Citizen science in pollinator monitoring: current approaches, challenges and recommendations

Citizen science in pollinator monitoring: current approaches, challenges and recommendations | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Oikos This is a preprint and has not been peer reviewed. Data may be preliminary. 5 May 2025 V1 Latest version Share on Citizen science in pollinator monitoring: current approaches, challenges and recommendations Authors : Joseph Justine , Mohankumar Ahirbudhnyan , Mechikottil Ebrahim Ashik , and Peroth Balakrishnan 0000-0002-3697-1322 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174645728.83653118/v1 Published Oikos Version of record Peer review timeline 741 views 204 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The current decline in pollinator abundance and diversity poses a significant threat to the natural world and the food and economic security of human societies. A major challenge faced by the scientific community in pollinator conservation is the lack of sufficient data. Citizen science has emerged as a promising avenue for addressing this issue. In this article, we present the global perspective of citizen science projects focused on pollinator monitoring. Our analysis reveals a notable underrepresentation of developing and tropical countries in citizen science-driven data generation efforts. More than 70% of the listed studies are conducted in North America (n:64), followed by Europe (n:22). Together, Europe and North America account for ~ 97% (n:86) of all the projects listed. Thirty-three percent of the projects are hosted on iNaturalist. The majority of projects focus on insects as pollinators, with only 52% recording the pollinated species. We classified these projects into structured, semi-structured, and unstructured categories based on their methodologies. Linear regression analysis was performed to evaluate the influence of various factors on the potential for generating outputs. The regression model explained 82% of the variance in document production (Adjusted R-squared = 0.766, F(10, 33) = 15.06, p < 0.001). Structured projects significantly contributed to document output (Estimate = 12.44, p < 0.001), as did the inclusion of training (Estimate = 6.89, p < 0.001). Fisher’s Exact Test for Count Data also revealed a significant association for outputs generated with the structured methodology (p < 0.001). Additionally, we discuss the merits and drawbacks of different approaches and propose recommendations for subsequent research. Citizen science in pollinator monitoring: current approaches, challenges and recommendations CONFLICT OF INTEREST STATEMENT The authors report that there are no competing interests to declare. ABSTRACT The current decline in pollinator abundance and diversity poses a significant threat to the natural world and the food and economic security of human societies. A major challenge faced by the scientific community in pollinator conservation is the lack of sufficient data. Citizen science has emerged as a promising avenue for addressing this issue. In this article, we present the global perspective of citizen science projects focused on pollinator monitoring. Our analysis reveals a notable underrepresentation of developing and tropical countries in citizen science-driven data generation efforts. More than 70% of the listed studies are conducted in North America (n:64), followed by Europe (n:22). Together, Europe and North America account for ~ 97% (n:86) of all the projects listed. Thirty-three percent of the projects are hosted on iNaturalist. The majority of projects focus on insects as pollinators, with only 52% recording the pollinated species. We classified these projects into structured, semi-structured, and unstructured categories based on their methodologies. Linear regression analysis was performed to evaluate the influence of various factors on the potential for generating outputs. The regression model explained 82% of the variance in document production (Adjusted R-squared = 0.766, F(10, 33) = 15.06, p < 0.001). Structured projects significantly contributed to document output (Estimate = 12.44, p < 0.001), as did the inclusion of training (Estimate = 6.89, p < 0.001). Fisher’s Exact Test for Count Data also revealed a significant association for outputs generated with the structured methodology (p < 0.001). Additionally, we discuss the merits and drawbacks of different approaches and propose recommendations for subsequent research. KEYWORDS : neglected pollinators, citizen science, pollinator monitoring, policy development, community contribution, volunteer retention 1 | INTRODUCTION Pollinators play a crucial role in global biodiversity, delivering essential ecosystem services that support both agricultural crops and wild plants. Crop productivity, livelihoods, and access to nutrition are among the many ecosystem functions reliant on pollinators (Genung et al., 2017; Garibaldi et al., 2020). Given that 35% of all food crops depend on animal pollination and 71% of the top five global commodities achieve higher production with the help of animal pollinators, conserving pollinators is essential to sustain food production for the growing human population (Klein et al., 2006). The global estimated economic value of ecosystem services by pollinators ranges between US$195 and US$387 billion annually (Porto et al., 2020). Among pollinators, the contribution of insects- the neglected pollinators , is pivotal in enhancing crop yield and quality, making their effective management critical for a sustainable future (Dymond et al., 2021). Additionally, the evolutionary significance of pollinators is substantial. Over 80% of all angiosperms rely on animal pollinators, and the selection pressure exerted by pollinators on floral traits has been instrumental in the evolution and diversification of numerous plant families, contributing significantly to the rich diversity of flowers seen today (Ollerton et al., 2011, 2019; van der Niet & Johnson, 2012). Looking at the available records of pollinator populations, a global trend of decline across multiple