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

Recruitment, Retention, and Training of Citizen Scientists in Translational Medicine Research: A Citizen Science Initiative on Non-Alcoholic Fatty Liver Disease

Citizen science is a participatory science approach in which members of the public (citizens) collaborate with scientists and professional researchers and become involved in research and innovation activities, resulting in the co-creation of scientific knowledge and innovation. Citizen science has been widely applied in research, particularly in the social sciences, environmental sciences, information and communication technologies, and public health. However, the application of this approach in clinical sciences, particularly in translational medicine research, is still nascent. This exploratory study involved members of the public (citizen scientists) in a translational medicine experiment on non-alcoholic fatty liver disease that incorporated a lifestyle and weight-loss intervention. The aim of this paper is to report successful methods and approaches for the recruitment, retention, and training of citizen scientists. For the citizen scientists' recruitment, online calls placed on the websites of our research project and biomedical research center and targeted emails were the most helpful. Of the 14 members of the public who expressed interest in our study, six were recruited as citizen scientists. Citizen scientists were mostly female (n = 5, 83%), white (n = 3, 50%), over 50 years of age (n = 4, 67%), educated to postgraduate level (n = 5, 83%), and either retired or not in employment (n = 5, 83%). The retention rate was 83% (n = 5), and the dropout rate was 17% (n = 1). We arranged instructor-led interactive online training sessions (an hour-long one-on-one session and two-hour group sessions). Research skills training covered ethics in research and qualitative and quantitative data analysis. Citizen scientists were given several incentives, such as reimbursement of travel and care costs, selection as citizen scientists of the month, publications of their blogs and perspective articles, and co-authorship and acknowledgement in papers and project deliverables. To conclude, members of the public (particularly middle-aged white women with postgraduate education) are interested in becoming citizen scientists in translational medicine research. Their retention rate is higher, and they can contribute to different research activities. However, they need training to develop their research skills and expertise. The training should be simple, comprehensive, and flexible to accommodate the schedules of individual citizen scientists. They deserve incentives as they work on a voluntary basis.

Sex-dependent phenological responses to climate vary across species' ranges

Anthropogenic climate change has significantly altered the flowering times (i.e., phenology) of plants worldwide, affecting their reproduction, survival, and interactions. Recent studies utilizing herbarium specimens have uncovered significant intra- and inter-specific variation in flowering phenology and its response to changes in climate but have mostly been limited to animal-pollinated species. Thus, despite their economic and ecological importance, variation in phenological responses to climate remain largely unexplored among and within wind-pollinated dioecious species and across their sexes. Using both herbarium specimens and volunteer observations of cottonwood (Populus) species, we examined how phenological sensitivity to climate varies across species, their ranges, sexes, and phenophases. The timing of flowering varied significantly across and within species, as did their sensitivity to spring temperature. In particular, male flowering generally happened earlier in the season and was more sensitive to warming than female flowering. Further, the onset of flowering was more sensitive to changes in temperature than leaf out. Increased temporal gaps between male and female flowering time and between the first open flower date and leaf out date were predicted for the future under two climate change scenarios. These shifts will impact the efficacy of sexual reproduction and gene flow among species. Our study demonstrates significant inter- and intra-specific variation in phenology and its responses to environmental cues, across species' ranges, phenophases, and sex, in wind-pollinated species. These variations need to be considered to predict accurately the effects of climate change and assess their ecological and evolutionary consequences.

Documenting butterflies with the help of citizen science in Darjeeling-Sikkim Himalaya, India

The availability of information on the distribution and occurrence of different species in a landscape is crucial to developing an informed conservation and management plan, however such information in the Himalaya is often limited. Citizen science, which builds on the knowledge and interest of communities to contribute to science, can be a solution to this problem. In this study, we used butterflies as a model taxon in the Darjeeling-Sikkim Himalaya which shows how citizen science can aid in documenting biodiversity. The study employed both citizen science, and researcher-survey approaches to collect data, and the collective effort resulted in 407 species, which is the highest by any study carried out in the region. Results show that citizen science can be helpful as a supplementary tool for data collection in biodiversity documentation projects, and can aid in adding to the diversity and distribution records of species, including those that are unique, rare, seasonal, and nationally protected. Citizen science outreach was used to muster potential participants from the local community to participate in the study. Thus, it is advisable for citizen science projects to find means to recruit a larger pool of contributors, and citizen science outreach can be key to their success.

