The social fabric of citizen science—drivers for long-term engagement in the German butterfly monitoring scheme

Insect conservation needs sound information on species distribution trends. Developing this evidence relies—in practice—on long-term engagement of volunteers who observe and record species over large spatial and temporal scales. Many biodiversity monitoring schemes, including those for insects, are highly dependent on conservation-based citizen science programs with a long-term continuity. As these schemes are built entirely on good will, the nature of social relations and networks is pivotal to success. We assess the working mechanism of a monitoring scheme that is citizen-based as a case study. The German Butterfly Monitoring Scheme (hereafter TMD for “Tagfalter-Monitoring Deutschland”) operates, as many other citizen science monitoring schemes, through an overarching national network of regional subnetworks of volunteers and a central scientific coordination. Using a questionnaire survey paired with a visual social network assessment, we investigate how participants interact within these networks and assess their motivations to engage. We characterise the functionality of this social network based on mechanism of coordination and participation, flows of information and knowledge exchange among recorders, regional and central coordinators, academic scientists and institutions. By analyzing the interactions, we show how the social network facilitates and ensures various communication modes and thereby fosters long-term engagement, stability and growth of the scheme. We identify the central role of project coordination and the importance of social relations within citizen-based monitoring programs for engagement and personal satisfaction. Based on our empirical study, we derive a set of recommendations for establishing and maintaining successful volunteer networks in insect citizen-based monitoring programs.     

Improving and integrating data on invasive species collected by citizen scientists

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

Challenges for biodiversity monitoring using citizen science in transitioning social–ecological systems

Biodiversity monitoring requires sound data collection over large temporal and spatial scales in order to inform policy and conservation management. Citizen science programmes, if designed appropriately, can make valuable contributions to data collection and analyses. Moreover, citizen science has potential for both environmental education and civic participation. Recommendations on effective citizen science are available in the literature, but most existing work has come from relatively rich, industrialized countries. By contrast, there is very little knowledge on citizen science projects in transitioning economic, social and cultural settings. This paper seeks to adjust this deficit by contributing insights from our attempt to initiate a new monitoring scheme in Romania. We draw on our experience of conducting workshops, training events and camps to strengthen citizen engagement in a butterfly monitoring scheme, and discussions with many stakeholders engaged in other monitoring programmes inside and outside of Europe. We highlight four general themes that are worth considering when initiating new citizen science projects in socio-economically challenging settings: (i) engaging citizens requires a combination of formal and informal support; (ii) a culture of volunteering requires education as well as building capacity and confidence; (iii) citizen science needs active integration of both national experts and local stakeholders; and (iv) successful monitoring schemes require effective leadership. We conclude that particular attention should be paid to the cultural legacies of the target area.     

An expert-assisted citizen science program involving agricultural high schools provides national patterns on bee species assemblages

Ecology studies often require large datasets. The benefits of citizen science for collecting such datasets include the extension of spatial and temporal scales, and cost reduction. In classical citizen science, citizens collect data and send them directly to scientists. This may not be possible for the many biological groups for which specimen identification is difficult and requires high-level expertise. Here we report the results of an expert-assisted citizen science program where teachers from 20 French agricultural high schools collected bees, which were identified to species level by a panel of expert bee taxonomists. Overall the dataset included 70 collections (year?×?sampling site combinations) that resulted in 4574 specimens belonging to 195 species. We analysed this dataset using data freely available at a national scale on agriculture intensity and landscape composition. We found that species richness increased with increasing proportion of herbaceous semi-natural elements; species dominance decreased with increasing crop diversity; the proportion of above ground nesting species and specimens increased as the intensity of agricultural practices decreased. Comparing the results obtained with identification to species level and those obtained with higher taxa or parataxonomic approaches, we found that the loss of taxonomic resolution resulted in the non-significance of some results on the effects of environmental variables on bee assemblage-level attributes. Our study suggests that identification to species level is of great importance to detect the effects of global change on bees and that an expert-assisted citizen science paradigm could provide relevant results to guide conservation measures at a national scale.     

