Citizen Science Research Articles

Detection of the jellyfish Chrysaora pacifica by RPA-CRISPR-Cas12a environmental DNA (eDNA) assay and its evaluation through field validation and comparative eDNA analyses

Climate-driven environmental changes and anthropogenic activities can result in the proliferation of non-indigenous aquatic species such as jellyfish that may cause envenomation and various ecological disruptions. Here we developed a two-step RPA-CRISPR-Cas12a eDNA assay, consisting of target eDNA amplification followed by a CRISPR-Cas12 reaction, for the early detection of Chrysaora pacifica, a jellyfish species often considered non-indigenous to South Korea. The assay demonstrated high sensitivity, with a detection limit of two copies COI/μL for eDNA derived from C. pacifica, using target specific RPA primers and crRNA sequences. Field validation of the assay using eDNA samples from Jinhae Bay collected over eight months of time-series monitoring, revealed temporal distribution of the jellyfish which correlated with results of digital polymerase chain reaction (dPCR) and eDNA metabarcoding. The C. pacifica eDNA assays were also corroborated (R-square 0.7891) by reports from a citizen science–based jellyfish-monitoring program operated by the National Institute of Fisheries Science, South Korea. Our RPA-CRISPR-Cas eDNA assay can therefore, be an efficient alternative to traditional tools for the early detection of outbreaks of non-indigenous or harmful species in marine ecosystems.

Environmental and ecological factors associated with stinging jellyfish at a tourism hotspot in the Gulf of Thailand

The waters of Thailand are associated with some of the most severe cases of envenomation from jellyfish in Asia, often attributed to the extensive marine recreational tourism industry it supports. The western Gulf of Thailand in particular has been shown to host among the highest abundances of box jellyfish, while also being home to some of the highest marine recreational tourism, such as along the Koh Samui archipelago. Here we utilise aspects of the same tourism industry to develop a deeper understanding of stinging jellyfish population dynamics, by combining citizen science at the island of Koh Tao, with dedicated in-situ surveys and remote sensing data. Our analyses of 170 box jellyfish sightings and 81 blooms of Pelagia sp. across a six-year period revealed a strong seasonal trend, with both groups being most abundant between July and December. Decision tree analysis of the complete observation dataset and multiple environmental variables revealed a combination of precipitation and lunar cycle to be the strongest predictors of the assessed stinging jellyfish at Koh Tao. Furthermore, blooms of Pelagia sp. were associated with extensive medusivory by a large diversity of predators, including reef building corals. These observations add to the growing literature showing that scleractinian corals are readily able to ingest larger prey items, either by solitary or protocooperative feeding strategies. Meanwhile, no consumption of box jellyfish medusae was recorded during the study. These findings may allow for improved tourism management practices while also supporting a wider biogeographic assessment of stinging jellyfish in south-east Asia.

Population monitoring and conservation implications of intra- and interspecific nest occupation rates in swallows.

With the exception of a few groups of birds, such as large raptors and colonial seabirds, direct counts of nests cannot be conducted over very large areas for most of the abundant and widely distributed species, and thus indirect methods are used to estimate their relative abundances and population sizes. However, many species of the Family Hirundinidae (swallows and martins) build their mud nests in discrete, predictable and accessible sites, which are reused across years. Therefore, the direct count of active nests could constitute a reliable method for estimating breeding population sizes and their changes at large spatial and temporal scales. We illustrate the feasibility of this monitoring approach through a single year survey of >2700 nests of three coexisting Old-World species, the barn swallow (Hirundo rustica), the red-rumped swallow (Cecropis daurica), and the crag martin (Ptyonoprogne rupestris), distributed across Portugal and Spain. Our results revealed changes in the use of nesting substrates and increases in interspecific nest usurpation rates over recent decades. While 56% of the nests of C. daurica were located in rocks five decades ago, almost 100% are nowadays located in anthropogenic substrates such as bridges, road culverts, and abandoned buildings, which could have favored the range expansion of this species. Nest occupation rates were surprisingly low (12% in C. daurica, 21% in H. rustica, and 37% in P. rupestris), and the proportion of abandoned nesting sites was very high (65% in C. daurica, 50% in H. rustica, and 27% in P. rupestris). Abandonment rates reflect the population decline reported for H. rustica. Notably, the usurpation of nests of C. daurica by house sparrows Passer domesticus, which is the main cause of breeding failure, has increased from 2.4% in 1976-1979 to 34.7% of the nests nowadays. The long-term monitoring of nests may constitute a reliable and affordable method, with the help of citizen science, for assessing changes in breeding population sizes and conservation threats of these and other mud-nest building hirundines worldwide.

