Introduction

Citizen scienceFootnote 1 is mired in two questions from its critics and advocates alike. First, how consequential is the science produced? A meta-analysis of 895 citizen science projects found 75% did not produce at least one peer-reviewed publication (Davis et al. 2023); Kullenberg and Kasperowski (2016) found similar results (84%). By measure of the primary means for advancing science—peer-reviewed publications—Davis et al. (2023) state most citizen science projects are “not conducting meaningful science” (p. 8). Others question the limited impact of citizen science on environmental policy (Requier et al. 2020; Wehn et al. 2021; cf. Danielsen et al. 2021). Second, scholars increasingly question the ethics of the relationships between those collecting and those using the data, expressing concerns that they are one sided, extractive, and even exploitative (Pagès et al. 2019; Rasmussen and Cooper 2019; Reiheld and Gay 2020; Guerrini et al. 2021; Harris et al. 2021; Riley and Mason-Wilkes 2023). Researchers take data, but do not always make the effort to close the loop by taking study results back to their contributors (Kim et al. 2011; Cooper and Lewenstein 2016; Lowry and Stepenuck 2021; Schölvinck et al. 2022). Citizens being “used” as data-collection instruments precipitates disengagement and disinterest in making further contributions (Druschke and Seltzer 2012; Tulloch et al. 2013; Turrini et al. 2018; Crowell 2019; Egerer et al. 2019). In our ambition for new data, scientists overlook citizen scientists’ expertise (Hall et al. 2024). Opportunities to make research more meaningful to society and local places are generally lost (Haywood 2016).

In this paper, we answer calls to overcome these dilemmas by showcasing an evidence-based approach to improve relations between those collecting the data and those using it. We aim at the question of what is meaningful science to citizen scientists, scientists, and society by asking: How can more thoughtful engagement of citizen scientists’ needs, motivations, and interests improve the process and outputs of science? For contributory citizen science projects, in what ways can scientists see the value of citizen scientist volunteers beyond just providing data? We argue that citizen science projects can become more meaningful and useful to both professional scientists and citizen scientists, thereby increasing participation, by being designed to address topics of interest to citizen scientists that have a stake in shared resources and by directly engaging them to identify the motivations of citizen scientists for participation, identify the outputs desired by citizen scientists, and iteratively incorporate these participant-driven insights into the projects to improve the salience of outputs. This co-created approach illustrates the benefits of establishing greater ties between scientists and society called for in hydrology (Njue et al. 2019) and citizen science at large (Roche et al. 2020; Eckhardt et al. 2021; Senabre Hidalgo et al. 2021).

We designed an approach for a citizen science project that used crowdsourced data to parameterize an ecohydrological model of the Boyne River, Michigan, USA—a small coldwater trout stream. Citizen scientist-generated observational data of streamwater level and water temperature via text message were used to generate a near real-time Soil and Water Assessment Tool (SWAT; Srinivasan et al. 1998) hydrologic model using a high-performance computer to forecast 7-day stream conditions (see Avellaneda et al. 2020). From the project’s inception, we treated the social system as an equally interesting site of inquiry and aimed to close the loop between data collection and products created. Through pre- and post-model interviews, we solicited detailed feedback from citizen scientists about their motivations for participating, ambitions for the collected data, and anticipated uses of the model. Following a co-creation design (Mauser et al. 2013), we aimed to listen carefully, then use what we learned from citizen scientists to shape the process and outputs of the science.

This approach for engaging citizen scientists as experts rather than data-collection instruments can be customized for existing projects or integrated from a project’s inception. We present the approach in four steps and illustrate it via the Boyne River citizen science project. First, we outline the project rationale. Then, we describe the citizen scientist engagement process used to understand the social infrastructure behind the contributed data. Finally, we explain how our social research, coupled with commitments to avoid one-way interpersonal interactions, yielded specific actions that improved data collection, the SWAT model development and its validation, and our ability to communicate this work to professional and lay audiences.