New Directions for Understanding the Spatial Resilience of Social–Ecological Systems

The concept of spatial resilience has brought a new focus on the influence of multi-scale processes on the dynamics of ecosystems. Initial ideas about spatial resilience focused on coral reefs and emphasized escalating anthropogenic disturbances across the broader seascape. This perspective resonated with a new awareness of global drivers of change, such as growth in international trade and shifts in climate, and the need to respond by scaling up governance and management. We review recent trends and emerging ideas in spatial resilience, using coral reefs and dependent communities as exemplars of multi-scale social–ecological systems. Despite recent advances, management and governance of ecosystems remain spatially fragmented and constrained to small scales. Temporally, many interventions still miss or ignore warning signals and struggle to cope with history, politics, long-term cumulative pressures, feedbacks, and sudden surprises. Significant recent progress has been made in understanding the relevance of spatial and temporal scale, heterogeneity, networks, the importance of place, and multi-scale governance. Emerging themes include better integration of ecology and conservation with social and economic science, and incorporating temporal dynamics in spatial analyses. A better understanding of the multi-scale spatial and temporal processes that drive the resilience of linked social-ecosystems will help address the widespread mismatch between the scales of ongoing ecological change and effective long-term governance of land- and seascapes.     

Reconstructing Ancient Hohokam Irrigation Systems in the Middle Gila River Valley,Arizona, United States of America

We explore the concept of scales to examine emerging irrigation realities, i.e., connecting more agents within larger spaces - relates to the complexity of irrigation systems. Modern hydraulic models allow the inclusion of emerging multi-scale issues over time, including social issues related to different spatial and temporal scales. We show that the time needed to manage irrigation efficiently relates to the size of a system. By reconstructing ancient Hohokam irrigation systems in Arizona, we identify how longer-term extension of spatial scales created management problems beyond the scope of available technology. This approach allows greater understanding of how stresses in daily irrigation management may have impacted longer-term societal stability.     

Many eyes on the ground: citizen science is an effective early detection tool for biosecurity

Early detection of target non-indigenous species is one of the most important determinants of a successful eradication campaign. For early detection to be successful, and provide the highest probability of achieving eradication, intense surveillance is often required that can involve significant resources. Volunteer based monitoring or “citizen science” is one potential tool to address this problem. This study differs from standard citizen science projects because the participants are personnel or contractors of a company working on Barrow Island, Western Australia. We show that personnel can contribute successfully to a surveillance program aimed at detecting a broad taxonomic range of non-indigenous vertebrate and invertebrate species. Using data collected over a five year surveillance period on Barrow Island, we show that eighteen of the nineteen (95%) non-indigenous invertebrate species new to the island were detected by personnel working on the island, and that the number of detections made by these workers was significantly related to the number of personnel on the island at any one time. Most personnel detections (91%) were made inside buildings where the majority of active surveillance tools could not be implemented. For vertebrates, 4 NIS species detections (100% of detections) were made in the built environment by personnel. Although reporting of suspect non-indigenous species is voluntary, personnel are required to attend inductions and toolboxes where reporting of suspect biosecurity risk material is encouraged. These results demonstrate the value of industry led ‘citizen science’ programs, resulting in sustained stewardship and conservation of areas with high environmental value.     

Misidentifications in citizen science bias the phenological estimates of two hard-to-identify Elaenia flycatchers