Assessing Sustainability Literacy in Green School through Citizen Science Project on Biodiversity Learning

Assessing Sustainability Literacy in Green School through Citizen Science Project on Biodiversity Learning

Similarity learning networks uniquely identify individuals of four marine and terrestrial species

AbstractEstimating the size of animal populations plays an important role in evidence‐based conservation and management. Some methods for estimating population size rely on animals being individually identifiable. Traditionally, this has been done by marking physically captured animals, but increasingly, animals with distinctive natural markings are surveyed noninvasively using cameras. Animal reidentification from photographs is usually done manually, which is expensive, laborious, and requires considerable skill. An alternative is to develop computer vision methods that can support or replace the manual identification task. We developed an automated approach using deep learning to identify whether a pair of photographs is of the same individual or not. The core of the approach is a similarity learning network that uses paired convolutional neural networks with a triplet loss function to summarize image pairs and decide whether they are from the same individual. Prior to the main matching step, two additional convolutional neural networks perform image segmentation, cropping the animal object within the image, and orientation prediction, deciding which side of the animal was photographed. We applied the approach to four species, with images of the same individual often spanning several years: systematic surveys of bottlenose dolphins (Tursiops truncatus, 2008–2019) and harbor seals (Phoca vitulina, 2015–2019), a citizen science dataset of western leopard toads (Sclerophrys pantherina, unknown dates), and a publicly available repository of humpback whale images (Megaptera novaeangliae, unknown dates). For these species, our best‐performing models were able to identify whether a pair of images were from the same individual or different individuals in 95.8%, 94.6%, 88.2%, and 83.8% of the cases, respectively. We found that triplet loss functions outperformed binary cross‐entropy loss functions and that data augmentation and additional manual curation of training data provided small but consistent improvements in performance. These results demonstrate the potential of deep learning to replace or, more likely, support and facilitate manual individual identification efforts.

Guardians of the clams: a decadal monitoring effort of endangered giant clams by citizen scientists

ABSTRACT Giant clams are highly conspicuous marine bivalves in the tropical oceans because of their large shells and colourful outer mantle tissues. This makes them an appropriate iconic species for both trained and untrained citizen science surveys. In Singapore, because of good public awareness of giant clams, they are often reported by citizen scientists, who document marine species using photography. Furthermore, these opportunistic records are captured in a comprehensive public blog (Wild Shores of Singapore; https://wildshores.blogspot.com) with specific details of the field surveys. This study presents the first feasible effort in establishing a long-term dataset based on citizen science observations to report giant clam species-level population status and trends over time. Between 2011 and 2020, citizen scientists covered 8.53 km2 of intertidal reef areas and consistently recorded only two giant clam species (Tridacna squamosa and T. crocea) annually. Despite the more comprehensive coverage and sampling of reef areas, the citizen scientists did not encounter the other native species: Hippopus hippopus, T. gigas and T. maxima. Also, the respective densities of T. squamosa and T. crocea were 0.00120 and 0.00034 individuals per 100 m2, which makes them the smallest known giant clam populations in the Indo-Pacific region. Regardless, the sampling efforts of citizen scientists were generally similar across the years, but future surveys could adapt different survey protocols for the respective giant clam species. Findings from this study also support the marine conservation efforts in Singapore, such as using active intervention measures to protect their giant clams. Additionally, lessons learnt from these citizen science observations can be harnessed to encourage monitoring of giant clams throughout the Indo-Pacific region.