taxonomic groups is reflected with 37% of native wild species facing a global drop (Sánchez-Bayo & Wyckhuys, 2021). Assessment of European pollinators reveals a decline of 37% of European bee species and 31% of butterflies. while 9% of both taxa are found to be threatened (IPBES, 2016). Due to their economic significance, honeybees have been extensively studied (Klein et al., 2006). An alarming decline of 59% of domestic honey bee colonies has been recorded from 1947 to 2005 in North America while the US alone recorded a loss of 0.75 to 1 million honey bee colonies from 2007 to 2008 ( Status of Pollinators in North America 2007; van Engelsdorp et al., 2008). Similarly, bumblebees are declining globally in range and abundance ( Colla & Packer, 2008; Cameron & Sadd, 2020). Few species have depleted rapidly by 96% in the North Americas while their distribution range contracted by 23-27% (Cameron et al., 2011). Lepidopterans are also facing a similar fate, with an abysmal population decline of 50% reported for butterflies in the Netherlands and Britain while the extinction rates are a whopping 20% in the Netherlands and 8% in Britain. Some species have also been contracted to 20% of their natural distribution range from the 1890s to the 1940s (Warren et al., 2021). Moth populations, especially positively phototaxic, dietary and ecological specialists, have been suffering population decline due to several factors including light pollution (Gurule & Nikam, 2011; van Langevelde et al., 2018; Wagner et al., 2021). The drastic fall in pollinators is likely due to factors such as habitat loss, monoculture, agrochemicals, alien invasives, climate change and diseases (Ghazoul, 2005; González-Varo et al., 2013). Urbanization acts as a selective agent on pollinator diversity by favouring few generalist species over species with specialized foraging strategies and narrow diet breadth (Geslin et al., 2013; Bates et al., 2014; Wenzel et al., 2020). In addition, increasingly impervious sealed and built up areas instigate a considerable drop in pollinator diversity and abundance, particularly impacting vulnerable species (Bates et al., 2014; Wenzel et al., 2020). Higher densities of invasive pollinators depletes floral resources making native pollinators fall prey to competition and consequent decline (Morales et al., 2017; LeCroy et al., 2020; Page & Williams, 2023). On the other hand, many invasive plants compete for pollinators, lauring in the majority of native insect pollinators disrupting the natural plant-pollinator network (Bartomeus et al., 2008; Rather et al., 2023). Similarly, isolated agricultural fields support fewer pollinators owing to inadequate floral trait diversity (Tews et al., 2004; Ollerton et al., 2014; Habel et al., 2019). While nitrogen-fortified fertilisers diminish floral resources by promoting grass growth over wild flowering plants (Wedin & Tilman, 1996), pollinator health and reproduction is affected by pesticide exposure and consumption (Bourlière, 1960; Boyle et al., 2019; Cham et al., 2019; Danforth et al., 2019). Perturbing concentrations of systemic pesticides, fungicides, miticides, and herbicides were found in apiaries, with 92% of all samples containing at least two pesticides (Mullin et al., 2010). Such an exposure can trigger acute, lethal or sublethal effects (Mullin et al., 2010; Habel et al., 2019; Raine & Rundlöf, 2024). Demand for national and international policies are escalating over the declining pollinator diversity, abundance and distribution (Breeze et al., 2021; Uwingabire & Gallai, 2024). Evidence based management policies should be developed and implemented for the conservation and monitoring of pollinators (Breeze et al., 2021). Unfortunately factual uncertainties persist obstructing the way forward (Drivdal & van der Sluijs, 2021). Especially in tropical and underdeveloped countries, native pollinator diversity, distribution and abundance is poorly documented. Majority of pollinator related studies are from Europe and North America (Baldock, 2020; IPBES, 2016). Also pollinators other than charismatic and economically important species are overlooked (Potts et al., 2010). These inconsistencies can be attributed to the lack of coordinated monitoring programmes (Wenzel et al., 2020). Long term, standardized pollinator monitoring programmes on large scale are still scarce as it can be expensive and labour intensive (Breeze et al., 2021; Hellwig et al., 2024). Citizen science presents an alternative in tackling the challenges associated with establishing such initiatives (Birkin & Goulson, 2015; Plummer et al., 2024; Sheard et al., 2024). Citizen science, also known as participatory science, is the data collection performed by volunteers who may not have any affiliation to the field of interest (Bonney et al., 2009). The proliferation of CS platforms, advancement of mobile phones and easy access to the internet equips CS and facilitates managers to focus on project design, implementation and management (Pocock et al., 2017). However, CS is not devoid of any flaws and challenges. Many scientists are of the view that the nature of volunteer generated, opportunistic data may not fit the requirements for policy developments and management practices (Lebuhn et al., 2013; O’Connor et al., 2019; Breeze et al., 2021). But there is evidence suggesting semi structured citizen science programmes are effective in pollinator monitoring and policy development (Birkin & Goulson, 2015; Roy et al., 2016, 2024). Also intense research on modifying and improving protocols incorporating modern technologies are backing the significance of citizen science in pollinator monitoring (Hellwig et al., 2024; Sheard et al., 2024). Here, we analyze and discuss the role of citizen science in pollinator monitoring by assessing the established, completed or running citizen science projects across the globe. The aim is to examine how citizen science initiatives are contributing to the monitoring of pollinators on a global scale. This includes understanding the scope of existing projects, their methodologies, outcomes, and ongoing efforts. 