Citizen science initiatives contain a large volume of observations that can be useful to address ecological questions for a wide array of organisms. However, one limitation of citizen science data is the potential for species misidentification. Although recent studies have shown that citizen science data are relatively accurate for many taxa, the effect of misidentification errors in hard-to-identify species remains poorly explored. If misidentification events occur at large scales, ecological estimates can be compromised. Here, we show that misidentifications contained in citizen science databases biased phenological estimates in a pair of migratory and partially overlapping Neotropical flycatchers: the Chilean Elaenia Elaenia chilensis and the Small-billed Elaenia Elaenia parvirostris. We reviewed and re-classified 4399 photos of these species from Argentina, Chile and Uruguay. We found that overall identification accuracy was high (c. 90%) for both species when they were allopatric, but dramatically low for Small-billed Elaenia during autumn migration (from 28.6% to 84.6%) because migrating individuals of Chilean Elaenia were systematically reported as Small-billed. The phenological estimates for both Elaenias were biased due to the large number of misidentifications concentrated towards the autumn migration period. These errors caused a 1-week advancement in the estimated arrival, and a 2-week delay in the estimated departure for Small-billed Elaenia. For Chilean Elaenia, errors caused a 1-week delay for the estimated spring peak passage and underestimation of the magnitude of the autumn passage. Our results highlight the importance of performing critical assessments of records when using citizen science databases to describe ecological patterns in species that are hard to identify. The large volume of information provided by citizen science initiatives is useful to describe spatio-temporal patterns in birds, particularly in those of poorly known regions. However, to further enhance the usefulness of such databases, it is imperative actively to post-process and contrast patterns derived from documented (and undocumented) records, with a special focus on misidentifications. This will only be possible through a thorough review of the documented data, together with an intimate understanding of the natural history of the study species.     

Evaluating citizen science data for forecasting species responses to national forest management

The extensive spatial and temporal coverage of many citizen science datasets (CSD) makes them appealing for use in species distribution modeling and forecasting. However, a frequent limitation is the inability to validate results. Here, we aim to assess the reliability of CSD for forecasting species occurrence in response to national forest management projections (representing 160,366 km²) by comparison against forecasts from a model based on systematically collected colonization–extinction data. We fitted species distribution models using citizen science observations of an old‐forest indicator fungus Phellinus ferrugineofuscus. We applied five modeling approaches (generalized linear model, Poisson process model, Bayesian occupancy model, and two MaxEnt models). Models were used to forecast changes in occurrence in response to national forest management for 2020‐2110. Forecasts of species occurrence from models based on CSD were congruent with forecasts made using the colonization–extinction model based on systematically collected data, although different modeling methods indicated different levels of change. All models projected increased occurrence in set‐aside forest from 2020 to 2110: the projected increase varied between 125% and 195% among models based on CSD, in comparison with an increase of 129% according to the colonization–extinction model. All but one model based on CSD projected a decline in production forest, which varied between 11% and 49%, compared to a decline of 41% using the colonization–extinction model. All models thus highlighted the importance of protected old forest for P. ferrugineofuscus persistence. We conclude that models based on CSD can reproduce forecasts from models based on systematically collected colonization–extinction data and so lead to the same forest management conclusions. Our results show that the use of a suite of models allows CSD to be reliably applied to land management and conservation decision making, demonstrating that widely available CSD can be a valuable forecasting resource.     

Developing a flexible learning activity on biodiversity and spatial scale concepts using open‐access vegetation datasets from the National Ecological Observatory Network

Biodiversity is a complex, yet essential, concept for undergraduate students in ecology and other natural sciences to grasp. As beginner scientists, students must learn to recognize, describe, and interpret patterns of biodiversity across various spatial scales and understand their relationships with ecological processes and human influences. It is also increasingly important for undergraduate programs in ecology and related disciplines to provide students with experiences working with large ecological datasets to develop students’ data science skills and their ability to consider how ecological processes that operate at broader spatial scales (macroscale) affect local ecosystems. To support the goals of improving student understanding of macroscale ecology and biodiversity at multiple spatial scales, we formed an interdisciplinary team that included grant personnel, scientists, and faculty from ecology and spatial sciences to design a flexible learning activity to teach macroscale biodiversity concepts using large datasets from the National Ecological Observatory Network (NEON). We piloted this learning activity in six courses enrolling a total of 109 students, ranging from midlevel ecology and GIS/remote sensing courses, to upper‐level conservation biology. Using our classroom experiences and a pre/postassessment framework, we evaluated whether our learning activity resulted in increased student understanding of macroscale ecology and biodiversity concepts and increased familiarity with analysis techniques, software programs, and large spatio‐ecological datasets. Overall, results suggest that our learning activity improved student understanding of biological diversity, biodiversity metrics, and patterns of biodiversity across several spatial scales. Participating faculty reflected on what went well and what would benefit from changes, and we offer suggestions for implementation of the learning activity based on this feedback. This learning activity introduced students to macroscale ecology and built student skills in working with big data (i.e., large datasets) and performing basic quantitative analyses, skills that are essential for the next generation of ecologists.     

