Greg Newman

The future of citizen science: emerging technologies and shifting paradigms

Citizen science creates a nexus between science and education that, when coupled with emerging technologies, expands the frontiers of ecological research and public engagement. Using representative technologies and other examples, we examine the future of citizen science in terms of its research processes, program and participant cultures, and scientific communities. Future citizen-science projects will likely be influenced by sociocultural issues related to new technologies and will continue to face practical programmatic challenges. We foresee networked, open science and the use of online computer/video gaming as important tools to engage non-traditional audiences, and offer recommendations to help prepare project managers for impending challenges. A more formalized citizen-science enterprise, complete with networked organizations, associations, journals, and cyberinfrastructure, will advance scientific research, including ecology, and further public education.

User-friendly web mapping: lessons from a citizen science website

Citizen science websites are emerging as a common way for volunteers to collect and report geographic ecological data. Engaging the public in citizen science is challenging and, when involving online participation, data entry, and map use, becomes even more daunting. Given these new challenges, citizen science websites must be easy to use, result in positive overall satisfaction for many different users, support many different tasks, and ensure data quality. To begin reaching these goals, we built a geospatially enabled citizen science website, evaluated its usability, and gained experience by working with and listening to citizens using the website. We sought to determine general perceptions, discover potential problems, and iteratively improve website features. Although the website was rated positively overall, map-based tasks identified a wide range of problems. Given our results, we redesigned the website, improved the content, enhanced the ease of use, simplified the map interface, and added features. We discuss citizen science websites in relation to online Public Participation Geographic Information Systems, examine the role(s) websites may play in the citizen science research model, discuss how citizen science research advances GIScience, and offer guidelines to improve citizen-based web mapping applications.

The art and science of multi-scale citizen science support

Abstract Citizen science and community-based monitoring programs are increasing in number and breadth, generating volumes of scientific data. Many programs are ill-equipped to effectively manage these data. We examined the art and science of multi-scale citizen science support, focusing on issues of integration and flexibility that arise for data management when programs span multiple spatial, temporal, and social scales across many domains. Our objectives were to: (1) briefly review existing citizen science approaches and data management needs; (2) propose a framework for multi-scale citizen science support; (3) develop a cyber-infrastructure to support citizen science program needs; and (4) describe lessons learned. We find that approaches differ in scope, scale, and activities and that the proposed framework situates programs while guiding cyber-infrastructure system development. We built a cyber-infrastructure support system for citizen science programs ( www.citsci.org ) and show that carefully designed systems can be adept enough to support programs at multiple spatial and temporal scales across many domains when built with a flexible architecture. The advantage of a flexible, yet controlled, cyber-infrastructure system lies in the ability of users with different levels of permission to easily customize the features themselves, while adhering to controlled vocabularies necessary for cross-discipline comparisons and meta-analyses. Program evaluation tied to this framework and integrated into cyber-infrastructure support systems will improve our ability to track effectiveness. We compare existing systems and discuss the importance of standards for interoperability and the challenges associated with system maintenance and long-term support. We conclude by offering a vision of the future of citizen science data management and cyber-infrastructure support.

Does Participation in Citizen Science Improve Scientific Literacy? A Study to Compare Assessment Methods.

This study investigated the use of a contextually sensitive instrument to assess the effect of invasive species monitoring training on the scientific literacy of citizen volunteers. The authors measured scientific literacy scores before and after 57 citizens participated in a 2-day event to learn to monitor invasive species with an instrument including 1 general-measures (Science and Engineering Indicator [SEI]) item and 4 newly developed contextual items. Ninety control subjects were also tested with a mailed survey that included the SEI item and the contextual items. Control scores, compared with trainees’ pretest scores with the chi-square (SEI) and independent-samples t-test (contextual), did not differ significantly from the pretest scores of trainees on either the SEI (p = .68) or the contextual (p = .11) items. The authors compared trainees’ pretest scores with their posttest scores using McNemars chi-square (SEI) and a paired-samples t-test (contextual). Posttest scores on the SEI item were not significantly (p = .52) different from pretest scores. However, posttest scores on the contextual instrument were significantly (p = .007) higher than those on the pretest. The authors’ multi-item context-sensitive instrument detected significant science literacy gains that were not detected by the single generalized SEI item. Multi-item contextual instruments may offer a promising, feasible approach for the development of new instruments to assess the effect of training in invasive species monitoring, and possibly other types of citizen science programs, on the scientific literacy of citizen scientists.