Practice makes the expert: The importance of training volunteers in the generation of phenological data from photographs of biodiversity observation platforms

Phenology studies the time at which events in the life cycle of a species occur sand how they are related to environmental cues. Patterns of change in phenology at different scales can be used as an indicator of ecosystem changes and climate change, but the data necessary to detect these changes can be difficult to obtain due to their temporal and regional dimensions. Citizen science can contribute to generate large amounts of data on phenological changes at wide geographical scales that would be almost impossible for professional scientists to generate, but the quality and reliability of these data are often questioned. The objective of this study was to evaluate the use of a biodiversity observation citizen science platform based on photographic information as a potential source of large-scale phenological information, and to identify the key benefits and limitations of this type of information source. We used the Naturalista photographic databases for two invasive species in a tropical region: Leonotis nepetifolia and Nicotiana glauca. The photographs were classified into different phenophases (initial growth, immature flower, mature flower, dry fruit) by three groups of volunteers: a group of experts, a trained group with information on the biology and phenology of both species, and an untrained group. The degree of reliability of the phenological classifications was estimated for each group of volunteers and each phenophase. The degree of reliability of the phenological classification of the untrained group was generally very low for all phenophases. The group of trained volunteers showed accuracy levels for the reproductive phenophases that equaled the degree of reliability among the expert group, regardless of species, and was consistent across phenophases. We conclude that volunteer classification of photographic information contained in biodiversity observation platforms can provide phenological information with high geographic coverage and an increasing temporal coverage on general phenological patterns of species with wide distributions but has limited applicability in the identification of exact start and end dates. and peaks of the different phenophases.

Identifying, reducing, and communicating uncertainty in community science: a focus on alien species

Community science (also often referred to as citizen science) provides a unique opportunity to address questions beyond the scope of other research methods whilst simultaneously engaging communities in the scientific process. This leads to broad educational benefits, empowers people, and can increase public awareness of societally relevant issues such as the biodiversity crisis. As such, community science has become a favourable framework for researching alien species where data on the presence, absence, abundance, phenology, and impact of species is important in informing management decisions. However, uncertainties arising at different stages can limit the interpretation of data and lead to projects failing to achieve their intended outcomes. Focusing on alien species centered community science projects, we identified key research questions and the relevant uncertainties that arise during the process of developing the study design, for example, when collecting the data and during the statistical analyses. Additionally, we assessed uncertainties from a linguistic perspective, and how the communication stages among project coordinators, participants and other stakeholders can alter the way in which information may be interpreted. We discuss existing methods for reducing uncertainty and suggest further solutions to improve data reliability. Further, we make suggestions to reduce the uncertainties that emerge at each project step and provide guidance and recommendations that can be readily applied in practice. Reducing uncertainties is essential and necessary to strengthen the scientific and community outcomes of community science, which is of particular importance to ensure the success of projects aimed at detecting novel alien species and monitoring their dynamics across space and time.

A comparison of herbarium and citizen science phenology datasets for detecting response of flowering time to climate change in Denmark

Phenology has emerged as a key metric to measure how species respond to changes in climate. Innovative means have been developed to extend the temporal and spatial range of phenological data by obtaining data from herbarium specimens, citizen science programs, and biodiversity data repositories. These different data types have seldom been compared for their effectiveness in detecting environmental impacts on phenology. To address this, we compare three separate phenology datasets from Denmark: (i) herbarium specimen data spanning 145 years, (ii) data collected from a citizen science phenology program over a single year observing first flowering, and (iii) data derived from incidental biodiversity observations in iNaturalist over a single year. Each dataset includes flowering day of year observed for three common spring-flowering plant species: Allium ursinum (ramsons), Aesculus hippocastanum (horse chestnut), and Sambucus nigra (black elderberry). The incidental iNaturalist dataset provided the most extensive geographic coverage across Denmark and the largest sample size and recorded peak flowering in a way comparable to herbarium specimens. The directed citizen science dataset recorded much earlier flowering dates because the program objective was to report the first flowering, and so was less compared to the other two datasets. Herbarium data demonstrated the strongest effect of spring temperature on flowering in Denmark, possibly because it was the only dataset measuring temporal variation in phenology, while the other datasets measured spatial variation. Herbarium data predicted the mean flowering day of year recorded in our iNaturalist dataset for all three species. Combining herbarium data with iNaturalist data provides an even more effective method for detecting climatic effects on phenology. Phenology observations from directed and incidental citizen science initiatives will increase in value for climate change research in the coming years with the addition of data capturing the inter-annual variation in phenology.

