification SBR. Bioprocess Biosyst. Eng. 44, 1649–1658 (2021).CAS 
PubMed 
Article 

Google Scholar 
Spataru, P., Povar, I., Mosanu, E. & Trancalan, A. Study of stable nitrogen forms in natural surface waters in the presence of mineral substrates. Chem. J. Moldova 10, 26–32 (2015).CAS 
Article 

Google Scholar 
Spataru, P. et al. Influence of the interaction of calcium carbonate particles with surfactants on the degree of water pollution in small rivers. Ecol. Process. https://doi.org/10.1186/s13717-017-0086-4#article-dates-history (2017).Article 

Google Scholar 
Spataru, P., Povar, I., Lupascu, T., Alder, A. C. & Mosanu, E. Study of nitrogen forms in seasonal dynamics and kinetics of nitrification and denitrification in Prut and Nistru river waters. Environ. Eng. Manag. J. 17(7), 1711–1719 (2018).CAS 
Article 

Google Scholar 
Cui, Z. G., Cui, Y. Z., Cui, C. F., Chen, Z. & Binks, B. P. Aqueous foams stabilized by in situ surface activation of CaCO3 Nanoparticles via Adsorption of Anionic Surfactant. Langmuir 26(15), 12567–12574 (2010).CAS 
PubMed 
Article 

Google Scholar 
Cui, Z.-G., Cui, C.-F., Zhu, Y. & Binks, B. P. Multiple phase inversion of emulsions stabilized by in situ surface activation of CaCO3 nanoparticles via adsorption of Fatty acids. Langmuir 28(1), 314–320 (2012).PubMed 
Article 
CAS 

Google Scholar 
Tanaka, T. et al. Biodegradation of endocrine-disrupting chemical aniline by microorganisms. J. Health Sci. 55(4), 625–630 (2009).CAS 
Article 

Google Scholar 
Ahmed, S. et al. Isolation and characterization of a bacterial strain for aniline degradation. Afr. J. Biotechnol. 9(8), 1173–1179 (2010).CAS 
Article 

Google Scholar 
Spataru, P. Transformations of organic substances in surface waters of Republic of Moldova. PhD Dissertation, State University of Moldova (2011).Sandu, M. et al. The dynamic of nitrification process in the presence of cationic surfactants. Proc. SIMI Bucharest 1, 277–281 (2007).
Google Scholar 
Spataru, P., Fernandez, F., Povar, I. & Spataru, T. Behavior of nitrogen soluble forms in natural water in the presence of anionic and cationic surfactants and mineral substrates. Adv. Sci. Eng. 11(2), 70–77. https://doi.org/10.32732/ase.2019.11.2.70 (2019).Article 

Google Scholar 
Reifferscheid, G., Buchinger, S., Cao, Z. & Claus, E. Identification of mutagens in freshwater sediments by the Ames-fluctuation assay using nitroreductase and acetyltransferase overproducing test strains. Environ. Mol. Mutagen. https://doi.org/10.1002/em.20638 (2011).Osadchyy, V., Nabyvanets, B., Linnik, P., Osadcha, N. & Nabyvanets, Y. Characteristics of Surface Water Quality in Processes Determining Surface Water Chemistry, Springer Link, 1–9. https://doi.org/10.1007/978-3-319-42159-9_1 (2016).Matveeva, N. P., Klimenko, O. A. & Trunov, N. M. Simulation of self-purification of natural treatment of organic pollutants in the laboratory, Gidrometeoizdat, Leningrad, 26–31 (in Russian) (1988).ISO 7150-1:2001.Water quality – Determination of ammonium – Spectrometric method.ISO 8466-1:1990. Water quality – Calibration and evaluation of analytical methods and estimation of performance characteristics, 1: Statistical evaluation of the linear calibration function.SR ISO 7890-3:2000 Water quality – The determination of the content of nitrates, 3: The spectrometric method with sulfosalicylic acid.SM SR EN 26777:2006 Water quality – determination of the content of nitrites. The method of the spectrometry of molecular absorption.Sandu, M. et al. Method for nitrate determination in water in the presence of nitrite. Chem. J. Moldova 9, 8–13 (2014).CAS 
Article 

Google Scholar 
Bentzon-Tilia, M. et al. Significant N2 fixation by heterotrophs, photoheterotrophs and heterocystous cyanobacteria in two temperate estuaries. ISME 9, 273–285 (2015).CAS 
Article 

Google Scholar 
Farnelid, H. et al. Active nitrogen-fixing heterotrophic bacteria at and below the chemocline of the central Baltic Sea. ISME J. 7, 1413–1423. https://doi.org/10.1038/ismej.2013.26 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Nagatani, H., Shimizu, M. & Valentine, R. C. The mechanism of ammonia assimilation in nitrogen fixing bacteria. Arch. Mikrobiol. 79, 164–175 (1971).CAS 
PubMed 
Article 

Google Scholar 
Allen, A. E., Booth, M. G., Verity, P. G. & Frischer, M. E. Influence of nitrate availability on the distribution and abundance of heterotrophic bacterial nitrate assimilation genes in the Barents Sea during summer. Aquat. Microb. Ecol. 39, 247–255 (2005).Article 

Google Scholar 
Davidson, K. et al. The influence of the balance of inorganic and organic nitrogen on the trophic dynamics of microbial food webs. Limnol. Oceanogr. 52(5), 2147–2163 (2007).ADS 
CAS 
Article 

