Editor’s Choice: A fast-moving area of citizen science is in tackling “Big Data” problems by coupling the ability of machine algorithms to quickly process well-defined images with the human ability to discern patterns in images that are not as well-defined. This article describes Gravity Spy – a recent project that is at the vanguard of exploring the incredibly rich ways in which the combination of humans and machines can lead to the best possible outcomes.  –LFF–


Gravity Spy is a citizen science project that draws on the contributions of both humans and machines to achieve its scientific goals. The system supports the Laser Interferometer Gravitational Observatory (LIGO) by classifying “glitches” that interfere with observations. The system makes three advances on the current state of the art: explicit training for new volunteers, synergy between machine and human classification and support for discovery of new classes of glitch. As well, it provides a platform for human-centred computing research on motivation, learning and collaboration. The system has been launched and is currently in operation.

Source: Gravity Spy: Humans, machines and the future of citizen science

Editor’s Choice: One of the more interesting issues in data collection citizen science is how to know whether a “zero counts” measurement really means nothing was there. This paper describes a Bayesian inference method to obtain occupancy probabilities providing a potential solution to this issue.  — LFF —


Occupancy monitoring is particularly suitable for freshwater turtles because many species are relatively easy to detect due to their basking behavior. However, the probability of detecting turtles can be highly variable. There are sophisticated methods available for accounting for detection probability in occupancy monitoring, but standard sampling designs involve surveying all sites several times. Here I illustrate a method whereby an accessible reference site was repeatedly surveyed to obtain models of detection probability for three turtle species, and these models then applied to surveys of 23 water bodies in two nearby conservation reserves with similar habitat. The explanatory variables for detection probability included the date, time of day, and a set of environmental measurements designed to capture the key factors likely to affect basking. The estimated probability of detecting a species present at a water body ranged from 0.10–0.99 for Painted Turtles (Chrysemys picta), 0.03–0.84 for Blanding’s Turtles (Emydoidea blandingii), and 0.01–0.60 for Snapping Turtles (Chelydra serpentina). All species were unlikely to be detected in overcast conditions, but the other factors affecting detection varied among species. I used Bayesian inference to estimate the posterior (post-survey) occupancy probabilities for water bodies where a species was not detected, illustrating that the surveys gave strong evidence of absence in some cases but provided little information in others. I believe the method could be usefully applied to regional monitoring programs for some turtle species, as the field surveying does not require specialist equipment or training, so lends itself to citizen science.

Source: Using Reference Sites to Account for Detection Probability in Occupancy Surveys for Freshwater Turtles


Turning meadow restoration into cleaner air is the goal of researchers at the University of Nevada, Reno. The Soil Science Laboratory at the University recently partnered with the Earthwatch Institute, an international citizen science research organization, to better understand how restoration and plant communities relate to the soil carbon in Sierra Nevada mountain meadows.

While the research area stretches much of the length of the Sierra Nevada from Mt. Lassen to Bishop, Calif., this recent research was performed in four meadows in the Truckee River watershed and involved about 75 volunteer participants from northern California and the Reno area, including a student organization at Hug High School in Reno; students in a University of Nevada, Reno English composition class on “Science and Society;” and students from the University of California, Berkeley Forestry Camp. The project focused on measuring the carbon stored in plants and determining if vegetation is an important indicator of the amount of carbon below ground.

Source: Meadow restoration studied for potential to build carbon credits in California


Pokémon Go, an augmented reality (AR) smartphone game, replicates many aspects of real-world wildlife watching and natural history by allowing players to find, capture, and collect Pokémon, which are effectively virtual animals. In this article, we consider how the unprecedented success of Pokémon Go as a smartphone game might create opportunities and challenges for the conservation movement. By encouraging players to go outside and consider various aspects of virtual species’ biology, the game could increase awareness and engagement with real-world nature. However, interacting with Pokémon could alternatively encourage exploitation of wildlife or replace players’ desire to interact with real-world nature. We suggest a number of ways in which Pokémon Go could be adapted to increase its conservation impact, and how new conservation-orientated AR games could be created. We conclude that Pokémon Go sets a precedent for well-implemented AR games from which the conservation movement could borrow a number of ideas.

Source: Pokémon Go: benefits, costs, and lessons for the conservation movement.


Although participation of citizen scientists is critical for a success of citizen science projects (a distinctive form of crowdsourcing), little attention has been paid to what types of messages can effectively recruit citizen scientists. Derived from previous studies on citizen scientists’ motivations, we created and sent participants one of four recruiting messages for a new project, Gravity Spy, appealing to different motivations (i.e., learning about science, social proof, contribution to science, and altruism). Counter to earlier studies on motivation, our results showed that messages appealing to learning, contribution and social proof were more effective than a message appealing to altruism. We discuss the inconsistency between the present and prior study results and plans for future work.

Source: Recruiting messages matter: Message strategies to attract citizen scientists


Since 2012, three organizations advancing the work of citizen science practitioners have arisen in different regions: The primarily US-based but globally open Citizen Science Association (CSA), the European Citizen Science Association (ECSA), and the Australian Citizen Science Association (ACSA). These associations are moving rapidly to establish themselves and to develop inter-association collaborations. We consider the factors driving this emergence and the significance of this trend for citizen science as a field of practice, as an area of scholarship, and for the culture of scientific research itself.