Step 1: develop research that is important to communities

The first step in engaging participants in citizen science projects is to develop research that will be of interest to communities beyond knowledge acquisition (Irwin 1995; Garcia and Brown 2009). This follows the principles of co-creation and co-production of aligning interests to structure interactions that yield mutual aid: relevant research (Mauser et al. 2013; Cooper and Lewenstein 2016; Chambers et al. 2021). Ideal sites for citizen science research will be those where shared resources are valued by the community. Asking research questions that will motivate participation requires consideration of the ways resources meaningfully affect people’s lives. What follows is a description of the background of our case study and crowdsourced hydrology data, which illustrates the usefulness of involving citizen scientists in freshwater research.

Case study: crowdsourced hydrology modeling in the Boyne River, Michigan, USA

Data-impoverished freshwater systems

Freshwater systems are some of the most imperiled habitats globally. Changes in precipitation, air temperature, and land use will continue to alter water resources and the biodiversity dependent on these systems (Knouft and Ficklin 2017; Van Compernolle et al. 2019). Accurate representations of processes regulating these systems are needed. Yet, water resource research and management suffer from a lack of information about tributary stream systems (Ward and Packman 2019). In aggregate, these smaller first- and second-order streams constitute 70–80% of the total channel length of river systems (Wohl 2017; Colvin et al. 2019), have more land use than our largest rivers, and have the greatest diversity of aquatic species habitat (Meyer et al. 2007). Globally, we are data poor about the simplest of metrics, such as water temperature and volume, in these systems (Fehri et al. 2020).

The United States Geological Survey (USGS) maintains approximately 7600 gauges across the USA for gathering streamflow and temperature data essential for a variety of uses. Maintaining the availability and quality of long-term data is expensive. For the USGS, one stream gauge requires about $7800 USD to install and ~ $18,300 annually to service the box and house the data (Patterson 2018; Miller and Kilpatrick 2020). Because of these limited resources, smaller streams do not have gauges. The resulting absence of basic water resource information challenges management, risks increasing water-use conflicts, and threatens the sustainability of the resource.

Study site: Boyne River

The Boyne River (Michigan, USA) is one of the world’s many streams without government-funded stream gauges. The Boyne River has 34 km of stream between its north and south branches and drains an area of 182 km2. The river is located within Charlevoix County, Michigan, population 26,000 (2020 U.S. Census) and flows through the Boyne Mountain Ski Resort and downtown Boyne City where it enters Lake Charlevoix, the third largest inland lake in Michigan with over 90 km of shoreline. The Boyne River hosts several species of native and non-native fishes (Hettinger 2012).

In May 2013, Friends of Boyne River (FoBR)—a non-profit citizen group that strives to improve and conserve the environmental health and recreational quality of the Boyne River and its watershed—contacted C.S. Lowry, co-founder of CrowdHydrology.org, about installing a river-height gauge for monitoring streamflow. CrowdHydrology.org provides basic stream-stage data (Lowry and Fienen 2013) for smaller streams and tributaries often not selected for USGS gauges. Citizen scientists read water height gauges from a stream bank (Fig. 1) and send the water height information via text message to a central server that generates hydrographs based on the readings. The gauges in the streams are installed, calibrated, and managed by volunteers. Gauges are located in public access spaces such as parks, greenways, trails, or land-trust lands (Lowry et al. 2019).

Fig. 1
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a Stream height gauge in the Boyne River. b Instructional sign, with gauge in the background

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In May 2014, five gauges were installed at hydrologically and socially significant locations (Fig. 2). Trout Unlimited trained seven members of FoBR to develop a baseline river rating curve for understanding the relationship between stream height and discharge (flow). We selected the Boyne River for this study because its size enabled testing hydrologic modeling infrastructure and its participation rates in CrowdHydrology.org offered opportunities to experiment with engaging citizen scientists.