Measures of spatial memory, peripheral inflammation and negative emotional state predict social-cognitive skills in healthy aging cats

Aging is a complex, multidimensional process. Normative aging is typically accompanied by declines in several cognitive domains, including memory and social cognition. As aging progresses, the often-overlooked early manifestations of cognitive decline can advance, leading to an inability to adapt to new situations and environments. Potential mechanisms influencing cognitive decline remain elusive, but, among other mechanisms, inflammaging, an age-related increase in the chronic production of pro-inflammatory mediators, and emotional stress are implicated. Cognitive function in cats is under-researched, with no studies on the link between potential risk factors and cognitive signs in aging subjects. Understanding this link is essential as it may facilitate the early identification of cognitive decline and encourage preventative, individualized interventions. We carried out a citizen science study to investigate, for the first time, the relationship between cognitive functions, stress, and the inflammatory markers interleukin (IL)-1β and IL-10 in 44 healthy, privately owned pet cats (7–14 years of age), who underwent standardized testing in their home environment. A task was employed to test spatial memory, requiring the cat to recall the location of a food-baited container. An unsolvable task was used to assess cats' social cognition and cognitive flexibility by measuring how they used referential communication (i.e., gaze alternation between the apparatus and the caregivers) compared to their persistence in trying to obtain food which was out of reach. Generalized linear regression analysis, conducted while adjusting for demographic factors and stress behaviors, revealed: 1) a predictive positive relationship connecting spatial memory and the use of social referencing with the caregiver (more gaze alternation) in the unsolvable task, highlighting the importance of the interplay between cognitive domains in sustaining complex behaviors, and 2) a reduced engagement in social referencing toward caregivers (less gaze alternation) in the presence of higher serum levels of IL-1β in the unsolvable task, especially as cats get older. A cat’s social cognitive performance was also negatively predicted by stress-related behaviors, which is not surprising given that cats are sentient creatures whose everyday behavior is also influenced by emotional states. These findings provide new insights into the crosstalk between different cognitive domains during aging and the potential contributions of emotions and inflammation to cognitive changes in healthy aging cats. Further research using this multidimensional approach will help explore these mechanisms of cognitive aging and potential markers for early detection of cognitive changes in older cats, which is critical for timely interventions.

Becoming Scientific-Environmental Citizens Through Citizen Science in China

This article advocates for a closer study of the forms of citizenship nurtured among individual participants in citizen science (CS) projects by highlighting some salient features of CS in China. Through a detailed examination of the experiences of students participating in the CS activities of a Chinese environmental NGO, it proposes that attention to CS as a means of democratizing science should be complemented by a similar attention to the ways in which CS fosters “scientific-environmental citizenship.” The article argues that these emergent forms of citizenship may be revealed by focusing on the experiences, perspectives, values and skills acquired by participants, and the specific polity in which CS initiatives are situated. This attention to the development of scientific-environmental citizenship as an outcome of CS is particularly valuable in contexts where democratic participation is otherwise constrained, but also yields a more nuanced understanding of citizenship enacted through CS in democratic contexts.