Source: Associations for Citizen Science: Regional Knowledge, Global Collaboration

Editor’s Choice:  This work makes an excellent case for strategic interplay between citizen science projects and monitoring biodiversity by remote sensing. By combining the power of each method, greater gains can be made on critical biodiversity and conservation goals.  –LFF–


To meet collective obligations towards biodiversity conservation and monitoring, it is essential that the world’s governments and non-governmental organisations as well as the research community tap all possible sources of data and information, including new, fast-growing sources such as citizen science (CS), in which volunteers participate in some or all aspects of environmental assessments. Through compilation of a database on CS and community-based monitoring (CBM, a subset of CS) programs, we assess where contributions from CS and CBM are significant and where opportunities for growth exist. We use the Essential Biodiversity Variable framework to describe the range of biodiversity data needed to track progress towards global biodiversity targets, and we assess strengths and gaps in geographical and taxonomic coverage. Our results show that existing CS and CBM data particularly provide large-scale data on species distribution and population abundance, species traits such as phenology, and ecosystem function variables such as primary and secondary productivity. Only birds, Lepidoptera and plants are monitored at scale. Most CS schemes are found in Europe, North America, South Africa, India, and Australia. We then explore what can be learned from successful CS/CBM programs that would facilitate the scaling up of current efforts, how existing strengths in data coverage can be better exploited, and the strategies that could maximise the synergies between CS/CBM and other approaches for monitoring biodiversity, in particular from remote sensing. More and better targeted funding will be needed, if CS/CBM programs are to contribute further to international biodiversity monitoring.

Detail of Figure 2 from the article: Distribution of species records made available to the Global Biodiversity Information Facility (GBIF) by citizen science data providers for Aves.

Figure 2 [detail]: Distribution of species records made available to the Global Biodiversity Information Facility (GBIF) by citizen science data providers for Aves.

Image credit: A detail from Figure 2, appearing in the original article.

Source: Contribution of citizen science towards international biodiversity monitoring

Editor’s Choice: I picked this video for two reasons (1) because iNaturalist is a great platform and Mary Ellen Hannibal eloquently explains why platforms such as iNaturalist are key tools for conservation science and (2) because this is a link to a YouTube video, I wanted to highlight the point of Citizen Science Today as an aggregator for citizen science information of all content types. –LFF–

Video summary:

In her latest book, Citizen Scientist: Searching for Heroes and Hope in an Age of Extinction, science and culture writer Mary Ellen Hannibal writes about the history of citizen science and many of its current forms, including the California Academy of Sciences’ Citizen Science program, including iNaturalist! Here’s a short chat we [California Academy of Sciences] had with her about citizen science, and iNat, and more!

Video still: A talk with author Mary Ellen Hannibal about citizen science and iNaturalist

Video still: A talk with author Mary Ellen Hannibal about citizen science and iNaturalist

Image source: Video still, published by California Academy of Sciences

Source: A talk with author Mary Ellen Hannibal about citizen science and iNaturalist – YouTube


Recent freshwater policy reforms in New Zealand promote increased community involvement in freshwater decision making and management. Involving community members in scientific monitoring increases both their knowledge and their ability to discuss this knowledge with professionals, potentially increasing their influence in decision-making processes. However, these interactions rarely occur because, in particular, of perceptions that volunteer-collected data are unreliable. We assessed the agreement between volunteer (community group) and local government (regional council) data at nine stream sites across New Zealand. Over 18 months, community groups and regional council staff monitored, in parallel, a common set of water quality variables, physical habitat, periphyton and benthic macroinvertebrates that are routinely used by regional councils for statutory state of environment reporting. Community groups achieved close agreement (correlations ≥ 0.89, bias < 1%) with regional councils for temperature, electrical conductivity, visual water clarity, and Escherichia coli. For dissolved oxygen, nitrate, and pH, correlations were weaker (0.2, 0.53, and 0.4, respectively). Volunteer assessments of physical habitat were as consistent over time as those of councils. For visual assessments of thick periphyton growths (% streambed cover), volunteers achieved a correlation of 0.93 and bias of 0.1% relative to councils. And for a macroinvertebrate biotic index that indicates water and habitat quality, correlation was 0.88, bias was < 5%, and the average difference was 12% of the index score. Volunteers showed increased awareness of local freshwaters, understanding of stream ecosystems, and attentiveness to local and national freshwater issues. Most volunteers had shared their knowledge and interest with others in their community. Most groups had developed relationships with their regional council, and some volunteers became more interested in engaging in freshwater decision making. Given adequate professional support, community-based water monitoring can provide data reliable enough to augment professionally collected data, and increase the opportunities, confidence, and skills of community members to engage in freshwater decision making.

Source: Volunteer stream monitoring: Do the data quality and monitoring experience support increased community involvement in freshwater decision making?


Ecological and environmental citizen-science projects have enormous potential to advance scientific knowledge, influence policy, and guide resource management by producing datasets that would otherwise be infeasible to generate. However, this potential can only be realized if the datasets are of high quality. While scientists are often skeptical of the ability of unpaid volunteers to produce accurate datasets, a growing body of publications clearly shows that diverse types of citizen-science projects can produce data with accuracy equal to or surpassing that of professionals. Successful projects rely on a suite of methods to boost data accuracy and account for bias, including iterative project development, volunteer training and testing, expert validation, replication across volunteers, and statistical modeling of systematic error. Each citizen-science dataset should therefore be judged individually, according to project design and application, and not assumed to be substandard simply because volunteers generated it.

Source: Assessing data quality in citizen science