Google Scholar 
Domingues, R. B., Barbosa, A. B., Sommer, U. & Galvão, H. M. Ammonium, nitrate and phytoplankton interactions in a freshwater tidal estuarine zone: potential effects of cultural eutrophication. Aquat. Sci. 73, 331–343. https://doi.org/10.1007/s00027-011-0180-0 (2011).CAS 
Article 

Google Scholar 
Hollibaugh, J. T., Gifford, S., Sharma, S., Bano, N. & Moran, M. A. Metatranscriptomic analysis of ammonia-oxidizing organisms in an estuarine bacterioplankton assemblage. ISME J. 5, 866–878 (2011).CAS 
PubMed 
Article 

Google Scholar 
Yang, J. et al. Ecogenomics of zooplankton community reveals ecological threshold of ammonia nitrogen. Environ. Sci. Technol. 51, 3057–3064 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Jones, Z. L., Jasper, J. T., Sedlak, D. L. & Sharpa, J. O. Sulfide-Induced dissimilatory nitrate reduction to ammonium supports anaerobic Ammonium oxidation (anammox) in an open-water unit process wetland. Appl. Environ. Microbiol. 83(15), 1–14 (2017).Article 

Google Scholar 
Nizzoli, D., Carraro, E., Nigro, V. & Viaroli, P. Effect of organic enrichment and thermal regime on denitrification and dissimilatory nitrate reduction to ammonium (DNRA) in hypolimnetic sediments of two lowland lakes. Water Res. 4, 2715–2724 (2010).Article 
CAS 

Google Scholar 
Rutting, T., Boeckx, P., Muller, C. & Klemedtsson, L. Assessment of the importance of dissimilatory nitrate reduction to ammonium for the terrestrial nitrogen cycle. Biogeosciences 8, 1779–1791 (2011).ADS 
Article 
CAS 

Google Scholar 
Roberts, K. L., Kessler, A. J., Grace, M. R. & Cook, P. L. M. Increased rates of dissimilatory nitrate reduction to ammonium (DNRA) under oxic conditions in a periodically hypoxic estuary. Cosmochim. Acta 133, 313–324 (2014).ADS 
CAS 
Article 

Google Scholar 
Broman, E. et al. Active DNRA and denitrification in oxic hypereutrophic waters. Water Res. 194, 116954 (2021).CAS 
PubMed 
Article 

Google Scholar 
Han, X., Peng, S., Zhang, L., Lu, P. & Zhang, D. The Co-occurrence of DNRA and Anammox during the anaerobic degradation of benzene under denitrification. Chemosphere 247, 125968 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Rantanen, P.-L. et al. Decreased natural organic matter in water distribution decreases nitrite formation in non-disinfected conditions, via enhanced nitrite oxidation. Water Res. X 9, 100069 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Raimonet, M., Cazier, T., Rocher, V. & Laverman, A. M. Nitrifying kinetics and the persistence of nitrite in the Seine River, France. J. Environ. Qual. https://doi.org/10.2134/jeq2016.06.0242 (2017).Article 
PubMed 

Google Scholar 
Philip, S., Laanbroek, H. J. & Verstraete, W. Origin, causes and effects of increased nitrite concentrations in aquatic environments. Rev. Environ. Sci. Biotechnol. 1, 115–141. https://doi.org/10.1023/A:1020892826575 (2002).Article 

Google Scholar 
Baneshi, M. M. et al. Aniline bio-adsorption from aqueous solutions using dried activated sludge: Aniline bio-adsorption from aqueous solutions using dried activated sludge. Poll. Res. 36(3), 403–409 (2017).CAS 

Google Scholar 
Börnick, H., Eppinger, P., Grischek, T. & Worch, E. Simulation of biological degradation of aromatic amines in river bed sediments. Water Res. 35(3), 619–624 (2001).PubMed 
Article 

Google Scholar 
Norzaee, S., Djahed, B., Khaksefidi, R. & Mostafapour, F. K. Photocatalytic degradation of aniline in water using CuO nanoparticles. Water Supply 66(3), 178–185 (2017).Article 

Google Scholar 
Paździor, K. et al. Integration of nanofiltration and biological degradation of textile wastewater containing azo dye. Chemosphere 75, 250–255 (2009).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Babcock, R. W., Chen, W., Ro, K. S., Mah, R. A. & Stenstrom, M. K. Enrichment and kinetics of biodegradation of 1-naphthylamine in activated sludge. Appl. Microbiol. Biotechnol. 39, 264–269 (1993).CAS 
Article 

Google Scholar 
Shin, K. A. & Spain, J. C. Pathway and evolutionary implications of diphenylamine biodegradation by Burkholderia sp. Strain JS667. Appl. Microbiol. Biotechnol. 75(9), 2694–2704 (2009).ADS 
CAS 