An integrated,modular approach to data science education in microbiology

We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms are transforming biology into an information science. This has shifted major challenges in biological research from data generation and processing to interpretation and knowledge translation. However, postsecondary training in bioinformatics, or more generally data science for life scientists, lags behind current demand. In particular, development of accessible, undergraduate data science curricula has the potential to improve research and learning outcomes as well as better prepare students in the life sciences to thrive in public and private sector careers. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competency across several years of integrated practice. Through EDUCE, students complete data science modules integrated into required and elective courses augmented with coordinated cocurricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants (TAs), postdocs, instructors, and research faculty from multiple disciplines to overcome several reported barriers to data science for life scientists, including instructor capacity, student prior knowledge, and relevance to discipline-specific problems. Preliminary survey results indicate that even a single module improves student self-reported interest and/or experience in bioinformatics and computer science. Thus, EDUCE provides a flexible and extensible active learning framework for integration of data science curriculum into undergraduate courses and programs across the life sciences.     

Chimpanzee identification and social network construction through an online citizen science platform

Citizen science has grown rapidly in popularity in recent years due to its potential to educate and engage the public while providing a means to address a myriad of scientific questions. However, the rise in popularity of citizen science has also been accompanied by concerns about the quality of data emerging from citizen science research projects. We assessed data quality in the online citizen scientist platform Chimp&See, which hosts camera trap videos of chimpanzees (Pan troglodytes) and other species across Equatorial Africa. In particular, we compared detection and identification of individual chimpanzees by citizen scientists with that of experts with years of experience studying those chimpanzees. We found that citizen scientists typically detected the same number of individual chimpanzees as experts, but assigned far fewer identifications (IDs) to those individuals. Those IDs assigned, however, were nearly always in agreement with the IDs provided by experts. We applied the data sets of citizen scientists and experts by constructing social networks from each. We found that both social networks were relatively robust and shared a similar structure, as well as having positively correlated individual network positions. Our findings demonstrate that, although citizen scientists produced a smaller data set based on fewer confirmed IDs, the data strongly reflect expert classifications and can be used for meaningful assessments of group structure and dynamics. This approach expands opportunities for social research and conservation monitoring in great apes and many other individually identifiable species.     

协同分布式实验2.0 (CDE 2.0):生态学野外研究新方法

全球野外实验网络是近年来兴起的生态学实验方法,产生了“协同分布式实验”(CDE)、“分布式协作实验”(DCE)的野外实验网络和英国生物学记录中心(BRC)的野外观察网络等具体方法,但均存在尺度小、周期短或数据偏差问题.野外实验网络的协议设计应秉持实验优于观察测量、数据数量优于数据质量和尺度优于操作的原则.综合上述方法,本研究提出利用公众科学(citizen science)获得大范围、多尺度和长周期的数据,采用环境因子作为协变量对公众参与者的实验数据集进行验证后,将其作为后验概率与作为主观概率的生态学家实验数据集进行比对,以完成数据有效性验证,克服公众参与者数据质量瑕疵,并提出了相应的统计学模型.讨论了生态学实验数据中应用先验概率、先验与后验概率的逻辑关系以及后验概率发现演化过程新关系的可能性.这种方法更符合统计学采样规范,并增加了大尺度时空的样本量.该方法有望为生态学研究所寻找的一般性理论提供方法支持,新方法可称之为“协同分布式实验2.0”(CDE 2.0).     

