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Negative feedback: ants choose unoccupied over occupied food sources and lay more pheromone to them.

ABSTRACT: In order to make effective collective decisions, ants lay pheromone trails to lead nest-mates to acceptable food sources. The strength of a trail informs other ants about the quality of a food source, allowing colonies to exploit the most profitable resources. However, recruiting too many ants to a single food source can lead to over-exploitation, queuing, and thus decreased food intake for the colony. The nonlinear nature of pheromonal recruitment can also lead colonies to become trapped in suboptimal decisions, if the environment changes. Negative feedback systems can ameliorate these problems. We investigated a potential source of negative feedback: whether the presence of nest-mates makes food sources more or less attractive. Lasius niger workers were trained to food sources of identical quality, scented with different odours. Ants fed alone at one odour. At the other odour ants fed either with other feeding nest-mates, or with dummy ants (black surface lipid-coated glass beads). Ants tended to avoid food sources at which other nest-mates were present. They also deposited less pheromone to occupied food sources, suggesting an active avoidance behaviour, and potentiating negative feedback. This effect may prevent crowding at a single food source when other profitable food sources are available elsewhere, leading to a higher collective food intake. It could also potentially protect colonies from becoming trapped in local feeding optima. However, ants did not avoid the food associated with dummy ants, suggesting that surface lipids and static visual cues alone may not be sufficient for nest-mate recognition in this context.

SUBMITTER: Wendt S

PROVIDER: S-EPMC7061701 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Negative feedback: ants choose unoccupied over occupied food sources and lay more pheromone to them.

Wendt Stephanie S Kleinhoelting Nico N Czaczkes Tomer J TJ

Journal of the Royal Society, Interface 20200226 163

In order to make effective collective decisions, ants lay pheromone trails to lead nest-mates to acceptable food sources. The strength of a trail informs other ants about the quality of a food source, allowing colonies to exploit the most profitable resources. However, recruiting too many ants to a single food source can lead to over-exploitation, queuing, and thus decreased food intake for the colony. The nonlinear nature of pheromonal recruitment can also lead colonies to become trapped in sub ...[more]

PMID: 32093538

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Project description:High-dimensional biological data collection across heterogeneous groups of samples has become increasingly common, creating high demand for dimensionality reduction techniques that capture underlying structure of the data. Discovering low-dimensional embeddings that describe the separation of any underlying discrete latent structure in data is an important motivation for applying these techniques since these latent classes can represent important sources of unwanted variability, such as batch effects, or interesting sources of signal such as unknown cell types. The features that define this discrete latent structure are often hard to identify in high-dimensional data. Principal component analysis (PCA) is one of the most widely used methods as an unsupervised step for dimensionality reduction. This reduction technique finds linear transformations of the data which explain total variance. When the goal is detecting discrete structure, PCA is applied with the assumption that classes will be separated in directions of maximum variance. However, PCA will fail to accurately find discrete latent structure if this assumption does not hold. Visualization techniques, such as t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP), attempt to mitigate these problems with PCA by creating a low-dimensional space where similar objects are modeled by nearby points in the low-dimensional embedding and dissimilar objects are modeled by distant points with high probability. However, since t-SNE and UMAP are computationally expensive, often a PCA reduction is done before applying them which makes it sensitive to PCAs downfalls. Also, tSNE is limited to only two or three dimensions as a visualization tool, which may not be adequate for retaining discriminatory information. The linear transformations of PCA are preferable to non-linear transformations provided by methods like t-SNE and UMAP for interpretable feature weights. Here, we propose iterative discriminant analysis (iDA), a dimensionality reduction technique designed to mitigate these limitations. iDA produces an embedding that carries discriminatory information which optimally separates latent clusters using linear transformations that permit post hoc analysis to determine features that define these latent structures.

Dataset Information

Negative feedback: ants choose unoccupied over occupied food sources and lay more pheromone to them.

Publications

Negative feedback: ants choose unoccupied over occupied food sources and lay more pheromone to them.

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