Google Scholar  More

Effects of aqueous plant extracts on germination and early growth of B. bipinnata by in vitro bioassaysSeed germination and seedling growth of B. bipinnata were investigated after treatment with DT, RC, PT, and JG aqueous extracts to explore the allelopathic effects of these plant species. The pH of the aqueous extracts corresponded to 6.62 for DL, 5.59 for RC, 7.20 for PT, and 7.42 for JG, with no significant difference in pH values between DL and RC extracts or between PT and JG extracts; however, the pH of DL and RC extracts differed significantly (p  1000 cm−1 were attributed to the C − H out-of-plane bending vibration of aliphatic alkenes and aromatic benzene rings49,50.The range between 1800 and 600 cm−1 of the infrared spectra was selected for the PCA, as it is the most representative region of the differences present in the spectra. In the PC1 versus PC2 score plot (Fig. 6), representing 85.78% of the total variance, it is possible to observe the separation of the samples into three distinct groups. The samples of DL and RC extracts formed two distinct groups, since they showed a significant separation in the PC1 axis, with positive and negative scores for PC1, respectively. The samples of JG and PT extracts formed a single group, remaining superimposed and located close to the zero value of PC1, indicating intermediate spectral characteristics in relation to the DL and RC extracts. These results may be correlated with the allelopathic activity of these extracts, since the RC extract showed better performance, followed by the JG and PT extracts, with intermediate performance, and the DL extract showed lower activity compared to the others.Figure 6PCA score plot (PC1 × PC2) of D. lacunifera (DL), R. communis (RC), P. tuberculatum (PT), and J. gossypiifolia (JG) extracts.Full size imageThe PC1 loading plot (Fig. S1) has as main contributors the negative bands associated with signals at approximately 1732, 1595, 1404, 1200–1025, 1049, and 780–600 cm−1, which significantly contributed to the separation of RC extract samples that presented greater intensity than in DL extract samples. On the other hand, the positive bands in PC1 in the region of 780–970 cm−1 were more intense in DL extracts. When evaluating the negative region of the PC1 loading plot, it is possible to observe that the functional groups responsible for the discrimination are probably those present in flavonoids and phenolic acids, corroborating the data in the literature that demonstrate the identification of these compound classes in RC leaves, such as gallic acid, quercetin, gentisic acid, rutin, epicatechin, ellagic acid, etc.51,52,53.The presence of flavonoids can be observed due to the stretching of C=O at approximately 1732 cm−1, C=C of aromatics at 1600 cm−1, C–O at 1200–1000 cm−1, and O–H at 3284–3174 cm−1. Phenolic acids can be verified due to stretching of the O–H of carboxylic acid, C=O and aromatic ring, as well as the C − H out-of-plane bending vibration of aromatic benzene ring at  More

Pigliucci, M. Phenotypic plasticity: Beyond Nature and Nurture (Johns Hopkins Univ. Press, 2001).Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).Article 

Google Scholar 
Aerts, R., Boot, R. G. A. & Van Der Aart, P. J. M. The relation between above- and belowground biomass allocation patterns and competitive ability. Oecologia 87, 551–559 (1991).CAS 
Article 

Google Scholar 
Ashton, I. W., Miller, A. E., Bowman, W. D. & Suding, K. N. Niche complementarity due to plasticity in resource use: plant partitioning of chemical N forms. Ecology 91, 3252–3260 (2010).Article 

Google Scholar 
Pfennig, D. W., Rice, A. M. & Martin, R. A. Ecological opportunity and phenotypic plasticity interact to promote character displacement and species coexistence. Ecology 87, 769–779 (2006).Article 

Google Scholar 
van Kleunen, M. & Fischer, M. Adaptive evolution of plastic foraging responses in a clonal plant. Ecology 82, 3309–3319 (2001).Article 

Google Scholar 
Relyea, R. A. Competitor-induced plasticity in tadpoles: consequences, cues, and connections to predator-induced plasticity. Ecol. Monogr. 72, 523–540 (2002).Article 

Google Scholar 
Broekman, M. J. E. et al. Signs of stabilisation and stable coexistence. Ecol. Lett. 22, 1957–1975 (2019).Article 

Google Scholar 
Callaway, R. M., Pennings, S. C. & Richards, C. L. Phenotypic plasticity and interactions among plants. Ecology 84, 1115–1128 (2003).Article 

Google Scholar 
Turcotte, M. M. & Levine, J. M. Phenotypic plasticity and species coexistence. Trends Ecol. Evol. 31, 803–813 (2016).Article 

Google Scholar 
Chesson, P. in Unity in Diversity: Reflections on Ecology after the Legacy of Ramon Margalef (eds F. Valladares et al.) 119–164 (Fundación Banco Bilbao Vizcaya Argentaria, 2008).Ellner, S. P., Snyder, R. E. & Adler, P. B. How to quantify the temporal storage effect using simulations instead of math. Ecol. Lett. 19, 1333–1342 (2016).Article 

Google Scholar 
Vasseur, D. A., Amarasekare, P., Rudolf, V. H. W. & Levine, J. M. Eco-evolutionary dynamics enable coexistence via neighbor-dependent selection. Am. Nat. 178, E96–E109 (2011).Article 

Google Scholar 
Hendry, A. P. Key questions on the role of phenotypic plasticity in eco-evolutionary dynamics. J. Hered. 107, 25–41 (2016).Article 

Google Scholar 
Hart, S. P., Turcotte, M. M. & Levine, J. M. Effects of rapid evolution on species coexistence. Proc. Natl Acad. Sci. USA 116, 2112–2117 (2019).CAS 
Article 

Google Scholar 
Hart, S. P., Freckleton, R. P. & Levine, J. M. How to quantify competitive ability. J. Ecol. 106, 1902–1909 (2018).Article 

Google Scholar 
Grainger, T. N., Levine, J. M. & Gilbert, B. The invasion criterion: a common currency for ecological research. Trends Ecol. Evol. 34, 925–935 (2019).Article 

Google Scholar 
Letten, A. D., Ke, P.-J. & Fukami, T. Linking modern coexistence theory and contemporary niche theory. Ecol. Monogr. 87, 161–177 (2017).Article 

Google Scholar 
Kraft, N. J. B., Godoy, O. & Levine, J. M. Plant functional traits and the multidimensional nature of species coexistence. Proc. Natl Acad. Sci. USA 112, 797–802 (2015).CAS 
Article 