Fig. 2
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Citizen science gauge locations, Boyne River, Michigan, USA

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Closing the loop in contributory citizen science

We, the authors, designed our project with two aims. The first was a traditional, science-focused goal to use CrowdHydrology data for advancing a frontier in hydrology: Can citizen science observations be used to generate a near real-time and predictive (up to seven days) ecohydrological model of stream conditions (streamflow and water temperature) on a small stream (Fig. 3)? The ecohydrological model uses a high-performance computing cluster (HPCC) to incorporate weather forecasts into a SWAT hydrological model to estimate habitat suitability for representative aquatic species (brook trout and brown trout). We successfully completed this aspect of the project and detail this first objective in Avellaneda et al. (2020). Because of equipment failures, the citizen scientists proved to be not just instrumental, but essential to the model (Box 1).

Fig. 3
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Project schematic of the research cycle

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Social system research objectives

From the project’s origin, we were equally curious about the social, environmental, and modeling questions and, particularly, how they interacted. We wanted to (1) catalog the range of value propositions citizen scientists get from texting readings from their phones (i.e., their motivations for participating), (2) gather their ideas for improving participation, and (3) ensure the model we created was useful, then (4) shape our ecohydrological model to meet the citizen scientists’ expressed ambitions.

Two underlying and compounding assumptions about participation, not statistically tested here, informed our approach to social research design. First, more frequent engagement with the research team via social research activities (interviews, focus groups) would yield greater participation. By asking citizen scientists to consider the value of the stream data and the ecohydrological model, they would effectively be reminded of the potential benefits of submitting readings. Second, a more locally useful ecohydrological model would increase the number of observations. Knowing that the model outputs are partially shaped by citizen scientists’—a proxy for the local community’s—expressed interests should inspire submitting gauge readings.

Step 2: identify citizen scientist motivations for participation

Understanding features of the community of citizen scientists can improve how scientists engage communities around project sites. These features can be elicited by talking with citizen scientists about their motivations for participation and their ambitions for their shared resources. Not engaging citizen scientists is a “mistake” of contributory citizen science projects (Lowry et al. 2019). For projects with shared resources (waterways, wildlife, street trees, etc.), researchers may assume that reasons for participation include the established range of pedagogical/didactic aims (Kelemen-Finan et al. 2018), the desire to participate in “real science,” and the cadre of intrinsic and extrinsic motivations (Capdevila et al. 2020; Land-Zandstra et al. 2021). Importantly, however, motivations may encircle the quality of the natural resource (Church et al. 2019; Agnello et al. 2022) and may include a broad array of motivations (Tinati et al. 2017; Zhou et al. 2020) that researchers would never come up with without engaging the citizen scientists involved in the project and directly asking “why?”.

Case study: identifying Boyne River citizen scientist motivations

Following stakeholder engagement literature and participatory modeling best practices (van den Belt 2004; Hall 2019; Hall et al. 2016), we sought to privilege how citizens voiced their ambitions for their participation, the project, and the value of the model over the research team’s assumptions about citizen scientists’ perspectives (Hall et al. 2012; Horton et al. 2017). To do so, we needed to collect information about citizen scientist participants’ views, which required hosting and documenting targeted conversations.

Interview methods

To gather information about citizen scientists’ motivations and their ideas for improving participation and the model (i.e., the research output from this project), we conducted targeted interviews with a sample (Table 1) of the 415 citizen scientistsFootnote 2 who had contributed readings. We recruited interviews from citizen scientists who had contributed data and who had a record of interest in Boyne River data evidenced by their attendance at our project’s kick-off presentation open to the public (Fig. 4). These participants represent an expanded interest in collaboration beyond texting readings (Cooper and Lewenstein 2016). We also recruited interviewees from FoBR contacts and through a web search of organizations with interests in water resources management in northern Michigan. We categorized interviewees by “perspective” not because perspectives are particularly important to the research outcomes, as rarely participants fall into just one perspective category, but to evaluate the diversity of perspectives voiced. For example, early in the process, we noticed we lacked government perspectives and then used snowball sampling (Noy 2008) to improve the diversity of perspectives interviewed. The final number of pre-model interviewees was 40 (Table 1). Because the scope of the study was narrow, the overall population of river users was small, the nature of the topic was easily talked about, and the interviewer was experienced, the number of targeted interviews was appropriate (Morse 2015, 2000; Malterud et al. 2016). The interview protocol followed the research objectives above. We asked questions that aimed to elicit: Who is texting information? Why do people participate? What motivates Boyne River citizen scientists to submit observations? Each interviewee confirmed they had submitted at least one stream gauge reading. Interviews were audio recorded, transcribed, and analyzed line by line using thematic cluster analysis (Guest et al. 2017; Creswell and Poth 2018).

