Data-derived metrics describing the behaviour of field-based citizen scientists provide insights for project design and modelling bias.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
03 07 2020
03 07 2020
Historique:
received:
30
10
2019
accepted:
11
06
2020
entrez:
5
7
2020
pubmed:
6
7
2020
medline:
15
12
2020
Statut:
epublish
Résumé
Around the world volunteers and non-professionals collect data as part of environmental citizen science projects, collecting wildlife observations, measures of water quality and much more. However, where projects allow flexibility in how, where, and when data are collected there will be variation in the behaviour of participants which results in biases in the datasets collected. We develop a method to quantify this behavioural variation, describing the key drivers and providing a tool to account for biases in models that use these data. We used a suite of metrics to describe the temporal and spatial behaviour of participants, as well as variation in the data they collected. These were applied to 5,268 users of the iRecord Butterflies mobile phone app, a multi-species environmental citizen science project. In contrast to previous studies, after removing transient participants (those active on few days and who contribute few records), we do not find evidence of clustering of participants; instead, participants fall along four continuous axes that describe variation in participants' behaviour: recording intensity, spatial extent, recording potential and rarity recording. Our results support a move away from labelling participants as belonging to one behavioural group or another in favour of placing them along axes of participant behaviour that better represent the continuous variation between individuals. Understanding participant behaviour could support better use of the data, by accounting for biases in the data collection process.
Identifiants
pubmed: 32620931
doi: 10.1038/s41598-020-67658-3
pii: 10.1038/s41598-020-67658-3
pmc: PMC7334204
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
11009Références
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).
pubmed: 25061202
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45 (2015).
pubmed: 25832402
pmcid: 25832402
Seebens, H., Gastner, M. T. & Blasius, B. The risk of marine bioinvasion caused by global shipping. Ecol. Lett. 16, 782–790 (2013).
pubmed: 23611311
Hooper, D. U., Chapin, F. S. III. & Ewel, J. J. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
Ehrenfeld, J. G. Ecosystem consequences of biological invasions. Annu. Rev. Ecol. Evol. Syst. 41, 59–80 (2010).
Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: challenges and benefits. Annu. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).
Eitzel, M. V. et al. Citizen science terminology matters: exploring key terms. Citizen Sci. Theory Pract. 2, 1 (2017).
August, T. et al. Emerging technologies for biological recording. Biol. J. Linn. Soc. 115, 731–749 (2015).
Follett, R. & Strezov, V. An analysis of citizen science based research: usage and publication patterns. PLoS ONE 10, e0143687 (2015).
pubmed: 26600041
pmcid: 4658079
Pocock, M. J. O. et al. Developing the global potential of citizen science: assessing opportunities that benefit people, society and the environment in East Africa. J. Appl. Ecol. https://doi.org/10.1111/1365-2664.13279 (2019).
doi: 10.1111/1365-2664.13279
Mason, S. C. et al. Geographical range margins of many taxonomic groups continue to shift polewards. Biol. J. Linn. Soc. 115, 586–597 (2015).
Pearce-Higgins, J. W. et al. A national-scale assessment of climate change impacts on species: assessing the balance of risks and opportunities for multiple taxa. Biol. Conserv. https://doi.org/10.1016/j.biocon.2017.06.035 (2017).
doi: 10.1016/j.biocon.2017.06.035
Woodcock, B. A. et al. Impacts of neonicotinoid use on long-term population changes in wild bees in England. Nat. Commun. 7, 12459 (2016).
pubmed: 27529661
pmcid: 4990702
Delaney, D. G., Sperling, C. D., Adams, C. S. & Leung, B. Marine invasive species: validation of citizen science and implications for national monitoring networks. Biol. Invasions 10, 117–128 (2008).
Roy, H. E. et al. Invasive alien predator causes rapid declines of native European ladybirds. Divers. Distrib. 18, 717–725 (2012).
Liebenberg, L. et al. Smartphone Icon User Interface design for non-literate trackers and its implications for an inclusive citizen science. Biol. Conserv. 208, 155–162 (2017).