Google Scholar 
Pfennig, D. W. & Murphy, P. J. How fluctuating competition and phenotypic plasticity mediate species divergence. Evolution 56, 1217–1228 (2002).Article 

Google Scholar 
Adler, P., HilleRisLambers, J. & Levine, J. A niche for neutrality. Ecol. Lett. 10, 95–104 (2007).Article 

Google Scholar 
Barabás, G., D’Andrea, R. & Stump Simon, M. Chesson’s coexistence theory. Ecol. Monogr. 88, 277–303 (2018).Article 

Google Scholar 
Pfennig, D. W. & Pfennig, K. S. Evolution’s Wedge: Competition and the Origins of Diversity (Univ. California Press, 2012).Ayala, F. J. Reversal of dominance in competing species of Drosophila. Am. Nat. 100, 81–83 (1966).Article 

Google Scholar 
Pease, C. M. On the evolutionary reversal of competitive dominance. Evolution 38, 1099–1115 (1984).Article 

Google Scholar 
Pimentel, D., Feinberg, E. H., Wood, P. W. & Hayes, J. T. Selection, spatial distribution, and the coexistence of competing fly species. Am. Nat. 99, 97–109 (1965).Article 

Google Scholar 
Lankau, R. A. & Strauss, S. Y. Mutual feedbacks maintain both genetic and species diversity in a plant community. Science 317, 1561–1563 (2007).CAS 
Article 

Google Scholar 
Kunstler, G. et al. Plant functional traits have globally consistent effects on competition. Nature 529, 204–207 (2016).CAS 
Article 

Google Scholar 
Stuart, Y. E. & Losos, J. B. Ecological character displacement: glass half full or half empty? Trends Ecol. Evol. 28, 402–408 (2013).Article 

Google Scholar 
Abrams, P. A. Alternative models of character displacement and niche shift. 2. Displacement when there is competition for a single resource. Am. Nat. 130, 271–282 (1987).Article 

Google Scholar 
Chevin, L. M., Lande, R. & Mace, G. M. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biol. 8, e1000357 (2010).Article 

Google Scholar 
Harmon, E. A. & Pfennig, D. W. Evolutionary rescue via transgenerational plasticity: evidence and implications for conservation. Evol. Dev. 23, 292–307 (2021).Article 

Google Scholar 
Forsman, A. Rethinking phenotypic plasticity and its consequences for individuals, populations and species. Heredity 115, 276–284 (2015).CAS 
Article 

Google Scholar 
Brass, D. P. et al. Phenotypic plasticity as a cause and consequence of population dynamics. Ecol. Lett. 24, 2406–2417 (2021).Article 

Google Scholar 
Macarthur, R. H. & Levins, R. The limiting similarity, convergence, and divergence of coexisting species. Am. Nat. 101, 377–385 (1967).Article 

Google Scholar 
Beverton, R. J. H. & Holt, S. J. On the Dynamics of Exploited Fish Populations (UK Ministry of Agriculture, Fisheries and Food, 1957).Landolt, E. Biosystematic Investigations in the Family of Duckweeds (Lemnaceae), Vol. 2: The Family of Lemnaceae—A Monographic Study, Vol.1 (Geobotanischen Institute, ETH Zürich, 1986).Wang, W. et al. The Spirodela polyrhiza genome reveals insights into its neotenous reduction fast growth and aquatic lifestyle. Nat. Commun. 5, 3311 (2014).CAS 
Article 

Google Scholar 
Hoagland, D. R. & Arnon, D. I. The Water-Culture Method for Growing Plants without Soil (College of Agriculture, Agricultural Experiment Station, Univ. California, 1950).Inouye, B. D. Response surface experimental designs for investigating interspecific competition. Ecology 82, 2696–2706 (2001).Article 

Google Scholar 
Law, R. & Watkinson, A. R. Response-surface analysis of two-species competition: an experiment on Phleum arenarium and Vulpia fasciculata. J. Ecol. 75, 871–886 (1987).Article 

Google Scholar 
MATLAB v.9.0 (MathWorks, 2016).Stan Modeling Language Users Guide and Reference Manual, v.2.27 (Stan Development Team, 2021); https://mc-stan.orgVehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).Article 

Google Scholar 
Bürkner, P.C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. https://doi.org/10.18637/jss.v080.i01 (2017).Vehtari, A. et al. loo: efficient leave-one-out cross-validation and WAIC for Bayesian models, v.2.4.1 (2020).ImageJ (US NIH, 1997–2016). More