Table 1 The 2018 interview and 2019 focus group participants
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Below, we present the most meaningful themes used to inform the model. Italics denote verbatim phrases and terms heard from several interviewees (unattributed) and italics with quotation marks signify verbatim quotes from an individual interviewee (attributed). We deliberately present verbatim quotes of citizen scientists’ voices because their voices are often silent in peer-reviewed studies. Further, we wish to show the value of citizen scientists’ natural language when treated as data worth analyzing.

Findings: citizen scientist motivations

Citizen scientists told us they text stream height and temperature data for a variety of reasons. First, citizen scientists told us they want to help out the river, their community, our research team, and CrowdHydrology. One person said she participates because it is real science that helps the Boyne.

Second, people told us about how they love the Boyne River. Citizen scientists described many natural amenities and recreational opportunities provided by the Boyne. One recreationalist called it “undeveloped, in good condition, natural, quiet, beautiful. It is a clean and winding river. It’s a jewel.” Several long-term residents told us a redemption story of the river as once polluted from local industry—essentially a sewer—that became a tourist destination that the community rallied behind to improve the quality of the natural amenities. As industrial uses became tourism, “it seems to get more and more love every year that I can see. People care about it for so many different reasons and there’s a very core group that would do anything for that river.” Texting data, i.e., participating in citizen science, continues the restoration of the resource. Caring for the river in this way is an atonement for the legacy of pollution.

Third, others spoke of participating in something larger than oneself. Citizens within the Boyne River catchment are self-described as “very water aware... the health of our water often equals the health of us, and our wildlife” (Business). Citizen scientists articulated watershed thinking in a variety of ways, “whether it’s the Boyne River or Lake Charlevoix, the Jordan River, I mean they’re all, you know, we’re all... it’s all connected. It’s in the same Lake Charlevoix watershed—it’s in my backyard” (Recreationalist). Texting hydrology readings aligns with being a citizen of the watershed and the community. As a city official noted below, everyone must do their part.

If we want to keep these rivers in really good shape and keep good fishing opportunities and keep fishing access open, everybody’s got to kinda lend a part. And for me that’s the value in the CrowdHydrology project is it encourages people to at least do a little bit of their part. I think it encourages people to think about the fact that you can’t, as an agency or a university or an NGO or whatever the heck you might be, you can’t provide all those resources on the river by yourself, and that—that people need to—to chip in and help with . . . help keep things nice, help keep things accessible, help keep fisheries good (Government).

Finally, many citizen scientists said they text the hydrology data because of the value of having the data as a permanent data collection for future uses. Some spoke of the state’s tracking of water information as haphazard and not consistent. They spoke of the CrowdHydrology gauges as contributing complementary data to form a baseline understanding of the river, useful for keeping the quality of water high. A biologist at an environmental non-governmental organization (eNGO) explained, “nothing is really being tracked up here closely. Not having USGS stations on many of our rivers, the CrowdHydrology with its stream gauges can be a filler to help track things.”

Step 3: identify citizen scientists’ priorities for research outputs

Citizen scientists have reasons for participating that are aligned with protecting and improving a shared resource. These desires intersect with the real or perceived end uses of the science. The outputs of the research (data, analyses, reports, models, tools, etc.) likely inform the shared resource’s management. Identifying the full spectrum of overlapping interests between citizen scientists and scientists yields opportunities to improve the outputs of the science by broadening its appeal and utility to multiple audiences.