Citizen science and wildlife biology: Synergies and challenges

Citizen science (CS) has evolved over the past decades as a working method involving interested citizens in scientific research, for example by reporting observations, taking measurements or analysing data. In the past, research on animal behaviour has been benefitting from contributions of citizen scientists mainly in the field of ornithology but the full potential of CS in ecological and behavioural sciences is surely still untapped. Here, we present case studies that successfully applied CS to research projects in wildlife biology and discuss potentials and challenges experienced. Our case studies cover a broad range of opportunities: large‐scale CS projects with interactive online tools on bird song dialects, engagement of stakeholders as citizen scientists to reduce human–wildlife conflicts, involvement of students of primary and secondary schools in CS projects as well as collaboration with the media leading to successful recruitment of citizen scientists. Each case study provides a short overview of the scientific questions and how they were approached to showcase the potentials and challenges of CS in wildlife biology. Based on the experience of the case studies, we highlight how CS may support research in wildlife biology and emphasise the value of fostering communication in CS to improve recruitment of participants and to facilitate learning and mutual trust among different groups of interest (e.g., researchers, stakeholders, students). We further show how specific training for the participants may be needed to obtain reliable data. We consider CS as a suitable tool to enhance research in wildlife biology through the application of open science procedures (i.e., open access to articles and the data on publicly available repositories) to support transparency and sharing experiences.     

Counting Cats: The integration of expert and citizen science data for unbiased inference of population abundance

Free‐roaming animal populations are hard to count, and professional experts are a limited resource. There is vast untapped potential in the data collected by nonprofessional scientists who volunteer their time to population monitoring, but citizen science (CS) raises concerns around data quality and biases. A particular concern in abundance modeling is the presence of false positives that can occur due to misidentification of nontarget species. Here, we introduce Integrated Abundance Models (IAMs) that integrate citizen and expert data to allow robust inference of population abundance meanwhile accounting for biases caused by misidentification. We used simulation experiments to confirm that IAMs successfully remove the inflation of abundance estimates caused by false‐positive detections and can provide accurate estimates of both bias and abundance. We illustrate the approach with a case study on unowned domestic cats, which are commonly confused with owned, and infer their abundance by analyzing a combination of CS data and expert data. Our case study finds that relying on CS data alone, either through simple summation or via traditional modeling approaches, can vastly inflate abundance estimates. IAMs provide an adaptable framework, increasing the opportunity for further development of the approach, tailoring to specific systems and robust use of CS data.     

Dispatches from the neighborhood watch: Using citizen science and field survey data to document color morph frequency in space and time

Heritable color polymorphisms have a long history of study in evolutionary biology, though they are less frequently examined today than in the past. These systems, where multiple discrete, visually identifiable color phenotypes co‐occur in the same population, are valuable for tracking evolutionary change and ascertaining the relative importance of different evolutionary mechanisms. Here, we use a combination of citizen science data and field surveys in the Great Lakes region of North America to identify patterns of color morph frequencies in the eastern gray squirrel (Sciurus carolinensis). Using over 68,000 individual squirrel records from both large and small spatial scales, we identify the following patterns: (a) the melanistic (black) phenotype is often localized but nonetheless widespread throughout the Great Lakes region, occurring in all states and provinces sampled. (b) In Ohio, where intensive surveys were performed, there is a weak but significantly positive association between color morph frequency and geographic proximity of populations. Nonetheless, even nearby populations often had radically different frequencies of the melanistic morph, which ranged from 0% to 96%. These patterns were mosaic rather than clinal. (c) In the Wooster, Ohio population, which had over eight years of continuous data on color morph frequency representing nearly 40,000 records, we found that the frequency of the melanistic morph increased gradually over time on some survey routes but decreased or did not change over time on others. These differences were statistically significant and occurred at very small spatial scales (on the order of hundreds of meters). Together, these patterns are suggestive of genetic drift as an important mechanism of evolutionary change in this system. We argue that studies of color polymorphism are still quite valuable in advancing our understanding of fundamental evolutionary processes, especially when coupled with the growing availability of data from citizen science efforts.     

