Publications by authors named "R Senthil Nathan"

Background: Species host diverse microbial communities that can impact their digestion and health, which has led to much interest in understanding the factors that influence their microbiota. We studied the developmental, environmental, and social factors that influence the microbiota of nestling barn owls (Tyto alba) through a partial cross-fostering experiment that manipulated the social and nest environment of the nestlings. We then examined the nestling microbiota before and three weeks after the exchange of nestlings between nests, along with the microbiota of the adults at the nest and nestlings in unmanipulated nests.

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The destructive and life-threatening nature of flood events calls for fast and accurate methods to predict dynamic flood behaviour. Data-driven surrogate models have been developed to quickly predict flood inundation, though their accuracy relies on the available flood information for model training and validation. Flood observations are rarely available at high spatial and temporal scales, and thus computationally expensive high-resolution hydrodynamic (high-fidelity) models are often used to generate training data through simulation of selected flood events.

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In wildlife populations, parasites often go unnoticed, as infected animals appear asymptomatic. However, these infections can subtly alter behaviour. Field evidence of how these subclinical infections induce changes in movement behaviour is scarce in free-ranging animals, yet it may be crucial for zoonotic disease surveillance.

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Bats are known for their ability to use echolocation for obstacle avoidance and orientation. However, the extent to which bats utilize their highly local and directional echolocation for kilometer-scale navigation is unknown. In this study, we translocated wild Kuhl's pipistrelle bats and tracked their homing abilities while manipulating their visual, magnetic, and olfactory sensing and accurately tracked them using a new reverse GPS system.

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Both humans and computational methods struggle to discriminate the depths of objects hidden beneath foliage. However, such discrimination becomes feasible when we combine computational optical synthetic aperture sensing with the human ability to fuse stereoscopic images. For object identification tasks, as required in search and rescue, wildlife observation, surveillance, and early wildfire detection, depth assists in differentiating true from false findings, such as people, animals, or vehicles vs.

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