Hostname: page-component-8448b6f56d-qsmjn Total loading time: 0 Render date: 2024-04-20T01:14:22.377Z Has data issue: false hasContentIssue false
  • English
  • Français

The people vs science: can passively crowdsourced internet data shed light on host–parasite interactions?

Published online by Cambridge University Press:  09 June 2021

Jean-François Doherty ,
Antoine Filion ,
Jerusha Bennett ,
Upendra Raj Bhattarai ,
Xuhong Chai ,
Daniela de Angeli Dutra ,
Erica Donlon ,
Fátima Jorge Open the ORCID record for Fátima Jorge [Opens in a new window] ,
Marin Milotic  and
Eunji Park Open the ORCID record for Eunji Park [Opens in a new window]
...Show all authors
Jean-François Doherty
Affiliation:
Department of Zoology, University of Otago, P.O. Box 56, Dunedin, New Zealand
Antoine Filion
Affiliation:
Department of Zoology, University of Otago, P.O. Box 56, Dunedin, New Zealand
Jerusha Bennett
Affiliation:
Department of Zoology, University of Otago, P.O. Box 56, Dunedin, New Zealand
Upendra Raj Bhattarai
Affiliation:
Department of Anatomy, University of Otago, P.O. Box 56, Dunedin, New Zealand
Xuhong Chai
Affiliation:
Department of Zoology, University of Otago, P.O. Box 56, Dunedin, New Zealand
Daniela de Angeli Dutra
Affiliation:
Department of Zoology, University of Otago, P.O. Box 56, Dunedin, New Zealand
Erica Donlon
Affiliation:
Department of Zoology, University of Otago, P.O. Box 56, Dunedin, New Zealand
Fátima Jorge
Affiliation:
Department of Zoology, University of Otago, P.O. Box 56, Dunedin, New Zealand
Marin Milotic
Affiliation:
Department of Zoology, University of Otago, P.O. Box 56, Dunedin, New Zealand
Eunji Park
Affiliation:
Department of Zoology, University of Otago, P.O. Box 56, Dunedin, New Zealand
Amandine J. M. Sabadel
Affiliation:
Department of Zoology, University of Otago, P.O. Box 56, Dunedin, New Zealand
Leighton J. Thomas
Affiliation:
Department of Zoology, University of Otago, P.O. Box 56, Dunedin, New Zealand
Robert Poulin*
Affiliation:
Department of Zoology, University of Otago, P.O. Box 56, Dunedin, New Zealand
*
Author for correspondence: Robert Poulin, E-mail: robert.poulin@otago.ac.nz
Get access Rights & Permissions [Opens in a new window]

Abstract

Every internet search query made out of curiosity by anyone who observed something in nature, as well as every photo uploaded to the internet, constitutes a data point of potential use to scientists. Researchers have now begun to exploit the vast online data accumulated through passive crowdsourcing for studies in ecology and epidemiology. Here, we demonstrate the usefulness of iParasitology, i.e. the use of internet data for tests of parasitological hypotheses, using hairworms (phylum Nematomorpha) as examples. These large worms are easily noticeable by people in general, and thus likely to generate interest on the internet. First, we show that internet search queries (collated with Google Trends) and photos uploaded to the internet (specifically, to the iNaturalist platform) point to parts of North America with many sightings of hairworms by the public, but few to no records in the scientific literature. Second, we demonstrate that internet searches predict seasonal peaks in hairworm abundance that accurately match scientific data. Finally, photos uploaded to the internet by non-scientists can provide reliable data on the host taxa that hairworms most frequently parasitize, and also identify hosts that appear to have been neglected by scientific studies. Our findings suggest that for any parasite group likely to be noticeable by non-scientists, information accumulating through internet search activity, photo uploads, social media or any other format available online, represents a valuable source of data that can complement traditional scientific data sources in parasitology.

Keywords

Citizen sciencecrowdsourcingGoogle TrendshairwormsiNaturalistinternet searchiParasitology
Type
Research Article
Information
Parasitology , Volume 148 , Issue 11 , September 2021 , pp. 1313 - 1319
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

These authors contributed equally to this article.

