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As part of a new large-scale exhibit at Australia's National Gallery of Victoria in Melbourne, Chinese artist Cai Guo-Qiang ( previously) has created a swarm of 10,000 porcelain birds, titled Murmuration (Landscape). The multi-part winter exhibition at the museum combines Cai's contemporary work with the display of a selection of China's famed ancient terracotta warriors. Cai, who is best known for his enormous artworks that utilize fireworks, assembled the vast quantity of birds and smudged them black with gunpowder. The installation fills an entire gallery and the birds are suspended to create a 3D impression of a calligraphic drawing of Mount Li, where the tomb of the ancient warriors was located. Terracotta Warriors and Cai Guo-Qiang opens to the public today and is on view through October 13, 2019. Watch a time-lapse of the labor-intensive installation here and explore more of the artist's diverse works on Instagram.

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Murmuration: 10,000 Porcelain Birds Create a Calligraphic Landscape at the National Gallery of Victoria

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As part of a new large-scale exhibit at Australia's National Gallery of Victoria in Melbourne, Chinese artist Cai Guo-Qiang (previously) has created a swarm of 10,000 porcelain birds, titled Murmuration (Landscape). The multi-part winter exhibition at the museum combines Cai's contemporary work with

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Søren Solkær murmuration photos at National Nordic Museum

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In his photographic series “Black Sun,” Søren Solkær studies the mesmerizing murmuration of starlings across Europe.

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Flight of the Starlings: Watch This Eerie but Beautiful Phenomenon | Short Film Showcase

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Bruissements au loin, mouvements d’air : une nuée se forme et s’élève, dessinant d’élégantes arabesques dans un ciel aux reflets irisés. Les images d’Alain Delorme nous transportent par le caractère féérique de la beauté éphémère d’un vol d’oiseaux, d’une Murmuration?. La séduction initiale est cependant rapidement contrecarrée par une attention…

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Still water by Sue Side

https://www.sueside.com/murmurations

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Current Biology Vol 22 N°4

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The mesmerizing act is typically

seen at dusk throughout Europe,

between November and February.

Even though starling numbers have been

falling across northern Europe since 40 years

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There is no real proofs in that matter. Murmurations likely help them avoid predators. They react faster in big groups, and waves in their movements can confuse predators and make attacks less successful.

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Study on the method of forming murmurations

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Study on the method of forming murmurations

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This article presents a study on the behavior of starling groups (or flocks). The birds manage to remain coherent and organized despite their environments. The researchers discovered that each bird responds to the movements of its seven closest neighbors. This number is not chosen at random: it optimizes group cohesion while reducing individual effort. This means that starlings are able to manage uncertainty and external disturbances more effectively by operating in this way.

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Starling Flock Networks Manage Uncertainty in Consensus at Low Cost

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Author Summary Starling flocks move in beautiful ways that both captivate and intrigue the observer. Previous work has shown that starlings pay attention to their seven closest neighbors, but until now it was not understood why this number is seven. Our paper explains the mystery: when uncertainty in sensing is present, interacting with six or seven neighbors optimizes the balance between group cohesiveness and individual effort. To prove this result we develop a new systems-theoretic approach for understanding noisy consensus dynamics. The approach allows the evaluation of robustness over a family of hypothesized sensing strategies using observations of the spatial positions of birds within the flock. We apply this approach to experimental data from wild starling flocks, and find that six or seven neighbors yield maximal robustness. The implication that robustness of cohesion may have been a factor in the evolution of flocking has significant consequences for evolutionary biology. In addition, the results and the versatility of the approach have implications for uncertainty management in social and technological networks.

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This study explores how starling flocks use coordinated patterns to escape predators, like creating waves, splitting into groups, or changing their shape. Researchers used videos of flocks being chased by a robot predator and computer simulations to study these behaviors. Each bird's movement is based on reacting to its neighbors and the predator, creating synchronized group dynamics that protect the flock. The study highlights the importance of individual decisions and information speed.

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Movements of individual birds are linked across vast distances within the flock. This allows the flock to respond cohesively as if it were a single organism.

Unlike earlier beliefs, starlings do not rely on the entire flock but interact only with their nearest neighbors, typically seven birds. These local interactions are sufficient to drive the global patterns of movement observed in murmurations.

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Starling murmurations rely on rapid information transfer, enabling flocks to execute synchronized turns crucial for evading predators. This information spreads at 20–40 m/s through local interactions, where each bird adjusts based on its nearest neighbors.

Research shows that behavioral inertia, or resistance to abrupt changes, stabilizes this process, maintaining cohesion during turns.

Using 3D tracking, scientists observed that directional changes originate locally and spread linearly through the flock, challenging traditional diffusive models.

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 Starling Murmuration Optimizer (SMO), a bio-inspired algorithm based

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 Starling Murmuration Optimizer (SMO), a bio-inspired algorithm based

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The Starling Murmuration Optimizer (SMO) is a computational algorithm inspired by the flight patterns of starling birds. Researchers have developed SMO to solve complex optimization problems, it demonstrates superior performance compared to other algorithms.

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In the study researchers introduced a binary version of SMO (BSMO) made for feature selection tasks. This feature selection is key in medical data to remove irrelevant and redundant information. The performance of BSMO was evaluated on medical databases from diseases such as diabetes, heart conditions, hepatitis, and coronavirus.