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The rise of citizen science (also called community science) has led to vast quantities of species observation data collected by members of the public. Citizen science data tend to be unevenly distributed across space and time, but the treatment of sampling bias varies between studies, and interactions between different biases are often overlooked. We present a method for conceptualizing and estimating spatial and temporal sampling biases, and interactions between them. We use this method to estimate sampling biases in an example ornithological citizen science dataset from eBird in Brisbane City, Australia. We then explore the effects of these sampling biases on subsequent model inference of population trends, using both a simulation study and an application of the same trend models to the Brisbane eBird dataset. We find varying levels of sampling bias in the Brisbane eBird dataset across temporal and spatial scales, and evidence for interactions between biases. Several of the sampling biases we identified differ from those described in the literature for other datasets, with protected areas being undersampled in the city, and only limited seasonal sampling bias. We demonstrate variable performance of trend models under different sampling bias scenarios, with more complex biases being associated with typically poorer trend estimates. Sampling biases are important to consider when analysing ecological datasets, and analysts can use this method to ensure that any biologically relevant sampling biases are detected and given due consideration during analysis. With appropriate model specification, the effects of sampling biases can be reduced to yield reliable information about biodiversity. [ABSTRACT FROM AUTHOR]

Most citizen science research inherently separates the observer (citizen science participant) from the observation (e.g. data point), placing artificial boundaries around what matters and how it comes to matter. We apply three elements of the philosophical framework of agential realism to reveal a more complex picture of how data arise within citizen science programmes, and its meaning to both the practice of science and the citizen science participant: 'intra‐action' (all entities have agency and are entangled with one another); 'material becoming' (what comes to matter); and 'responsibility' (accountability for what comes to matter and what is excluded from mattering).We draw on a case study of FrogID—an Australia‐wide citizen science program focused on calling frogs, with over 42,000 participants and over 1 million frog records. We conducted semi‐structured interviews with 30 FrogID users, completing two rounds of thematic and relational coding.Our findings reveal that as a consequence of their recording behaviours, FrogID participants become increasingly entangled with the nocturnal environment, with sound and with their own self. Expanding and reciprocal relationships and experiences shape the nature and frequency of their recordings.Second, meaning influences what comes to matter (i.e. what is recorded and submitted) for FrogID participants. We reveal meaning related to feedback (recognition and thus reciprocity), others (social networks and participation with family and friends) and the self (physical and mental well‐being and identity formation/becoming). These different forms of meaning influenced engagement with app use.Third, participants communicated responsibilities related to their involvement in citizen science, including responsibilities to create knowledge (e.g. longitudinal data collection), to conserve (e.g. actively conserving frog, formally committing areas to conservation) and to educate self and others (e.g. skills and competencies required for environmental action).Synthesis and applications: By recognizing a more comprehensive set of intra‐actions, beyond the observer and the observation, agential realism can reveal when, why and how citizen science observations are made; what observations come to matter and why; and how people can create a more just world. Agential realism can shape how citizen science participation, retention and biodiversity data generation are founded. We propose three opportunities for citizen science programs based on these findings. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Engagement in biodiversity citizen science initiatives can confer health and wellbeing benefits to individuals and communities. Yet, few biodiversity citizen science initiatives are explicitly planned to optimize health and wellbeing as a potential co-benefit, leading to missed opportunities for biodiversity conservation and human health. In this perspective, we use a dose-response approach to discuss the components that determine how engagement in biodiversity citizen science initiatives map onto opportunities to foster health and wellbeing benefits. We considered aspects related to the duration and frequency of contact with nature, and the intensity of interactions with nature and between individuals to highlight the different health benefits across the variety of citizen science initiatives. To illustrate these aspects, we use a sample of 95 citizen science initiatives from seven English and non-English-speaking countries and show how careful project design can increase the potential to confer health and wellbeing benefits to participants. We conclude with considerations on how to enhance the health and wellbeing benefits from citizen science initiatives, and propose potential research avenues to assess synergies and trade-offs between benefits to biodiversity and human health from these initiatives. [ABSTRACT FROM AUTHOR]

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

Citizen science is a popular approach to biodiversity surveying, whereby data that are collected by volunteer naturalists may help analysts to understand the distribution and abundance of wild organisms. In Australia, birdwatchers have contributed to two major citizen science programs, eBird (run by the Cornell Lab of Ornithology) and Birdata (run by Birdlife Australia), which collectively hold more than 42 million records of wild birds from across the country. However, these records are not evenly distributed across space, time, or taxonomy, with particularly significant variation in the number of records of each species in these datasets. In this paper, we explore this variation and seek to determine which Australian bird species are least known as determined by rates of citizen science survey detections. We achieve this by comparing the rates of survey effort and species detection across each Australian bird species' range, assigning all 581 species to one of the four groups depending on their rates of survey effort and species observation. We classify 56 species into a group considered the most poorly recorded despite extensive survey effort, with Coxen's Fig Parrot Cyclopsitta coxeni, Letter-winged Kite Elanus scriptus, Night Parrot Pezoporus occidentalis, Buff-breasted Buttonquail Turnix olivii and Red-chested Buttonquail Turnix pyrrhothorax having the very lowest numbers of records. Our analyses provide a framework to identify species that are poorly represented in citizen science datasets. We explore the reasons behind why they may be poorly represented and suggest ways in which targeted approaches may be able to help fill in the gaps. [ABSTRACT FROM AUTHOR]