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To adaptively express inducible defenses, prey must gauge risk based on indirect cues of predation. However, the information contained in indirect cues that enable prey to fine-tune their phenotypes to variation in risk is still unclear. In aquatic systems, research has focused on cue concentration as the key variable driving threat-sensitive responses to risk. However, while risk is measured as individuals killed per time, cue concentration may vary with either the number or biomass killed. Alternatively, fine-grained variation in cue, that is, frequency of cue pulses irrespective of concentration, may provide a more reliable signal of risk. Here, we present results from laboratory experiments that examine the relationship between red-eyed treefrog tadpole growth and total cue, cue per pulse, and cue pulse frequency. We also reanalyze an earlier study that examined the effect of fine-grained variation in predator cues on wood frog tadpole growth. Both studies show growth declines with increasing cue pulse frequency, even though individual pulses in high-frequency treatments contained very little cue. This result suggests that counter to earlier conclusions, tadpoles are using fine-grained variation in cue arising from the number of predation events to assess and respond to predation risk, as predicted by consumer-resource theory.
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http://dx.doi.org/10.1002/ece3.1552 | DOI Listing |
Front Artif Intell
August 2025
Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada.
Precision livestock farming increasingly relies on non-invasive, high-fidelity systems capable of monitoring cattle with minimal disruption to behavior or welfare. Conventional identification methods, such as ear tags and wearable sensors, often compromise animal comfort and produce inconsistent data under real-world farm conditions. This study introduces Dairy DigiD, a deep learning-based biometric classification framework that categorizes dairy cattle into four physiologically defineda groups-young, mature milking, pregnant, and dry cows-using high-resolution facial images.
View Article and Find Full Text PDFAudio-visual event localization (AVEL) aims to recognize events in videos by associating audio-visual information. However, events involved in existing AVEL tasks are usually coarse-grained events. Actually, finer-grained events are sometimes necessary to be distinguished, especially in certain expert-level applications or rich-content-generation studies.
View Article and Find Full Text PDFJ Acoust Soc Am
September 2025
IMVIA UR 7535, Université Bourgogne Europe, 21000 Dijon, France.
The narrowband components of ship-radiated noise are critical for the passive detection and identification of ship targets. However, the intricate underwater environment poses challenges for conventional acoustic signal processing methods, particularly at low signal-to-noise ratios. Previous studies have suggested the use of deep learning for denoising, but there is a significant lack of research on underwater narrowband signals.
View Article and Find Full Text PDFSci Rep
September 2025
Sanko School, Gaziantep, Turkey.
This study presents a novel privacy-preserving deep learning framework for accurately classifying fine-grained hygiene and water-usage events in restroom environments. Leveraging a comprehensive, curated dataset comprising approximately 460 min of stereo audio recordings from five acoustically diverse bathrooms, our method robustly identifies 11 distinct events, including nuanced variations in faucet counts and flow rates, toilet flushing, and handwashing activities. Stereo audio inputs were transformed into triple-channel Mel spectrograms using an adaptive one-dimensional convolutional neural network (1D-CNN), dynamically synthesizing spatial cues to enhance discriminative power.
View Article and Find Full Text PDFFront Artif Intell
August 2025
Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI, United States.
AI-enabled microscopy is emerging for rapid bacterial classification, yet its utility remains limited in dynamic or resource-limited settings due to imaging variability. This study aims to enhance the generalizability of AI microscopy using domain adaptation techniques. Six bacterial species, including three Gram-positive () and three Gram-negative ( Enteritidis, Typhimurium), were grown into microcolonies on soft tryptic soy agar plates at 37°C for 3-5 h.
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