The fascinating world of bird feeding behaviors has been further explored by researchers, who have discovered that Chilean flamingos use their unique beak and foot structure to create water tornados and skimming techniques to trap their prey.

Victor Ortega Jiménez, an assistant professor at the University of California, Berkeley, and his collaborators have published a study in the Proceedings of the National Academy of Sciences detailing how these birds employ various strategies to capture brine shrimp, a crucial food source for them.

One of the key findings is that flamingos use their floppy webbed feet to churn up the water and create vortices around their beaks. This allows them to concentrate particles of food and increase their chances of capturing prey.

Another technique employed by flamingos is skimming, which involves moving the lower beak in a rapid chattering motion to create symmetrical vortices on either side of the beak. This helps to recirculate particles in the water and bring them into the beak, making it easier for the bird to capture its prey.

The study also highlights the importance of fluid dynamics in understanding how flamingos feed. Researchers employed computational fluid dynamics to simulate the 3D flow around the beak and feet, confirming that the vortices do indeed concentrate particles, similar to experiments using a 3D-printed head in a flume.

This research has significant implications for our understanding of bird feeding behaviors and could potentially inform the design of robots that need to navigate water or muddy environments.

The study on the reproducibility of behavioral experiments with insects has now been published, providing evidence that some results cannot be fully reproduced. This “reproducibility crisis” affects different disciplines, including biomedical research and behavioral studies on mammals. However, there have been no comparable systematic studies on insects – until now.

A team of researchers from the Universities of Münster, Bielefeld, and Jena (Germany) conducted a multi-laboratory approach to test the reproducibility of ecological insect studies. They performed three different behavioral experiments using different insect species: the turnip sawfly, meadow grasshopper, and red flour beetle.

Each experiment was carried out in laboratories in Münster, Bielefeld, and Jena, and the results were compared. The studies examined the effects of starvation on behavior in larvae of the turnip sawfly, the relationship between body color and preferred substrate color in grasshoppers, and the choice of habitat in red flour beetles.

To the research team’s knowledge, this study is the first to systematically demonstrate that behavioral studies on insects can also be affected by poor reproducibility. This was surprising, as insect studies generally use large sample sizes and could provide more robust results. However, reproducibility was higher compared to other systematic replication studies not carried out on insects.

The results are of particular interest to scientists in behavioral biology and ecology but also for all disciplines where behavioral experiments are conducted with animals. The research team concludes that deliberately introducing systematic variations could improve reproducibility in studies with living organisms.

The amazing diversity of life on our planet is a testament to the multitude of biological solutions that have evolved to secure and maintain energy. However, despite its central role in biology, measuring energy consumption remains a challenging task. One major drain for many animals is movement, making it an ideal lens through which to estimate energy usage. While methods exist for measuring animal movement, they are often limited by the physical size of the equipment used.

In a groundbreaking study published in the Journal of Experimental Biology, researchers from the Marine Biophysics Unit at the Okinawa Institute of Science and Technology (OIST) have developed an innovative method for measuring energy usage during movement using video and 3D-tracking via deep learning. This new approach opens up the possibility of studying energy consumption in animals that were previously inaccessible due to the reliance on wearable equipment.

The current state-of-the-art method, Dynamic Body Acceleration (DBA), involves measuring oxygen consumption while an animal performs a specific behavior in a lab setting. However, this method has limitations when applied in the wild, where reliably measuring oxygen consumption is impossible. To overcome these challenges, researchers have used physical accelerometers that weigh at least ten times less than the animal, but this still rules out the study of many small species.

The OIST researchers’ solution to this problem is elegantly simple: they use two cameras to capture video footage of an animal’s behavior from multiple angles, reconstructing its movement in 3D space. A deep learning neural network is then trained on a few frames of the videos to track the position of body features such as eyes, allowing researchers to subsequently measure the movement-related acceleration.

This new video-based DBA method has opened up possibilities for studying energy consumption in animals that were previously inaccessible, potentially enabling many new research avenues into the breadth of life on our planet. For example, researchers can now investigate the energy expenditure during schooling of small fish, which has long remained mysterious. By accurately measuring energy usage during free-ranging animal behavior, scientists can gain a deeper understanding of the ecology and evolution of various species.