2 | MATERIALS AND METHODS 2.1 | Data collection We gathered data on citizen science projects related to pollinator monitoring from online sources using web harvesting techniques. Searches were performed using different keyword combinations such as “Pollinator” AND “Project” OR “Monitoring” OR “Survey” OR “Find” OR “Search” OR “Count”. The search was carried out across four platforms: Google, SciStarter, Zooniverse, and iNaturalist. Relevant projects were identified from the first 100 results of each keyword combination on each platform. Thirty-three parameters including the objectives of the project, location, methods followed, platforms used, number of participants, number of observations generated, outputs from the project such as research articles and other documents produced, etc., provided on the project websites were collected. While compiling metadata, we classified websites/apps that allow the general public to access and create their own projects as platforms; and individual websites facilitating data collection for the specific project as websites. All the details pertaining to each project were collected on or before August 18, 2024. The following criteria were set for a project to be included in the final analysis. (1). sufficient information: at least 20 of the 33 recorded parameters must be publically available, (2). volunteer participation: no fee or remuneration was required, (3). keyword relevance: the project website must mention ”pollinator” or ”pollination” (as many projects focus on specific taxonomic groups that may not align with general pollinator conservation goals), (4). volunteer accountability: registration or joining for participation, and (5). taxonomic focus: all focal taxonomic groups must be involved in pollination. 2.2 | Analysis The selected projects were classified into three categories: semi-structured, structured and unstructured, based on their method of data collection following Haelewaters et al. (2024). Structured projects had designated survey paths or plots and specific time limits for each count, while projects focusing on the pollinators of a single individual plant within a limited time are categorized as semi-structured. Projects which followed opportunistic sampling without any spatial or temporal scale were classified as unstructured. To explore the suitability of each category, we cross verified the outputs generated from each project. To evaluate the success of each citizen driven pollinator project with reference to the objectives listed, we listed various outputs such as peer reviewed articles, technical reports, policy documents and other reports such as annual reviews for further analysis. The outputs listed in the websites for individual projects or website links provided on the “about” page for iNaturalist projects, GBIF database or listed in google scholar search were included in the analysis. Fisher’s exact test was used to determine if there was a significant association between the method categories and the type of outputs generated from projects (see, Bower, 2003; Nowacki, 2017). Further, a linear regression analysis was carried out to evaluate the impact of various factors on the total number of outputs generated from pollinator monitoring projects after removing correlated variables using Variance Inflation Factor analysis (VIF) (van Hulst, 2010). The model included predictors such as project class (structured, semi-structured, unstructured), presence of training, number of observations, number of observers, presence of interactive sessions, keys provided for plants and pollinator identification, the frequency of interactive sessions, newsletters, etc. All statistical analysis were done on R studio version 2023.06.1+524. 3 | RESULTS 3.1 | Types, platforms, taxa and coverage of pollinator projects We compiled a total of 196 projects based on web harvesting results, including 76 from Google search, 115 from iNaturalist, and 5 from Zooniverse. After careful review, 109 projects were excluded (24 from Google, 83 from iNaturalist, and 2 from Zooniverse) as they did not meet any of the specified criteria (Figure 1). As a result, we identified 87 pollinator counting projects, categorized into structured (n=17), unstructured (n=44), and semi-structured (n=27) projects. Some projects employ multiple methods and were classified into the appropriate categories during analysis. More than 70% of the listed studies are conducted in North America (n=64), followed by Europe (n=22). Together, Europe and North America account for approximately 97% (n=86) of all listed projects (Figure 2). Six projects have a global scope, while three and two projects cover the entirety of Europe and Africa, respectively (Figure 3). One project, titled “Pollinators in the Mediterranean and Europe” in iNaturalist, spans both Africa and Europe. All other projects are either international, covering a few countries, or are national or regional in scope. A notable increase in citizen science projects on pollinators is observed from 2016 onwards (Figure 4). An overwhelming share (33%) of the listed projects use the iNaturalist platform for data collection, while another 33% have developed their own websites (Figure 5). The majority of projects (85%) focus on insects as pollinators, while 11% include birds and 4% include mammals, such as bats. Additionally, some projects record specific groups and species (Table 1). Among the listed projects, only 75% identified pollinators to the species level. Similarly, only 58.6% of projects recorded the plant species visited by pollinators. Merely 40.2% of projects provided identification keys for plants, while 77% provided keys for pollinators. Among the projects focus on insects as pollinators, only 52% record the species being pollinated. Data is accessible for 73.5% of the projects, while 4.8% provide only a summary of the collected data. However, 16.8% of the projects do not make their data available, and 4.8% do not provide any information regarding data accessibility. Among the platforms used for data collection, iNaturalist is the most common, used by 33% of projects, followed by the FIT Count app (5.6%), Zooniverse (2.8%), and the eBMS app (1.8%). Additionally, 33% of projects use their dedicated websites for data collection, while some gather data through online forms and printed data sheets. Below, we discuss some of the major projects. 