Population and Evolutionary Dynamics based on Predator-Prey Relationships in a 3D Physical Simulation

Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics

Prey-predator interactions play a pivotal role in elucidating the evolution and adaptation of various organism's traits. Numerous approaches have been employed to study the dynamics of prey-predator interaction systems, with agent-based methodologies gaining popularity. However, existing agent-based models are limited in their ability to handle multi-modal interactions, which are believed to be crucial for understanding living organisms. Conversely, prevailing prey-predator integration studies often rely on mathematical models and computer simulations, neglecting real-world constraints and noise. These elusive attributes, challenging to model, can lead to emergent behaviors and embodied intelligence. To bridge these gaps, our study designs and implements a prey-predator interaction scenario that incorporates visual and olfactory sensory cues not only in computer simulations but also in a real multi-robot system. Observed emergent spatial-temporal dynamics demonstrate successful transitioning of investigating prey-predator interactions from virtual simulations to the tangible world. It highlights the potential of multi-robotics approaches for studying prey-predator interactions and lays the groundwork for future investigations involving multi-modal sensory processing while considering real-world constraints.

Metapopulation model for a prey-predator system: Nonlinear migration due to the finite capacities of patches

Many species live in spatially separated patches, and individuals can migrate between patches through paths. In real ecosystems, the capacities of patches are finite. If a patch is already occupied by the individuals of some species, then the migration into the patch is impossible. In the present paper, we deal with prey-predator system composed of two patches. Each patch contains a limited number of cells, where the cell is either empty or occupied by an individual of prey or predator. We introduce "swapping migration" defined by the exchange between occupied and empty cells. An individual can migrate, only when there are empty cells in the destination patch. Reaction-migration equations in prey-predator system are presented, where the migration term forms nonlinear function of densities. We numerically solve equilibrium densities, and find that the population dynamics are largely affected by nonlinear migration. Not only extinction points but also the responses to the environmental changes crucially depend on the patch capacities.

Deep-Reinforcement Learning-Based Co-Evolution in a Predator-Prey System

Understanding or estimating the co-evolution processes is critical in ecology, but very challenging. Traditional methods are difficult to deal with the complex processes of evolution and to predict their consequences on nature. In this paper, we use the deep-reinforcement learning algorithms to endow the organism with learning ability, and simulate their evolution process by using the Monte Carlo simulation algorithm in a large-scale ecosystem. The combination of the two algorithms allows organisms to use experiences to determine their behavior through interaction with that environment, and to pass on experience to their offspring. Our research showed that the predators' reinforcement learning ability contributed to the stability of the ecosystem and helped predators obtain a more reasonable behavior pattern of coexistence with its prey. The reinforcement learning effect of prey on its own population was not as good as that of predators and increased the risk of extinction of predators. The inconsistent learning periods and speed of prey and predators aggravated that risk. The co-evolution of the two species had resulted in fewer numbers of their populations due to their potentially antagonistic evolutionary networks. If the learnable predators and prey invade an ecosystem at the same time, prey had an advantage. Thus, the proposed model illustrates the influence of learning mechanism on a predator-prey ecosystem and demonstrates the feasibility of predicting the behavior evolution in a predator-prey ecosystem using AI approaches.

Effect of directional migration on Lotka-Volterra system with desert

Migration is observed across many species. Several authors have studied ecological migration by applying cellular automaton (CA). In this paper, we present a directional migration model with desert on a one-dimensional lattice where a traffic CA model and a lattice Lotka-Volterra system are connected. Here predators correspond to locomotive animals while prey is immobile plants. Predators migrate between deserts and fertile lands repeatedly. Computer simulations reveal the two types of phase transition: coexistence of both species and prey dominance, which is caused by both benefit and cost of migration. In the coexistence phase, the steady-state density of predators usually increases by migration as long as the desert size is small and their mortality rate is low. In contrast, the prey density increases, even if the desert size becomes large. Such a paradox comes from the indirect effect: predators go extinct by the increase of desert size, so that the plant density can increase. Moreover, we find several self-organized spatial patterns: 1) predators form a stripe pattern; namely swarms. 2) The velocity of predators is high on deserts, but very low on fertile land. 3) Predators give birth only on fertile lands.