References

Aiello, AE, Renson, A and Zivich, PN (2020) Social media- and internet-based disease surveillance for public health. Annual Review of Public Health 41, 101–118.CrossRefGoogle ScholarPubMed
Anaya, C, Hanelt, B and Bolek, MG (2021) Field and laboratory observations on the life history of Gordius terrestris (phylum Nematomorpha), a terrestrial nematomorph. Journal of Parasitology 107, 48–58.CrossRefGoogle Scholar
Bolek, MG, Schmidt-Rhaesa, A, de Villalobos, C and Hanelt, B (2015) Phylum Nematomorpha. In Thorp, JH and Rogers, DC (eds), Thorp and Covich's Freshwater Invertebrates, Vol. 1: Ecology and General Biology. New York: Academic Press, pp. 303–326.CrossRefGoogle Scholar
Bornmann, L, Haunschild, R and Patel, VM (2020) Are papers addressing certain diseases perceived where these diseases are prevalent? The proposal to use Twitter data as social-spatial sensors. PLoS ONE 15, e0242550.CrossRefGoogle ScholarPubMed
Bürkner, P-C (2017) Brms: an R package for Bayesian multilevel models using stan. Journal of Statistical Software 80, 1–28.CrossRefGoogle Scholar
Carneiro, HA and Mylonakis, E (2009) Google Trends: a web-based tool for real-time surveillance of disease outbreaks. Clinical Infectious Diseases 49, 1557–1564.CrossRefGoogle ScholarPubMed
Dormann, CF, Elith, J, Bacher, S, Buchmann, C, Carl, G, Carre, G, Garcia Marquez, JR, Gruber, B, Lafourcade, B, Leitao, PJ, Muenkemueller, T, McClean, C, Osborne, PE, Reineking, B, Schroeder, B, Skidmore, AK, Zurell, D and Lautenbach, S (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46.CrossRefGoogle Scholar
Elmer, F, Kohl, ZF, Johnson, PTJ and Peachey, RBJ (2019) Black spot syndrome in reef fishes: using archival imagery and field surveys to characterize spatial and temporal distribution in the Caribbean. Coral Reefs 38, 1303–1315.CrossRefGoogle Scholar
Estrada-Peña, A, Ostfeld, RS, Peterson, AT, Poulin, R and de la Fuente, J (2014) Effects of environmental change on zoonotic disease risk: an ecological primer. Trends in Parasitology 30, 205–214.CrossRefGoogle ScholarPubMed
Hanelt, B, Schmidt-Rhaesa, A and Bolek, MG (2015) Cryptic species of hairworm parasites revealed by molecular data and crowdsourcing of specimen collections. Molecular Phylogenetics and Evolution 82, 211–218.CrossRefGoogle ScholarPubMed
Jarić, I, Correia, RA, Brook, BW, Buettel, JC, Courchamp, F, Di Minin, E, Firth, JA, Gaston, KJ, Jepson, P, Kalinkat, G, Ladle, R, Soriano-Redondo, A, Souza, AT and Roll, U (2020) iEcology: harnessing large online resources to generate ecological insights. Trends in Ecology and Evolution 35, 630–639.CrossRefGoogle ScholarPubMed
Jones, KE, Patel, NG, Levy, MA, Storeygard, A, Balk, D, Gittleman, JL and Daszak, P (2008) Global trends in emerging infectious diseases. Nature 451, 990–993.CrossRefGoogle ScholarPubMed
Karvonen, A, Seppälä, O and Valtonen, ET (2004) Eye fluke-induced cataract formation in fish: quantitative analysis using an ophthalmological microscope. Parasitology 129, 473–478.CrossRefGoogle ScholarPubMed
Kiernan, N (2020) usa: updated US state facts and figures. R package version 0.1.0. Available at https://CRAN.R-project.org/package=usa.Google Scholar
Martin, LJ, Blossey, B and Ellis, E (2012) Mapping where ecologists work: biases in the global distribution of terrestrial ecological observations. Frontiers in Ecology and Environment 10, 195–201.CrossRefGoogle Scholar