Teaching Citizen Science Skills Online: Implications for Invasive Species Training Programs

Citizen science programs are emerging as an efficient way to increase data collection and help monitor invasive species. Effective invasive species monitoring requires rigid data quality assurances if expensive control efforts are to be guided by volunteer data. To achieve data quality, effective online training is needed to improve field skills and reach large numbers of remote sentinel volunteers critical to early detection and rapid response. The authors evaluated the effectiveness of online static and multimedia tutorials to teach citizen science volunteers (n = 54) how to identify invasive plants; establish monitoring plots; measure percent cover; and use Global Positioning System (GPS) units. Participants trained using static and multimedia tutorials provided less (p < .001) correct species identifications (63% and 67%) than did professionals (83%) across all species, but they did not differ (p = .125) between each other. However, their ability to identify conspicuous species was comparable to that of professionals. The variability in percent plant cover estimates between static (±10%) and multimedia (±13%) participants did not differ (p = .86 and .08, respectively) from those of professionals (±9%). Trained volunteers struggled with plot setup and GPS skills. Overall, the online approach used did not influence conferred field skills and abilities. Traditional or multimedia online training augmented with more rigorous, repeated, and hands-on, in-person training in specialized skills required for more difficult tasks will likely improve volunteer abilities, data quality, and overall program effectiveness.

Studying citizen science through adaptive management and learning feedbacks as mechanisms for improving conservation

Citizen science has generated a growing interest among scientists and community groups, and citizen science programs have been created specifically for conservation. We examined collaborative science, a highly interactive form of citizen science, which we developed within a theoretically informed framework. In this essay, we focused on 2 aspects of our framework: social learning and adaptive management. Social learning, in contrast to individual-based learning, stresses collaborative and generative insight making and is well-suited for adaptive management. Adaptive-management integrates feedback loops that are informed by what is learned and is guided by iterative decision making. Participants engaged in citizen science are able to add to what they are learning through primary data collection, which can result in the real-time information that is often necessary for conservation. Our work is particularly timely because research publications consistently report a lack of established frameworks and evaluation plans to address the extent of conservation outcomes in citizen science. To illustrate how our framework supports conservation through citizen science, we examined how 2 programs enacted our collaborative science framework. Further, we inspected preliminary conservation outcomes of our case-study programs. These programs, despite their recent implementation, are demonstrating promise with regard to positive conservation outcomes. To date, they are independently earning funds to support research, earning buy-in from local partners to engage in experimentation, and, in the absence of leading scientists, are collecting data to test ideas. We argue that this success is due to citizen scientists being organized around local issues and engaging in iterative, collaborative, and adaptive learning.

Bringing Modeling to the Masses: A Web Based System to Predict Potential Species Distributions

Predicting current and potential species distributions and abundance is critical for managing invasive species, preserving threatened and endangered species, and conserving native species and habitats. Accurate predictive models are needed at local, regional, and national scales to guide field surveys, improve monitoring, and set priorities for conservation and restoration. Modeling capabilities, however, are often limited by access to software and environmental data required for predictions. To address these needs, we built a comprehensive web-based system that: (1) maintains a large database of field data; (2) provides access to field data and a wealth of environmental data; (3) accesses values in rasters representing environmental characteristics; (4) runs statistical spatial models; and (5) creates maps that predict the potential species distribution. The system is available online at www.niiss.org, and provides web-based tools for stakeholders to create potential species distribution models and maps under current and future climate scenarios.

Regional data refine local predictions: modeling the distribution of plant species abundance on a portion of the central plains

Species distribution models are frequently used to predict species occurrences in novel conditions, yet few studies have examined the consequences of extrapolating locally collected data to regional landscapes. Similarly, the process of using regional data to inform local prediction for species distribution models has not been adequately evaluated. Using boosted regression trees, we examined errors associated with extrapolating models developed with locally collected abundance data to regional-scale spatial extents and associated with using regional data for predictions at a local extent for a native and non-native plant species across the northeastern central plains of Colorado. Our objectives were to compare model results and accuracy between those developed locally and extrapolated regionally, those developed regionally and extrapolated locally, and to evaluate extending species distribution modeling from predicting the probability of presence to predicting abundance. We developed models to predict the spatial distribution of plant species abundance using topographic, remotely sensed, land cover and soil taxonomic predictor variables. We compared model predicted mean and range abundance values to observed values between local and regional. We also evaluated model prediction performance based on Pearson’s correlation coefficient. We show that: (1) extrapolating local models to regional extents may restrict predictions, (2) regional data can help refine and improve local predictions, and (3) boosted regression trees can be useful to model and predict plant species abundance. Regional sampling designed in concert with large sampling frameworks such as the National Ecological Observatory Network may improve our ability to monitor changes in local species abundance.

CitSci.org: A New Model for Managing, Documenting, and Sharing Citizen Science Data.

Citizen science projects have the potential to advance science by increasing the volume and variety of data, as well as innovation. Yet this potential has not been fully realized, in part because citizen science data are typically not widely shared and reused. To address this and related challenges, we built CitSci.org (see www.citsci.org), a customizable platform that allows users to collect and generate diverse datasets. We hope that CitSci.org will ultimately increase discoverability and confidence in citizen science observations, encouraging scientists to use such data in their own scientific research.

Correction: CitSci.org: A New Model for Managing, Documenting, and Sharing Citizen Science Data

The Funding section incorrectly states that “the authors received no specific funding for this work.” The authors would like to correct the statement as follows: Funding: YW was supported by the NSF grant 1430508. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.