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Currea, J. P.

Publications and source records attributed to Currea, J. P..

2 recordsLinked to original sources

An inexpensive setup for robust activity tracking in small animals: Portable Locomotion Activity Monitor (pLAM)

O_LIAdvances in computer vision and deep learning have automated animal behaviour studies that previously required tedious manual input. However, tracking activity of small and fast flying animals remains a hurdle, especially in a field setting with variable light conditions. Commercial locomotor activity monitors (LAMs) can be expensive, closed source, and generally limited to laboratory settings. C_LIO_LIHere, we present a portable locomotion activity monitor (pLAM), a mobile activity detector to quantify small animal circadian activity. Our setup uses inexpensive components, is based on open-source motion tracking software, and is easy to assemble and use in the field. It runs off-grid, supports low-light tracking with infrared lights, and can implement arbitrary light cycle colours and brightnesses with programmable LEDs. We provide a user-friendly guide to assembling pLAM hardware and accessing its pre-configured software and guidelines for using it in other systems. C_LIO_LIWe benchmarked pLAM for insects under various lab and field conditions, then compared results to a commercial activity detector. They offer broadly similar activity measures, but our setup captures flight and bouts of motion that are often missed by beam-breaking activity detection. C_LIO_LIpLAM will enable high-throughput quantification of small animal location and activity in a low-cost and accessible manner, crucial to studying behaviour that can help inform conservation and management decisions. C_LI

animal behavior and cognition

Measuring Compound Eye Optics with Microscope and MicroCT Images

With a great variety of shapes and sizes, compound eye morphologies give insight into visual ecology, development, and evolution, and inspire novel engineering. In contrast to our own camera-type eyes, compound eyes reveal their resolution, sensitivity, and field of view externally, provided they have spherical curvature and orthogonal ommatidia. Non-spherical compound eyes with skewed ommatidia require measuring internal structures, such as with MicroCT (CT). Thus far, there is no efficient tool to characterize compound eye optics, from either 2D or 3D data, automatically. Here we present two open-source programs: (1) the ommatidia detecting algorithm (ODA), which measures ommatidia count and diameter in 2D images, and (2) a CT pipeline (ODA-3D), which calculates anatomical acuity, sensitivity, and field of view across the eye by applying the ODA to 3D data. We validate these algorithms on images, images of replicas, and CT eye scans from ants, fruit flies, moths, and a bee.

neuroscience