3.2 | Objectives and outputs of pollinator projects Major objectives of the projects can be categorized into five: distribution of pollinators, diversity of pollinators, abundance of pollinators, distribution of both pollinators and pollinated plants, and interaction mapping of pollinated species. A few projects also targeted specific objectives, including nesting preferences (n=4), migration (n=3), reproduction (n=3), flower phenology (n=1), pollen collection (n=1), pesticide usage (n=1), and relationships with abiotic factors (n=1). The projects targeting quantified data, such as the abundance of pollinators and pollinated plants, interactions, and other specific objectives, used either structured or semi-structured designs. As expected the structured projects produced the most number of outputs (total documents/number of projects; p= <0.001) (Table 2). A subset of projects (n= 44), which satisfy the criteria of having at least 25 of the collected parameters were used to develop a linear regression model to evaluate the relationship between the number of total outputs and various predictors. After removing auto correlated parameters, nine variables were selected for modelling, namely; method category, presence of training, number of observations, number of newsletters, keys provided for pollinators and plants, frequency of interactive sessions and number of observers. The model explained a significant portion of the variance in total outputs ( R 2 =0.8203; adjusted R 2 =0.7658; F(10,33)=15.06, p<0.001). Significant predictors included the structured methods (β=12.44, p<0.001), indicating that structured projects are associated with a higher number of total outputs compared to unstructured projects. The presence of training (β=6.89, p<0.001) was also a significant predictor, suggesting that projects incorporating training tend to produce more outputs. The number of interactive sessions not showing a positive effect (β=−3.83, p=0.042), indicating need for more frequent interactions for better outputs in pollinator projects. Other variables, such as the semi-structured methods, number of observations, newsletters, number of observers, key plants and key pollinators, were not significant predictors (p>0.05). The Durbin-Watson test conducted to validate the model indicates no significant evidence of strong positive autocorrelation in the residuals (DW = 1.8721, p = 0.2094). However, caution should be exercised when interpreting these results, as most unstructured projects primarily focus on accumulating observations and have the potential to generate multiple outputs over the long term. 3.3 | Characteristics of major pollinator projects Structured projects : Most of the prominent structured projects are associated with national or international biodiversity monitoring schemes hosted by respective national agencies or management bodies. The European/UK Pollinator Monitoring Scheme (EU/UK-PoMS) stands out as one of the remarkable efforts in citizen-powered pollinator monitoring. It follows a structured monitoring protocol, which include sampling insects by trapping using five pan traps per plot (1 km²). Collected samples are then sent to the organizers for identification. Approximately, 90 plots are selected across the UK, with each plot visited twice a year. The project also utilizes a semi-structured method called Flower-Insect-Timed (FIT) Count, which can be conducted independently or alongside pan trapping. To date, 24,097 bee and hoverfly specimens have been identified from pan traps across the UK, including 4,428 in 2023, along with 1,79,700 observations of flower-insect visits (46,581 in 2023), including FIT Count (UK Pollinator Monitoring Scheme, 2024). The project provides training, resources, and equipment for participants. This model has been replicated across the EU, with many countries adopting the method and joining the scheme. The outcomes of the initiative, including research and technical reports, are listed on the project’s website. UK-PoMS has produced five research articles and six technical reports from 2018 to 2024 as part of the program. A slightly different method is followed in the Australian Pollinator Count , in which insects are captured, identified, and released within a ten-minute count period. Data collection is facilitated via a website, and volunteers are required to undergo a thorough training program for capturing and identifying insects. An estimated 2,000 species of pollinators have been recorded so far by the volunteers. However, information regarding the status of the collected data or the outputs generated by the programme are not available for analysis. The European Butterfly Monitoring Scheme specifically focuses on butterfly populations and distribution. It has a dedicated website and Butterfly Count app for data collection. Transects or polygons (plots) are laid out at the convenience of the observer. The eBMS works together with the ‘Assessing ButterfLies in Europe (ABLE)’ project and the ‘Strengthening Pollinator Recovery through INdicators and monitorinG (SPRING)’ project. Overall, this programme has produced over 18,00,000 individual butterfly records across 28 European countries. Together, they have produced nine newsletters and two technical reports and have been instrumental in the formulation of several EU policies on pollinator conservation. The Native Bee Watch is a North Colorado Citizen science initiative recording plant bee interactions. This project mandates a training session for all participants where they will learn to identify both plants and bees. The project was able to generate two peer reviewed articles out of the data collected. In the All-Ireland Bumblebee Monitoring Scheme , a 5 m³ recording box method is followed. Bumblebees are counted from the 5-15 sections of recording boxes laid on the permanent transects of 1-2 km. Eight