MaterialsData sourcesSupplementary Table S1 summarizes the definitions of all the variables and Supplementary Table S2 displays the descriptive statistics of the variables. A detailed description of our data sources is summarized in Supplementary Table S3.In summary, our mobile phone data, containing Jan 2018 to Apr 2021 visitation records to each national park and the visitors’ respective census block groups, are courtesy of SafeGraph Inc47. The geographical boundaries of national parks that are used to extract records only relevant to national parks are provided by the NPS Land Resources Division48. Finally, the racial and population demographics of each census block group are provided by the 2015-2019 American Community Survey (ACS)16.The utilization of each distinct dataset towards the extraction of our materials of interest are elaborated in the subsequent sections.Validation of SafeGraph’s mobile-phone datasetThe validation of SafeGraph’s mobile-phone dataset in its application to national parks has been previously validated by Yun et al17. Specifically, Yun et al’s17 work showed a close resemblance between the NPS visitor use survey and SafeGraph’s mobile-phone dataset in terms of visitation counts, temporal visitation patterns, racial demographics, and state-level residential origins of the visitors to Yellowstone National Park. However, SafeGraph’s POI classification of “National Parks” remains inconsistent with the NPS’s official definition of National Park. To circumvent this problem, we have utilized shapefiles courtesy of the NPS OpenData48 to extract the most visited POIs that fall within the shapefiles of each respective “National Park”. This process would be detailed in the subsequent sub-sections below.Selection of mainland US national parksWe adopted the official and formal definition of national parks as defined and listed by the NPS System49.We selected national parks within the 48 states encompassing the contiguous U.S. We chose to omit the parks that fall within the states of Alaska, Hawaii, Puerto Rico and other US minor Islands considering the fact that air travel is a necessity for out-of-state visitors to visit these select parks. These separate travel behavioral patterns could result in confounding variables towards our analysis, particularly since air travel faced major disruptions amidst the COVID-19 pandemic50.It is worth noting that New River George National Park was declared as a national park only following the COVID-19 pandemic51. Hence, it is excluded from our study.Finally, we lack the data availability for White Sands National Park and Dry Tortugas National Park. The former is due to its proximity to White Sands Missile Range and security concerns on mobile device data52. The latter’s lack of data availability could be attributed to the fact that the park is an island off the coast of Key West, FL53.Henceforth, we included a grand total of 48 national parks in our study.Extraction of POIsWe selected our points-of-interests (POIs) based on the dataset made available by SafeGraph47. While SafeGraph does provide its own classification of “national parks”, its classification methodology remains inconsistent with the NPS’s official definition and formal list of “national parks”17,49.Hence, we extracted POIs that fall within the encompassed polygon shapefiles of each respective national park. The polygon shapefiles are courtesy of the NPS OpenData48.We then selected the POI with the highest average monthly visitation records for each distinct national park.The choice to select the POI with the highest visitation record could be attributed to the fact that a brief analysis reveals that in many parks, the top 5 most populated POIs tends to fall within the same vicinity17. Specifically, the top 5 most populated POIs for many large national parks, like Cuyahoga National Park, Indiana Dunes National park, and Yellowstone National Park, typically encompass the areas surrounding the park entrances17. This remains rational since visitors would have to pass through park entrances to enter the parks and gain access other areas of the park. Hence, selecting only the POI with the highest visitation record for each park prevents us from making duplicate counts from separate POIs.Computing census block group-based racial demographicsThe aforementioned Safegraph47 data provides us with the census block group origins of the visitors to each distinct POI. The census block group origins are identified by its 12-digit Federal Information Processing Standard (FIPS) code. We are thus able to retrieve our racial demographics of interests (% of non-whites, % of African-, % Hispanics-, % of Asian-, and % Native Americans) pertaining to each visitors census block origins.Our study only considered all visitations across mainland U.S. As such, we have excluded visitors from Hawaii, Alaska, Puerto Rico and other minor US islands for their visitation patterns are expected to be abruptly disrupted following the pandemic due to restrictions put in place from air travel50. This decision would prevent the effects of confounding variables and avoid drastically skewing our data.Computing distance travelled by visitor to each national parkLikewise, we obtain the variables of distance through the utilization of the Haversine formula54 between the POIs coordinates and the centroids of the visitors census block group. We standardize the units of distance to kilometers in our analysis.Categorization of visitation records falling before and after COVID-19We categorize pre-COVID era as any time-period that occurs prior to the month of March 2020. Hence, we classify the COVID era as any time period from the month of March 2020 onward. We selected March 2020 for it was the month in which the UN declared COVID-19 a global pandemic55. This declaration was proceeded by numerous state and local lockdown measures which drastically impacted American commerce56 and the lifestyles of many Americans57.Methods and ModelOffsetting visitation counts with the census block group populationWe offset our dependent variable of visitation counts per census block population because racial demographics of the visitors’ census origins are measured at a census block level. This allows us to account for the fact that one would naturally expect higher visitation counts from more populated census block groups. Hence, the visitation counts per thousand population of the census block group would serve as a function of our independent variables (COVID-19 era, distance and racial demographics). This could be illustrated in Eq. (1) in the introduction section.Gravity ModelWe incorporated gravity models into our methodology. In the context of tourism, the gravity model explores the behavior and travel patterns over distances between two unique POIs.The gravity model was adopted from Newton’s law of universal gravitation in physics58. Newton’s law of universal gravitation states that distance and mass determine the gravitational forces between two objects. The gravity model has since been adapted by numerous disciplines in the social sciences. These topics include trade21, tourism19,20, and migration22. For instance, the gravity model is popular in studies involving bilateral trade21. This is because the gravity model allows economists to measure how specific economic indicators (such as GDP) could attract trade between two countries, given the distances between them21.We thus elected to use the gravity model because it best represents our research theme of seeking to analyze the changes in visitations to national parks amongst individual racial communities across the U.S. Henceforth, the gravity model allows us to best analyze the change in visitations from different racial communities to each specified national park given the required distance of travel. The selection of our variables, in seeking to optimally represent the gravity model, while preserving its assumptions, would be elaborated in the subsequent subsections below.Our application of the gravity model works as such: given (i{mathrm{th}}) census block group and (j{mathrm{th}}) national park where (alpha _k) symbolizes each respective coefficient towards the determined independent variable, the gravity model could be demonstrated as such:$$begin{aligned} frac{visitation_{ijt}}{left( frac{population_i}{1000}right) }propto frac{race_i^{alpha _1}*interaction_terms^{alpha _2}}{distance_{ij}^{alpha _3}} end{aligned}$$
(2)
which can be remodelled as:$$begin{aligned} visitation_{ijt}propto frac{race_i^{alpha _1}*(interaction~terms)^{alpha _2}*left( frac{population_i}{1000}right) ^{alpha _4}}{distance_{ij}^{alpha _3}} end{aligned}$$
(3)
using natural logarithms could be transformed to:$$begin{aligned} ln (visitation_{ijt})propto {alpha _1}ln (race_i)+{alpha _2}ln (interaction~terms)+alpha _3ln (distance_{ij})+ {alpha _4}ln left( frac{population_i}{1000}right) end{aligned}$$
(4)
Model SpecificationThe gravity model is incorporated using panel data with interaction terms19,21. Incorporating panel data allows us to control for unobservable individual effects19,21, such as time invariant monthly and seasonal fluctuations in park visitations, as best illustrated in the peaks and troughs witnessed in Fig. 1. The interaction terms allows us to measure the impact of COVID-19 towards our selected predictors. Specifically, the random-effects panel approach was selected in favor of the fixed-effects panel model and the pooled ordinary least squares (OLS) model as evident by the results of the F-tests, Hausman’s Chi-Squared, and the Breusch-Pagan (BP) Lagrange Multiplier59 tests displayed in Supplementary Table S4.This results in Eq. (5), given each (i{mathrm{th}}) census block group’s visitation to (j{mathrm{th}}) national parks during (t{mathrm{th}}) month over specified race (race_i).$$begin{aligned} begin{aligned} ln left( visitation_{ijt}right)&= beta _0+beta _1(COVID~era)+beta _2[ln (race_{i})] +beta _3[ln (distance_{ij})] +beta _4left[ ln left( frac{population_{i}}{1000}right) right] \ {}&quad +,beta _5[COVID~eratimes ln (race_{i})] +beta _6[(COVID~eratimes ln (distance_{ij})] +beta _7[ln (distance_{ij})times ln (race_i)] \ {}&quad +,beta _8[(COVID~eratimes ln (distance_{ij})times ln (race_i)]+V_{ijt} \ end{aligned} end{aligned}$$
(5)
The assumptions of log-linearity and multi-collinearity19,20,21 in our specified model, per Eq. (5), have been tested and could be referenced in Supplementary Table S5.Consideration of variables in our modelWe explored using the size area (in km(^2)) of each respective park, instead of distance travelled, as the denominator of our gravity model per Eq. (2). However, the substantially lower (R^2) values obtained when using a park’s size suggests that a park’s area is a poor factor in explaining visitation trends across socio-economic variables. These are detailed in Supplemental Table S6.We also initially considered fitting other socio-economic independent variables into the same analysis. We did so in the hopes of gaining further insights on COVID-19’s impact towards park visitation. Some other independent variables that were considered included median income and median age. However, fitting them into same analysis resulted in high multi-collinearity. These are detailed in Supplemental Table S6. Multi-collinearity occurs when an independent variable is highly correlated with another independent variable in an analysis involving multiple independent variables60. This could consequently “undermine the statistical significance of an independent variable”60.To mitigate concerns of multi-collinearity in our analysis involving different racial groups, we adopt the procedures outlined by Lewis-Beck and Lewis-Beck60. Lewis-Beck and Lewis-Beck recommends separating our analysis of each racial composition. This means that we would analyze the composition of non-whites, African-, Asian-, Hispanic-, and Native American with our other variables separately.Finally, we considered analyzing the variables of income and age separately. However, the variables of income and age still resulted in high multi-collinearity amongst the existing independent variables. Furthermore, the different characteristics displayed amongst our analysis involving variables like income and age (compared to race) meant that our suggested random-effects gravity model is not a one-size-fits-all model for other analysis involving separate variables. These are detailed in Supplemental Table S6. For this reason, we hope to study variables like age and income in some of our future studies, using a different model. More

Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Syst. 37, 637–669 (2006).
Google Scholar 
Forrest, J. & Miller-Rushing, A. J. Toward a synthetic understanding of the role of phenology in ecology and evolution. Philos. Trans. R. Soc. B Biol. Sci. 365, 3101–3112 (2010).
Google Scholar 
Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change/631/158/2165/2457/631/158/2039/129/141/139 letter. Nat. Clim. Chang. 8 (2018).Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Mushegian, A. A. et al. Ecological mechanism of climate-mediated selection in a rapidly evolving invasive species. Ecol. Lett. 24, 698–707 (2021).PubMed 
PubMed Central 

Google Scholar 
Visser, M. E. & Both, C. Shifts in phenology due to global climate change: the need for a yardstick. Proc. R. Soc. B Biol. Sci. 272, 2561–2569 (2005).
Google Scholar 
Mayor, S. J. et al. Increasing phenological asynchrony between spring green-up and arrival of migratory birds. Sci. Rep. 7, 1–10 (2017).ADS 

Google Scholar 
Beard, K. H., Kelsey, K. C., Leffler, A. J. & Welker, J. M. The missing angle: Ecosystem consequences of phenological mismatch. Trends Ecol. Evol. 34 (2019).Youngflesh, C. et al. Migratory strategy drives species-level variation in bird sensitivity to vegetation green-up. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01442-y (2021).PubMed 

