Building a More Scientifically Informed Community in the Delaware River Basin David W. Bressler, John K. Jackson, Matthew J. Ehrhart, and David B. Arscott Citizen Science (CS) programs inherently broaden societal science literacy by providing experiential scientific learning opportunities to a diverse cross-section of the public. Here we describe an expanding CS program that supports more than 50 nonprofit organizations in the Delaware River Basin (DRB). The motivation for this effort has been generated by investment from the William Penn Foundation to create the Delaware River Watershed Initiative (DRWI), a multi-year effort to support organizations working to protect and restore stream health in the DRB. In direct support of this initiative, the Stroud Water Research Center is facilitating CS efforts to improve the capacity of watershed groups to conduct scientific investigations associated with DRWI watershed protection and restoration projects, as well as to build general knowledge on the ecology of their watersheds and the broader DRB. This project benefits from cooperative efforts among a wide variety of citizen scientists, as well as professional scientists and environmental planners. Participants in these CS activities have diverse backgrounds ranging from volunteers with minimal or no formal training in science to retired Ph.D.-level scientists. There are full-time and part-time environmental professionals who volunteer in their spare time, college and high school students, teachers and professors, and many other individuals from a wide variety of science and non-science backgrounds. Some volunteers work multiple days per week carrying out or assisting the goals of the DRWI, while others put in a few hours per month—all helping to build valuable datasets on water quality and related outcomes of restoration and land protection. Through their engagement, these citizen scientists gain personal knowledge and experience that can inform the greater community and influence local environmental policy. Citizen Science depends on the experience and expertise of the individuals involved. In our case, professional scientists, environmental planners, and even environmental regulators help to frame monitoring approaches and guide groups and individuals on collecting samples, doing field measurements, analyzing data, and researching policy. Our vision of success is a collaborative environment that supports watershed groups and their citizen scientists in asking and answering their own ecological questions about local streams and rivers, and in translating this knowledge and experience into regional policies and practices that result in healthier streams and, subsequently, cleaner drinking water for future generations. Volunteers contributing to this initiative are not exclusively collecting data to feed into a single large study; nonetheless, combined across tributaries, this effort is also building an increasingly comprehensive and publicly accessible dataset for the whole DRB. Citizen Science enables certain things that conventional science does not. We are supporting CS programs to not only generate robust data sets but also to build a scientifically informed community in the DRB. Citizen Science is no different than ordinary science in that it follows the same [End Page 24] processes of developing and testing hypotheses (i.e., asking questions, making predictions, and coming up with ways to answer the questions), Quality Assurance (QA) and Quality Control (QC) (i.e., making plans to ensure data accuracy [QA] and then confirming data accuracy [QC] via specific data replication protocols), and summarizing and communicating results (i.e., preparing data summaries, reports, etc.). Citizen Science is different from ordinary science, however, in that it involves a far greater diversity of individuals with wide-ranging backgrounds and skills. From certain professional science perspectives, this variation among individuals may be considered a hindrance to the science. However, with improvements in technology and with people more often changing careers and increasing volunteer involvement during these transitions, in spare time, and in retirement, there continue to be more opportunities to build large viable datasets with new and unconventional CS methods. Perhaps most importantly, as societal and cultural pursuits are increasingly directed toward improving the environmental awareness and science-knowledge of the general population, CS not only presents opportunities to build useful datasets but also to make strides in building a scientifically informed community, which is rarely a goal in conventional science endeavors. Ideally, this building of science literacy then leads to communities making better environmental decisions...
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Jan 1, 2024
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