The Catalan butterfly monitoring scheme has the capacity to detect effects of modifying agricultural practices

Concurrent Butterfly, Bat and Small Mammal Monitoring Programmes Using Citizen Science in Catalonia (NE Spain): A Historical Review and Future Directions

The Biodiversity and Bioindicators research group (BiBIO), based at the Natural Sciences Museum of Granollers, has coordinated four long-term faunal monitoring programmes based on citizen science over more than two decades in Catalonia (NE Spain). We summarize the historical progress of these programmes, describing their main conservation outputs, the challenges overcome, and future directions. The Catalan Butterfly Monitoring Scheme (CBMS) consists of a network of nearly 200 recording sites where butterfly populations have been monitored through visual censuses along transects for nearly three decades. This programme provides accurate temporal and spatial changes in the abundance of butterflies and relates them to different environmental factors (e.g., habitat and weather conditions). The Bat Monitoring Programme has progressively evolved to include passive acoustic monitoring protocols, as well as bat box-, underground- and river-bat surveys, and community ecological indices have been developed to monitor bat responses at assemblage level to both landscape and climatic changes. The Monitoring of common small mammals in Spain (SEMICE), a common small mammal monitoring programme with almost 80 active live-trapping stations, provides information to estimate population trends and has underlined the relevance of small mammals as both prey (of several predators) and predators (of insect forest pests). The Dormouse Monitoring Programme represents the first monitoring programme in Europe using specific nest boxes for the edible dormouse, providing information about biological and demographic data of the species at the southern limit of its distribution range. The combination and complementarity of these monitoring programmes provide crucial data to land managers to improve the understanding of conservation needs and develop efficient protection laws.

The Fluctuating Asymmetry of the Butterfly Wing Pattern Does Not Change along an Industrial Pollution Gradient

The rapid and selective responses to changes in habitat structure and climate have made butterflies valuable environmental indicators. In this study, we asked whether the decline in butterfly populations near the copper-nickel smelter in Monchegorsk in northwestern Russia is accompanied by phenotypic stress responses to toxic pollutants, expressed as a decrease in body size and an increase in fluctuating asymmetry. We measured the concentrations of nickel and copper, forewing length, and fluctuating asymmetry in two elements of wing patterns in Boloria euphrosyne, Plebejus idas, and Agriades optilete collected 1–65 km from Monchegorsk. Body metal concentrations increased toward the smelter, confirming the local origin of the collected butterflies. The wings of butterflies from the most polluted sites were 5–8% shorter than those in unpolluted localities, suggesting adverse effects of pollution on butterfly fitness due to larval feeding on contaminated plants. However, fluctuating asymmetry averaged across two hindwing spots did not change systematically with pollution, thereby questioning the use of fluctuating asymmetry as an indicator of habitat quality in butterfly conservation projects.

Deep learning identification for citizen science surveillance of tiger mosquitoes

Global monitoring of disease vectors is undoubtedly becoming an urgent need as the human population rises and becomes increasingly mobile, international commercial exchanges increase, and climate change expands the habitats of many vector species. Traditional surveillance of mosquitoes, vectors of many diseases, relies on catches, which requires regular manual inspection and reporting, and dedicated personnel, making large-scale monitoring difficult and expensive. New approaches are solving the problem of scalability by relying on smartphones and the Internet to enable novel community-based and digital observatories, where people can upload pictures of mosquitoes whenever they encounter them. An example is the Mosquito Alert citizen science system, which includes a dedicated mobile phone app through which geotagged images are collected. This system provides a viable option for monitoring the spread of various mosquito species across the globe, although it is partly limited by the quality of the citizen scientists’ photos. To make the system useful for public health agencies, and to give feedback to the volunteering citizens, the submitted images are inspected and labeled by entomology experts. Although citizen-based data collection can greatly broaden disease-vector monitoring scales, manual inspection of each image is not an easily scalable option in the long run, and the system could be improved through automation. Based on Mosquito Alert’s curated database of expert-validated mosquito photos, we trained a deep learning model to find tiger mosquitoes (Aedes albopictus), a species that is responsible for spreading chikungunya, dengue, and Zika among other diseases. The highly accurate 0.96 area under the receiver operating characteristic curve score promises not only a helpful pre-selector for the expert validation process but also an automated classifier giving quick feedback to the app participants, which may help to keep them motivated. In the paper, we also explored the possibilities of using the model to improve future data collection quality as a feedback loop.