repetitions are carried out from March to October (once a month). Similarly, multiple transect surveys are required for the Colorado Butterfly Monitoring Scheme (CBMN). One to two hours of transects are carried out on predetermined paths allocated by CBMN or new paths chosen by volunteers upon approval. The project has recorded 17,980 bumblebees across 14 species in the year 2022 alone. It is part of the National Biodiversity Data Centre, and numerous articles including monthly newsletters, annual reviews (n=12), and checklists (n=9) are generated from the Centre. Most of these articles are not produced exclusively by the bumblebee monitoring program; instead, they include studies regarding multiple taxa and multiple programmes such as the FIT Count Ireland which is discussed later. Semi structured projects : The Great Southeast Pollinator Census focuses on a single plant at a time, counting all pollinators landing on it for 15 minutes and uploading the data onto the project website. Pollinators are classified into categories such as carpenter bees, bumblebees, honey bees, etc. In 2023 alone, the project recorded 2,53,443 observations contributed by 12,293 observers. The programme has so far generated four peer reviewed articles. Similarly, the FIT Count is performed by all associating projects of EU-PoMS. For instance, FIT Count Ireland records pollinators visiting a particular flower for a period of 10 minutes, as part of the All Ireland Pollinator Plan. Similarly, many semi-structured projects such as the Wild Pollinator Count , CU Pollinator Count , University of Georgia Pollinator Count , etc., follow a unified method in which pollinators visiting a single plant are counted for a given period of time. Most of them utilize websites and online forms for data collection. The Great Sunflower Project examines the effect of pesticides on pollinators. Volunteers are requested to grow a sunflower plant using non-pesticide-treated seeds, and observations of pollinators visiting the plant are recorded. This project also facilitates data collection in four other ways: the stationary method involves observing a single flowering plant, the travel method involves making observations during a hike or movement, the area method covers a selected local area, and the casual method involves opportunistic observations. Operating since 2008, it is one of the largest citizen science projects with more than 1,00,000 volunteers. However, information regarding the output and data generated are not available. McLaughlin Reserve Pollinators using the iNaturalist platform is an example for a project using semi structured method. This project records pollinators with plots of 100 m 2 for a period of 30 minutes. However, predefined grids are not a requirement and the volunteers were asked to mark or visualize the grids by themselves. It should be noted that the project so far has only 36 participants together contributing 3,342 observations. Another noteworthy community initiative is the PolliNation Project focused on pollinator conservation and awareness. Participants are requested to provide nesting habitat for native bees by installing insect hotels provided by the project, free of cost. Pollinators visiting and utilizing the insect hotel are recorded through this project. Data is collected through two apps; PolliNation ID and Pollinator Hotel app. Milkweeds and Monarchs is a semi structured project in which the data is collected to address a specific objective; whether monarch butterflies prefer young and non-flowering milkweeds for egg laying. Data regarding participation and outputs are not available for the project. Unstructured projects : Projects that use opportunistic sampling without adhering to a specific sampling method are categorised as unstructured. iNaturalist hosts numerous unstructured pollinator projects however, this platform does not allow participants to record the abundance of a species, as they are purely focused on species occurrence inventory. Among the several projects, Global Pollinator Watch is worth giving attention. Being a collection project, it doesn’t require any specific fields related to pollinated species. However, volunteers are requested, not mandated, to upload such information using additional fields, which may later be incorporated into the GloBI (Global Biotic Interactions: www.globalbioticinteractions.org) dataset of interactions. The project has so far recorded more than 6,13,000 observations of 25,600+ species, including insects and birds as pollinators along with a few pollen recipients. The project is hosted by the Earth Watch Institute, which works on multiple species and areas. Apparently the specific outcomes of the project were not distinguishable out of the many outcomes they have listed. Among other pollinator projects hosted by iNaturalist, Pollinators in the Mediterranean and Europe stands out for its collection of extensive data on pollination and flower visitation by animals. The project takes advantage of a traditional approach by including additional fields and lookup functions related to flower visitation. To date, more than 11,000 observations have been recorded in this project. Similarly, Pollinator associations is another traditional project that has recorded more than 79,000 observations globally. Western monarch milkweed mapper is an unstructured project organized as part of a tri-nation monarch mapping initiative called Monarch Blitz spanning over USA, Canada and Mexico. The initiative hosts multiple projects, including Monarch Larvae Monitoring, Milkweed Mapping, and Mission Monarch, with a total of over 63,800 observations. Another interesting project to look at is the Pollinator watch hosted in Zooniverse. Here the project managers are developing a neural network where time-lapse images can be used to identify pollinators and pollen receptors for understanding seasonal variations. Volunteers are asked to identify pollinators using the provided time lapse images. The data generated is being used for the development and training of the neural network. Buzzy Bee - African Canopy Pollinators is a similar project in Zooniverse, where videos of insect pollinators of central African timber trees are shared online. Participants are requested to identify pollinator species by observing the video. Though low in number a couple of outputs are also generated from such unstructured projects. 