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1. Spatial learning overshadows learning novel odors and sounds in both predatory and frugivorous bats

   https://doi.org/10.1093/beheco/arad001  

   Dixon, Marjorie May; Carter, Gerald G.; Ryan, Michael J.; Page, Rachel A.; Snell-Rood, ed., Emilie (February 2023, Behavioral Ecology)

   Abstract To forage efficiently, animals should selectively attend to and remember the cues of food that best predict future meals. One hypothesis is that animals with different foraging strategies should vary in their reliance on spatial versus feature cues. Specifically, animals that store food in dispersed caches or that feed on spatially stable food, such as fruits or flowers, should be relatively biased towards learning a meal’s location, whereas predators that hunt mobile prey should instead be relatively biased towards learning feature cues such as odor or sound. Several authors have predicted that nectar-feeding and fruit-feeding bats would rely relatively more on spatial cues, whereas closely related predatory bats would rely more on feature cues, yet no experiment has compared these two foraging strategies under the same conditions. To test this hypothesis, we compared learning in the frugivorous bat, Artibeus jamaicensis, and the predatory bat, Lophostoma silvicolum, which hunts katydids using acoustic cues. We trained bats to find food paired with a unique and novel odor, sound, and location. To assess which cues each bat had learned, we then dissociated these cues to create conflicting information. Rather than finding that the frugivore and predator clearly differ in their relative reliance on spatial versus feature cues, we found that both species used spatial cues over sounds or odors in subsequent foraging decisions. We interpret these results alongside past findings on how foraging animals use spatial cues versus feature cues, and explore why spatial cues may be fundamentally more rich, salient, or memorable. 

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2. Sensory perception plays a larger role in foraging efficiency than does heavy-tailed movement strategies.

   https://doi.org/doi:10.1016/j.ecolmodel.2019.02.015  

   LaScala-Gruenewald, DE; Mehta, RS; Denny, MW (January 2019, Ecological modelling)

   Animals must balance their rates of energetic intake and expenditure while foraging. Several mathematical models have been put forward as energetically optimal foraging strategies when the food environment is sparse (i.e., the distance between food patches in the environment is much larger than the distance from which the forager can perceive food). In particular, Lévy walks with a power law exponent approaching 1 are considered optimal for destructive foragers. However, these models have yet to explore the role of sensory perception in foraging success as the distance between food patches approaches the distance from which the forager can perceive food. Here, we used an agent-based modeling approach to address this question. Our results concur that lower values of the power law exponent (i.e. values approaching 1) result in the most food found, but in contrast to previous studies, we note that, in many cases, lower exponents are not optimal when we consider food found per unit distance traveled. For example, higher values of the exponent resulted in comparable or higher foraging success relative to lower values when the forager's range of sensory perception was restricted to an angle±30° from its current heading. In addition, we find that sensory perception has a larger effect on foraging success than the power law exponent. These results suggest that a deeper examination of how animals perceive food sources from a distance may affect longstanding assumptions regarding the optimality of Lévy walk foraging patterns, and lend support to the developing theoretical shift towards models that place increasing emphasis on how organisms interact with their environments. 

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3. Consistent long-distance foraging flights across years and seasons at colony level in a neotropical bat

   https://doi.org/10.1098/rsbl.2024.0424  

   Calderón-Capote, María C; van_Toor, Mariëlle L; O’Mara, M Teague; Bayer, Travis D; Crofoot, Margaret C; Dechmann, Dina_K N (December 2024, Biology Letters)

   All foraging animals face a trade-off: how much time should they invest in exploitation of known resources versus exploration to discover new resources? For group-living central place foragers, this balance is challenging. Due to the nature of their movement patterns, exploration and exploitation are often mutually exclusive, while the availability of social information may discourage individuals from exploring. To examine these trade-offs, we GPS-tracked groups of greater spear-nosed bats (Phyllostomus hastatus) from three colonies on Isla Colón, Panamá. During the dry season, when these omnivores forage on the nectar of unpredictable balsa flowers, bats consistently travelled long distances to remote, colony-specific foraging areas, bypassing flowering trees closer to their roosts. They continued using these areas in the wet season, when feeding on a diverse, presumably ubiquitous diet, but also visited other, similarly distant foraging areas. Foraging areas were shared within but not always between colonies. Our longitudinal dataset suggests that bats from each colony invest in long-distance commutes to socially learned shared foraging areas, bypassing other available food patches. Rather than exploring nearby resources, these bats exploit colony-specific foraging locations that appear to be culturally transmitted. These results give insight into how social animals might diverge from optimal foraging. 

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4. Adaptive collective responses to local stimuli in anonymous dynamic networks

   Oh, Shunhao; Randall, Dana; Richa, Andrea (January 2025, Theoretical Computer Science)

   Doty, David; Spirakis, Paul (Ed.)

   We develop a framework for self-induced phase changes in programmable matter in which a collection of agents with limited computational and communication capabilities can collectively perform appropriate global tasks in response to local stimuli that dynamically appear and disappear. Agents are represented by vertices in a dynamic graph whose edge set changes over time, and stimuli are placed adversarially on the vertices of where each agent is only capable of recognizing a co-located stimulus. Agents communicate via token passing along edges to alert other agents to transition to an Aware state when stimuli are present and an Unaware state when the stimuli disappear. We present an Adaptive Stimuli Algorithm that can handle arbitrary adversarial stimulus dynamics, while an adversary (or the agents themselves) reconfigures the connections (edges) of over time in a controlled way. This algorithm can be used to solve the foraging problem on reconfigurable graphs where, in addition to food sources (stimuli) being discovered, removed, or shifted arbitrarily, we would like the agents to consistently self-organize, using only local interactions, such that if the food remains in a position long enough, the agents transition to a gather phase in which many collectively form a single large component with small perimeter around the food. Alternatively, if no food source has existed recently, the agents should undergo a self-induced collective phase change and switch to a search phase in which they distribute themselves randomly throughout the graph to search for food. Unlike previous approaches to foraging, this process is indefinitely repeatable, withstanding competing broadcast waves of state transition that may interfere with each other. Like a physical phase change, such as the ferromagnetic models underlying the gather and search algorithms used for foraging, microscopic changes in the environment trigger these macroscopic, system-wide transitions as agents share information and respond locally to get the desired collective response. 

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5. Shield Decentralization for Safe Multi-Agent Reinforcement Learning

   Melcer, Daniel; Amato, Christopher; Tripakis, Stavros (January 2022, Advances in neural information processing systems)

   Learning safe solutions is an important but challenging problem in multi-agent reinforcement learning (MARL). Shielded reinforcement learning is one approach for preventing agents from choosing unsafe actions. Current shielded reinforcement learning methods for MARL make strong assumptions about communication and full observability. In this work, we extend the formalization of the shielded reinforcement learning problem to a decentralized multi-agent setting. We then present an algorithm for decomposition of a centralized shield, allowing shields to be used in such decentralized, communication-free environments. Our results show that agents equipped with decentralized shields perform comparably to agents with centralized shields in several tasks, allowing shielding to be used in environments with decentralized training and execution for the first time. 

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