Google Scholar 
Forrest, J. R. Complex responses of insect phenology to climate change. Curr. Opin. Insect Sci. 17 (2016).Crimmins, T. M. et al. Short-term forecasts of insect phenology inform pest management. Ann. Entomol. Soc. Am. 113 (2020).Brakefield, P. M. Geographical variability in, and temperature effects on, the phenology of Maniola jurtina and Pyronia tithonus (Lepidoptera, Satyrinae) in England and Wales. Ecol. Entomol. 12 (1987).Dell, D., Sparks, T. H. & Dennis, R. L. H. Climate change and the effect of increasing spring temperatures on emergence dates of the butterfly Apatura iris (Lepidoptera: Nymphalidae). Eur. J. Entomol. 102, 161–167 (2005).
Google Scholar 
Van Der Kolk, H. J., Wallisdevries, M. F. & Van Vliet, A. J. H. Using a phenological network to assess weather influences on first appearance of butterflies in the Netherlands. Ecol. Indic. 69 (2016).Abarca, M. et al. Inclusion of host quality data improves predictions of herbivore phenology. Entomol. Exp. Appl. 166 (2018).Abarca, M. & Lill, J. T. Latitudinal variation in the phenological responses of eastern tent caterpillars and their egg parasitoids. Ecol. Entomol. 44 (2019).Karlsson, B. Extended season for northern butterflies. Int. J. Biometeorol. 58, 691–701 (2014).ADS 
PubMed 

Google Scholar 
Kharouba, H. M., Paquette, S. R., Kerr, J. T. & Vellend, M. Predicting the sensitivity of butterfly phenology to temperature over the past century. Glob. Chang. Biol. 20 (2014).Diamond, S. E., Frame, A. M., Martin, R. A. & Buckley, L. B. Species’ traits predict phenological responses to climate change in butterflies. Ecology 92 (2011).Diamond, S. E. et al. Unexpected phenological responses of butterflies to the interaction of urbanization and geographic temperature. Ecology 95 (2014).Cayton, H. L., Haddad, N. M., Gross, K., Diamond, S. E. & Ries, L. Do growing degree days predict phenology across butterfly species?. Ecology 96, 1473–1479 (2015).
Google Scholar 
Stewart, J. E., Illán, J. G., Richards, S. A., Gutiérrez, D. & Wilson, R. J. Linking inter-annual variation in environment, phenology, and abundance for a montane butterfly community. Ecology 101 (2020).Roy, D. B. et al. Similarities in butterfly emergence dates among populations suggest local adaptation to climate. Glob. Chang. Biol. 21 (2015).Dennis, R. L. H. et al. Turnover and trends in butterfly communities on two British tidal islands: Stochastic influences and deterministic factors. J. Biogeogr. 37, 2291–2304 (2010).
Google Scholar 
Sparks, T. H. & Yates, T. J. The effect of spring temperature on the appearance dates of British butterflies 1883–1993. Ecography (Cop.). 20 (1997).Michielini, J. P., Dopman, E. B. & Crone, E. E. Changes in flight period predict trends in abundance of Massachusetts butterflies. Ecol. Lett. 24, 249–257 (2021).PubMed 

Google Scholar 
Zografou, K. et al. Species traits affect phenological responses to climate change in a butterfly community. Sci. Rep. 11 (2021).Belitz, M. W., Larsen, E. A., Ries, L. & Guralnick, R. P. The accuracy of phenology estimators for use with sparsely sampled presence-only observations. Methods Ecol. Evol. 11, 1273–1285 (2020).
Google Scholar 
Van Strien, A. J., Plantenga, W. F., Soldaat, L. L., Van Swaay, C. A. M. & WallisDeVries, M. F. Bias in phenology assessments based on first appearance data of butterflies. Oecologia 156, 227–235 (2008).ADS 
PubMed 

Google Scholar 
Pollard, E. A method for assessing changes in the abundance of butterflies. Biol. Conserv. 12 (1977).Taron, D. & Ries, L. Butterfly Monitoring for Conservation. in Butterfly Conservation in North America 35–57 (Springer Netherlands, 2015). https://doi.org/10.1007/978-94-017-9852-5_3.Schmucki, R. et al. A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes. J. Appl. Ecol. 53, 501–510 (2016).
Google Scholar 
Prudic, K., Oliver, J., Brown, B. & Long, E. Comparisons of citizen science data-gathering approaches to evaluate urban butterfly diversity. Insects 9, 186 (2018).PubMed Central 

Google Scholar 
Prudic, K. L. et al. eButterfly: Leveraging massive online citizen science for butterfly conservation. Insects 8 (2017).Barve, V. V. et al. Methods for broad-scale plant phenology assessments using citizen scientists’ photographs. Appl. Plant Sci. 8 (2020).Seltzer, C. Making biodiversity data social, shareable, and scalable: Reflections on iNaturalist & citizen science. Biodivers. Inf. Sci. Stand. 3 (2019).Wittmann, J., Girman, D. & Crocker, D. Using inaturalist in a coverboard protocol to measure data quality: Suggestions for project design. Citiz. Sci. Theory Pract. 4 (2019).Dorazio, R. M. Accounting for imperfect detection and survey bias in statistical analysis of presence-only data. Glob. Ecol. Biogeogr. 23 (2014).Ries, L., Zipkin, E. F. & Guralnick, R. P. Tracking trends in monarch abundance over the 20th century is currently impossible using museum records. In Proceedings of the National Academy of Sciences of the United States of America vol. 116 (2019).Larsen, E. A. & Shirey, V. Method matters: Pitfalls in analysing phenology from occurrence records. Ecol. Lett. https://doi.org/10.1111/ele.13602 (2021).PubMed 
PubMed Central 