Equivalence of citizen science and scientific data for modelling species distribution of birds from a tropical savanna

The Wallacean deficit continues to be a challenge to species distribution modelling. Although some authors have suggested that data collected by citizen scientists can be relevant for a better understanding of biodiversity, to our knowledge, no work has quantitatively tested the equivalence between scientific and citizen science data. Here, we investigate the hypothesis that data collected by citizen scientists can be equivalent to data collected by professional scientists when generating species spatial distribution models. For 42 bird species in the Cerrado region we generated and compared species distribution models based on three data sources: (1) scientific data, (2) citizen science data and (3) sample size corrected citizen science data. To test our hypothesis, we compared the equivalence of these datasets. We rejected the hypothesis of equivalence for about one-third (38%) of the evaluated species, revealing that, for most of the species considered, the models generated were equivalent irrespective of the data set used. The distances between centroids of the models that were equivalent were on average smaller than the distances between non-equivalent models. Also, the direction of change in the models showed no pattern, with no trend towards more populated regions. Our results show that the use of data collected by citizen scientists can be an ally in filling the Wallacean deficit gap. In fact, the lack of use of this wide range of data collected by citizen scientists seems to be an unjustified caution. We indicate the potential of using citizen science data for modelling the distribution of species, mainly due to the large set of data collected, which is impracticable for scientists alone to collect. Conservation measures will be favoured by the union of professional and amateur data, aiming for a better understanding of species distribution and, consequently, biodiversity conservation.     

A unique role for citizen science in ecological restoration: a case study in streams

Citizen science has the potential to generate valuable biologic data for use in restoration monitoring, while also providing a unique opportunity for public participation in local restoration projects. In this article, we describe and evaluate a citizen science program designed to monitor the effect of stream restoration construction disturbance on the macroinvertebrate community. We present the results of a 7‐year stream restoration study conducted by citizen scientists utilizing a Before‐After‐Control‐Impact (BACI) design. Trait‐based macroinvertebrate data showed a strong response to restoration construction disturbance and return to pre‐restoration conditions within 2 years. The findings of this study suggest that citizen science can generate meaningful BACI‐oriented data about ecological restoration; however, until more research is conducted, citizen data should only be used to augment professional data intended to demonstrate restoration success.     

New conservation opportunities: Using citizen science in monitoring non-native species in Neotropical region

The combination of highly equipped smartphones, with the increased use of social media, has offered a wide database. Given this, citizen science can be used to record and monitor non-native fish fauna, target new samples and collaborate with monitoring occurrences in new areas. We aimed to demonstrate the efficiency of social media in citizen science as a tool to cooperate with monitoring studies of non-native species. Consequently, we determined the occurrence points of S. brasiliensis in the Iguaçu River basin, indicating sites of greatest occurrence and analyzing the impact of the invasion on the native fauna of the basin. Files and information available on the YouTube^® and Facebook^® media platforms were used as data, was carried out from April 2019 to April 2020. The results were 40 records, 22 videos obtained from Youtube, and seven videos and 11 photos from Facebook, the oldest record was from April 2013, while the largest number of posts was in 2016. Fish records available from online platforms can reveal the occurrence and progressive dispersion of species, in the context of biological invasions, these tools can be of great value in studies that aim to follow the progress of introduced species, contributing by helping to direct new sampling programs and corroborating the occurrence of species in new areas in conjunction with standard monitoring programs. Based on citizen science records, it was possible to update the range of occurrence of the non-native S. brasiliensis in the Iguaçu River basin, cooperating with scientific knowledge. Innovative monitoring and control measures are necessary to deal with invasive species, with citizen science proving to be competent for determining the occurrence of species and showing promise in the entire field of ichthyology.