Monitoring biological water quality by volunteers complements professional assessments

Progressively more community initiatives have been undertaken over last decades to monitor water quality. Biological data collected by volunteers has been used for biodiversity and water quality studies. Despite the many citizen science projects collecting and using macroinvertebrates, the number of scientific peer-reviewed publications that use this data, remains limited. In 2018, a citizen science project on biological water quality assessment was launched in the Netherlands. In this project, volunteers collect macroinvertebrates from a nearby waterbody, identify and count the number of specimens, and register the catch through a web portal to instantaneously receive a water quality score based on their data. Water quality monitoring in the Netherlands is traditionally the field of professionals working at water authorities. Here, we compare the data from the citizen science project with the data gathered by professionals. We evaluate information regarding type and distribution of sampled waterbodies and sampling period, and compare general patterns in both datasets with respect to collected animals and calculated water quality scores. The results show that volunteers and professionals seldomly sample the same waterbody, that there is some overlap in sampling period, and that volunteers more frequently sampled urban waters and smaller waterbodies. The citizen science project is thus yielding data about understudied waters and this spatial and temporal complementarity is useful. The character and thoroughness of the assessments by volunteers and professionals are likely to differentiate. Volunteers collected significantly lower numbers of animals per sample and fewer animals from soft sediments like worms and more mobile individuals from the open water column such as boatsmen and beetles. Due to the lack of simultaneous observations at various locations by volunteers and professionals, a direct comparison of water quality scores is impossible. However, the obtained patterns from both datasets show that the water quality scores between volunteers and professionals are dissimilar for the different water types. To bridge these differences, new tools and processes need to be further developed to increase the value of monitoring biological water quality by volunteers for professionals.

Narrative review of citizen science in environmental epidemiology: Setting the stage for co-created research projects in environmental epidemiology

Several citizen science (CS) initiatives have been adopted in environmental science to monitor air and noise pollution, and water quality related to civic concerns. Nevertheless, CS projects in environmental epidemiology remain scarce. This is because little attention has been paid to evaluate associations of environmental exposures with health effects directly. This narrative review aims to promote the understanding and application of CS in environmental epidemiology. There are many commonalities between CS and other participatory approaches in environmental epidemiology. Yet, CS can foster the democratization of scientific governance and enhance the sustainability of research projects more effectively than other existing participatory approaches. This is especially the case in projects where citizens are invited to participate, engage and become involved throughout all the phases of a research project (co-created projects). This paper identifies various challenges and opportunities specific to the implementation of co-created CS projects in environmental epidemiology. The development of more locally relevant research designs, using local knowledge, obtaining medical ethical clearance, and co-analysing the association between exposure and health, are examples of opportunities and challenges that require epidemiologists to go beyond the traditional research framework and include more outreach activities. Continued efforts, particularly the sharing of information about projects' collaborative processes, are needed to make CS a more concrete and cohesive approach in environmental epidemiology.

Conducting a Community "Street Survey" to Inform an Obesity Intervention: The WE Project

Using a community-based participatory research approach, a citywide survey was conducted to explore perceptions of obesity and interventions to reduce obesity within an African American urban community. More than 1300 surveys were collected within 3 months; 92.9% of respondents agreed or strongly agreed that obesity was an important health issue in the community and the majority indicated that family-based interventions were the preferred pathway for improving physical activity (86.0%) and nutrition (85.2%). Engaging community members in survey development and implementation was an effective approach to build local research capacity and establish a shared agenda of reaching a diverse sample of community residents.

Outlier detection methods to improve the quality of citizen science data

Citizen science involves public participation in research, usually through volunteer observation and reporting. Data collected by citizen scientists are a valuable resource in many fields of research that require long-term observations at large geographic scales. However, such data may be perceived as less accurate than those collected by trained professionals. Here, we analyze the quality of data from a plant phenology network, which tracks biological response to climate change. We apply five algorithms designed to detect outlier observations or inconsistent observers. These methods rely on different quantitative approaches, including residuals of linear models, correlations among observers, deviations from multivariate clusters, and percentile-based outlier removal. We evaluated these methods by comparing the resulting cleaned datasets in terms of time series means, spatial data coverage, and spatial autocorrelations after outlier removal. Spatial autocorrelations were used to determine the efficacy of outlier removal, as they are expected to increase if outliers and inconsistent observations are successfully removed. All data cleaning methods resulted in better Moran's I autocorrelation statistics, with percentile-based outlier removal and the clustering method showing the greatest improvement. Methods based on residual analysis of linear models had the strongest impact on the final bloom time mean estimates, but were among the weakest based on autocorrelation analysis. Removing entire sets of observations from potentially unreliable observers proved least effective. In conclusion, percentile-based outlier removal emerges as a simple and effective method to improve reliability of citizen science phenology observations.

