The role of automated feedback in training and retaining biological recorders for citizen science

Exploring the landscape of automated species identification apps: Development, promise, and user appraisal

Two decades ago, Gaston and O'Neill (2004) deliberated on why automated species identification had not become widely employed. We no longer have to wonder: This AI-based technology is here, embedded in numerous web and mobile apps used by large audiences interested in nature. Now that automated species identification tools are available, popular, and efficient, it is time to look at how the apps are developed, what they promise, and how users appraise them. Delving into the automated species identification apps landscape, we found that free and paid apps differ fundamentally in presentation, experience, and the use of biodiversity and personal data. However, these two business models are deeply intertwined. Going forward, although big tech companies will eventually take over the landscape, citizen science programs will likely continue to have their own identification tools because of their specific purpose and their ability to create a strong sense of belonging among naturalist communities.

Emerging technologies in citizen science and potential for insect monitoring

Emerging technologies are increasingly employed in environmental citizen science projects. This integration offers benefits and opportunities for scientists and participants alike. Citizen science can support large-scale, long-term monitoring of species occurrences, behaviour and interactions. At the same time, technologies can foster participant engagement, regardless of pre-existing taxonomic expertise or experience, and permit new types of data to be collected. Yet, technologies may also create challenges by potentially increasing financial costs, necessitating technological expertise or demanding training of participants. Technology could also reduce people's direct involvement and engagement with nature. In this perspective, we discuss how current technologies have spurred an increase in citizen science projects and how the implementation of emerging technologies in citizen science may enhance scientific impact and public engagement. We show how technology can act as (i) a facilitator of current citizen science and monitoring efforts, (ii) an enabler of new research opportunities, and (iii) a transformer of science, policy and public participation, but could also become (iv) an inhibitor of participation, equity and scientific rigour. Technology is developing fast and promises to provide many exciting opportunities for citizen science and insect monitoring, but while we seize these opportunities, we must remain vigilant against potential risks. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Farmland biodiversity monitoring through citizen science: A review of existing approaches and future opportunities

Biodiversity monitoring in agricultural landscapes is important for assessing the effects of both land use change and activities that influence farmland biodiversity. Despite a considerable increase in citizen science approaches to biodiversity monitoring in recent decades, their potential in farmland-specific contexts has not been systematically examined. This paper therefore provides a comprehensive review of existing citizen science approaches involving biodiversity monitoring on farmland. Using three complementary methods, we identify a range of programmes at least partially covering farmland. From these, we develop a typology of eight programme types, reflecting distinctions in types of data collected and nature of volunteer involvement, and highlight their respective strengths and limitations. While all eight types can make substantial contributions to farmland biodiversity monitoring, there is considerable scope for their further development-particularly through increased engagement of farmers, for whom receiving feedback on the effects of their own practices could help facilitate adaptive management.

Citizen Science and Monitoring Forest Pests: a Beneficial Alliance?

Purpose of the Review

One of the major threats to tree health, and hence the resilience of forests and their provision of ecosystem services, is new and emerging pests. Therefore, forest health monitoring is of major importance to detect invasive, emerging and native pest outbreaks. This is usually done by foresters and forest health experts, but can also be complemented by citizen scientists. Here, we review the use of citizen science for detection and monitoring, as well as for hypothesis-driven research and evaluation of control measures as part of forest pest surveillance and research. We then examine its limitations and opportunities and make recommendations on the use of citizen science for forest pest monitoring.

Recent Findings

The main opportunities of citizen scientists for forest health are early warning, early detection of new pests, monitoring of impact of outbreaks and scientific research. Each domain has its own limitations, opportunities and recommendations to follow, as well as their own public engagement strategies. The development of new technologies provides many opportunities to involve citizen scientists in forest pest monitoring. To enhance the benefits of citizen scientists' inclusion in monitoring, it is important that they are involved in the cocreation of activities.

Summary

Future monitoring and research may benefit from tailor-made citizen science projects to facilitate successful monitoring by citizen scientists and expand their practice to countries where the forest health sector is less developed. In this sense, citizen scientists can help understand and detect outbreaks of new pests and avoid problems in the future.

