The Role of Citizen Science in Educating and Engaging City Dwellers on Urban Wildlife Ecology
Citizen science (CS) provides an exceptional opportunity to connect urban dwellers with the natural world through scientific data collection, natural resource management, and educational activities. Technological advancements, such as the development of smartphones, have helped instigate the rapid growth of CS in the past two decades, allowing millions of individuals to contribute to science (Bonney et al., 2016; Roger and Motion, 2022). Widespread participation has allowed CS to reach a degree of temporal and spatial granularity that is not typical of traditional scientific research (Chandler et al., 2017; Adler et al., 2020). The large-scale and interdisciplinary nature of CS practices has the potential to bridge the gap between scientists, policymakers, and the public, while simultaneously mitigating the human-wildlife conflict.
Many factors affect humanity's erratic relationship with the natural world, especially unchecked population growth that results in infringement on plant and animal habitats. This trend, along with climate change, is putting undue strain on shared resources (Day et al., 2022; Davis et al., 2018; Driscoll et al., 2018). Both people and nature are forced to find new ways to adapt to these changes as well as to each other. Recording and interpreting these evolutions and adaptations through CS is crucial to engaging the general public and policymakers in the hope of inspiring positive change. Previous research indicates that citizen science may already play a role in encouraging the public to be more attentive to nature and to actively work to protect it (Santori et al., 2021; Bruckermann et al., 2021).
Within the field of urban wildlife ecology, citizen science (CS) has contributed to a variety of conservation, scientific, and policy initiatives, such as informing natural resource management (Serret et al., 2022), tracking bird migrations (Fuentes et al., 2022), and recording the impacts of urban private gardens on insects (Pendl et al., 2022). Yet, disagreement remains between scientists on the role that citizen science participants should play and how reliable and valid CS data is (Ceccaroni et al., 2017). According to Haklay (2013), citizen science is when non-professional scientists voluntarily participate in each step of a scientific project. Irwin (1995) describes CS as “the democratization of science and public engagement” (p. 1). Nevertheless, many scientists remain skeptical about the public’s capacity to carefully and thoroughly collect quality data (Hughes et al., 2022).
Other drawbacks to the success of citizen science (CS) include the lack of long-term funding, resources, program leadership, and staff (Hughes et al., 2022). Additionally, the effect of citizen science on pro-environmental beliefs and activities is not well understood. Current impact assessment methods and frameworks for citizen science are outdated (Kieslinger et al., 2017) and lack key measurements, including the assessment of environmental attitudes, behavior and knowledge (Somerwill and Wehn, 2022; Wehn et al., 2021). To tackle these challenges, governmental agencies, such as the Government of Alberta (Hughes et al., 2022) and the European Commission (Figueiredo et al., 2016; Rautio et al., 2022), are establishing a clear set of principles to guide the process and evaluation of citizen science initiatives. These parameters may improve the public’s scientific literacy through collaborative learning and knowledge exchange and may thus enhance the public’s ability to collect quality data.
This research brief investigates the role of citizen science (CS) in educating and engaging city dwellers on urban wildlife ecology. CS allows urban citizens to participate in scientific data collection while reaping the benefits of open science. This democratization of science has the potential to foster innovation at a societal level. To better understand the influence of CS on environmental attitude and behavior, policymakers may use this brief to re-adapt citizen science frameworks and impact assessment methods. Scientists may also use this analysis to discover more effective strategies to recruit, educate, and engage citizen science participants in urban areas.
Literature Review
This research brief explores literature related to four core topics in urban citizen science: (1) conflicting definitions of citizen science, (2) contributions of citizen science towards global biodiversity monitoring, (3) effects of citizen science on knowledge, attitude, and behavior towards nature, and (4) frameworks for evaluating citizen science initiatives.
Conflicting Definitions of Citizen Science
Disagreement over the level of public participation in the scientific process and the reliability of data collected in citizen science (CS) initiatives is common. Many argue that citizen scientists are active participants in scientific research, and CS is a movement that aims to democratize science (Adler et al., 2020; Rautio et al., 2022). This notion stems from Irwin (1995), who argues that CS aims to bridge the gap between science and the public by incorporating public opinion in decision-making around scientific issues (e.g., public health and environmental risks). Ceccaroni et al. (2017) expand the concept of science democratization by arguing that CS should teach people how to perform their civic duties and empower them in the process. More specifically, they define citizen science as the “work undertaken by civic educators together with citizen communities to advance science, foster a broad scientific mentality, and/or encourage democratic engagement” (p. 1). When explaining the reasoning behind their definition, Ceccaroni et al. (2017) emphasize the importance of semantics in understanding citizen science.
