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

Influences of Anthropogenic Noise on Flight Initiation Distance, Foraging Behavior, And Feeder Community Structure of Wild Birds

Throughout the world, birds represent the primary type of wildlife that people experience on a daily basis. However, a growing body of evidence suggests that alterations to the acoustic environment can negatively affect birds as well as humans in a variety of ways, and altered acoustics from noise pollution has the potential to influence human interactions with wild birds. In this thesis, I investigated how anthropogenic noise impacts daily behavior as well as community structure of wild birds. In the first component of this thesis, I assessed the distance at which a bird initiates flight or escape behavior (i.e., flight initiation distance or FID) in varying acoustic conditions. I surveyed 12 songbird species from three foraging guilds, ground foragers, canopy gleaners, and hawking flycatchers, and I predicted FIDs to decrease, remain the same, and increase with noise exposure, respectively. Contrary to expectations, the canopy gleaning and flycatching guilds exhibited mixed responses, with some species exhibiting unchanged FIDs with noise while others exhibited increased FIDs with noise. However, FIDs of all ground foraging species and one canopy gleaner decreased with noise levels. In the second component, I examined the feeding of wild birds, an increasingly popular recreational activity throughout North America that promotes increased sense of wellbeing by connecting people with wildlife and nature. I tested how experimental noise influences abundance, species richness, community structure and foraging behavior of songbirds at maintained bird feeders. By measuring activity levels of all species that utilized the feeders exposed to intervals of quiet and noisy conditions, I found noise to be a significant predictor of community turnover. Specifically, noise exposure resulted in increased feeder activity for two species, and decreased activity for one species. I also confirmed previous research conducted in the laboratory indicating white-crowned sparrows decrease their foraging rate under noise conditions, presumably as a trade off with visual vigilance. Considering the interactions of humans and wild birds, the results from my two thesis components indicate that the acoustic environment can play a role in how species of different foraging guilds respond to birdwatchers and what species visit bird feeders

Social Factors Driving Grouping Dynamics in Bighorn Sheep Ewe

Understanding and predicting movement is critical for conservation planning and disease risk mitigation, and important environmental drivers of animal movement have received extensive attention in the ecological literature. Social factors surrounding group fission and fusion events also directly affect movement. However, these events are infrequently measured in the wild and rarely linked to underlying mechanisms such as relatedness, agreement in reproductive status, or shared life stage. While some social factors cannot be directly observed in the field, individual animals congregating in groups and moving about a landscape can. In animal societies, groups may merge together in a fusion event, and a group may split in a fission event. These events, repeating over time within a population, constitute fission-fusion dynamics. Here, we study group structure and how individuals navigate the social environment that structure imposes, using a long-term, individual-level dataset on female bighorn sheep gathered at the Confederated Salish and Kootenai Tribal (CSKT) Bison Range in Montana, USA. In Chapter 2, we describe three dimensions of fission-fusion dynamics that clarify the context that informs an individual’s social environment: group size, composition, and spatial cohesion in this population. In Chapter 3, we relate these group attributes from the previous chapter to social processes and underlying drivers using a discrete choice model. Our analysis opens a door for understanding how social mechanisms precipitate observed space use behaviors, with implications for how we view animal societies in research. We find that general group-level attributes like group size and demographic makeup along with specific, dyadic social attributes like mother and cohort relationships each influence bighorn sheep group configurations at the Bison Range and drive fission and fusion events

Approximating Optimal Behavioural Strategies Down to Rules-of-Thumb: Energy Reserve Changes in Pairs of Social Foragers