Case study: identifying Boyne River citizen scientist priorities for outputs

Focus group methods

Once we had a working prototype of the ecohydrological model, we hosted a presentation in August 2019 to explain the hydrological model and what information it could provide. In addition to inviting each of the citizen scientists interviewed 1.5 years earlier, we announced the presentation via press release. All attendees reported submitting at least one reading via text message. Immediately following the presentation, we broke the attendees (n = 16; Table 1) into two focus groups of eight citizen scientists each and solicited their ideas for improving the utility of the model for local and regional needs (Box 2). We aimed to discover: What might citizen scientists and regional water managers want from an ecohydrological model? How can we design the model interface and outputs to be meaningful to those citizens texting readings?

Findings: citizen scientists’ anticipated uses of the data and model’s real-time and predictive outputs

Pre-modeling interviews revealed citizen scientists envisioned several useful purposes for the data and model that exceeded what our research team of hydrologists, biologists, and modelers anticipated. The science team wanted to test whether citizen science readings alone could be sufficient to run a SWAT predictive hydrological model of streamflow and water temperature. In turn, these parameters indicate habitat suitability for fish. Citizen scientists saw other uses (Fig. 5) that would appeal to diverse audiences (i.e., natural resource professionals, riverfront town planners, recreationalists, educators, and eNGO members). These desired uses served two purposes: they articulated the motivations behind each submitted data reading and helped our modeling team consider the outputs of the model that would meet citizen scientists’ and the community’s interests (Fig. 5). Below, we present quotes that evidence the spectrum of citizen-supplied expected uses of these data.

Fig. 5
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The research team’s stated uses of the data (left) compared with citizen scientist-generated ideas about the value of this research project, the data, and model recorded from interviews and focus groups (right)

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Anticipated uses of the model and data for natural resource management

Citizen scientists who work for state natural resource management agencies and eNGOs reported that the model and texted gauge data would be useful for stream management activities. Some said that the model outputs would help them know when stream conditions are best for working in the river. For others, the annual discharge data are more helpful than model outputs. They said the real-time data and projected streamflow and temperature conditions from the model are helpful for those working in and on the water. Temperature is very helpful to us and so is discharge data for an annual basis. The real-time [model] isn’t real critical for us but having actual data on baseflow or low flow data of systems throughout the state is very important... because of our groundwater withdrawal protections, is very important [to know] the amount of water that can be taken out of the system... is really, really helpful for understanding the watershed (eNGO).

“It’s nice to have that information before you get in the field and think, ‘Oh crap, well this was a waste of the day’” (eNGO). One government employee said that streamflow data

especially in the spring and . . . the fall and then during storm events in the summer . . . it was helpful to have that as we continue to do dam removal and channel work . . . to know what flows the contractors would be up against with heavy equipment around the river. (Government)

Another government biologist echoed this sentiment:

If we’re planning fish surveys or if I’m planning a day that we’re gonna stock Brown trout there, uhm same thing! If we get rain earlier in the week, I need to make sure that that river has come back into shape enough that it’s relatively normal to survey without having, you know, weather conditions potentially alter our results. Same thing with fish stocking, if it’s rained or if we’re just getting past the thaw . . . water temperature becomes critically important when we’re talking about getting fish that we’re pulling out of a hatchery at one temperature, putting ‘em into a truck to acclimate them at another temperature, and then making sure that the receiving waters are—are, at least, in the range of acceptability for stocking those fish. (Government)

Anticipated uses of model and data for riverfront town planning

Citizen scientists serving as municipal officials reported the real-time data and model’s predictive flow data as helping them keep tabs on what’s happening on the river and waterfront to keep it pristine. They expressed wanting to know what is the normal water height and how long it [high water upstream] takes to get to us because the height of the water level in the summer is important for managing the lake.

Another municipal official thought the model would help her deal with census flood data... where they’ve got flood districts set up along the lake shore and along the riverbanks. Officials also reported interest in using the model for understanding invasive species and sedimentation.

Anticipated uses of the model and data for recreation

Citizen scientists who recreated on the Boyne had several ideas for how they could use the model’s predictive outputs of streamflow forecasts. For anglers, the model can “tell me and others when it’s time to go fishing” (Recreationalist). One government official spoke from the perspective of a nearby angler who must decide where to spend their day off.

