Alycia Crall

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.

Improving and integrating data on invasive species collected by citizen scientists

Limited resources make it difficult to effectively document, monitor, and control invasive species across large areas, resulting in large gaps in our knowledge of current and future invasion patterns. We surveyed 128 citizen science program coordinators and interviewed 15 of them to evaluate their potential role in filling these gaps. Many programs collect data on invasive species and are willing to contribute these data to public databases. Although resources for education and monitoring are readily available, groups generally lack tools to manage and analyze data. Potential users of these data also retain concerns over data quality. We discuss how to address these concerns about citizen scientist data and programs while preserving the advantages they afford. A unified yet flexible national citizen science program aimed at tracking invasive species location, abundance, and control efforts could be designed using centralized data sharing and management tools. Such a system could meet the needs of multiple stakeholders while allowing efficiencies of scale, greater standardization of methods, and improved data quality testing and sharing. Finally, we present a prototype for such a system (see www.citsci.org).

The impacts of an invasive species citizen science training program on participant attitudes, behavior, and science literacy:

Citizen science can make major contributions to informal science education by targeting participants’ attitudes and knowledge about science while changing human behavior towards the environment. We examined how training associated with an invasive species citizen science program affected participants in these areas. We found no changes in science literacy or overall attitudes between tests administered just before and after a one-day training program, matching results from other studies. However, we found improvements in science literacy and knowledge using context-specific measures and in self-reported intention to engage in pro-environmental activities. While we noted modest change in knowledge and attitudes, we found comparison and interpretation of these data difficult in the absence of other studies using similar measures. We suggest that alternative survey instruments are needed and should be calibrated appropriately to the pre-existing attitudes, behavior, and levels of knowledge in these relatively sophisticated target groups.

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.

Show me the numbers: what data currently exist for non-native species in the USA?

Non-native species continue to be introduced to the United States from other countries via trade and transportation, creating a growing need for early detection and rapid response to new invaders. It is therefore increasingly important to synthesize existing data on non-native species abundance and distributions. However, no comprehensive analysis of existing data has been undertaken for non-native species, and there have been few efforts to improve collaboration. We therefore conducted a survey to determine what datasets currently exist for non-native species in the US from county, state, multi-state region, national, and global scales. We identified 319 datasets and collected metadata for 79% of these. Through this study, we provide a better understanding of extant non-native species datasets and identify data gaps (ie taxonomic, spatial, and temporal) to help guide future survey, research, and predictive modeling efforts.

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.

Using habitat suitability models to target invasive plant species surveys

Managers need new tools for detecting the movement and spread of nonnative, invasive species. Habitat suitability models are a popular tool for mapping the potential distribution of current invaders, but the ability of these models to prioritize monitoring efforts has not been tested in the field. We tested the utility of an iterative sampling design (i.e., models based on field observations used to guide subsequent field data collection to improve the model), hypothesizing that model performance would increase when new data were gathered from targeted sampling using criteria based on the initial model results. We also tested the ability of habitat suitability models to predict the spread of invasive species, hypothesizing that models would accurately predict occurrences in the field, and that the use of targeted sampling would detect more species with less sampling effort than a nontargeted approach. We tested these hypotheses on two species at the state scale (Centaurea stoebe and Pastinaca sativa) in Wisconsin (USA), and one genus at the regional scale (Tamarix) in the western United States. These initial data were merged with environmental data at 30-m2 resolution for Wisconsin and 1-km2 resolution for the western United States to produce our first iteration models. We stratified these initial models to target field sampling and compared our models and success at detecting our species of interest to other surveys being conducted during the same field season (i.e., nontargeted sampling). Although more data did not always improve our models based on correct classification rate (CCR), sensitivity, specificity, kappa, or area under the curve (AUC), our models generated from targeted sampling data always performed better than models generated from nontargeted data. For Wisconsin species, the model described actual locations in the field fairly well (kappa = 0.51, 0.19, P < 0.01), and targeted sampling did detect more species than nontargeted sampling with less sampling effort (chi2 = 47.42, P < 0.01). From these findings, we conclude that habitat suitability models can be highly useful tools for guiding invasive species monitoring, and we support the use of an iterative sampling design for guiding such efforts.

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.