Decision-making of citizen scientists when recording species observations

Citizen scientists play an increasingly important role in biodiversity monitoring. Most of the data, however, are unstructured—collected by diverse methods that are not documented with the data. Insufficient understanding of the data collection processes presents a major barrier to the use of citizen science data in biodiversity research. We developed a questionnaire to ask citizen scientists about their decision-making before, during and after collecting and reporting species observations, using Germany as a case study. We quantified the greatest sources of variability among respondents and assessed whether motivations and experience related to any aspect of data collection. Our questionnaire was answered by almost 900 people, with varying taxonomic foci and expertise. Respondents were most often motivated by improving species knowledge and supporting conservation, but there were no linkages between motivations and data collection methods. By contrast, variables related to experience and knowledge, such as membership of a natural history society, were linked with a greater propensity to conduct planned searches, during which typically all species were reported. Our findings have implications for how citizen science data are analysed in statistical models; highlight the importance of natural history societies and provide pointers to where citizen science projects might be further developed.

Citizen science projects in freshwater monitoring. From individual design to clusters?

Citizen science projects spring up in freshwater monitoring, with an increasing number of projects in river, lake, and groundwater monitoring around the globe. Citizen science scholars assume that these projects have different designs, including diverse characteristics of citizens, institutions, and forms of interactions, potentially affecting the outcomes of these projects. Given the strong focus on case studies or reviews in the field, there is, however, little comparative evidence of the different types of citizen science projects in freshwater monitoring. Based on a global survey, this study provides a systematic comparative analysis of the design of 85 citizen science projects in the field of freshwater monitoring. Descriptive statistics reveal how projects differ along 45 literature-based design variables raised in the survey. Factor analysis based on 31 of these variables yields ten key design factors, and cluster analysis, based on these design factors, allows to identify seven distinguished clusters of citizen science projects. While these clusters are rather heterogeneous, they reveal differences between groups of projects regarding institutional motivation, citizen characteristics, and interaction forms. These results significantly add to a systematic comparison of citizen science projects in freshwater monitoring and enable a more effective involvement of citizens in environmental management.

The Role of Citizen Science in Promoting Health Equity

While there are many definitions of citizen science, the term usually refers to the participation of the general public in the scientific process in collaboration with professional scientists. Citizen scientists have been engaged to promote health equity, especially in the areas of environmental contaminant exposures, physical activity, and healthy eating. Citizen scientists commonly come from communities experiencing health inequities and have collected data using a range of strategies and technologies, such as air sensors, water quality kits, and mobile applications. On the basis of our review, and to advance the field of citizen science to address health equity, we recommend (a) expanding the focus on topics important for health equity, (b) increasing the diversity of people serving as citizen scientists, (c) increasing the integration of citizen scientists in additional research phases, (d) continuing to leverage emerging technologies that enable citizen scientists to collect data relevant for health equity, and (e) strengthening the rigor of methods to evaluate impacts on health equity. Expected final online publication date for the Annual Review of Public Health, Volume 43 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Using citizen science for early detection of tree pests and diseases: perceptions of professional and public participants

Early detection of new tree pests and diseases is a vital element of national strategies to prevent their establishment and spread into a country or region, based on the rationale that it increases the chances of successful eradication. Given the limited capacity and financial resources of most national plant protection authorities, the use of public participants has recently been explored in a range of citizen science projects for its ability to supplement official surveillance. However, little is known about the motivations, expectations and experiences of members of the public involved in such activities and even less about the views of professionals and officials. In this study, evidence was obtained from structured interviews with professionals and volunteers engaged in five projects related to tree health surveillance. Some differences were noted between the two groups with a greater focus on personal aspects by members of the public and on strategic and institutional aspects by professionals. A striking feature was the agreement of the two groups that the projects had met or exceeded their expectations, and provided the proof of concept that tree health surveillance capacity can be increased by engaging and training citizens. Many participants shared concerns about the importance of securing both project longevity and volunteer participation over the long term. The paper discusses ways in which the motivations of tree health surveillance participants can be sustained over long periods with particular attention to recognition and reward.

The Partnership of Citizen Science and Machine Learning: Benefits, Risks, and Future Challenges for Engagement, Data Collection, and Data Quality

Advances in artificial intelligence (AI) and the extension of citizen science to various scientific areas, as well as the generation of big citizen science data, are resulting in AI and citizen science being good partners, and their combination benefits both fields. The integration of AI and citizen science has mostly been used in biodiversity projects, with the primary focus on using citizen science data to train machine learning (ML) algorithms for automatic species identification. In this article, we will look at how ML techniques can be used in citizen science and how they can influence volunteer engagement, data collection, and data validation. We reviewed several use cases from various domains and categorized them according to the ML technique used and the impact of ML on citizen science in each project. Furthermore, the benefits and risks of integrating ML in citizen science are explored, and some recommendations are provided on how to enhance the benefits while mitigating the risks of this integration. Finally, because this integration is still in its early phases, we have proposed some potential ideas and challenges that can be implemented in the future to leverage the power of the combination of citizen science and AI, with the key emphasis being on citizen science in this article.

