Florencia Sangermano

Understanding the spatial and seasonal distribution of mosquito vectors is essential for improving surveillance and control strategies for arboviral diseases. In this study, we modeled the potential distribution of Aedes aegypti and Aedes albopictus in Eldorado city, Misiones province, Argentina, using MaxEnt and satellite-derived environmental predictors. Occurrence data collected between 2016 and 2018 were combined with land-cover variables, vegetation and water indices, land surface temperature, and distance-based metrics representing urbanization and environmental features. Models were developed separately for each species and season and evaluated using internal cross-validation. Our results indicate that environmentally suitable conditions for both species are concentrated in urban and suburban areas, with seasonal variation in the extent of suitable habitat. Urban-related predictors consistently contributed to model performance for both species, while vegetation-related variables were particularly relevant for Ae. albopictus. Although model evaluation metrics varied across species and seasons, the outputs consistently identified areas with favorable environmental conditions rather than predicting abundance or epidemiological risk. The models are therefore most reliable within the study period and spatial extent considered. These findings highlight the importance of urban landscape structure in shaping mosquito habitat suitability in subtropical cities. The modeling framework, based on publicly available data and reproducible methods, provides a transferable tool to support vector surveillance and spatial prioritization, while emphasizing the need for complementary entomological data to inform operational control decisions.

Emerging zoonotic parasites such as Plasmodium spp. in remote ecosystems demand rapid, field-deployable surveillance. We developed a drone-based environmental DNA (eDNA) sampling approach integrated with a portable quantitative PCR (qPCR) assay for in situ detection of Plasmodium DNA. Field trials confirmed malaria parasite DNA in canopy swabs, coinciding spatially with acoustic detection of howler monkeys ( Alouatta sp.), suspected reservoir hosts. No Anopheles mosquitoes were captured in simultaneously deployed insect traps. The end-to-end workflow, from drone deployment to diagnostic readout, averaged 1.5 hours per assay, required only basic mobile-laboratory infrastructure, and bypassed cold-chain logistics. This rapid, non-invasive method delivers actionable surveillance data in real-time, enabling near-real-time trigger points for vector-control interventions and clinical response in remote settings. By merging environmental genomics with portable diagnostics, we establish a scalable One Health framework adaptable to diverse vector-borne parasites and ecosystems, with potential for early detection and spillover risk mitigation in under-monitored, high-risk environments.

Tropical rainforests are among the most biodiverse ecosystems on Earth, and also among the most threatened by anthropogenic pressures such as deforestation and climate change. Understanding human impact and the efficacy of conservation and preservation efforts requires scalable and comprehensive biodiversity monitoring solutions. As a winning finalist of the XPRIZE Rainforest Competition, ETH BiodivX collected biodiversity data from 100 ha of rainforest in the Amazon, in 24 hours. A suite of complementary data types were captured, from remote sensing maps and close-up images to surface and water environmental DNA (eDNA), along with canopy rafts which collect specimens, close-up images, and bioacoustic recordings. A distributed mesh communication network allows for a persistent link to the drone, up to the edges of the competition area. Optimized workflows allow for a full RGB and digital surface map (DSM) after only one-and-a-half hours. The captured DSM was then used to collect surface eDNA fully autonomously, and using the communication network, surface eDNA was collected at distances up to 1.4 km from the base station. Pre-processed multispectral satellite remote sensing provides indicators of water locations, which were then sampled for water eDNA. The canopy rafts can act as communication nodes or data collection stations, providing long-term bioacoustic recordings, insect images, and specimens. By utilizing a commercial drone platform with modular payloads for diverse tasks, the solutions are robust and easy to use. These field-proven systems mark a major step towards scalable biodiversity monitoring, including in some of the world’s most remote and biodiverse regions: tropical rainforests.