4 | DISCUSSION 4.1 | Status of citizen science in pollinator monitoring and conservation In the context of global concerns over the decline of pollinators, citizen science is crucial for monitoring pollinators, especially the lesser known taxa; their trends and interactions. Our review shows the global diversity, geographical representation, methods and tools, and the outputs leading to policy formulations for pollinator monitoring and conservation using citizen science. Majority of the pollinator monitoring schemes, especially object oriented ones are restricted to North America and Europe. This is similar to that of the general trends of citizen science programs globally (Chandler et al., 2017). Though a variety of platforms or specific websites are used for data collection globally, majority are mere collection projects and lack specific objectives and methodology, and therefore less productive. In this context, the present study sheds some light on the output oriented formulation of pollinator monitoring projects. The key factors identified while developing and operating such projects are: (1) parametric objectives, (2) accurate and scientific methods, (3) full-fledged platform, (4) consistent training and interactive sessions for volunteers (volunteer recruitment and retention activities). All these factors are interconnected as the objectives play a crucial role in the selection of other factors. From the analysis of major ongoing pollinator monitoring citizen science projects, it is evident that the method used in a project has a significant impact on the production of outputs, such as the number of research articles, technical reports, and policy documents. Specific sampling protocols and species lists provided by structured and semi-structured citizen science projects allow managers to streamline the data contributions towards the parametric objectives of the projects. Similar conclusions were also drawn in a recent review of the citizen science projects on fungi (Haelewaters et al., 2024). The Flower-Insect Timed Pollinator Count (FIT Count) app developed by the UK Pollinator Monitoring Scheme (PoMS), serves as a prime example for a focused and sophisticated platform for pollinator monitoring. It was later adopted by the European Union Pollinator Monitoring Scheme (EU PoMS). The app is specifically designed to track pollinator interactions, population trends, distribution, and the abundance of focal species using in-app pictorial guides. Participants are required to have some knowledge of plant identification to accurately record plant-pollinator interactions. When combined with supporting data sources, data collected through FIT Count enhances the understanding of population trends for many focal species (e.g., Powney et al., 2019). The European Butterfly Monitoring Scheme (eBMS) offers a similar example, where population trends are tracked using a dedicated eBMS platform (e.g., Dennis et al., 2017). However, these platforms and other dedicated websites, do not allow citizens to create or execute their own projects. Further, these platforms are restricted in spatial coverage, while non-dedicated platforms like iNaturalist offer global reach. Our review of citizen science projects on pollinators reveals that most of them are hosted on iNaturalist platform. However, these projects typically focus on recording species occurrences rather than addressing broader objectives like pollinator interactions or population abundance. While presence-only data from iNaturalist can aid in modelling habitat diversity, species distribution, and temporal fluctuations, it is less suited for understanding population trends (Huettmann et al., 2024). Designed primarily for occurrence records, iNaturalist offers limited fields such as ”pollinated by” and ”visited by”, which are not compiled in a usable format for policy development on pollinator interactions. Despite these limitations, iNaturalist’s species suggestion feature reduces the learning curve for participants, making it more accessible and less intimidating. Although its functionality is not ideal for pollinator monitoring, it excels in global species occurrence recording and allows anyone to start a citizen science project. Projects like McLaughlin Reserve Pollinators show that iNaturalist can still be leveraged for abundance recording. The present study reveals a clear need for a global, open-access, dedicated platform that provides flexibility in choosing methodologies. Incorporating features like species suggestions, aided by artificial intelligence, as seen in iNaturalist, would be highly beneficial for generating more comprehensive data on pollinator monitoring and conservation. This is also crucial for mapping the role of lesser known and neglected pollinators. Looking at the scientific literature on pollinator monitoring produced within the last ten years reveals a spike in demand for citizen driven pollinator monitoring (Roy et al., 2024). Pollinator research and conservation, particularly for non-bee insects, receive comparatively poor funding and attention, leading to insufficient data for policy making and conservation. The integration of citizen science can bring a paradigm shift in generating spatial and temporal data for understudied groups (Rondeau et al., 2023; León-Cortés et al., 2024). 