PLOS ONE is organizing a Twitter chat on citizen science methodologies on 2nd April- see details below Citizen science (CS) encompasses a broad range of research methodologies that involve public participation for data collection, transcription
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Maximizing the rigor and reproducibility of citizen science in ecological research
PLOS ONE is organizing a Twitter chat on citizen science methodologies on 2nd April- see details below
Citizen science (CS) encompasses a broad range of research methodologies that involve public participation for data collection, transcription or analysis. Applications of CS have been found in many disciplines, but ecology has consistently been at the forefront. While some CS-based ecological monitoring schemes- such as the UK Butterfly Monitoring Scheme, established in 1976- have been running for decades, the popularity of CS has grown rapidly in more recent years. A wide range of projects based on CS methodologies are now being undertaken around the world, at local, national and international scales. The value of volunteer participation in activities ranging from transect-based species monitoring (Wepprich et al., 2019) and collection of biological specimens for lab-based analysis (Larson et al., 2020; Rasmussen et al., 2020) to crowdsourcing of creative thinking for study design (Can et al., 2017), has been repeatedly demonstrated. Studies have also highlighted the particular utility of CS methodologies in supporting long-term ecological monitoring in resource-limited contexts, including in economically developing countries (Gouraguine et al., 2019). Meanwhile, examples of the real-world impact of CS research are abundant, both in specific ecological interventions and the wider political discourse. For instance, the influential UK State of Nature 2019 report, likely to be a key source of evidence for future environmental legislation, cites the outcomes of a wealth of CS projects.
With the expansion of CS research, there is lively debate about how to maximise rigor and reproducibility in different types of CS methodologies. One of the crucial aspects of a successful CS study is an appropriately designed protocol, which features a realistic degree of complexity and accounts for the specific challenges of handling CS-derived data. An example of this is provided by a recent comprehensive report of the design, launch and assessment of the UK National Plant Monitoring Scheme (Pescott et al., 2019). Pre-testing of protocols prior to project launch can provide confidence in the robustness of the study design. When designing a CS study, it is also important to understand volunteer motivation and ensure that this is appropriately matched with the nature of the task to be performed (Lyons & Zhang, 2019). Some CS studies utilize narrower demographic groups to meet the required level of motivation and understanding, such as amateur naturalists (Hallmann et al., 2017) or students who are following a course in a related topic (Chiovitti et al., 2019). Depending on the type of study, researchers may also plan to support CS volunteers with training or technological aids, increasingly in the form of mobile apps (Ožana et al., 2019; Appenfeller et al., 2020).
A certain amount of error, either random or systematic, is likely to be introduced by the collection of data by CS volunteers, and study designs must account for this. The level of error can be reduced by allowing volunteers to provide clarifying metadata or to register uncertainty (Torre et al., 2019), or using incentives to reduce sampling bias (Callaghan et al., 2019), but researchers should also ensure that they have means to assess the accuracy of contributed data (Falk et al., 2019; Gibson et al., 2019). Much ecological research is based on large public databases of volunteer-contributed records of species distributions, phenological events and other observational data (e.g. Siljamo et al., 2020). There is an active discussion in the ecological research community about how to maximize the reliability and utility of such data (Ball-Damerow et al., 2019).
The particular considerations that have to be made in the design, execution and evaluation of CS studies has led to calls for dedicated standards and guidelines for CS research. Of course, any such tools must strike the balance between promoting appropriate levels of standardization and allowing the flexibility required for applications of CS methodologies across diverse settings and research questions. Whilst some progress has been made towards this goal, maintaining an open and constructive dialogue among CS practitioners and other stakeholders remains critical to ensure that researchers, volunteers and society are able to realize the full potential of CS.
To foster discussion of these important issues, PLOS ONE (@plosone) will be moderating a Twitter chat on citizen science methodologies on Thursday 2nd April starting at 4pm BST (8am PDT, 11am EDT, 5pm CET). This is a chance for the CS community to share perspectives, experiences and suggestions for best practice. We’ll aim to cover the following questions (and more!):
- How far can methods in CS projects be standardized?
- What steps should be taken to maximize CS data quality?
- Is there a need for clearer guidelines for the design and execution of CS studies?
- How should credit for data collection be apportioned?
You can take part by using the hashtag #citscichat– we hope to see you there!