Community-Driven Research with People Who Use Drugs: A Virtual Project During Multiple Epidemics

Sociological approaches to digital and community-engaged research experienced significant innovation in recent years. This article examines developing and implementing a primarily virtual community-driven research (CDR) project with the National Survivors Union, the American national drug-users union, during the COVID-19 pandemic. Relationships between researchers and directly impacted people, such as people who use drugs, face many barriers. These issues were exacerbated during COVID-19 when in-person research decreased while drug-related harms increased. In response, this project modified the CDR model for drug-use research. The CDR model is particularly beneficial for studies with marginalized populations who may mistrust researchers. In CDR, impacted community members are fundamental project drivers. This project’s data are based on 29 months of weekly group meetings in National Survivors Union online spaces, group and individual text conversations, phone calls, and shared-document group work. The project co-developed methods for CDR with directly impacted people, including community-initiated research questions, low-threshold methods, collaborative writing strategies, coauthorship practices foregrounding directly impacted perspectives, and multiple dissemination forms. Modified CDR expands sociological methods for digital research, citizen science, and community-engaged research with vulnerable, criminalized groups. This approach may aid inclusive, innovative sociological scholarship and effective public health policy for reducing morbidity and mortality during multiple crises.

Estimating Rainfall Intensity Using an Image-Based Convolutional Neural Network Inversion Technique for Potential Crowdsourcing Applications in Urban Areas

High-quality rainfall data are essential in many water management problems, including stormwater management, water resources management, and more. Due to the high spatial–temporal variations, rainfall measurement could be challenging and costly, especially in urban areas. This could be even more challenging in tropical regions with their typical short-duration and high-intensity rainfall events, as some of the undeveloped or developing countries in those regions lack a dense rain gauge network and have limited resources to use radar and satellite readings. Thus, exploring alternative rainfall estimation methods could be helpful to back up some shortcomings. Recently, a few studies have examined the utilisation of citizen science methods to collect rainfall data as a complement to the existing rain gauge networks. However, these attempts are in the early stages, and limited works have been published on improving the quality of such data. Therefore, this study focuses on image-based rainfall estimation with potential usage in citizen science. For this, a novel convolutional neural network (CNN) model is developed to predict rainfall intensity by processing the images captured by citizens (e.g., by smartphones or security cameras) in an urban area. The developed model is merely a complementary sensing tool (e.g., better spatial coverage) to the existing rain gauge network in an urban area and is not meant to replace it. This study also presents one of the most extensive datasets of rain image data ever published in the literature. The estimated rainfall data by the proposed CNN model of this study using images captured by surveillance cameras and smartphone cameras are compared with observed rainfall by a weather station and exhibit strong R2 values of 0.955 and 0.840, respectively.

Unlocking Solutions: Innovative Approaches to Identifying and Mitigating the Environmental Impacts of Undocumented Orphan Wells in the United States.

In the United States, hundreds of thousands of undocumented orphan wells have been abandoned, leaving the burden of managing environmental hazards to governmental agencies or the public. These wells, a result of over a century of fossil fuel extraction without adequate regulation, lack basic information like location and depth, emit greenhouse gases, and leak toxic substances into groundwater. For most of these wells, basic information such as well location and depth is unknown or unverified. Addressing this issue necessitates innovative and interdisciplinary approaches for locating, characterizing, and mitigating their environmental impacts. Our survey of the United States revealed the need for tools to identify well locations and assess conditions, prompting the development of technologies including machine learning to automatically extract information from old records (95%+ accuracy), remote sensing technologies like aero-magnetometers to find buried wells, and cost-effective methods for estimating methane emissions. Notably, fixed-wing drones equipped with magnetometers have emerged as cost-effective and efficient for discovering unknown wells, offering advantages over helicopters and quadcopters. Efforts also involved leveraging local knowledge through outreach to state and tribal governments as well as citizen science initiatives. These initiatives aim to significantly contribute to environmental sustainability by reducing greenhouse gases and improving air and water quality.