Haklay (2013) proposes a different yet popular approach to defining citizen science (Greving et al., 2022; Hughes et al., 2022; Bonney et al., 2016). The author argues that there should not be a precise definition of CS with fixed boundaries. Instead, we should adopt a broad definition of CS that highlights its core attributes: “scientific activities in which non-professional scientists voluntarily participate in data collection, analysis, and dissemination of a scientific project” (p. 105). Although it seems neutral, this definition excludes study participants (e.g., volunteers in medical studies) and raises the question of who is considered a scientist. Professional scientists are recognized as those employed to conduct scientific research (Haklay, 2013). However, it is more complicated to identify unpaid scientists since many do not classify themselves as real scientists. These conflicting definitions of citizen science (CS) can make it challenging to develop and implement distinct CS projects, comprehensive evaluation frameworks, and standardized impact assessments. These inconsistencies may make it harder to attract and engage citizens in CS projects, particularly in urban environments where citizens are more disconnected from nature.
Contributions of Citizen Science Towards Global Biodiversity Monitoring
Contributions of Citizen Science Towards Global Biodiversity Monitoring
Few studies investigate the practicality and accuracy of citizen science data on large-scale biodiversity monitoring. Chandler et al. (2017) use the “Essential Biodiversity Variable” (EBV) framework as well as databases on citizen science (CS) and community-based monitoring (CBM) to evaluate the contributions of CS and CBM on global biodiversity research. Contrary to the belief of most scientists (Hughes et al., 2022; Roger and Motion, 2022), Chandler et al. (2017) argue that CS and CBM data is as reliable as data collected by professional scientists. Their results indicate that CS and CBM data are valuable for large-scale projects on species distribution, phenology, population abundances, and ecosystem functions. Yet, most CS projects (70%) target a single species (Geijzendorffer et al., 2015) and are biased toward monitoring birds (86%; Amano et al., 2016). This limited approach to CS may have emerged because of a lack of funding and resources, poor communication, and low visibility of CS projects (Costello et al., 2015; Johnson et al., 2016). By inspiring more citizens to participate in CS and CBM projects, biodiversity researchers can increase the taxonomic and geographic scope.
Fuentes et al. (2022) argue that citizen science (CS) has the potential to advance scientific research, conservation efforts, and public outreach on a global level. The researchers propose a probabilistic modeling framework, BirdFlow, that uses CS data from the eBird database to follow the movements of migratory birds with GPS and satellite tracking data. Using CS data on 11 species of North American birds, the probabilistic BirdFlow model accurately predicted bird movement behavior and was more effective and pervasive than previous models that did not use CS data. These results align with the Chandler et al. (2017) study, which also demonstrates the validity and reliability of aggregate data in biodiversity monitoring.
Roger and Motion (2022) categorize citizen science projects more rigidly than previous studies. The researchers exclude ad hoc citizen science initiatives like eBird that do not have set timelines and geographic ranges. Using the Australian Citizen Science Association’s Citizen Science Project Finder, Roger and Motion (2022) compiled and assessed citizen science projects in Australia. They found that the quantity and diversity of CS projects are low, particularly in urban environments (5% of CS projects in Australia). Roger and Motion (2022) subsequently doubt the general effectiveness of CS projects in conservation efforts and public outreach. They claim that there is no easy way to participate, an overwhelming focus on birds, and no follow-up scientific paper publications from the CS data collected. To improve the credibility and validity of CS projects, Roger and Motion (2022) argue that instead of focusing on a global biodiversity monitoring approach, there must be a local approach to CS with narrower questions, set deadlines, and clear steps. The discrepancy between this study and the previous ones may be due to Roger and Motion’s (2022) adoption of a precise definition of citizen science (Haklay, 2013) and their focus on a single country.
All the studies mentioned above argue that to maximize the potential of citizen science projects, CS organizers need to inspire and mobilize more urban citizens, cover a wider taxonomic range, and focus on more vulnerable species. By utilizing online tools and smartphone applications such as ArcGIS and iNaturalist, scientists and policymakers can analyze the necessary information to help inspire pro-environmental attitudes and ultimately develop and implement urban conservation initiatives.
Effects of Citizen Science on Knowledge, Attitude, and Behavior Towards Nature
Some studies have explored the impacts of citizen science (CS) on participants’ knowledge, attitudes, and behaviors toward nature. Greving et al. (2022) conducted four field studies in metropolitan Germany and integrated pre/post-measurement surveys to explore the effects of a CS project on participants' (N = 64) knowledge of bat ecology and attitudes towards bats and engaging in CS. The results suggest that participant knowledge of urban bat ecology increased, and attitude towards bats and engagement in CS improved. However, the frequency of participation did not alter participants’ attitudes toward bats or engagement in CS.
Santori et al. (2021) surveyed 148 members of the Australian turtle mapping app, TurtleSAT, to examine the app’s impacts on knowledge and skills acquisition and whether these acquisitions affect participant behavior or attitude. The results suggest that self-reported gains in knowledge and skills from the app are positively associated with behavior and attitude changes. Respondents reveal that learning about threatened turtle populations inspired them to adopt new habits, such as volunteering at habitat restoration projects or moving turtles out of threatening situations. The results also indicate that the frequency of participation is not associated with behavior or attitude change. This consistency across the Greving et al. (2022) and Santori et al. (2021) studies imply that the quality and content of CS experiences may impact participants more than how often they participate. This concept directly contradicts previous research derived from “Arnstein’s ladder” (Arnstein, 1969; Haklay, 2013), which argues that the higher degree of participation, the more citizens are engaged and empowered.