4.2 | Contribution of citizen science projects in policy formulation Citizen driven pollinator monitoring programmes have played a significant role in generating tremendous data which can be critical in conservation and policy making, examples of successful structured and semi structured projects strengthen the notion of leaning towards CS for pollinator monitoring (Dennis et al., 2017; O’Connor et al., 2019; Rondeau et al., 2023; Plummer et al., 2024). The enormous data generated through pollinator monitoring also form the basis for many ecologically significant observations such as the colonization of exotic plants by invasive bee species facilitating the spread of both (Fontúrbel et al., 2023). Apart from data, growing flower rich gardens for pollinator observation and bee hotel maintenance are few examples for CS being used as a conservation evaluation and implementation tool (Persson et al., 2023). Practice of citizen science has also been proven to improve the conservation attitude, nature connectedness and happiness of the participants (Lakeman-Fraser et al., 2023; Pocock et al. 2023). There is also a recent surge in attempts to improve citizen science for pollinator monitoring, which promises a bright future for evidence-based policy making (Bloom & Crowder, 2020; Roy et al., 2024; Whipple and Moss, 2024). Our review shows that some of the existing pollinator monitoring programmes have contributed significantly for the development of policies. For example, the ‘European Butterfly Monitoring Scheme (eBMS)’ in association with the ‘Assessing Butterflies in Europe (ABLE)’ programme has produced policy briefs requiring the incorporation of eBMS data to (1) inform resource planning in Member State Prioritised Action Frameworks, (2) CAP strategic plans, (3) Land use and management focusing butterfly indicator species and (4) Assessment of conservation status of butterflies (ABLE policy brief, 2020). Similarly, the community science projects focusing on pollinators, including the Bumble Bee Atlas, Firefly Atlas, Bumble Bee Watch, and the Monarch Nectar Plant Database by the Xerces Society utilizes citizen-generated data for policy formulations. For example, data generated through citizen science projects has contributed to securing protection for the rare butterfly Euchloe ausonides insulana , red-listing Bombus affinis , and restoring habitats for monarch butterflies. These initiatives have also led to numerous policy recommendations. The most recent is the Strategy to Protect State and Federally Recognized Bumble Bees of Conservation Concern: Washington State (Martin et al., 2023). Additionally, proposals include the inclusion of various pollinator species under the Endangered Species Act, listing firefly species on the emergency list, and advocating for pollinator protections in the farm bill (Skinner, 2008). These examples amplify the notion that the incorporation of citizen-generated data for pollinator conservation is gaining momentum as the significance of citizen science is rising. Meanwhile, several members of the PoMS partnership were invited to serve as expert witnesses for the inquiry conducted by the House of Commons Science, Innovation and Technology Committee (SITC) regarding insect decline and UK food security in 2023, indicating the recognition of the potential of citizen generated data in policy formation (UK Pollinator Monitoring Scheme Annual Report, 2022). Apart from these, general insect monitoring programmes including collection projects also contributed in the development of policies for their conservation in general ( see, Roy et al., 2024; Wagner, 2020). 4.3 | Challenges and recommendations The quality of citizen-generated data is a significant challenge in biodiversity monitoring (MacPhail & Colla, 2020). Unstructured projects are major contributors to data inconsistencies, often leading to low-quality results. Variations in spatial and temporal monitoring protocols across different projects further exacerbate the problem (Pocock et al., 2017). Inconsistent protocols within projects and low observer accuracy among untrained volunteers can result in data that does not align with project objectives (Balázs et al., 2021). For instance, the Big Bumblebee Discovery Program found that one in four observations were misidentified, and the color type identification accuracy was as low as 43% among untrained participants (Roy et al., 2016). Unstructured projects with undefined sampling locations and protocols allow personal biases in data collection, often leading to non-random data (Roth et al., 2021). Spatial biases also arise from the concentration of observations in areas with higher economic status and greater human activity (Shirey et al., 2021). Specialization towards specific taxonomic groups among participants further skews the data; for instance, iNaturalist exhibits significant disproportionality in observations due to the preferences of frequent contributors (Figure 6). Large-bodied eusocial bees were observed more frequently by volunteers than by entomologists (Pereira et al., 2024). Additionally, data consistency varies significantly among contributors, with only the top 5% of iNaturalist volunteers being highly active, while the rest contribute sporadically (Di Cecco et al., 2021). Adopting structured or semi-structured project designs with robust protocols can help address these issues by reducing observational biases (Haelewaters et al., 2024). Timed counts are particularly effective for citizen science-driven pollinator monitoring (Roy et al., 2016; Dennis et al., 2017; Powney et al., 2019). However, structured projects can be expensive, and timed counts may limit identification to the genus level, though they can still provide valuable data on abundance and population trends (Lebuhn et al., 2013). Semi-structured projects analyzed in our study appear to be a promising alternative. For designing an effective long-term pollinator monitoring project, we recommend reviewing the six steps proposed by Hellwig et al. (2024). Collaborations with professional taxonomists can reduce misidentification rates, especially for insects, where up to 85% identification success has been achieved (Pereira et al., 2024). Challenges such as a lack of trained professionals (MacPhail et al., 2024), difficulties in photographing small insects, and the under sampling of species with complex identification traits or elusive behaviors persists (Hochmair et al., 2020; Unger et al., 2021; Schlesinger et al., 2023). Investing in volunteer training can mitigate spatial, temporal, and observational biases, helping participants understand data