Assessing and correcting neighborhood socioeconomic spatial sampling biases in citizen science mosquito data collection

Climatic, ecological, and socioeconomic factors are facilitating the spread of mosquito-borne diseases, heightening the importance of vector surveillance and control. Citizen science is proving to be an effective tool to track mosquito populations, but methods are needed to detect and account for small scale sampling biases in citizen science surveillance. In this article we combine two types of traditional mosquito surveillance records with data from the Mosquito Alert citizen science system to explore the ways in which the socioeconomic characteristics of urban neighborhoods result in sampling biases in citizen scientists’ mosquito reports, while also shaping the spatial distribution of mosquito populations themselves. We use Barcelona, Spain, as an example, and focus on Aedes albopictus, an invasive vector species of concern worldwide. Our results suggest citizen scientists’ sampling effort is focused more in Barcelona’s lower and middle income census tracts than in its higher income ones, whereas Ae. albopictus populations are concentrated in the city’s upper-middle income tracts. High resolution estimates of the spatial distribution of Ae. albopictus risk can be improved by controlling for citizen scientists’ sampling effort, making it possible to provide better insights for efficiently targeting control efforts. Our methodology can be replicated in other cities faced with vector mosquitoes to improve public health responses to mosquito-borne diseases, which impose massive burdens on communities worldwide.

Anglers as citizen scientists: a case study of resource monitoring in stream salmonids

Anglers as citizen scientists: a case study of resource monitoring in stream salmonids

Authorship Dynamics in Multidisciplinary Research: A Focus on Library and Information Science Professionals in RRIJM

This study looks into bibliometric studies in library and information science (LIS) to learn more about the influence of professional collaborations and multidisciplinary publication. It centers on user behaviour, digital libraries, and information management. The study uses bibliometric methodologies to examine authorship trends, geographic distribution, research paper length, and references. The results enhanced comprehension of the importance of library science in academic communities by emphasizing its function in handling intricate problems and promoting information sharing. Data from the Research Review International Journal of Multidisciplinary has been used in the study.

Citizen science in elementary classrooms: a tale of two teachers

Citizen science in elementary classrooms: a tale of two teachers

Citizen science tools for engaged research: Waterquality monitoring in remote communities

Remote areas that lack conventional water-provisioning infrastructure often rely on rainwater harvesting, rivers, pans, reservoirs and borehole-extracted water to meet domestic water requirements. These water sources often have poor microbial quality and chemical composition, the quality of which is not routinely monitored. This study explored citizen science as a tool for Engaged Research and Responsible Research and Innovation, detailing the co-creation of a sustained community-based water quality monitoring program in collaboration with communities in villages in Amakhala Game Reserve (Eastern Cape, South Africa). Without access to other water sources, participants predominantly used rainwater for drinking and cooking (80%), while borehole water was mainly used for cleaning and gardening due to its salty or bitter taste. A hydrogen sulphide (H2S) water testing kit was used by the citizen scientists to monitor the water quality. The H2S kits were effective in estimating bacterial contamination, showing a proportional relationship with Colilert® test results conducted in a laboratory. The alignment observed between community-based monitoring results and those derived from scientist-led testing underscores the value of data produced through citizen science initiatives. Sustained participant engagement throughout this research reflected a sense of community empowerment through access to tools that inform their decision-making around water use and treatment as well as investment in the research, indicative of the perceived relevance of the research to community interests. This integration of transdisciplinary data sources holds promise for informing evidence-based decision-making processes, facilitating more effective and contextually informed water management strategies that value and integrate community perspectives alongside scientific insights.

Bringing river health into being with citizen science: River commons co-learning and practice

Human health and well-being are directly and indirectly dependent on the life of river systems. Life in river systems is increasingly dependent on human actions that bring river health into being. Rather than describing river health as thing, problem or management challenge, this paper explores how river health is brought into being, through the citizen science practices of the Amanzi Ethu Nobuntu project in the upper uMngeni catchment in South Africa. The study draws on focal data produced by citizen science practitioners, as interpreted by them in collaboration with partners in the catchment, and their reflections on the meaning(s) of river health and how it came into being. Drawing on the concepts of citizen science as a co-learning process, integrative views of One Health, and commoning as activity, the study offers a rich interpretation on how river health comes into being. The study shows the complex interrelated practices involved, including practices of resolving leaks and pollution challenges, social and community engagement, and the co-learning involved in citizen science practices itself. It offers insight into the social-ecological and ethical-political ontological dynamics of river health commoning activity, thus offering alternatives to reductionist approaches to bringing river health into being, potentially also enriching tools for river health reporting.

