Phone-wielding and bare-armed, I follow Scott Edmunds and Mendel Wong to a small park in the Mid-Levels area of Hong Kong Island, where a dengue outbreak occurred last year. We hit the jackpot within five minutes – a swarm of mosquitoes around a tree. With his phone, Wong snaps a picture of one that lands on his arm, as well as the breeding site – a pile of discarded rubbish in the alley nearby. From the picture, we can clearly see the white lines on its legs, the distinctive characteristic of both the tiger mosquito (Aedes albopictus) and yellow fever mosquito (Aedes aegypti). “You don’t necessarily have to let it feed on you. You just need a clear picture of the front of its head,” says Wong. He uploads the picture to Mosquito Alert, an app which taps into the power of citizen science by allowing people to report sightings of mosquitoes and their breeding sites.

Source: Cheung, R., 2017. Hong Kong citizen scientists localise mosquito tracking app to let people report sightings of the disease carriers. South China Morning Post, 30 May.

As citizen science methodologies mature and number of participants increases, it is becoming more possible to understand the role and necessity of experts in relation to data quality. This article is a great example of how expertise can be assessed and utilized. — LFF —


  1. Citizen science data are increasingly making valuable contributions to ecological studies. However, many citizen science surveys are also designed to encourage wide participation and therefore the participants have a range of natural history expertise, leading to variation and potentially bias in the data.
  2. We assessed a recently proposed measure of observer expertise, calculated based on the average numbers of species recorded by observers. We investigated if this observer expertise score is associated with how often an observer records any individual species. Species reporting rates increased monotonically with the observer’s expertise score for 197 of 200 species, suggesting that this expertise score describes inter-observer variation in the detectability of individual species.
  3. Expertise scores were incorporated into single-species occupancy models as a covariate, to explain inter-observer variation in detectability. Including expertise as a detectability covariate led to improved model fit and improved predictive performance on validation data. The expertise score had a large effect on the estimated detectability, comparable in magnitude to the effect of the duration of the observation period.
  4. Expertise scores were also included into single-species occupancy models that estimated seasonal patterns in species occupancy and seasonal expertise effects. The addition of a seasonal effect of expertise led to improved model fit and increased predictive performance on validation data. The seasonal expertise variables accounted for bias that may be introduced by seasonal differences in the effect of expertise, caused by changes in the environment or species behaviour.
  5. Measures of observer expertise included in models as a covariate can account for heterogeneity and bias introduced by variable expertise, although in this example the differences in estimated occupancy were small. This method of incorporating observer expertise can be used in any regression model of species occurrence, occupancy, abundance, or density to produce more reliable ecological inference and may be most important where citizen science schemes encourage wide participation. Overall, the results highlight the value of recording observer identity and other detectability covariates, to control for sources of bias associated with the observation process.

Figure 3 from article, Johnston et al., 2017

Source: Johnston, A., Fink, D., Hochachka, W.M., Kelling, S., 2017. Estimates of observer expertise improve species distributions from citizen science data. Methods in Ecology and Evolution. DOI: 10.1111/2041-210X.12838

Citizen science opens new pathways that can complement traditional scientific practice. Intuition and reasoning often make humans more effective than computer algorithms in various realms of problem solving. In particular, a simple visual comparison of spatial patterns is a task where humans are often considered to be more reliable than computer algorithms. However, in practice, science still largely depends on computer based solutions, which inevitably gives benefits such as speed and the possibility to automatize processes. However, the human vision can be harnessed to evaluate the reliability of algorithms which are tailored to quantify similarity in spatial patterns. We established a citizen science project to employ the human perception to rate similarity and dissimilarity between simulated spatial patterns of several scenarios of a hydrological catchment model. In total, the turnout counts more than 2500 volunteers that provided over 43000 classifications of 1095 individual subjects. We investigate the capability of a set of advanced statistical performance metrics to mimic the human perception to distinguish between similarity and dissimilarity. Results suggest that more complex metrics are not necessarily better at emulating the human perception, but clearly provide auxiliary information that is valuable for model diagnostics. The metrics clearly differ in their ability to unambiguously distinguish between similar and dissimilar patterns which is regarded a key feature of a reliable metric. The obtained dataset can provide an insightful benchmark to the community to test novel spatial metrics.

Source: Koch, J., Stisen, S., 2017. Citizen science: A new perspective to advance spatial pattern evaluation in hydrology. PLoS ONE 12(5): e0178165. https://doi.org/10.1371/journal.pone.0178165

Editor’s Choice: This article will get your mental wheels turning about identity, power, and the nature of work at disciplinary boundaries. The authors carefully scrutinize words to unpack divergent connotations, examining how positive, neutral, and negative associations influence us. This is a must-read for all who aspire to make “citizen science” a just, inclusive, and equitable endeavor. — AWA —