Citizen science project characteristics: Connection to participants' gains in knowledge and skills

BACKGROUND

Biodiversity is being lost rapidly and its conservation is thus one of the most urgent tasks today. For biodiversity conservation to be successful, the public needs to gain an awareness and understanding of biodiversity and its importance. Moreover, species experts are needed who have the skills necessary for identifying and recording biodiversity. Previous research showed that citizen science projects can contribute to educating the public about biodiversity. However, it is still unclear how project characteristics connect to participants' knowledge and skills and how citizen science projects should be designed if they are to foster participants' learning.

AIM

We aimed to investigate specific characteristics of biodiversity citizen science projects that could potentially influence participants' learning. We explored the following project characteristics from both the project coordinators' and the participants' perspectives: information and training provided to participants, social interaction among participants, contact between participants and staff, and feedback and recognition provided to participants.

METHODS AND RESULTS

In order to examine the extent to which these project characteristics are connected to participants' gains in knowledge and skills, we conducted a comprehensive study across 48 biodiversity citizen science projects in Europe and Australia. We found that participants' perceived gains in knowledge and skills were significantly related to the five project characteristics as reported by the participants: information received by the participants, training received by the participants, social interaction among participants, contact between participants and staff, and feedback and recognition received by the participants.

CONCLUSION

We conclude that by deliberately designing citizen science projects to include features such as interaction and feedback, these projects could achieve higher learning outcomes for the participants. Thereby, suitable modes of communication between projects and their participants are crucial. We provide specific suggestions for the design of biodiversity citizen science projects and for future research on project characteristics and participant outcomes.

Reliability of Data Collected by Volunteers: A Nine-Year Citizen Science Study in the Red Sea

The quality of data collected by non-professional volunteers in citizen science programs is crucial to render them valid for implementing environmental resources management and protection plans. This study assessed the reliability of data collected by non-professional volunteers during the citizen science project Scuba Tourism for the Environment (STE), carried out in mass tourism facilities of the Red Sea between 2007 and 2015. STE involved 16,164 volunteer recreational divers in data collection on marine biodiversity using a recreational citizen science approach. Through a specifically designed questionnaire, volunteers indicated which of the seventy-two marine taxa surveyed were observed during their recreational dive, giving an estimate of their abundance. To evaluate the validity of the collected data, a reference researcher randomly dived with the volunteers and filled in the project questionnaire separately. Correlation analyses between the records collected by the reference researcher and those collected by volunteers were performed based on 513 validation trials, testing 3,138 volunteers. Data reliability was analyzed through 7 parameters. Consistency showed the lowest mean score (51.6%, 95% Confidence Interval CI 44.1–59.2%), indicating that volunteers could direct their attention to different taxa depending on personal interests; Percent Identified showed the highest mean score (66.7%, 95% CI 55.5–78.0), indicating that volunteers can correctly identify most surveyed taxa. Overall, results confirmed that the recreational citizen science approach can effectively support reliable data for biodiversity monitoring, when carefully tailored for the volunteer skills required by the specific project. The use of a recreational approach enhances massive volunteer participation in citizen science projects, thus increasing the amount of sufficiently reliable data collected in a reduced time.

Volunteering in the Citizen Science Project "Insects of Saxony"-The Larger the Island of Knowledge, the Longer the Bank of Questions

In a cross-sectional survey study (N = 116), volunteers of the project Insects of Saxony were asked about their current and past volunteering activities, their motivations, their rating of organisational offers, their knowledge, their satisfaction with the project and their personal contribution, and their intended future involvement. Participants in the study were mostly male, well-educated, over 50 years old, and had been volunteering in biodiversity projects for a long time. They were driven by both pro-social (altruistic) and self-serving (egoistic) motivations, but rated the pro-social functions as more important for their engagement. Communication and feedback were rated the most important organisational offers. Participants also reported a knowledge increase during project participation. While the volunteers were satisfied with the overall project, they were significantly less content with their own contribution. Results from the survey were followed up with a group discussion (N = 60). The anecdotes revealed the participants' regret of not having more time for their hobby, and they emphasised the challenges that arise from the different scientific approaches of the various disciplines. Most participants indicated that they want to continue their volunteering. Implications for measuring motivations in citizen science projects and for volunteer management are discussed.