From a personal standpoint, you know, if I’m going to go fish somewhere . . . if I’ve only got one day that I can make it up there and—and let’s say I can only fish it on Friday, we get rain on Wednesday . . . I really want to know, what did that do to temperature, you know, was it a cool rain? Was it warm rain? And really, what did that do to discharge. How many cfs are we up . . . it is wadeable? Is the increase going to cause fish that were holding in there to spawn to . . . drop back and start making their way out of the system? Where do I go? (Government)

Citizen scientists who were fishing guides and anglers told us they use the USGS gauges on the nearby Manistee, Au Sable, and Jordan rivers. Some get alerts, use the USGS website, or have phone apps that pull USGS data, like the app Fishhead. One angler said

We don’t have a gauge station here. This would be the next best thing to check stream flows . . . for recreationists, I think if they can look online and see if the river’s too high, they may not necessarily go fishing that day.” (Government)

A local fishing guide noted that the

gauge heights are very useful to me. . . . Temperature data’s super useful to me. . . . I talk to the biologists on a fairly regular basis, so . . . between those two and barometric pressure, that kind of dictates day-to-day stuff—for us in terms of the fishing aspect . . . I want this thing [the citizen science model project] to succeed as well as it can because any data that you can get that’s actually good quantifiable data is important. (Business)

Other citizen scientists noted that the model’s predictive streamflow would be useful for kayaking. One said, “it would be nice to know when the river’s up a little bit for kayaking, ‘cause it’s kind of a lot more fun.” (Recreation).

Anticipated uses of model and data for education

Other citizen scientists saw the educational value of the model and the data. An eNGO employee said, “having accurate and reliable discharge dramatically helps us be able to infer and discuss that with stakeholders.” Others spoke about using the model “to get people to be familiar with how much water is in a stream system and to care about it” (Recreation).

A member of the business community noted the importance of educating residents because, unlike nearby rivers that have USGS gauges, “we don’t have USGS on the Boyne, because there is not enough funding. The people who love this resource must do the work to get the measurements.” An eNGO leader said

here in northern Michigan, the property rights is a huge thing, so finding those ways to make people understand that . . . the water has always been yours. But now [through the citizen science gauges and model] you have—this is a way you can monitor this more yourself and understand what’s happening over time, which . . . people love.

Anticipated use of the model and data for regulatory purposes

Citizen scientists frequently spoke of using these data to monitor water quantity for new water withdraws, like hydrologic fracking for oil and gas extraction. A two-hour drive south of the Boyne River, a Nestle Waters North America water bottling plant was granted a controversial permit to double groundwater withdrawals to 2.2 M liters a day for the total cost of $200 per year (PRI, 2018; see Jaffee and Case 2018). The possibility of similar new groundwater withdrawals, coupled with a paucity of water quantity data and uncertainty of climate change effects, appeared throughout conversations with river recreationalists and eNGO staff. Preserving water quantity was a concern for many and emerged in comments such as,

Well, when we started doing it [CrowdHydrology gauges], my concern was, how do we know if someone’s draining our water. How will we know?! So, if we do this recording, then we have a database to know ahead of time. So that if somebody says ‘we want to frack’ or ‘we need more water for our agriculture’ we know how much water there is and maybe make those decisions about how much they could take or not take. But we didn’t have any data before this. (eNGO)

Others spoke of using the data to defend the river and “help justify the structural barrier and dam removals” (eNGO). Of course, some uses desired by citizen scientists exceeded the uses of the model we had imagined.

Ah, granted they [Project team] were talking about using it as a role model to be able to predict certain conditions. . . . But even more important, that may be used in the court of law, someday perhaps, because somebody’s come in and taken up too much water, or our river has become polluted, the water level has gone down, the water temperature has gone up due to agriculture or fracking, and things that we are all very concerned about (eNGO).

Finally, one environmental management eNGO in a leadership position mentioned using the model, the citizen science gauges, and the data to

help us get funding to implement projects . . . with climate change . . . there’s always a new section we have to write about how this project will help make resilient streams and things like that. So that’s a tough one up here, you know, and conditions are already so good, but just being able to kinda complete that story a little more. Your information can help.