Abstract: Much can be at stake depending on the choice of words used to describe citizen science, because terminology impacts how knowledge is developed. Citizen science is a quickly evolving field that is mobilizing people’s involvement in information development, social action and justice, and large-scale information gathering. Currently, a wide variety of terms and expressions are being used to refer to the concept of ‘citizen science’ and its practitioners. Here, we explore these terms to help provide guidance for the future growth of this field. We do this by reviewing the theoretical, historical, geopolitical, and disciplinary context of citizen science terminology; discussing what citizen science is and reviewing related terms; and providing a collection of potential terms and definitions for ‘citizen science’ and people participating in citizen science projects. This collection of terms was generated primarily from the broad knowledge base and on-the-ground experience of the authors, by recognizing the potential issues associated with various terms. While our examples may not be systematic or exhaustive, they are intended to be suggestive and invitational of future consideration. In our collective experience with citizen science projects, no single term is appropriate for all contexts. In a given citizen science project, we suggest that terms should be chosen carefully and their usage explained; direct communication with participants about how terminology affects them and what they would prefer to be called also should occur. We further recommend that a more systematic study of terminology trends in citizen science be conducted.

Source: Eitzel, M.V. et al., 2017. Citizen Science Terminology Matters: Exploring Key Terms. Citizen Science: Theory and Practice. 2(1), p.1. DOI: http://doi.org/10.5334/cstp.96

Scientists expect Africa to be hardest hit by climate change: its dependence on agriculture, hot temperatures, and poor infrastructure mean its citizens are likely to feel the pressure of a changing climate more than most others. But there are big gaps in our knowledge of how ecosystems and microclimates work.
“We don’t have an operating manual for the planet and we need one,” says Guy Midgley, a professor at Stellenbosch University who was part of the United Nations’ climate change panel. “It’s frustrating to see incomplete knowledge being implemented as policy.

“It’s why we need more science.”

That’s where rePhotoSA comes in: this ambitious citizen science project wants to recreate southern African historic landscapes through photography, and compare new and old vistas.

Source: Wild, S., 2017. Climate change’s impact on Southern Africa is captured in the photos of a citizen science project — Quartz. 3 June. Available at https://qz.com/996437/the-photos-from-a-citizen-science-project-capture-southern-africas-climate-change-future/ [Last accessed 3 July 2017].

Editor’s Choice: This is an excellent example of real “co-created” science between a non-salaried scientist (aka citizen scientist) and salaried scientists. –LFF–

Amateur naturalists from the UK have a distinguished pedigree, from Henry Walter Bates and Marianne North, to Alfred Russel Wallace and Mary Anning. But arguably, the rise of post-war academia in the fifties displaced them from mainstream scientific discourse and discovery. Recently, there has been a resurgence of the ‘citizen scientist’, like me, in the UK and elsewhere – although the term may refer to more than one kind of beast.

To me, the ‘citizen scientist’ label feels a little patronising – conveying an image of people co-opted en masse for top-down, scientist-led, large-scale biological surveys. That said, scientist-led surveys can offer valid contributions to conservation and documentation of the effects of climate change (among other objectives). They also engage the public (not least children) in science, although volunteers usually have an interest in natural history and science already. For me though, the real excitement comes in following a bottom-up path: making my own discoveries and approaching scientists for assistance with my projects.

Source: Grieves, Chris, 2017. Bottom-up Citizen Science and Biodiversity Statistics. 6 June. Available at methods.blog, https://methodsblog.wordpress.com/2017/06/06/citizen-science-biodiversity-statistics/

Reef Check Australia (RCA) has collected data on benthic composition and cover at > 70 sites along > 1000 km of Australia’s Queensland coast from 2002 to 2015. This paper quantifies the accuracy, precision and power of RCA benthic composition data, to guide its application and interpretation. A simulation study established that the inherent accuracy of the Reef Check point sampling protocol is high (<± 7% error absolute), in the range of estimates of benthic cover from 1% to 50%. A field study at three reef sites indicated that, despite minor observer- and deployment-related biases, the protocol does reliably document moderate ecological changes in coral communities. The error analyses were then used to guide the interpretation of inter-annual variability and long term trends at three study sites in RCA's major 2002–2015 data series for the Queensland coast. Source: Done, T., Roelfsema, C., Harvey, A., Schuller, L., Hill, J., Schläppy, M-L., Lea, A., Bauer-Civiello, A., Loder, J., 2017. Reliability and utility of citizen science reef monitoring data collected by Reef Check Australia, 2002–2015. Marine Pollution Bulletin: Volume 117, Issues 1–2, 15 April 2017, Pages 148–155. https://doi.org/10.1016/j.marpolbul.2017.01.054

Abstract: Small-scale pollution events involve the release of potentially harmful substances into the marine environment. These events can affect all levels of the ecosystem, with damage to both fauna and flora. Numerous reporting structures are currently available to document spills, however there is a lack of information on small-scale events due to their magnitude and patchy distribution. To this end, volunteers may provide a useful tool in filling this data gap, especially for coastal environments with a high usage by members of the public. The potential for citizen scientists to record small-scale pollution events is explored using the UK as an example, with a focus on highlighting methods and issues associated with using this data source. An integrated monitoring system is proposed which combines citizen science and traditional reporting approaches.

