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Abstract A growing body of information on vector-borne diseases has arisen as increasing research focus has been directed towards the need for anticipating risk, optimizing surveillance, and understanding the fundamental biology of vector-borne diseases to direct control and mitigation efforts. The scope and scale of this information, in the form of data, comprising database efforts, data storage, and serving approaches, means that it is distributed across many formats and data types. Data ranges from collections records to molecular characterization, geospatial data to interactions of vectors and traits, infection experiments to field trials. New initiatives arise, often spanning the effort traditionally siloed in specific research disciplines, and other efforts wane, perhaps in response to funding declines, different research directions, or lack of sustained interest. Thusly, the world of vector data – the Vector Data Ecosystem – can become unclear in scope, and the flows of data through these various efforts can become stymied by obsolescence, or simply by gaps in access and interoperability. As increasing attention is paid to creating FAIR (Findable Accessible Interoperable, and Reusable) data, simply characterizing what is ‘out there’, and how these existing data aggregation and collection efforts interact, or interoperate with each other, is a useful exercise. This study presents a snapshot of current vector data efforts, reporting on level of accessibility, and commenting on interoperability using an illustration to track a specimen through the data ecosystem to understand where it occurs for the database efforts anticipated to describe it (or parts of its extended specimen data). 

Mosquitoes and other blood feeding arthropods are vectors of pathogens causing serious human diseases, such as Plasmodium spp. (malaria), Wuchereria bancrofti (lymphatic filariasis), Borrelia burgdorferi (Lyme disease), and viruses causing dengue, Zika, West Nile, chikungunya, and yellow fever. Among the most effective strategies for the prevention of vector-borne diseases are those aimed at reducing human–vector interactions, such as insecticide applications and insecticide-treated bed nets (ITNs). In some areas where ITNs are widely used, behavioral adaptations have resulted in mosquitoes shifting their time of blood feeding to earlier or later in the night when the bed nets are not being employed. Little is known about the genetic basis of these behavioral shifts. We conducted quantitative trait locus (QTL) analysis using two strains of Culex pipiens sensu lato with contrasting blood feeding behaviors, wherein the lab adapted Shasta strain blood feeds at any time of the day or night, while the newly established Trinidad strain feeds only at night. We identified a single locus on chromosome 2 associated with the observed variation in feeding times. None of the core clock genes period, timeless, clock, cycle, PAR-domain protein 1, vrille, discs overgrown, cryptochrome 1, or cryptochrome 2 were located within the QTL region. We then monitored locomotor behavior to determine if they differed in their flight activity. The highly nocturnal Trinidad strain showed little daytime activity while the day-feeding Shasta strain was active during the day, suggesting blood feeding behavior and flight activity are physiologically linked.

 

Small Unmanned Aircraft Systems (sUAS) are an emerging application area for many industries including surveillance, agriculture monitoring, and vector-borne disease control. With drastically lower costs and increasing performance and autonomy, future application evolution will more than likely include the use of sUAS swarms. Several largely successful experiments in recent years, using off the shelf sUAS, have been conducted to address the long standing challenge of controlling and monitoring vector-borne diseases. In this paper we build on lessons learned from these prior efforts, and discuss ways in which swarms of sUAS could be deployed to place and monitor Autocidal Gravid Ovitraps for reducing the mosquito population. 

Arthropods play a dominant role in natural and human-modified terrestrial ecosystem dynamics. Spatially-explicit arthropod population time-series data are crucial for statistical or mathematical models of these dynamics and assessment of their veterinary, medical, agricultural, and ecological impacts. Such data have been collected world-wide for over a century, but remain scattered and largely inaccessible. In particular, with the ever-present and growing threat of arthropod pests and vectors of infectious diseases, there are numerous historical and ongoing surveillance efforts, but the data are not reported in consistent formats and typically lack sufficient metadata to make reuse and re-analysis possible. Here, we present the first-ever minimum information standard for arthropod abundance, Minimum Information for Reusable Arthropod Abundance Data (MIReAD). Developed with broad stakeholder collaboration, it balances sufficiency for reuse with the practicality of preparing the data for submission. It is designed to optimize data (re)usability from the “FAIR,” (Findable, Accessible, Interoperable, and Reusable) principles of public data archiving (PDA). This standard will facilitate data unification across research initiatives and communities dedicated to surveillance for detection and control of vector-borne diseases and pests.