Citizen science data reveals the need for keeping garden plant recommendations up-to-date to help pollinators

Widespread concern over declines in pollinating insects has led to numerous recommendations of which “pollinator-friendly” plants to grow and help turn urban environments into valuable habitat for such important wildlife. Whilst communicated widely by organisations and readily taken up by gardeners, the provenance, accuracy, specificity and timeliness of such recommendations remain unclear. Here we use data (6429 records) gathered through a UK-wide citizen science programme (BeeWatch) to determine food plant use by the nations’ bumblebee species, and show that much of the plant use recorded does not reflect practitioner recommendations: correlation between the practitioners’ bumblebee-friendly plant list (376 plants compiled from 14 different sources) and BeeWatch records (334 plants) was low (r = 0.57), and only marginally higher than the correlation between BeeWatch records and the practitioners’ pollinator-friendly plant list (465 plants from 9 different sources; r = 0.52). We found pollinator-friendly plant lists to lack independence (correlation between practitioners’ bumblebee-friendly and pollinator-friendly lists: r = 0.75), appropriateness and precision, thus failing to recognise the non-binary nature of food-plant preference (bumblebees used many plants, but only in small quantities, e.g. lavender—the most popular plant in the BeeWatch database—constituted, at most, only 11% of records for any one bumblebee species) and stark differences therein among species and pollinator groups. We call for the provision and use of up-to-date dynamic planting recommendations driven by live (citizen science) data, with the possibility to specify pollinator species or group, to powerfully support transformative personal learning journeys and pollinator-friendly management of garden spaces.

Artificial Intelligence Meets Citizen Science to Supercharge Ecological Monitoring

The development and uptake of citizen science and artificial intelligence (AI) techniques for ecological monitoring is increasing rapidly. Citizen science and AI allow scientists to create and process larger volumes of data than possible with conventional methods. However, managers of large ecological monitoring projects have little guidance on whether citizen science, AI, or both, best suit their resource capacity and objectives. To highlight the benefits of integrating the two techniques and guide future implementation by managers, we explore the opportunities, challenges, and complementarities of using citizen science and AI for ecological monitoring. We identify project attributes to consider when implementing these techniques and suggest that financial resources, engagement, participant training, technical expertise, and subject charisma and identification are important project considerations. Ultimately, we highlight that integration can supercharge outcomes for ecological monitoring, enhancing cost-efficiency, accuracy, and multi-sector engagement.

Citizen science and artificial intelligence (AI) are often used in isolation for ecological monitoring, but their integration likely has emergent benefits for management and scientific inquiry. We explore the complementarity of citizen science and AI for ecological monitoring, highlighting key opportunities and challenges. We show that strategic integration of citizen science and AI can improve outcomes for conservation activities. For example, coupling the public engagement benefits of citizen science with the advanced analytical capabilities of AI can increase multi-stakeholder accord on issues of public and scientific interest. Furthermore, both techniques speed up data collection and processing compared with conventional scientific techniques, suggesting that their integration can fast-track monitoring and conservation actions. We present key project attributes that will assist project managers in prioritizing the resources needed to implement citizen science, AI, or preferably both.

On the merits and pitfalls of introducing a digital platform to aid conservation management: Volunteer data submission and the mediating role of volunteer coordinators

Against a backdrop of accelerating digital innovation in nature conservation and environmental management, a real-world experiment was conducted with the research aims of assessing: 1) the effects of introducing a digital data-entry platform on volunteer data submission; and 2) the extent to which coordinators influence digital platform use by their volunteers. We focussed on a large-scale volunteer-based initiative aimed at eradicating the non-native American mink (Neovison vison) from northern Scotland. This geographically dispersed conservation initiative adopted a digital platform that allowed volunteers to submit records to a central database. We found that the platform had a direct and positive effect on volunteer data submission behaviour, increasing both the number and frequency of submissions. However, our analysis revealed striking differences in coordinator engagement with the platform, which in turn influenced the engagement of volunteers with this centrally introduced digital innovation. As a consequence, the intended organisation-wide rolling out of a digital platform translated into a diversely-implemented innovation, limiting the efficacy of the tool and revealing key challenges for digital innovation in geographically-dispersed conservation initiatives.