The value of citizen science for a just and sustainable water future

The value of citizen science for a just and sustainable water future

Immersive citizen science experiences and their role in changing perceptions of coastal wetlands

Abstract Citizen, or community, science initiatives are increasingly recognised as an effective strategy to connect society with nature, science and environmental issues. However, different approaches to the delivery of the programmes can have different impacts on participant awareness, perceptions and behaviour change—especially when working with ecosystems perceived as less popular or uncharismatic, such as coastal wetlands, mangroves and saltmarshes. Using the HSBC Blue Carbon Citizen Science Programme as a case study, we compared two groups of corporate employees from Australia and New Zealand (N = 89) who participated in either: (a) a short duration citizen science experience including educational workshops and fieldwork in a local wetland; or (b) in educational workshops‐only. Questionnaires assessed the impact on participants' knowledge and perception towards wetlands, as well as their likelihood of adopting sustainable behaviours. Results revealed that participants' knowledge and understanding of environmental concepts increased, independent of the type of experience attended. However, the citizen science experience was more effective at fostering participants' intentions to make behaviour changes, with 64% of citizen science participants implementing sustainable changes at home or work, compared to 45% of workshop‐only participants. Our results highlight the importance of immersive citizen science experiences that, even of short duration, can have a valuable role enhancing participant knowledge, perception, and importantly, intention to make long‐term behaviour changes. With the increasing challenges faced by coastal systems globally, incorporating hands‐on, immersive experiences into educational programmes can be a strategic solution to improve ocean and climate literacy, while facilitating the actions required for a sustainable future. Read the free Plain Language Summary for this article on the Journal blog.

Microplastic detectives: a citizen-science project reveals large variation in meso- and microplastic pollution along German coastlines

Plastic production and plastic waste have increased to such an extent that it has become globally ubiquitous. Several studies already have investigated the meso- and microplastic pollution along the German Baltic and North Sea coasts, but were all limited to a few locations. To obtain representative bulk samples from sandy beaches along the entire German coast, we initiated a citizen-science project entitled “Microplastic Detectives.” Here, we describe in detail 1) how we recruited, instructed, and engaged citizen scientists, 2) why we chose bulk sampling over reduced-volume sampling, and 3) the laboratory methods we used. The citizen scientists collected 1,139 samples from 71 locations along the German coast, totalling 2.2 tons of sand. After drying, sieving with a 1 mm sieve, and visual inspection of the retained fraction under a binocular microscope, all putative plastic particles ≥1 mm were analysed by attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy. 177 out of 1,139 samples (15.5%) contained a total of 260 plastic particles, with a large right-skewed variation among locations. Most of the particles were fragments, foils, foams, fibres, and pellets (96.2% in total), and 89.6% of the particles were made of polyethylene, polypropylene, polyester, and polystyrene. The unweighted mean pollution densities were 4.12 particles m−2, 0.17 particles kg−1 and 0.27 particles L−1, and the weighted mean pollution densities were 3.77 particles m−2, 0.11 particles kg−1 and 0.18 particles L−1. These densities are lower than in other similar studies, but previous studies had important methodological differences. We discuss how these differences could have influenced the results and make recommendations for improving future studies. Two important recommendations are 1) to use random or stratified random sampling and 2) to run transects perpendicular (rather than parallel) to the waterline. Our study highlights that large-scale, scientifically rigorous monitoring of meso- and microplastic pollution is possible at the national level, and possibly even at much larger spatial and temporal scales. With the help of local authorities, such a monitoring program could be established.