quality requirements (Roy et al., 2016; Balázs et al., 2021; Vilen et al., 2023). Incorporating citizen science into higher education can further enhance students’ ecological knowledge and skills (Griffiths-Lee et al., 2023). Structured approaches, such as those used by UK-PoMS, demonstrate higher volunteer retention rates (46.7% to 85.2%) compared to semi-structured methods like FIT-Count (18.1% to 30.8%) (UK Pollinator Monitoring Scheme Annual Report, 2022). Volunteer retention can also be enhanced through competitions and rewards, as seen in initiatives like the Pollinator Power Party Bioblitz 2024 on iNaturalist. Volunteers often find knowledge acquisition the most rewarding aspect of participation, followed by emotional fulfillment. Building emotional connections, direct communication, media exposure, and public recognition are additional ways to retain volunteers (Bloom & Crowder, 2020; Jansen et al., 2024). Social media is a powerful tool for project promotion and communication, while other channels like radio, conferences, and presentations can boost engagement (Rondeau et al., 2023). For further insights, the volunteer retention methods discussed by Robinson et al. (2021) are highly recommended. Based on the recommendations from individual project outputs and our insights, we suggest the following for successful execution of citizen science-driven pollinator monitoring: • Experiment with sampling techniques before full-scale implementation to ensure data compatibility with project objectives. • Use engaging and accessible methodologies for data collection that minimize barriers for participants and avoid creating a digital or economic divide. • Engage volunteers who are eager to learn, as they are more likely to contribute high-quality data consistently. • Provide proper training and regular reinforcement of methods to reduce biases. • Use simple and accessible platforms for data collection; iNaturalist is recommended for occurrence modeling when other options are limited. • Effectively communicate project updates, events, and results to foster a sense of fulfillment and improve volunteer retention. • Choose communication channels suited to the economic and social contexts of the volunteer community, such as social media for engagement, training, and recognition. • Collaborate with professionals to reduce identification errors. • Combine data sets from multiple sources, including museum and survey records, with citizen science data to enhance policy and conservation outcomes. • Integrate citizen science into higher education, especially in tropical regions, to build ecological knowledge and skills among students. To conclude, our study discusses best practices for designing and executing citizen driven pollinator monitoring along with the urgent need to develop object-oriented programmes to address the spatial and temporal disproportionality in CS-driven pollinator monitoring among developed and developing countries; better global platforms enabling collection of sufficient data points, and more emphasis for policy development for pollinators, especially the lesser known taxa. Data Availability Statement All the data collected as part of the manuscript is provided in the tables REFERENCES Balázs B, Mooney P, Nováková E, Bastin L, Jokar Arsanjani J, 2021. Data Quality in Citizen Science, in: Vohland, K., Land-Zandstra, A., Ceccaroni, L., Lemmens, R., Perelló, J., Ponti, M., Samson, R., Wagenknecht, K. (Eds.), The Science of Citizen Science (pp.139–157). Springer International Publishing, Cham. Baldock KC, 2020. Opportunities and threats for pollinator conservation in global towns and cities. 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Focal taxa of citizen science projects recorded in the study Taxa Group Species No Projects Insects Non-specific Non-specific 38 Insects Ants Non-specific 3 Insects Bees Non-specific 7 Insects Beetles Non-specific 3 Insects Bumble bee Non-specific 2 Insects Moths Non-specific 7 Insects Sawflies Non-specific 3 Insects Other Flies Non-specific 3 Insects Wasps Non-specific 5 Insects Butterflies Non-specific 10 Insects Butterflies Monarch Butterfly 5 Insects Butterflies Painted lady 1 Insects Butterflies Red admiral 1 Insects Butterflies West coast lady 1 Birds Non-specific Non-specific 1 Birds Songbirds Non-specific 2 Birds Hummingbirds Non-specific 4 Mammals Bats Non-specific 5 Table 2. Output generated from each class of projects Class Research Articles Technical Report Policy Recommendation Other reports Avg output Semi structured (n=28) 55 22 2 0 2.89 Structured (n=16) 27 15 6 12 3.62 Unstructured (n=44) 4 4 0 0 0.18 Figure 1: result of web harvesting Figure 2.: National and International CS projects on pollinators listed in our study Figure 3. Global representation of citizen driven pollinator projects Figure 4: Chronology of Citizen driven pollinator projects listed in our study Figure 5. Platforms hosting citizen science projects on pollinators Figure 6: Year wise observation pattern of few pollinator taxonomic groups recorded in iNaturalist (Research grade) Information & Authors Information Version history V1 Version 1 05 May 2025 Peer review timeline Published Oikos Version of Record 9 Dec 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Oikos Keywords abundance citizen science linear regression pollination pollinator regression analysis Authors Affiliations Joseph Justine Kerala Forest Research Institute View all articles by this author Mohankumar Ahirbudhnyan Kerala Forest Research Institute View all articles by this author Mechikottil Ebrahim Ashik Kerala Forest Research Institute View all articles by this author Peroth Balakrishnan 0000-0002-3697-1322 [email protected] Kerala Forest Research Institute View all articles by this author Metrics & Citations Metrics Article Usage 741 views 204 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Joseph Justine, Mohankumar Ahirbudhnyan, Mechikottil Ebrahim Ashik, et al. Citizen science in pollinator monitoring: current approaches, challenges and recommendations. Authorea . 05 May 2025. 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