Community science participants gain environmental awareness and contribute high quality data but improvements are needed: insights from Bumble Bee Watch

Bumble Bee Watch is a community science program where participants submit photos of bumble bees from across Canada and the United States for expert verification. The data can be used to help better understand bumble bee biology and aid in their conservation. Yet for community science programs like this to be successful and sustainable, it is important to understand the participant demographics, what motivates them, and the outcomes of their participation, as well as areas that are working well or could be improved. It is also important to understand who verifies the submissions, who uses the data and their views on the program. Of the surveyed users, most participate to contribute to scientific data collection (88%), because of a worry about bees and a desire to help save them (80%), to learn more about species in their property (63%) or region (56%), and because of a personal interest (59%). About 77% report increased awareness of species diversity, while 84% report improvement in their identification skills. We found that 81% had at least one college or university degree. There were more respondents from suburban and rural areas than urban areas, but area did not affect numbers of submissions. While half were between 45 and 64 years of age, age did not influence motivation or number of submissions. Respondents were happy with the program, particularly the website resources, the contribution to knowledge and conservation efforts, the educational values, and the ability to get identifications. Areas for improvement included app and website functionality, faster and more detailed feedback, localized resources, and more communication. Most respondents participate rarely and have submitted fewer than ten records, although about five percent are super users who participate often and submit more than fifty records. Suggested improvements to the program may increase this participation rate. Indeed, increased recruitment and retention of users in general is important, and advertising should promote the outcomes of participation. Fifteen experts responded to a separate survey and were favorable of the program although there were suggestions on how to improve the verification process and the quality of the submitted data. Suggested research questions that could be asked or answered from the data included filling knowledge gaps (species diversity, ranges, habitat, phenology, floral associations, etc.), supporting species status assessments, effecting policy and legislation, encouraging habitat restoration and management efforts, and guiding further research. However, only about half have used data from the project to date. Further promotion of Bumble Bee Watch and community science programs in general should occur amongst academia, conservationists, policy makers, and the general public. This would help to increase the number and scope of submissions, knowledge of these species, interest in conserving them, and the overall program impact.

Co-Designing a Citizen Science Program for Malaria Control in Rwanda

Good health and human wellbeing is one of the sustainable development goals. To achieve this goal, many efforts are required to control infectious diseases including malaria which remains a major public health concern in Rwanda. Surveillance of mosquitoes is critical to control the disease, but surveillance rarely includes the participation of citizens. A citizen science approach (CSA) has been applied for mosquito surveillance in developed countries, but it is unknown whether it is feasible in rural African contexts. In this paper, the technical and social components of such a program are described. Participatory design workshops were conducted in Ruhuha, Rwanda. Community members can decide on the technical tools for collecting and reporting mosquito species, mosquito nuisance, and confirmed malaria cases. Community members set up a social structure to gather observations by nominating representatives to collect the reports and send them to the researchers. These results demonstrate that co-designing a citizen science program (CSP) with citizens allows for decision on what to use in reporting observations. The decisions that the citizens took demonstrated that they have context-specific knowledge and skills, and showed that implementing a CSP in a rural area is feasible.

The feasibility of using citizens to segment anatomy from medical images: Accuracy and motivation

The development of automatic methods for segmenting anatomy from medical images is an important goal for many medical and healthcare research areas. Datasets that can be used to train and test computer algorithms, however, are often small due to the difficulties in obtaining experts to segment enough examples. Citizen science provides a potential solution to this problem but the feasibility of using the public to identify and segment anatomy in a medical image has not been investigated. Our study therefore aimed to explore the feasibility, in terms of performance and motivation, of using citizens for such purposes. Public involvement was woven into the study design and evaluation. Twenty-nine citizens were recruited and, after brief training, asked to segment the spine from a dataset of 150 magnetic resonance images. Participants segmented as many images as they could within three one-hour sessions. Their accuracy was evaluated by comparing them, as individuals and as a combined consensus, to the segmentations of three experts. Questionnaires and a focus group were used to determine the citizens' motivation for taking part and their experience of the study. Citizen segmentation accuracy, in terms of agreement with the expert consensus segmentation, varied considerably between individual citizens. The citizen consensus, however, was close to the expert consensus, indicating that when pooled, citizens may be able to replace or supplement experts for generating large image datasets. Personal interest and a desire to help were the two most common reasons for taking part in the study.