A comparison of ground-based methods for obtaining large-scale, high-resolution data on the spring leaf phenology of temperate tree species

Phenological variation in spring leafing between and within species can determine plant responses to warmer winter and spring temperatures in the short term. Methods are needed for monitoring canopy development that can be replicated on a large-scale, while retaining fine-scale resolution at the level of individual trees. Citizen science has the potential to provide this, but a range of approaches exist in terms of the phenophase recorded (e.g. budburst or leaf expansion), how the phenophase is characterised (first events or intensity monitoring) and the portion of tree crown assessed and observation frequency. A comparison of spring budburst and leaf expansion of four tree species (Fraxinus excelsior, Fagus sylvatica, Quercus robur and Acer pseudoplatanus) was monitored in one woodland using (1) counts of expanded leaves on three crown sections, (2) percentage estimates of expanded leaves across the whole crown and (3) a greenness index from photography. Logistic growth models were applied to make comparisons. First-event dates were found to be misleading due to high variation in leaf development rates within and between species. Percentage estimates and counts produced similar estimates of leaf expansion timing and rate. The greenness index produced similar estimates of timing, but not rate, and was compromised by practicalities of photographing individual crowns in closed-canopy woodland. Citizen scientists could collect data across the period of spring leafing, with visual counts and/or estimates made every 3-4 days, subject to tests of reliability in pilot citizen science studies.

Towards better species identification processes between scientists and community participants

Urban gardens are a model system for understanding the intersection between biodiversity conservation and citizen science. They contain high plant diversity that contributes to urban flora. However, this diversity is challenging to document due to site access and complex plant taxonomy with hybrids and cultivars. Community research participation provides a tool to measure plant diversity and distribution by engaging gardeners who are most familiar with their plants to report on their garden's species richness using citizen science. Yet there is little empirical exploration of plant identification consistency between citizen scientists and scientific researchers. This could lead to reporting differences (e.g., missing species, multiple reporting of the same species) due to spatial and temporal effects, different perspectives and knowledge systems, and cultural context. We leverage a scientific survey of garden plants and a questionnaire asking gardeners to report on the species in their gardens to perform an opportunistic comparison of gardener and researcher reported plant diversity in community gardens. The comparison shows that gardeners interpret instructions to report plants quite variably, with some reporting all species (including herbaceous weeds) and crop varieties, while others reporting only their main crop species. Scientist on the other hand seek clarity in terms of species and variety and report all species located in the plot, including the small weed species that are overlooked by some gardeners. Consistency could be improved if researchers are more specific about their reporting expectations when asking community members to participate in data collection. We use this case study to communicate that paired citizen scientist-researcher data collection and dialogue between groups is necessary to improve methods for conducting consistent and collaborative assessments of biological diversity.

Comparison of large-scale citizen science data and long-term study data for phenology modeling

Large‐scale observational data from citizen science efforts are becoming increasingly common in ecology, and researchers often choose between these and data from intensive local‐scale studies for their analyses. This choice has potential trade‐offs related to spatial scale, observer variance, and interannual variability. Here we explored this issue with phenology by comparing models built using data from the large‐scale, citizen science USA National Phenology Network (USA‐NPN) effort with models built using data from more intensive studies at Long Term Ecological Research (LTER) sites. We built statistical and process based phenology models for species common to each data set. From these models, we compared parameter estimates, estimates of phenological events, and out‐of‐sample errors between models derived from both USA‐NPN and LTER data. We found that model parameter estimates for the same species were most similar between the two data sets when using simple models, but parameter estimates varied widely as model complexity increased. Despite this, estimates for the date of phenological events and out‐of‐sample errors were similar, regardless of the model chosen. Predictions for USA‐NPN data had the lowest error when using models built from the USA‐NPN data, while LTER predictions were best made using LTER‐derived models, confirming that models perform best when applied at the same scale they were built. This difference in the cross‐scale model comparison is likely due to variation in phenological requirements within species. Models using the USA‐NPN data set can integrate parameters over a large spatial scale while those using an LTER data set can only estimate parameters for a single location. Accordingly, the choice of data set depends on the research question. Inferences about species‐specific phenological requirements are best made with LTER data, and if USA‐NPN or similar data are all that is available, then analyses should be limited to simple models. Large‐scale predictive modeling is best done with the larger‐scale USA‐NPN data, which has high spatial representation and a large regional species pool. LTER data sets, on the other hand, have high site fidelity and thus characterize inter‐annual variability extremely well. Future research aimed at forecasting phenology events for particular species over larger scales should develop models that integrate the strengths of both data sets.