Google Scholar 
de Keyzer, C. W., Rafferty, N. E., Inouye, D. W. & Thomson, J. D. Confounding effects of spatial variation on shifts in phenology. Glob. Chang. Biol. 23 (2017).Cima, V. et al. A test of six simple indices to display the phenology of butterflies using a large multi-source database. Ecol. Indic. 110, 105885 (2020).
Google Scholar 
Zipkin, E. F. et al. Addressing data integration challenges to link ecological processes across scales. Front. Ecol. Environ. 19 (2021).Polgar, C. A., Primack, R. B., Williams, E. H., Stichter, S. & Hitchcock, C. Climate effects on the flight period of Lycaenid butterflies in Massachusetts. Biol. Conserv. 160 (2013).Brooks, S. J. et al. The influence of life history traits on the phenological response of British butterflies to climate variability since the late-19th century. Ecography (Cop.) 40, 1152–1165 (2017).
Google Scholar 
van Strien, A. J., van Swaay, C. A. M., van Strien-van Liempt, W. T. F. H., Poot, M. J. M. & WallisDeVries, M. F. Over a century of data reveal more than 80% decline in butterflies in the Netherlands. Biol. Conserv. 234 (2019).Boggs, C. L. The fingerprints of global climate change on insect populations. Curr. Opin. Insect Sci. 17 (2016).Belitz, M. et al. Climate drivers of adult insect activity are conditioned by life history traits. Authorea Prepr. (2021).Kellner, K. F. & Swihart, R. K. Accounting for imperfect detection in ecology: A quantitative review. PLoS ONE 9 (2014).Park, D. S., Newman, E. A. & Breckheimer, I. K. Scale gaps in landscape phenology: challenges and opportunities. Trends Ecol. Evol. 36 (2021).Kerr, J. T., Vincent, R. & Currie, D. J. Lepidopteran richness patterns in North America. Écoscience 5, 448–453 (1998).
Google Scholar 
Taylor, S. D., Meiners, J. M., Riemer, K., Orr, M. C. & White, E. P. Comparison of large-scale citizen science data and long-term study data for phenology modeling. Ecology 100 (2019).Isaac, N. J. B. et al. Data integration for large-scale models of species distributions. Trends Ecol. Evol. 35 (2020).Miller, D. A. W., Pacifici, K., Sanderlin, J. S. & Reich, B. J. The recent past and promising future for data integration methods to estimate species’ distributions. Methods Ecol. Evol. 10 (2019).Fletcher, R. J. et al. A practical guide for combining data to model species distributions. Ecology https://doi.org/10.1002/ecy.2710 (2019).PubMed 

Google Scholar 
Wepprich, T., Adrion, J. R., Ries, L., Wiedmann, J. & Haddad, N. M. Butterfly abundance declines over 20 years of systematic monitoring in Ohio, USA. bioRxiv https://doi.org/10.1101/613786 (2019).
Google Scholar 
Crossley, M. S. et al. Recent climate change is creating hotspots of butterfly increase and decline across North America. Glob. Chang. Biol. 27, 2702–2714 (2021).CAS 
PubMed 

Google Scholar 
Forister, M. L. et al. Fewer butterflies seen by community scientists across the warming and drying landscapes of the American West. Science (80-) 371, 1042–1045 (2021).ADS 
CAS 

Google Scholar 
Macgregor, C. J. et al. Climate-induced phenology shifts linked to range expansions in species with multiple reproductive cycles per year. Nat. Commun. 10, (2019).Kerr, N. Z. et al. Developmental trap or demographic bonanza? Opposing consequences of earlier phenology in a changing climate for a multivoltine butterfly. Glob. Chang. Biol. 26, (2020).Belth, J. E. Butterflies of Indiana: A field guide. Butterflies of Indiana: A Field Guide (2012).Betros, B. A Photographic Field Guide to the Butterflies in the Kansas City Region (Kansas City Star Books, 2008).
Google Scholar 
Bouseman, J. K., Sternburg, J. G. & Wiker, J. R. Field guide to the skipper butterflies of Illinois. (Illinois Natural History Survey Manual 11, 2006).Clark, A. H. The butterflies of the District of Columbia and vicinity. Bull. United States Natl. Museum (1932).Glassberg, J. Butterflies through Binoculars: Boston—New York—Washington Region (Oxford University Press, 1993).
Google Scholar 
Glassberg, J. Butterflies through Binoculars: The East—A Field Guide to the Butterflies of Eastern North America (Oxford University Press, 1999).
Google Scholar 
Iftner, D. C., Shuey, J. A. & Calhoun, J. V. Butterflies and skippers of Ohio (Ohio State University, 1992).
Google Scholar 
Jeffords, M. R., Post, S. L. & Wiker, J. Butterflies of Illinois: a field guide (Illinois Natural History Survey, 2019).
Google Scholar 
Schlicht, D. W., Downey, J. C. & Nekola, J. C. The butterflies of Iowa (University of Iowa Press, 2007).
Google Scholar 
Schmucki, R., Harrower, C. A. & Dennis, E. B. rbms: Computing generalised abundance indices for butterfly monitoring count data. R package version 1.1.0. https://github.com/RetoSchmucki/rbms (2021).GBIF. GBIF Occurrence download. https://doi.org/10.15468/dl.1erh15 (2019).Thornton, P. E. et al. Daymet: Daily surface weather data on a 1-km grid for North America, version 3. ORNL DAAC. (Oak Ridge, TN, 2017).Baskerville, G. L. & Emin, P. Rapid estimation of heat accumulation from maximum and minimum temperatures. Ecology 50, (1969).R Development Core Team, R. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing vol. 1 409 (2011).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest: Tests for random and fixed effects for linear mixed effect models (lmer objects of lme4 package). R package version (2014).Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4 (2013).Kahle, D. & Wickham, H. ggmap: Spatial visualization with ggplot2. R J 5 (2013). More