Citizen Science Mosquito Surveillance by Ad Hoc Observation Using the iNaturalist Platform

Abstract

Citizen science mosquito surveillance has been growing in recent years due to both increasing concern about mosquito-borne disease and the increasing popularity of citizen science projects globally. Health authorities are recognising the potential importance of citizen science to expanding or enhancing traditional surveillance programs. Different programs have shown success in engaging communities to monitor species of medical importance through low-cost methods. The Mozzie Monitors project was established on iNaturalist—an open citizen science platform that allows participants to upload photos (i.e., observers) and assist identification (i.e., identifiers). This article describes the likelihood of citizen scientists submitting photos of mosquitoes, assesses user submission behaviour, and evaluates public health utility from these citizen science-derived data. From October 2018 to July 2021, the Mozzie Monitors project on iNaturalist received 2118 observations of 57 different species of mosquitoes across Australia. The number of observers in the system increased over time with more than 500 observers and 180 identifiers being active in the project since its establishment. Data showed species bias with large-bodied and colourful mosquitoes being over-represented. Analyses also indicate regional differentiation of mosquito fauna per state, seasonality of activity, and ecological information about mosquitoes. The iNaturalist citizen science platform also allows connectedness, facilitated communication and collaboration between overall users and expert entomologists, of value to medical entomology and mosquito management.

Bee monitoring by community scientists: comparing a collections-based program with iNaturalist

“…S3 in Supplemental 1 ). This is not a surprising finding given that larger bees are easier to spot in the wild, easier to photograph, and perhaps more likely to result in a research grade observation because they are more reliably able to be identified with photos ( Barbato et al 2021 , Braz Sousa et al 2022 ). Likely because of this bias, iNaturalist actually outperformed collections for documenting bumble bee ( Bombus ) biodiversity.…”

“…However, a common weakness of photo-based data for biodiversity studies is a lack of taxonomic resolution due to poor-quality photos, or the inability of many taxa to be identified from photos alone ( McMullin and Allen 2022 ). Additionally, observational data are likely to have some biases including a greater abundance and diversity of common, large-bodied, and colorful species, and a disproportionate amount of data from urban areas ( Barbato et al 2021 , Di Cecco et al 2021 , Braz Sousa et al 2022 , Mesaglio et al 2023 , Skvarla and Fisher 2023 ). However, this bias toward data coming from human population centers may make crowd-sourced photo data particularly useful for tracking the arrival and spread of non-native species ( Orr et al 2023 , Skvarla and Fisher 2023 ).…”

Online community photo-sharing in entomology: a large-scale review with suggestions on best practices

“…Mesaglio et al (2023a) reported that Lepidoptera and Odonata were the best recognized and most completely represented terrestrial invertebrate taxa in Australia on iNaturalist. There is also a bias to photograph larger, more colorful species within a given taxon compared to those that are smaller or less colorful, as Braz Sousa et al (2022) demonstrated with mosquitoes. So a bias toward large, charismatic entomological megafauna such as butterflies, moths, dragonflies, and damselflies is perhaps unsurprising.…”

Mapping the Landscape of Citizen Science in Africa: Assessing its Potential Contributions to Sustainable Development Goals 6 and 11 on Access to Clean Water and Sanitation and Sustainable Cities

“…There is growing awareness of the potential for citizen science data to fill data gaps and improve the basis for decision-making (Wehn and Almomani 2019). Citizen science (CS) as a field comprises different ways to engage citizens in the collection and analysis of data, ranging from local projects involving fieldwork and data collection (Andrianandrasana 2016) to broad-scale efforts to crowdsource data through internet-based platforms (e.g., volunteeeographicapic information) (Murindahabi et al 2018;Braz Sousa et al 2022). CS efforts have the potential to provide fine-resolution data over large spatial extents and long time periods.…”