Appenfeller LR, Lloyd S, Szendrei Z (2020) Citizen science improves our understanding of the impact of soil management on wild pollinator abundance in agroecosystems. PLoS ONE 15(3): e0230007. https://doi.org/10.1371/journal.pone.0230007
Ball-Damerow JE, Brenskelle L, Barve N, Soltis PS, Sierwald P, Bieler R, et al. (2019) Research applications of primary biodiversity databases in the digital age. PLoS ONE 14(9): e0215794. https://doi.org/10.1371/journal.pone.0215794
Callaghan CT, Rowley JJL, Cornwell WK, Poore AGB, Major RE (2019) Improving big citizen science data: Moving beyond haphazard sampling. PLoS Biol 17(6): e3000357. https://doi.org/10.1371/journal.pbio.3000357
Can ÖE, D’Cruze N, Balaskas M, Macdonald DW (2017) Scientific crowdsourcing in wildlife research and conservation: Tigers (Panthera tigris) as a case study. PLoS Biol 15(3): e2001001. https://doi.org/10.1371/journal.pbio.2001001
Chiovitti A, Thorpe F, Gorman C, Cuxson JL, Robevska G, Szwed C, et al. (2019) A citizen science model for implementing statewide educational DNA barcoding. PLoS ONE 14(1): e0208604. https://doi.org/10.1371/journal.pone.0208604
Falk S, Foster G, Comont R, Conroy J, Bostock H, Salisbury A, et al. (2019) Evaluating the ability of citizen scientists to identify bumblebee (Bombus) species. PLoS ONE 14(6): e0218614. https://doi.org/10.1371/journal.pone.0218614
Gibson KJ, Streich MK, Topping TS, Stunz GW (2019) Utility of citizen science data: A case study in land-based shark fishing. PLoS ONE 14(12): e0226782. https://doi.org/10.1371/journal.pone.0226782
Gouraguine A, Moranta J, Ruiz-Frau A, Hinz H, Reñones O, Ferse SCA, et al. (2019) Citizen science in data and resource-limited areas: A tool to detect long-term ecosystem changes. PLoS ONE 14(1): e0210007. https://doi.org/10.1371/journal.pone.0210007
Hallmann CA, Sorg M, Jongejans E, Siepel H, Hofland N, Schwan H, et al. (2017) More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12(10): e0185809. https://doi.org/10.1371/journal.pone.0185809
Larson RN, Brown JL, Karels T, Riley SPD (2020) Effects of urbanization on resource use and individual specialization in coyotes (Canis latrans) in southern California. PLoS ONE 15(2): e0228881. https://doi.org/10.1371/journal.pone.0228881
Lyons E, Zhang L (2019) Trade-offs in motivating volunteer effort: Experimental evidence on voluntary contributions to science. PLoS ONE 14(11): e0224946. https://doi.org/10.1371/journal.pone.0224946
Ožana S, Burda M, Hykel M, Malina M, Prášek M, Bárta D, et al. (2019) Dragonfly Hunter CZ: Mobile application for biological species recognition in citizen science. PLoS ONE 14(1): e0210370. https://doi.org/10.1371/journal.pone.0210370
Pescott OL, Walker KJ, Harris F, New H, Cheffings CM, Newton N, et al. (2019) The design, launch and assessment of a new volunteer-based plant monitoring scheme for the United Kingdom. PLoS ONE 14(4): e0215891. https://doi.org/10.1371/journal.pone.0215891
Rasmussen SL, Nielsen JL, Jones OR, Berg TB, Pertoldi C (2020) Genetic structure of the European hedgehog (Erinaceus europaeus) in Denmark. PLoS ONE 15(1): e0227205. https://doi.org/10.1371/journal.pone.0227205
Siljamo P, Ashbrook K, Comont RF, Skjøth CA (2020) Do atmospheric events explain the arrival of an invasive ladybird (Harmonia axyridis) in the UK? PLoS ONE 15(1): e0219335. https://doi.org/10.1371/journal.pone.0219335
Torre M, Nakayama S, Tolbert TJ, Porfiri M (2019) Producing knowledge by admitting ignorance: Enhancing data quality through an “I don’t know” option in citizen science. PLoS ONE 14(2): e0211907. https://doi.org/10.1371/journal.pone.0211907
Wepprich T, Adrion JR, Ries L, Wiedmann J, Haddad NM (2019) Butterfly abundance declines over 20 years of systematic monitoring in Ohio, USA. PLoS ONE 14(7): e0216270. https://doi.org/10.1371/journal.pone.0216270
All images used under Pixabay licence.
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