Leveraging the Expertise of the New Jersey Mosquito Control Community to Jump Start Standardized Tick Surveillance

Despite the rising incidence of tick-borne diseases (TBD) in the northeastern United States (US), information and expertise needed to assess risk, inform the public and respond proactively is highly variable across states. Standardized and well-designed tick surveillance by trained personnel can facilitate the development of useful risk maps and help target resources, but requires nontrivial start-up costs. To address this challenge, we tested whether existing personnel in New Jersey’s 21 county mosquito control agencies could be trained and interested to participate in a one-day collection of American dog ticks (Dermacentor variabilis), a presumably widespread species never before surveyed in this state. A workshop was held offering training in basic tick biology, identification, and standard operating procedures (SOPs) for surveillance, followed by a one-day simultaneous collection of D. variabilis across the state (the “NJ Tick Blitz”). In total, 498 D. variabilis were collected from 21 counties and follow-up participant surveys demonstrated an increase in knowledge and interest in ticks: 41.7% of respondents reported collecting ticks outside the Tick Blitz. We hope that the success of this initiative may provide a template for researchers and officials in other states with tick-borne disease concerns to obtain baseline tick surveillance data by training and partnering with existing personnel.

Evaluating the ability of citizen scientists to identify bumblebee (Bombus) species

Citizen science is an increasingly popular way of engaging volunteers in the collection of scientific data. Despite this, data quality remains a concern and there is little published evidence about the accuracy of records generated by citizen scientists. Here we compare data generated by two British citizen science projects, Blooms for Bees and BeeWatch, to determine the ability of volunteer recorders to identify bumblebee (Bombus) species. We assessed recorders' identification ability in two ways-as recorder accuracy (the proportion of expert-verified records correctly identified by recorders) and recorder success (the proportion of recorder-submitted identifications confirmed correct by verifiers). Recorder identification ability was low (<50% accuracy; <60% success), despite access to project specific bumblebee identification materials. Identification ability varied significantly depending on bumblebee species, with recorders most able to correctly identify species with distinct appearances. Blooms for Bees recorders (largely recruited from the gardening community) were markedly less able to identify bumblebees than BeeWatch recorders (largely individuals with a more specific interest in bumblebees). Within both projects, recorders demonstrated an improvement in identification ability over time. Here we demonstrate and quantify the essential role of expert verification within citizen science projects, and highlight where resources could be strengthened to improve recorder ability.

Hoping for optimality or designing for inclusion: Persistence, learning, and the social network of citizen science

The explosive growth in citizen science combined with a recalcitrance on the part of mainstream science to fully embrace this data collection technique demands a rigorous examination of the factors influencing data quality and project efficacy. Patterns of contributor effort and task performance have been well reviewed in online projects; however, studies of hands-on citizen science are lacking. We used a single hands-on, out-of-doors project-the Coastal Observation and Seabird Survey Team (COASST)-to quantitatively explore the relationships among participant effort, task performance, and social connectedness as a function of the demographic characteristics and interests of participants, placing these results in the context of a meta-analysis of 54 citizen science projects. Although online projects were typified by high (>90%) rates of one-off participation and low retention (<10%) past 1 y, regular COASST participants were highly likely to continue past their first survey (86%), with 54% active 1 y later. Project-wide, task performance was high (88% correct species identifications over the 31,450 carcasses and 163 species found). However, there were distinct demographic differences. Age, birding expertise, and previous citizen science experience had the greatest impact on participant persistence and performance, albeit occasionally in opposite directions. Gender and sociality were relatively inconsequential, although highly gregarious social types, i.e., "nexus people," were extremely influential at recruiting others. Our findings suggest that hands-on citizen science can produce high-quality data especially if participants persist, and that understanding the demographic data of participation could be used to maximize data quality and breadth of participation across the larger societal landscape.

