Today, our natural world is suffering rapid alterations at large scales as consequence of environmental problems like climate change, biodiversity loss, and pollution. Impacts like warming temperatures, modified rainfall patterns, as well as droughts and extreme events can shift species historic habitat ranges, as they “follow” livable conditions; or these impacts can also be factors in the local or total extinction of species. When habitat ranges change, species may come into contact with new wildlife, which could have implications for conservation and human health [1, 2]. Pollution can result in the release of toxins into the environment, impacting air and water quality, creating hazards for wildlife and humans alike. The loss of biodiversity can also negatively affect the functioning of ecosystems and their associated services . To learn more about these problems, identify their consequences, and develop effective solutions, scientists are in need of one important thing: data .
Without data, it is difficult for scientists to know both the current and previous state of the environment (one might consider this the baseline state, but there can be issues with that – see our previous article on the blog!). This makes it difficult to identify potentially harmful changes that could be occurring or even monitor progress of implemented solutions. Missing or unavailable information also hinders the ability of researchers to model future scenarios and make predictions. Without data, scientists cannot make specialized recommendations or inform policymakers of risks associated with various pathways. Despite this importance, data scarcity remains a problem in environmental science , partly due to a lack of access to existing data as well as insufficient monitoring or interpretation capacity.
What can help address this problem?
Lack of access to existing environmental or ecological data can be solved by things like supporting data sharing and open data or following FAIR principles . With data readily available, it can continue to be useful through time to a wider group who may provide reanalysis or reinterpretation. This can also allow for previously existing data to be integrated into new data, thus providing the opportunity for more robust results and conclusions. Additionally, in science it is important to make data available so that results can be replicated and trusted [4, 5]. Another part of the answer is to ensure that conditions are right for collection of new data . This means enhancing capacity building and technology transfer, particularly for researchers in vulnerable areas.
Yet, the investigation of many environmental problems requires both the continual collection and interpretation of very large amounts of data, in some cases from very different parts of the world – which can be a struggle for even the best equipped teams of professional researchers, as they face limitations in their available time and numbers. In such cases, so-called citizen scientists can step in.
Citizen Science: the public can be part of the solution
An increase in the availability and sophistication of technology, data storage and sharing options via the internet, and education have opened possibilities for a wider range of participation in the scientific process. This has contributed to the growth of citizen science, which is a term that describes when the public engages in scientific research. Through citizen science, individuals across many sectors or areas can collaborate with scientists and/or each other to contribute to an increase in scientific knowledge across social science, the arts, technology, medicine, or natural science. It allows participants to take part in the scientific process and creates other co-benefits for citizen scientist participants as well as researchers [6, 8].
Citizen science can provide opportunity to fill gaps in data collection across time and space. In the environmental area, volunteers might participate by submitting observations or tracking abundance of species, taking samples to determine water quality, interpreting images, or other tasks. Yet, participants’ roles may extend past data collection as well. Citizens may help by identifying needs or problems that research could focus on, refining scientific questions, stimulating engagement within the public and among stakeholders, and otherwise providing input from perspectives that often go underrepresented, including from indigenous and local community members [6, 7]. For instance, in community-led citizen science (CCS), participants – aided by professional scientists – direct their own projects, which provides both scientific understanding as well as empowerment and local ownership of the initiative and its outcomes .
One important CCS example lies in the Amazon, where the construction of the Belo Monte hydroelectric project has severely impacted the flow of the Xinga river since 2016. This has resulted in the loss of breeding habitat and the decimation of fish populations on which local communities like the Juruna rely. The Juruna reached out to scientists to help them document changes in the river’s fish and turtle populations. This collaboration has resulted in not only scientific publications, but has additionally helped the Juruna to document what has been lost to them culturally so that their history is not forgotten. The data have been used to propose more ecologically-sound water regimes- though the Brazilian Institute of Environment and Renewable Natural Resources is still reviewing the proposal- and have also fuelled lawsuits against Norte Energia, the company responsible for Belo Monte .
These projects are thus beneficial for both the participants and the professional scientists, resulting in opportunities for problem-solving, learning, and public action as well as the generation of data and the publication of research findings. Outcomes of citizen science can also inform management, conservation actions, education, or policy decisions [6, 7].
Nevertheless, it is important to note that methods of citizen science are not compatible with all research projects, particularly when an initiative requires expensive equipment, utilizes complex or rigorous methods for data collection, or calls for a large time commitment. Concerns may also arise about the data generated by citizen science. For instance, sampling bias may be a problem if data is collected opportunistically, leading to an overrepresentation of data from some areas versus others. Citizen scientists may make mistakes in identification, be inconsistent in following protocol or using equipment, or lack neutrality, which would all impact the data and how it can be used. However, by implementing sufficient training for participants, validation and filtering procedures, statistical approaches, and upholding inclusiveness, these issues can be minimized .
The benefits of citizen science are also being recognized by governments, who are increasingly supporting this approach. For example, in 2022 Germany introduced the Citizen Science Strategie 2030 (German language version here). This strategy includes recommendations and outlines opportunities to develop citizen science in Germany and interlink it within both science and society . Though this method is not appropriate for every research question, it is increasingly recognized as a useful and enriching approach that can, when properly executed, produce a variety of rewards for stakeholders across disciplines.
If this article has made you interested in learning more about citizen science, you might be glad to know that the University of Zurich and ETH Zurich will be hosting a Citizen Science Summer School from 04.06.2023 – 09.06.2023. Applications are now open until 01.03.2023!
 IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32. Link here.
 Pecl, G., Araújo, M.B., Bell, J.D., et al. 2017. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science (355) 6332. Link here.
 Hochkirch, A., Samways, M.J., Gerlach, J. 2020. A strategy for the next decade to address data deficiencyin neglected biodiversity. Conservation Biology 35 (2): 502–509. Link here.
 Tedersoo, L., Küngas, R., Oras, E. et al. 2021. Data sharing practices and data availability upon request differ across scientific disciplines. Sci Data (8) 192. Link here.
 Miyakawa, T. 2020. No raw data, no science: another possible source of the reproducibility crisis. Mol Brain (13) 24. Link here.
 Fraisl, D., Hager, G., Bedessem, B., et al. 2022. Citizen science in environmental and ecological sciences. Nat Rev Methods Primers (2) 64. Link here.
 McKinley, D.C., Miller-Rushing, A.J., Ballard, H.L., et al. 2017. Citizen science can improve conservation science, natural resource management, and environmental protection. Biological Conservation (208): 15-28. Link here.
 ECSA (European Citizen Science Association). 2015. Ten Principles of Citizen Science. Berlin. Link here.
 Moutinho, S. 2023. “A river’s pulse”. Science (379) 6627: 18-23. Link here.
 Bonn, A., Brink, W., Hecker, S., et al. 2022. White Paper Citizen Science Strategy 2030 for Germany. Helmholtz Association, Leibniz Association, Fraunhofer Society, universities and non-academic institutions. Link here.