Isaac, N. J. B. & Pocock, M. J. O. Bias and information in biological records. Biol. J. Linn. Soc. 115, 522–531 (2015).
Pocock, M. J. O., Roy, H. E., Preston, C. D. & Roy, D. B. The Biological Records Centre: a pioneer of citizen science. Biol. J. Linn. Soc. 115, 475–493 (2015).
Pocock, M. J. O., Tweddle, J. C., Savage, J., Robinson, L. D. & Roy, H. E. The diversity and evolution of ecological and environmental citizen science. PLoS ONE 12, e0172579 (2017).
pubmed: 28369087
pmcid: 5378328
Dennis, E. B., Morgan, B. J. T., Brereton, T. M., Roy, D. B. & Fox, R. Using citizen science butterfly counts to predict species population trends. Conserv. Biol. 31, 1350–1361 (2017).
pubmed: 28474803
Boakes, E. H. et al. Distorted views of biodiversity: spatial and temporal bias in species occurrence data. PLoS Biol. 8, e1000385 (2010).
pubmed: 20532234
pmcid: 2879389
Kelling, S. et al. Finding the signal in the noise of Citizen Science Observations. bioRxiv https://doi.org/10.1101/326314 (2018).
doi: 10.1101/326314
Hill, M. O. Local frequency as a key to interpreting species occurrence data when recording effort is not known. Methods Ecol. Evol. 3, 195–205 (2012).
Isaac, N. J. B., van Strien, A. J., August, T. A., de Zeeuw, M. P. & Roy, D. B. Statistics for citizen science: extracting signals of change from noisy ecological data. Methods Ecol. Evol. 5, 1052–1060 (2014).
Roberge, J. M. Using data from online social networks in conservation science: Which species engage people the most on Twitter?. Biodivers. Conserv. 23, 715–726 (2014).
Steen, V. A., Elphick, C. S. & Tingley, M. W. An evaluation of stringent filtering to improve species distribution models from citizen science data. Divers. Distrib. 25, 1857–1869 (2019).
Barata, I. M., Griffiths, R. A. & Ridout, M. S. The power of monitoring: optimizing survey designs to detect occupancy changes in a rare amphibian population. Sci. Rep. 7, 16491 (2017).
pubmed: 29184083
pmcid: 5705711
Kelling, S. et al. Using semistructured surveys to improve citizen science data for monitoring biodiversity. Bioscience 69, 170–179 (2019).
pubmed: 30905970
pmcid: 6422830
Johnston, A., Fink, D., Hochachka, W. M. & Kelling, S. Estimates of observer expertise improve species distributions from citizen science data. Methods Ecol. Evol. 2017, 1–10 (2017).
Boersch-Supan, P. H., Trask, A. E. & Baillie, S. R. Robustness of simple avian population trend models for semi-structured citizen science data is species-dependent. Biol. Conserv. 240, 108286 (2019).
Farmer, R. G., Leonard, M. L., Mills Flemming, J. E. & Anderson, S. C. Observer aging and long-term avian survey data quality. Ecol. Evol. 4, 2563–2576 (2014).
pubmed: 25360286
pmcid: 4203298
Horns, J. J., Adler, F. R. & Şekercioğlu, ÇH. Using opportunistic citizen science data to estimate avian population trends. Biol. Conserv. 221, 151–159 (2018).
Aagaard, K., Lyons, J. E. & Thogmartin, W. E. Accounting for surveyor effort in large-scale monitoring programs. J. Fish Wildl. Manag. 9, 459–466 (2018).
Neyens, T. et al. Mapping species richness using opportunistic samples: a case study on ground-floor bryophyte species richness in the Belgian province of Limburg. Sci. Rep. 9, 19122 (2019).
pubmed: 31836780
pmcid: 6911062
Ponciano, L. & Brasileiro, F. Finding volunteers’ engagement profiles in human computation for citizen science projects. Hum. Comput. 1, 245–264 (2014).
