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Small uncrewed aerial systems (sUASs) can be used to quantify emissions of greenhouse and other gases, providing flexibility in quantifying these emissions from a multitude of sources, including oil and gas infrastructure, volcano plumes, wildfire emissions, and natural sources. However, sUAS-based emission estimates are sensitive to the accuracy of wind speed and direction measurements. In this study, we examined how filtering and correcting sUAS-based wind measurements affects data accuracy by comparing data from a miniature ultrasonic anemometer mounted on a sUAS in a joust configuration to highly accurate wind data taken from a nearby eddy covariance flux tower (aka the Tower). These corrections had a small effect on wind speed error, but reduced wind direction errors from 50° to >120° to 20–30°. A concurrent experiment examining the amount of error due to the sUAS and the Tower not being co-located showed that the impact of this separation was 0.16–0.21 ms−1, a small influence on wind speed errors. Lower wind speed errors were correlated with lower turbulence intensity and higher relative wind speeds. There were also some loose trends in diminished wind direction errors at higher relative wind speeds. Therefore, to improve the quality of sUAS-based wind measurements, our study suggested that flight planning consider optimizing conditions that can lower turbulence intensity and maximize relative wind speeds as well as include post-flight corrections. 

Abstract Humans and other complex organisms exhibit intelligent behaviors as individual agents and as groups of coordinated agents. They can switch between independent and collective modes of behavior, and flexible switching can be advantageous for adapting to ongoing changes in conditions. In the present study, we investigated the flexibility between independent and collective modes of behavior in a simulated social foraging task designed to benefit from both modes: distancing among ten foraging agents promoted faster detection of resources, whereas flocking promoted faster consumption. There was a tradeoff between faster detection versus faster consumption, but both factors contributed to foraging success. Results showed that group foraging performance among simulated agents was enhanced by  loose coupling  that balanced distancing and flocking among agents and enabled them to fluidly switch among a variety of groupings. We also examined the effects of more sophisticated cognitive capacities by studying how human players improve performance when they control one of the search agents. Results showed that human intervention further enhanced group performance with loosely coupled agents, and human foragers performed better when coordinating with loosely coupled agents. Humans players adapted their balance of independent versus collective search modes in response to the dynamics of simulated agents, thereby demonstrating the importance of adaptive flexibility in social foraging.