Functional explanations of behaviour often propose optimal strategies for organisms to follow. These ‘best’ strategies could be difficult to perform given biological constraints such as neural architecture and physiological constraints. Instead, simple heuristics or ‘rules-of-thumb’ that approximate these optimal strategies may instead be performed. From a modelling perspective, rules-of-thumb are also useful tools for considering how group behaviour is shaped by the behaviours of individuals. Using simple rules-of-thumb reduces the complexity of these models, but care needs to be taken to use rules that are biologically relevant. Here, we investigate the similarity between the outputs of a two-player dynamic foraging game (which generated optimal but complex solutions) and a computational simulation of the behaviours of the two members of a foraging pair, who instead followed a rule-of-thumb approximation of the game's output. The original game generated complex results, and we demonstrate here that the simulations following the much-simplified rules-of-thumb also generate complex results, suggesting that the rule-of-thumb was sufficient to make some of the model outcomes unpredictable. There was some agreement between both modelling techniques, but some differences arose – particularly when pair members were not identical in how they gained and lost energy. We argue that exploring how rules-of-thumb perform in comparison to their optimal counterparts is an important exercise for biologically validating the output of agent-based models of group behaviour

Squirreling Around for Science: Observing Sciurid Rodents to Investigate Animal Behavior

Hands-on research experiences are important opportunities for students to learn about the nature of inquiry and gain confidence in solving problems. Here, we present an inquiry-based lesson plan that investigates the foraging behavior of sciurid rodents (squirrels) in local habitats. Squirrels are an ideal study system for student research projects because many species are diurnal, easy to watch, and inhabit a range of habitats including college campuses. In this activity, instructors identify appropriate field sites and focal species, while students generate questions and brainstorm predictions in small groups regarding factors that might influence behavioral trade-offs in sciurids. Students conduct observational surveys of local squirrels in pairs using a standardized protocol and upload their data to a national database as part of the multi-institutional Squirrel-Net (http://squirrel-net.org). Instructors access the nationwide dataset through the Squirrel-Net website and provide students with data for independent analysis. Students across the country observe and record a range of squirrel species, including behaviors and habitat characteristics. The national dataset can be used to answer student questions about why squirrels behave in the way they do and for students to learn about authentic analyses regarding behavior trade-offs. Additionally, the lesson is designed to be modified across a range of inquiry levels, from a single two-hour laboratory activity to a unit- or semester-long student-driven course-based research experience. Our activity highlights the value of using observational data to conduct research, makes use of the Squirrel-Net infrastructure for collaboration, and provides students equitable access to field-based projects with small mammals

Leaving safety to visit a feeding site:is it optimal to hesitate whilst exposed?

Animals living in complex environments experience differing risks of predation depending upon their location within the landscape. An animal could reduce the risk it experiences by remaining in a refuge site, but it may need to emerge from its refuge and enter more dangerous sites for feeding and other activities. Here, I consider the actions of an animal choosing to travel a short distance between a safe refuge and a dangerous foraging site, such as a bird leaving cover to visit a feeder. Although much work has been conducted examining the choice between a refuge and a foraging site when faced with a trade-off between starvation and predation risk, the work presented here is the first to consider the travel behaviour between these locations. Using state-dependent stochastic dynamic programming, I illustrate that there are several forms of optimal behaviour that can emerge. In some situations, the animal should choose to travel without stopping between sites, but in other cases, it is optimal for the animal to travel hesitantly towards the food, and to stop its travel at a point before it reaches the refuge. I discuss how this hesitant ‘dawdling’ behaviour may be optimal, and suggest further work to test these predictions

Temporal Variation in Predation Risk May Explain Daily Rhythms of Foraging Behavior in an Orb-Weaving Spider

Daily rhythms occur in numerous physiological and behavioral processes across an immense diversity of taxa, but there remain few cases in which mechanistic links between rhythms of trait expression and organismal fitness have been established. We construct a dynamic optimization model to determine whether risk allocation provides an adaptive explanation for the daily foraging rhythm observed in many species using the orb-weaving spider Cyclosa turbinata as a case study. Our model predicts that female C. turbinata should generally start foraging at lower levels of energy reserves (i.e., should be less bold) during midday when predators are most abundant. We also find that individuals’ foraging efficacy determines whether daily rates of encounters with predators or prey more strongly influences boldness under high risk. The qualitative model predictions are robust to variation in our parameter estimates and likely apply to a wide range of taxa. The predictions are also consistent with observed patterns of foraging behavior under both laboratory and field conditions. We discuss the implications of our study for understanding the evolution of daily rhythms and the importance of model predictions for interpreting empirical studies and generating additional hypotheses regarding behavioral evolution