O’Brien, H. L. & Toms, E. G. What is user engagement? A conceptual framework for defining user engagement with technology. J. Am. Soc. Inf. Sci. Technol. 59, 938–955 (2008).
Boakes, E. H. et al. Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers’ recording behaviour. Sci. Rep. 6, 33051 (2016).
pubmed: 27619155
pmcid: 5020317
Nov, O., Arazy, O. & Anderson, D. Scientists@Home: what drives the quantity and quality of online citizen science participation?. PLoS ONE 9, e90375 (2014).
pubmed: 24690612
pmcid: 3972171
West, S. & Pateman, R. Recruiting and retaining participants in citizen science: what can be learned from the volunteering literature?. Citiz. Sci. Theory Pract. https://doi.org/10.5334/cstp.8 (2016).
doi: 10.5334/cstp.8
Aristeidou, M., Scanlon, E. & Sharples, M. Profiles of engagement in online communities of citizen science participation. Comput. Hum. Behav. 74, 246–256 (2017).
Calenge, C. The package “adehabitat” for the R software: a tool for the analysis of space and habitat use by animals. Ecol. Model. 197, 516–519 (2006).
Dolnicar, S., Grabler, K. & Mazanec, J. A. A tale of three cities: perceptual charting for analysing destination images. In Consumer psychology of tourism, hospitality and leisure (ed. Woodside) 39–62 (CABI, London, 1999).
Struyf, A., Hubert, M. & Rousseeuw, P. Clustering in an object-oriented environment. J. Stat. Softw. 1, 1–30 (1996).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2017).
Oksanen, J. et al. Vegan: Community Ecology Package. R Package Version 2.5-1 (2018).
Maechler, M. et al. Cluster Analysis Basics and Extensions. R package version 2.0.7-1. R package version (2018).
Wood, C., Sullivan, B., Iliff, M., Fink, D. & Kelling, S. eBird: engaging birders in science and conservation. PLoS Biol. 9(12), e1001220. https://doi.org/10.1371/journal.pbio.1001220 (2011).
doi: 10.1371/journal.pbio.1001220
pubmed: 22205876
pmcid: 3243722
Haklay, M. Why is participation inequality important? In European Handbook of Crowdsourced Geographic Information. https://doi.org/10.5334/bax.c (2016).
Seymour, V. & Haklay, M. Exploring engagement characteristics and behaviours of environmental volunteers. Citiz. Sci. Theory Pract. https://doi.org/10.5334/cstp.66 (2017).
doi: 10.5334/cstp.66
August, T. A. et al. Citizen meets social science: predicting volunteer involvement in a global freshwater monitoring experiment. Freshw. Sci. https://doi.org/10.1086/703416 (2019).
doi: 10.1086/703416
Gura, T. Citizen science: amateur experts. Nature https://doi.org/10.1038/nj7444-259a (2013).
doi: 10.1038/nj7444-259a
pubmed: 23586092
Kelling, S. et al. Can observation skills of citizen scientists be estimated using species accumulation curves?. PLoS ONE 10, e0139600 (2015).
pubmed: 26451728
pmcid: 4599805
van Strien, A. J., van Swaay, C. A. M. & Termaat, T. Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models. J. Appl. Ecol. 50, 1450–1458 (2013).
Telfer, M. G., Preston, C. D. & Rothery, P. A general method for measuring relative change in range size from biological atlas data. Biol. Conserv. 107, 99–109 (2002).
Shirk, J. L. et al. Public participation in scientific research: a framework for deliberate design. Ecol. Soc. https://doi.org/10.5751/ES-04705-170229 (2012).
doi: 10.5751/ES-04705-170229
Domroese, M. C. & Johnson, E. A. Why watch bees? Motivations of citizen science volunteers in the Great Pollinator Project. Biol. Conserv. 208, 40–47 (2017).
Geoghegan, H., Dyke, A., Pateman, R., West, S. & Everett, G. Understanding Motivations for Citizen Science. Final report on behalf of UKEOF, University of Reading, Stockholm Environment Institute (University of York) and University of the West of England (2016).