Inviting the community to consider their desired uses and brainstorm other uses after seeing the model contributed to a sense of ownership over the model, the project, and the data collection. Evidence of this ownership can be seen on the FoBR’s website that stated

We should be able to predict Boyne River outflow from a simple gauge reading. Also, the baseline river outflow information could serve as an alert indicator for water depletion, as well as a tool for making regulatory decisions on future water use.

Step 4: iteratively incorporate citizen scientist-driven insights into the project

When beginning the engagement that documents citizen scientists’ motivations for participating (step 2 above) and ambitions for project results (step 3 above), the entire project team should meet regularly to hear the initial findings. We found it effective for the engagement team to present a spectrum of project-relevant themes by grouping 3–5 verbatim quotes that evidence each theme heard (for examples see the sections "Findings: citizen scientist motivations" and "Findings: citizen scientists’ anticipated uses of the data and model’s real-time and predictive outputs" above). The objective of these regular meetings is for everyone in the project team to listen to citizen scientists’ voices for instances where utterances affirm, as well as challenge, the project’s direction and administration with the aim of identifying practices project team members can adopt that improve the project and its outputs. Periodically revisiting the breadth of these findings from citizen scientists’ voices (i.e., documented motivations and ambitions) can ensure the project and its products are salient with and meaningful to communities and are, thus, legitimate (Cash et al. 2003; Hall et al. 2017). Listening to these social findings as a research team reserved space to discuss what we could incorporate into the project that would improve the value of participation and research outputs to citizen scientists and other audiences. Deciding as a team ensured the science team members agreed to the change and, importantly, understood its rationale and the plan for incorporating any particular change.

Case study: increasing citizen science participation by improving user experience

The success of citizen science projects relies on participation. Participation functions as a proxy for how meaningful the project is to individuals. Social research activities can explore and then connect what is mutually meaningful to the scientists and community, presumably to increase the value proposition of participating: aka greater participation rates. Targeting the salience of a project to citizen scientists can be built into survey and interview instruments. In the Boyne River project, we built the questions of what may prevent participation and what can improve participation into the interview protocol questions 8 and 9 (Box 2).

Case study: improving research outputs

Engaging citizen scientists in iterative model improvement constitutes an opportunity to produce more meaningful models for decision-making when compared with model building based solely on modelers’ understanding of the parameters and assumed local needs. We were surprised by the breadth of responses from homeowners, government, business, and recreationalists regarding how they would use the data and the model outputs, which broadened our views about both the model itself and the ways we communicated about the research.

In response to citizen scientist inputs, we adapted both our model and research outputs to reflect local uses and preferences (Fig. 5). For example, we chose brown trout and brook trout as target species because of angler interest. Also, instead of labeling the model outputs with the jargon-riddled terms from our grant proposal, we labeled them using the same words citizen scientists used when referring to the model in the focus groups: temperature maps and streamflow maps. We also changed how we communicated about the project and the model with broader community stakeholders by using the participant-derived anticipated uses to discuss the model. We used the natural language of citizens who put the project into their own words during the focus groups. For example, one angler referred to stream temperature model outputs as the fish happiness map. This label was used by several others at the meeting, and we incorporated it into our talks—both public and academic.

Finally, citizen scientists we interviewed shared with us how they were communicating about the project to others in the community. One FoBR member framed the importance of the citizen-supplied river data to others using a familiar metaphor.

I’ve always told people it’s kinda like going to the doctor. You take the blood pressure and do some tests along the river. We’re checking the level, which is what the texts are, and the temperature and so forth. . . . And in the long run, the more you know about that information . . . the more you will be able to use that information for a useful purpose. (Recreationalist)

Local talk about the project, the gauges, and the computer model is generative throughout local social networks, leading to new ways of framing the importance and salience of the research (Hall et al. 2012; Hall and Lazarus 2015).