Contrasting these previous studies, Bruckermann et al. (2021) did not find a correlation between pro-environmental attitudes and participating in CS projects. This discrepancy may result from Bruckermann et al.’s (2021) broader focus on urban wildlife in contrast to Santori et al.’s (2021) and Greving et al.’s (2022) narrow focus on one species. Using cross-lagged panel analyses, Bruckermann et al. (2021) surveyed 303 citizen science (CS) participants across three field studies that monitor urban wildlife populations at home gardens in Germany. The participants responded to two questionnaires, one at the start and one at the end of the CS project, related to their scientific reasoning skills, attitudes toward nature, topic-specific knowledge, and epistemological beliefs. The results indicate that topic-specific knowledge (i.e., locally-relevant knowledge) is a better predictor of positive attitudes toward urban wildlife ecology than general ecological information.
Frameworks for Evaluating Citizen Science Initiatives
The main challenges of citizen science (CS) include (1) scientists’ distrust of the public’s ability to collect quality data, (2) vague and infrequent communication to participants about project objectives and goals, (3) poor recruitment strategies by CS organizers, and (4) a lack of resources, including funds, program leadership, and public participants (Hughes et al., 2022; Burgess et al., 2017). Additionally, the impact assessment and frameworks for citizen science are outdated and lack critical measurements. These measurements include communal needs, concerns, and desires (Kieslinger et al., 2017) and individual participant behavior, attitude, and knowledge of the environment (When et al., 2021; Somerwill and When, 2022).
To address these concerns, governmental agencies like the Government of Alberta (Hughes et al. 2022) and the European Commission (Figueiredo et al., 2016) created a flexible set of principles that provide CS organizers with guidance on how to plan and assess various citizen science projects. Both frameworks highlight the importance of a clear objective and scientific outcome while fostering a meaningful, educational, and memorable experience for CS participants. Yet, the European Commission better addresses citizen needs, desires, and contributions by focusing on both engaging and "co-creating with citizens" (p. 32). This additional emphasis on co-creation likely emerged from Figueiredo et al.'s (2016) extensive research using focus groups, mobile phone data, social media content, and public sector information to interpret the dynamic relationship between environmental science and societal factors (e.g., political parties, citizens, and private entities).
After consulting four stakeholders, interviewing 20 communication science experts, and completing a systematic literature review on the evaluation of citizen science, Kieslinger et al. (2017) propose a framework to assess citizen science activities in Austria and Germany. Paralleling Figueiredo et al. (2016), the framework integrates open-ended questions related to (1) the scientific, (2) the social, and (3) the socio-ecological/economic aspects of citizen science. Nevertheless, after completing a systematic literature review of 77 publications and using convenience sampling to interview ten coordinators from citizen science projects, Wehn et al. (2021) argue that these three domains are insufficient. The researchers recommend expanding the three domains into five by integrating "science and technology" and "governance" since these two themes are often mentioned by practitioners (>45%). Wehn et al. (2021) argue that considering all five domains ensures that participants’ goals better align with the scientific process and that their insights are appropriately considered during policy decision-making. These unique conclusions may stem from the biasedness and limited generalizability of Wehn et al.’s (2021) sample population. With the expansion of urbanization into wildlife habitats and the abundance of potential participants available in cities, it is particularly critical to assess the impacts of citizen science projects in urban areas.
Conclusion
By promoting public engagement and innovation in the scientific process, citizen science (CS) has the potential to transform society by closing the gap between scientists, the public, and policymakers while helping moderate the human-wildlife conflict. Since over 25% of vulnerable species live in cities and 56% of the human population is urban (Aronson et al., 2014), CS has the potential to revolutionize conservation efforts by exposing city dwellers to the natural world through scientific data collection and knowledge sharing. The advent of smartphones has sparked the rapid growth and popularity of citizen science amongst millions of people. Such extensive participation has allowed many CS projects to exceed the temporal and spatial granularity of traditional scientific research. Establishing a flexible comprehensive evaluation framework and impact assessment method for citizen science (CS) is essential to understand the impacts of these initiatives on society, individuals, and the environment. Once CS organizers better understand the influence of CS, they can develop more effective marketing strategies to recruit and engage potential participants in cities.
Previous research indicates that a transparent and two-way dialogue between CS organizers and participants is necessary to establish guidelines, timelines, and expectations for projects. An open line of communication may increase trust and thus improve pro-environmental behavior, knowledge, and attitude amongst participants. In turn, CS organizers may mobilize more citizens and can accordingly expand their projects to a broader geographic and taxonomic range. This will bring more awareness to their project and conservation issues while building credibility and attracting more funders. By integrating pre/post-measurement surveys focused on the environment, society, and economy, citizen science (CS) organizers can create a flexible framework that applies to their locale. Leveraging technology, such as artificial intelligence, smartphone applications, and online trends, may provide policymakers and scientists with the data necessary to inspire and mobilize city dwellers to implement eco-friendly behaviors and habits.
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