Source: Hyder, K., Wright, S., Kirby, M., Brant, J, 2017. The role of citizen science in monitoring small-scale pollution events. Marine Pollution Bulletin in press. DOI: https://doi.org/10.1016/j.marpolbul.2017.04.038

Agricultural workers have long been collecting data on the natural world, but opening up opportunities for farmers to share information raises questions about who would participate and why. This study is unusual for involving farmers in developing countries as potential stakeholders in citizen science, for whom participation may be meaningfully connected to livelihood. While most farmers in all three countries (Honduras, India, & Ethiopia) owned and regularly used cell phones for information purposes, their usage patterns, preferences, and needs were quite different from the users targeted by most citizen science projects, so the results clearly reinforce the importance of culturally-sensitive project and technology designs. — AW, Guest Editor

Map of locations of study participants in Honduras, Ethiopia, and India.

Farmers were recruited from agricultural communities in three developing countries.

As the sustainability of agricultural citizen science projects depends on volunteer farmers who contribute their time, energy and skills, understanding their motivation is important to attract and retain participants in citizen science projects. The objectives of this study were to assess 1) farmers’ motivations to participate as citizen scientists and 2) farmers’ mobile telephone usage. Building on motivational factors identified from previous citizen science studies, a questionnaire based methodology was developed which allowed the analysis of motivational factors and their relation to farmers’ characteristics. The questionnaire was applied in three communities of farmers, in countries from different continents, participating as citizen scientists. We used statistical tests to compare motivational factors within and among the three countries. In addition, the relations between motivational factors and farmers characteristics were assessed. Lastly, Principal Component Analysis (PCA) was used to group farmers based on their motivations. Although there was an overlap between the types of motivations, for Indian farmers a collectivistic type of motivation (i.e., contribute to scientific research) was more important than egoistic and altruistic motivations. For Ethiopian and Honduran farmers an egoistic intrinsic type of motivation (i.e., interest in sharing information) was most important. While fun has appeared to be an important egoistic intrinsic factor to participate in other citizen science projects, the smallholder farmers involved in this research valued ‘passing free time’ the lowest. Two major groups of farmers were distinguished: one motivated by sharing information (egoistic intrinsic), helping (altruism) and contribute to scientific research (collectivistic) and one motivated by egoistic extrinsic factors (expectation, expert interaction and community interaction). Country and education level were the two most important farmers’ characteristics that explain around 20% of the variation in farmers motivations. For educated farmers, contributing to scientific research was a more important motivation to participate as citizen scientists compared to less educated farmers. We conclude that motivations to participate in citizen science are different for smallholders in agriculture compared to other sectors. Citizen science does have high potential, but easy to use mechanisms are needed. Moreover, gamification may increase the egoistic intrinsic motivation of farmers.

Source: Beza, E., Steinke,  J., van Etten, J., Reidsma, P., Fadda, C., Mittra, S., et al., 2017. What are the prospects for citizen science in agriculture? Evidence from three continents on motivation and mobile telephone use of resource-poor farmers. PLoS ONE 12(5): e0175700. https://doi.org/10.1371/journal.pone.0175700

These computing researchers took a critical look at the current practice of citizen science with the goal of informing the development of tools and technologies. They asked two seemingly simple questions: what drives people to participate, and what are productive ways for people to participate? In the analysis, I particularly like the nuanced understanding of the space that they developed, describing the complex multi-dimensional tradeoffs that must be made and the types of bottom-line impacts those decisions have on both participants and science. Ultimately, the authors recommend applying different design principles for three “ideal types” of project designs, which are readily recognizable through parallels to numerous other citizen science typology papers. — AW, Guest Editor

Excerpt: “An early focus on generalizable research – notably, on infrastructure for collaboration and data collection, things that computing research does well – risks losing sight of some of the particulars of citizen science as it is actually practiced. Such efforts often overlook: (1) the behavioral motivations of citizens to contribute to particular environmental causes, (2) the capacity of citizens to participate in activities necessary to a meaningful scientific campaign, and (3) the alignment of technologies with the ultimate goals of the collective scientific effort.”

Abstract: In this paper we consider various genres of citizen science from the perspective of citizen participants. As a mode of scientific inquiry, citizen science has the potential to “scale up” scientific data collection efforts and increase lay engagement with science. However, current technological directions risk losing sight of the ways in which citizen science is actually practiced. As citizen science is increasingly used to describe a wide range of activities, we begin by presenting a framework of citizen science genres. We then present findings from four interlocking qualitative studies and technological interventions of community air quality monitoring efforts, examining the motivations and capacities of citizen participants and characterizing their alignment with different types of citizen science. Based on these studies, we suggest that data acquisition involves complex multi-dimensional tradeoffs, and the commonly held view that citizen science systems are a win-win for citizens and science may be overstated.

Figure 1. Different types of data-centric community and science practice. Studies 2-4 show the focus of our fieldwork.

Source: Aoki, P., Woodruff, A., Yellapragada, B., Willett, W., 2017. Environmental Protection and Agency: Motivations, Capacity, and Goals in Participatory Sensing. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 3138-3150. DOI: 10.1145/3025453.3025667.