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5 results for “multicomponent signals”
Predator selection on multicomponent warning signals in an aposematic moth
<p>Aposematic prey advertise their unprofitability with conspicuous warning signals that are often composed of multiple color patterns. Many species show intraspecific variation in these patterns even though selection is expected to favor invariable warning signals that enhance predator learning. However, if predators acquire avoidance to specific signal components, this might relax selection on other aposematic traits and explain variability. Here we investigated this idea in the aposematic moth <em>Amata</em> <em>nigriceps</em> that has conspicuous black and orange coloration. The size of the orange spots in the wings is highly variable between individuals, whereas the number and width of orange abdominal stripes remain consistent. We produced artificial moths that varied in the proportion of orange in the wings or the presence of abdominal stripes. We presented these to a natural avian predator, the noisy miner (<em>Manorina</em> <em>melanocephala</em>), and recorded how different warning signal components influenced their attack decisions. When moth models had orange stripes on the abdomen, birds did not discriminate between different wing signals. However, when the stripes on the abdomen were removed, birds chose the model with smaller wing spots. In addition, we found that birds were more likely to attack moths with a smaller number of abdominal stripes. Together, our results suggest that bird predators primarily pay attention to the abdominal stripes of <em>A. nigriceps,</em> and this could relax selection on wing coloration. Our study highlights the importance of considering individual warning signal components if we are to understand how predation shapes selection on prey warning coloration.</p>
Predator selection on multicomponent warning signals in an aposematic moth
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Data from: I remember you! Multicomponent warning signals and predator memory
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Data from: Assessing the potential information content of multicomponent visual signals: a machine learning approach
Careful investigation of the form of animal signals can offer novel insights into their function. Here, we deconstruct the face patterns of a tribe of primates, the guenons (Cercopithecini), and examine the information that is potentially available in the perceptual dimensions of their multicomponent displays. Using standardized colour-calibrated images of guenon faces, we measure variation in appearance both within and between species. Overall face pattern was quantified using the computer vision 'eigenface' technique, and eyebrow and nose-spot focal traits were described using computational image segmentation and shape analysis. Discriminant function analyses established whether these perceptual dimensions could be used to reliably classify species identity, individual identity, age and sex, and, if so, identify the dimensions that carry this information. Across the 12 species studied, we found that both overall face pattern and focal trait differences could be used to categorize species and individuals reliably, whereas correct classification of age category and sex was not possible. This pattern makes sense, as guenons often form mixed-species groups in which familiar conspecifics develop complex differentiated social relationships but where the presence of heterospecifics creates hybridization risk. Our approach should be broadly applicable to the investigation of visual signal function across the animal kingdom.
Data from: Assessing the potential information content of multicomponent visual signals: a machine learning approach
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.