Discussion: from citizen silence to citizen salience

Citizen science projects are relational, and we propose that citizen science-generated data quality improves with reciprocity. That is, when communities receive something meaningful from participating in a project or through interactions with scientists or each other, they are more likely to continue or increase their participation, to contribute their local knowledge and insights, and to take more care with their data collection (Danielsen et al. 2021). Attending to these relational elements is a worthy investment and the right thing to do. In the words of a local business owner from our focus groups, “If it’s citizen science, make it available to the citizens!” Closing the loop in citizen science means adding points of interaction in two steps. First, poll the wisdom of the crowd of citizen scientist participants to learn the value they see in the project and listen to their ideas for improving the scientific process based on their experience as a participant. Second, share with citizen scientists the product or output envisioned during its development stage. Inviting invested perspectives into the scientific payoff—the product—engenders an opportunity to practice explaining the science to a lay audience—a good practice for science communication.

Communication is difficult and wrought with failure (Peters 2012). Researchers may not comprehend the range of practical uses their outputs could have for society—our training creates blind spots (Burke 1984). Similarly, our assumptions about how well we are communicating may also be askew. These interactions provide opportunities for researchers to witness citizen scientists making sense of the scientific product. In that moment, both project scientists and the citizen scientists can ask questions and learn from each other. Scientists can learn what appeals most to the lay audience about the scientific product, perhaps what is most interesting or useful, and to whom. These insights reveal the salience of the product to society. Failing to engage citizen scientists in the results or outputs of data analysis can lead to disengagement or disinterest in making further contributions (Druschke and Seltzer 2012; Tulloch et al. 2013; Turrini et al. 2018; Crowell 2019; Rasmussen and Cooper 2019). Conversely, when citizen scientists not only feel heard, but are able to see their input being integrated into project outputs, they are more likely to trust the researchers and the research, continue their participation, and communicate positively about the research within their community networks.

Through this study, we showed how treating the voiced input and feedback of citizen scientists as empirical accounts to be gathered and analyzed enabled integrating on-the-ground insights into the project. Further, hosting these conversations provided opportunities for building relationships and trust. Ongoing communication promotes long-term participation (Rotman et al. 2014) and sustains citizen scientists’ commitment over time (Devlin et al. 2001). Particularly for publicly funded citizen science projects on topics concerning shared natural resources (public lands, water, fish, wildlife, air quality, climate, etc.), science teams have an opportunity and responsibility to integrate citizen scientists’ voices into project outputs. Making science more responsive to the interests of society creates a more socially robust science, worthy of public funding and continued public support (Lubchenco 1998; Gibbons 1999).

Conclusion

The varying capacity and capabilities of communities is an underappreciated aspect of citizen science research. Citizen scientists are not homogenous data-collection instruments. We showed that citizen scientists are experts of their home locations who, when asked and listened to, provided a wealth of local insights that improved our study. Through our formal and informal time spent together, our team witnessed their attachment to this place and documented their voiced motivations for submitting readings, their desires for project outputs, and their ideas for how to improve participation and the model. We tailored our methods to this audience, leading to improved methods for data collection, making it easier, more rewarding, and fun for participants. In their response to the presentation of the Boyne River ecohydrological model, the same citizen scientists who texted us stream-stage data also helped our team see the model’s value for local recreational uses, water resources and fisheries management, and policy concerns. As potential model users, they gave our modeling team a real-world audience to target our products.

Foundational to citizen science projects are the relationships between citizen scientists and scientists, and lasting relationships have an element of reciprocity. We offered four steps for building this reciprocity into citizen science projects:

  1. 1.

    Develop research that is important to citizen scientists.

  2. 2.

    Identify citizen scientists’ motivations for participation.

  3. 3.

    Identify citizen scientists’ priorities for research outputs.

  4. 4.

    Iteratively incorporate citizen scientist-driven insights into the project.

Closing the loop in citizen science is accomplished by engaging citizen scientists as experts, documenting their voiced desires, and valuing interpersonal relationships with them (Hall et al. 2021). Attending to and integrating the wisdom of the crowd can improve the quality of participation, the productivity of the process, and the real-world usefulness of the science.