Massicotte, P and Eddelbuettel, D (2021) gtrendsR: perform and display Google Trends queries. R package version 1.4.8. Available at https://CRAN.R-project.org/package=gtrendsR.Google Scholar
McDavitt, MT and Kyne, PM (2020) Social media posts reveal the geographic range of the critically endangered clown wedgefish, Rhynchobatus cooki. Journal of Fish Biology 97, 1846–1851.CrossRefGoogle ScholarPubMed
Meguro, N, Kishida, O, Utsumi, S, Niwa, S, Igarashi, S, Kozuka, C, Naniwa, A and Sato, T (2020) Host phenologies and the life history of horsehair worms (Nematomorpha, Gordiida) in a mountain stream in northern Japan. Ecological Research 35, 482–493.CrossRefGoogle Scholar
Mikula, P, Hadrava, J, Albrecht, T and Tryjanowski, P (2018) Large-scale assessment of commensalistic–mutualistic associations between African birds and herbivorous mammals using internet photos. PeerJ 6, e4520.CrossRefGoogle ScholarPubMed
Morand, S and Krasnov, BR (2010) The Biogeography of Host-Parasite Interactions. Oxford: Oxford University Press.Google Scholar
Ning, S, Yang, S and Kou, SC (2019) Accurate regional influenza epidemics tracking using internet search data. Scientific Reports 9, 5238.CrossRefGoogle ScholarPubMed
Pernat, N, Kampen, H, Jeschke, JM and Werner, D (2021) Citizen science versus professional data collection: comparison of approaches to mosquito monitoring in Germany. Journal of Applied Ecology 58, 214–223.CrossRefGoogle Scholar
Poinar, G Jr (2000) Heydenius araneus n.sp. (Nematoda: Mermithidae), a parasite of a fossil spider, with an examination of helminths from extant spiders (Arachnida: Araneae). Invertebrate Biology 119, 388–393.CrossRefGoogle Scholar
Poulin, R, Bennett, J, Filion, A, Bhattarai, UR, Chai, X, de Angeli Dutra, D, Donlon, E, Doherty, J-F, Jorge, F, Milotic, M, Park, E, Sabadel, A and Thomas, LJ (2021) iParasitology: mining the internet to test parasitological hypotheses. Trends in Parasitology 37, 267–272.CrossRefGoogle ScholarPubMed
R Core Team (2021) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Schmidt-Rhaesa, A (2013) Nematomorpha. In Schmidt-Rhaesa, A (ed.), Handbook of Zoology, vol. 1. Berlin: De Gruyter Publisher, pp. 29–145.Google Scholar
Stephens, PR, Altizer, S, Smith, KF, Aguirre, AA, Brown, JH, Budischak, SA, Byers, JE, Dallas, TA, Davies, TJ, Drake, JM, Ezenwa, VO, Farrell, MJ, Gittleman, JL, Han, BA, Huang, S, Hutchinson, RA, Johnson, P, Nunn, CL, Onstad, D, Park, A, Vazquez-Prokopec, GM, Schmidt, JP and Poulin, R (2016) The macroecology of infectious diseases: a new perspective on global-scale drivers of pathogen distributions and impacts. Ecology Letters 19, 1159–1171.CrossRefGoogle ScholarPubMed
Thomas, F, Schmidt-Rhaesa, A, Martin, G, Manu, C, Durand, P and Renaud, F (2002) Do hairworms (Nematomorpha) manipulate the water seeking behaviour of their terrestrial hosts? Journal of Evolutionary Biology 15, 356–361.CrossRefGoogle Scholar
von Bergmann, J, Shkolnik, D and Jacobs, A (2021) cancensus: R package to access, retrieve, and work with Canadian Census data and geography. R package version 0.4.2. Available at https://CRAN.R-project.org/package=cancensus.Google Scholar
Supplementary material: File

Doherty et al. supplementary material

Doherty et al. supplementary material

Download Doherty et al. supplementary material(File)
File 5.6 MB