Designing online species identification tools for biological recording: the impact on data quality and citizen science learning

In recent years, the number and scale of environmental citizen science programmes that involve lay people in scientific research have increased rapidly. Many of these initiatives are concerned with the recording and identification of species, processes which are increasingly mediated through digital interfaces. Here, we address the growing need to understand the particular role of digital identification tools, both in generating scientific data and in supporting learning by lay people engaged in citizen science activities pertaining to biological recording communities. Starting from two well-known identification tools, namely identification keys and field guides, this study focuses on the decision-making and quality of learning processes underlying species identification tasks, by comparing three digital interfaces designed to identify bumblebee species. The three interfaces varied with respect to whether species were directly compared or filtered by matching on visual features; and whether the order of filters was directed by the interface or a user-driven open choice. A concurrent mixed-methods approach was adopted to compare how these different interfaces affected the ability of participants to make correct and quick species identifications, and to better understand how participants learned through using these interfaces. We found that the accuracy of identification and quality of learning were dependent upon the interface type, the difficulty of the specimen on the image being identified and the interaction between interface type and ‘image difficulty’. Specifically, interfaces based on filtering outperformed those based on direct visual comparison across all metrics, and an open choice of filters led to higher accuracy than the interface that directed the filtering. Our results have direct implications for the design of online identification technologies for biological recording, irrespective of whether the goal is to collect higher quality citizen science data, or to support user learning and engagement in these communities of practice.

Opportunistic citizen science data transform understanding of species distributions, phenology, and diversity gradients for global change research

Opportunistic citizen science (CS) programs allow volunteers to report species observations from anywhere, at any time, and can assemble large volumes of historic and current data at faster rates than more coordinated programs with standardized data collection. This can quickly provide large amounts of species distributional data, but whether this focus on participation comes at a cost in data quality is not clear. Although automated and expert vetting can increase data reliability, there is no guarantee that opportunistic data will do anything more than confirm information from professional surveys. Here, we use eButterfly, an opportunistic CS program, and a comparable dataset of professionally collected observations, to measure the amount of new distributional species information that opportunistic CS generates. We also test how well opportunistic CS can estimate regional species richness for a large group of taxa (>300 butterfly species) across a broad area. We find that eButterfly contributes new distributional information for >80% of species, and that opportunistically submitting observations allowed volunteers to spot species ~35 days earlier than professionals. Although eButterfly did a relatively poor job at predicting regional species richness by itself (detecting only about 35-57% of species per region), it significantly contributed to regional species richness when used with the professional dataset (adding ~3 species that had gone undetected in professional surveys per region). Overall, we find that the opportunistic CS model can provide substantial complementary species information when used alongside professional survey data. Our results suggest that data from opportunistic CS programs in conjunction with professional datasets can strongly increase the capacity of researchers to estimate species richness, and provide unique information on species distributions and phenologies that are relevant to the detection of the biological consequences of global change.

How training citizen scientists affects the accuracy and precision of phenological data

Monitoring plant and animal phenology is a critical step to anticipating and predicting changes in species interactions and biodiversity. Because phenology necessarily involves frequent and repeated observations over time, citizen scientists have become a vital part of collecting phenological data. However, there is still concern over the accuracy and precision of citizen science data. It is possible that training citizen scientists can improve data quality though there are few comparisons of trained and untrained citizen scientists in the ability of each to accurately and precisely measure phenology. We assessed how three types of observers-experts, trained citizen scientists that make repeated observations, and untrained citizen scientists making once-per-year observations-differ in quantifying temporal change in flower and fruit abundance of American mountain ash trees (Sorbus americana Marsh.) and arthropods in Acadia National Park, Maine, USA. We found that trained more so than untrained citizen science observers over- or under-estimated abundances leading to precise but inaccurate characterizations of phenological patterns. Our results suggest a new type of bias induced by repeated observations: A type of learning takes place that reduces the independence of observations taken on different trees or different dates. Thus, in this and many other cases, having individuals make one-off observations of marked plants may produce data as good if not better than individuals making repeated observations. For citizen science programs related to phenology, our results underscore the importance of (a) attracting the most number of observers possible even if they only make one observation, (b) producing easy-to-use and informative data sheets, and

Digital technology and the conservation of nature

Digital technology is changing nature conservation in increasingly profound ways. We describe this impact and its significance through the concept of 'digital conservation', which we found to comprise five pivotal dimensions: data on nature, data on people, data integration and analysis, communication and experience, and participatory governance. Examining digital innovation in nature conservation and addressing how its development, implementation and diffusion may be steered, we warn against hypes, techno-fix thinking, good news narratives and unverified assumptions. We identify a need for rigorous evaluation, more comprehensive consideration of social exclusion, frameworks for regulation and increased multi-sector as well as multi-discipline awareness and cooperation. Along the way, digital technology may best be reconceptualised by conservationists from something that is either good or bad, to a dual-faced force in need of guidance.