Evolution of swarming behavior is shaped by how predators attack

Animal grouping behaviors have been widely studied due to their implications for understanding social intelligence, collective cognition, and potential applications in engineering, artificial intelligence, and robotics. An important biological aspect of these studies is discerning which selection pressures favor the evolution of grouping behavior. In the past decade, researchers have begun using evolutionary computation to study the evolutionary effects of these selection pressures in predator-prey models. The selfish herd hypothesis states that concentrated groups arise because prey selfishly attempt to place their conspecifics between themselves and the predator, thus causing an endless cycle of movement toward the center of the group. Using an evolutionary model of a predator-prey system, we show that how predators attack is critical to the evolution of the selfish herd. Following this discovery, we show that density-dependent predation provides an abstraction of Hamilton's original formulation of ``domains of danger.'' Finally, we verify that density-dependent predation provides a sufficient selective advantage for prey to evolve the selfish herd in response to predation by coevolving predators. Thus, our work corroborates Hamilton's selfish herd hypothesis in a digital evolutionary model, refines the assumptions of the selfish herd hypothesis, and generalizes the domain of danger concept to density-dependent predation.Comment: 25 pages, 11 figures, 5 tables, including 2 Supplementary Figures. Version to appear in "Artificial Life

Sensing Enhancement on Social Networks: The Role of Network Topology

Sensing and processing information from dynamically changing environments is essential for the survival of animal collectives and the functioning of human society. In this context, previous work has shown that communication between networked agents with some preference towards adopting the majority opinion can enhance the quality of error-prone individual sensing from dynamic environments. In this paper, we compare the potential of different types of complex networks for such sensing enhancement. Numerical simulations on complex networks are complemented by a mean-field approach for limited connectivity that captures essential trends in dependencies. Our results show that whilst bestowing advantages on a small group of agents, degree heterogeneity tends to impede overall sensing enhancement. In contrast, clustering and spatial structure play a more nuanced role depending on overall connectivity. We find that ring graphs exhibit superior enhancement for large connectivity and random graphs outperform for small connectivity. Further exploring the role of clustering and path lengths in small world models, we find that sensing enhancement tends to be boosted in the small-world regime

Collective predator evasion: Putting the criticality hypothesis to the test

According to the criticality hypothesis, collective biological systems should operate in a special parameter region, close to so-called critical points, where the collective behavior undergoes a qualitative change between different dynamical regimes. Critical systems exhibit unique properties, which may benefit collective information processing such as maximal responsiveness to external stimuli. Besides neuronal and gene-regulatory networks, recent empirical data suggests that also animal collectives may be examples of self-organized critical systems. However, open questions about self-organization mechanisms in animal groups remain: Evolutionary adaptation towards a group-level optimum (group-level selection), implicitly assumed in the "criticality hypothesis", appears in general not reasonable for fission-fusion groups composed of non-related individuals. Furthermore, previous theoretical work relies on non-spatial models, which ignore potentially important self-organization and spatial sorting effects. Using a generic, spatially-explicit model of schooling prey being attacked by a predator, we show first that schools operating at criticality perform best. However, this is not due to optimal response of the prey to the predator, as suggested by the "criticality hypothesis", but rather due to the spatial structure of the prey school at criticality. Secondly, by investigating individual-level evolution, we show that strong spatial self-sorting effects at the critical point lead to strong selection gradients, and make it an evolutionary unstable state. Our results demonstrate the decisive role of spatio-temporal phenomena in collective behavior, and that individual-level selection is in general not a viable mechanism for self-tuning of unrelated animal groups towards criticality

Ecological interactions between Antarctic krill (Euphausia superba) and baleen whales in the South Sandwich Islands region – Exploring predator-prey biomass ratios

Following the cessation of whaling, the southwest Atlantic humpback whale (Megaptera novaeangliae) population is thought to be close to pre-exploitation size, reversing 20th century changes in abundance. Using a model-based approach applied to concurrently collected data on baleen whale abundance and Antarctic krill (Euphausia superba) biomass in the South Sandwich Islands (SSI) region, we explore ecological interactions between these taxa. Krill biomass and baleen whale density were highest to the north and northeast of the SSI, where the Antarctic Circumpolar Current (ACC) is deflected around the island chain. Humpback whale density was elevated at locations of krill biomass density >150 gm-2. Krill consumption by baleen whales was estimated at 19–29% of the available krill standing stock. We used historic whaling data to confirm the plausibility of these consumption rates and found evidence of rapid weight gain in humpback whales, such that blubber depleted during the breeding season could be restored in a much shorter period than previously assumed. Little is known about krill replenishment rates in the flow of the ACC, or about niche separation between recovering baleen whale populations; both factors may affect species carrying capacities and further monitoring will be required to inform the management of human activities in the region

Simulating predator attacks on fish schools: Evolving composite tactics with variable energies

We used a computer model containing a flock of fish and a predator to evaluate the effect of energy on predator speed and success of various attack tactics. We also looked into predator confusability and tried to determine whether fish form flocks as a form of a defense mechanism. Results were obtained using a genetic algorithm. We simulated natural evolution of predators and prey, and came to conclusion that the best attack tactic is highly dependant on predator's method of confusability. Moreover, we showed flocking increases the chance of individual survival in certain attack tactics, while it decreases the chance of individual survival in others