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Abstract Characterising the frequency and timing of biological processes such as locomotion, eclosion or foraging, is often needed to get a complete picture of a species' ecology. Automated trackers are an invaluable tool for high‐throughput collection of activity data and have become more accurate and efficient with advances in computer vision and deep learning. However, tracking activity of small and fast flying animals remains a hurdle, especially in a field setting with variable light conditions. Commercial activity monitors can be expensive, closed source and generally limited to laboratory settings.Here, we present a portable locomotion activity monitor (pLAM), a mobile activity detector to quantify small animal activity. Our setup uses inexpensive components, builds upon 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, accessing its pre‐configured software and guidelines for using it in other systems.We benchmarked pLAM for insects under various laboratory 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.pLAM can automate laboratory and field monitoring of activity and timing in a wide range of biological processes, including circadian rhythm, eclosion and diapause timing, pollination and flower foraging, or pest feeding activity. This low cost and easy setup allows high‐throughput animal behaviour studies for basic and applied ecology and evolution research.
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FINDeM : A CRISPR ‐based, molecular method for rapid, inexpensive and field‐deployable organism detection
Abstract The field of ecology has undergone a molecular revolution, with researchers increasingly relying on DNA‐based methods for organism detection. Unfortunately, these techniques often require expensive equipment, dedicated laboratory spaces and specialized training in molecular and computational techniques; limitations that may exclude field researchers, underfunded programmes and citizen scientists from contributing to cutting‐edge science.It is for these reasons that we have designed a simplified, inexpensive method for field‐based molecular organism detection—FINDeM (Field‐deployableIsothermalNucleotide‐basedDetectionMethod). In this approach, DNA is extracted using chemical cell lysis and a cellulose filter disc, followed by two body‐heat inducible reactions—recombinase polymerase amplification and a CRISPR‐Cas12a fluorescent reporter assay—to amplify and detect target DNA, respectively.Here, we introduce and validate FINDeM in detectingBatrachochytrium dendrobatidis, the causative agent of amphibian chytridiomycosis, and show that this approach can identify single‐digit DNA copies from epidermal swabs in under 1 h using low‐cost supplies and field‐friendly equipment.This research signifies a breakthrough in ecology, as we demonstrate a field‐deployable platform that requires only basic supplies (i.e. micropipettes, plastic consumables and a UV flashlight), inexpensive reagents (~$1.29 USD/sample) and emanated body heat for highly sensitive, DNA‐based organism detection. By presenting FINDeM in an ecological system with pressing, global biodiversity implications, we aim to not only highlight how CRISPR‐based applications promise to revolutionize organism detection but also how the continued development of such techniques will allow for additional, more diversely trained researchers to answer the most pressing questions in ecology.
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- Award ID(s):
- 2120084
- PAR ID:
- 10470949
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 14
- Issue:
- 12
- ISSN:
- 2041-210X
- Format(s):
- Medium: X Size: p. 3055-3067
- Size(s):
- p. 3055-3067
- Sponsoring Org:
- National Science Foundation
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