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To successfully navigate dynamic social environments, animals must manage their relationships by deciding who to interact with, how often, and when. Relationships may develop between familiar group members, but novel relationships can also form as strangers join groups. The process through which relationships form among strangers is not well-known for most species. We used a captive population of monk parakeets (Myiopsitta monachus) with known familiar and stranger relationships to test how novel relationships form among strangers. We established a novel social group by combining 22 parakeets captured from four geographically distinct locations. We quantified how familiar relationships differed from stranger relationships and whether we could detect convergence of the patterning and timing of spatial and affiliative associations across three contexts: general spatial proximity, nearest neighbor identity, and affiliative interactions. We found that familiar networks were consistently more well-connected during the experiment, but relationships did form between former stranger birds across all three behavioral contexts. Spatial proximity associations formed readily for both familiar and stranger birds, while nearest neighbor and affiliative interactions occurred more quickly among familiar dyads, indicating that the rate and patterning of how these relationships were formed differed. We found that the birds consistently preferred familiars across all behavioral contexts with no clear patterns of convergence between familiars and strangers, but the degree of preference depended on the social context. Overall, these findings suggest that parakeets recognize and differentiate between birds they might have encountered previously and that relationships with familiar and stranger conspecifics are not interchangeable. 

Initiating and developing social relationships with strangers can provide fitness benefits, but it is an inherently risky process. To mitigate potential risks and develop trust, strangers may ‘test the waters’ by gradually escalating the type of social investment from low-cost to high-cost. Opportunities to capture the moment animals first encounter one another in the wild are rare, and detailed quantitative assessments of when and how animals initiate relationships are limited. We introduced four unfamiliar groups of feral monk parakeets together into a single 22-bird group and observed the sequence of social behaviours that occurred as relationships developed over 22 days. We tested the effect of relationship status (stranger versus familiar) on the probability of dyads following predicted sequences and whether strangers who progressed their relationships maintained higher rates of no-contact proximity compared with dyads that did not. We found that stranger dyads, but not familiar dyads, were more likely to (i) approach each other without contact before making contact and (ii) follow predicted sequences of affiliative behaviours. Strangers that progressed to contact also had higher rates of associations than did birds that never made contact. These findings provide support for ‘Testing the Waters’ during new relationship formation in a socially and cognitively complex species. 

Recent studies in group-living species suggest that being a valuable group member (a source of information or other resources) should increase social connectedness. This is because individuals may recognize and associate more with valuable individuals to increase the chances of benefiting from their activity, a process we refer to here as adaptive social plasticity. However, it is still unclear what minimum cognitive abilities are required for animals to alter their social interactions based on the value provided by different group members. We varied the cognitive skills of individuals in an agent-based model and evaluated changes in how access to a food resource impacts an informed agent's social connectedness. We modeled a social foraging scenario in an arena with one food patch, which only one informed individual (i.e., producer) can make accessible. Agents’ movement decisions were driven by three cognitive-based parameters: attention (probability of perceiving successful foragers), preference (probability of following successful foragers), and memory (number of time steps a successful forager was remembered). To understand what combination of these parameters may facilitate adaptive social plasticity, we compared the producer's strength (number of interactions) in a proximity network and the foraging success of non-producers between simulations with different combinations of parameter values. We found that non-zero values of each of our parameters are necessary for increases in producer strength and non-producer foraging success to occur. The largest increases in producer strength were seen at intermediate memory values and high values of attention and preference. Unless foragers were programmed to be able to move directly to the food patch when it was accessible to them, a non-zero value of memory was needed for them to experience an increase in foraging success. Furthermore, relationships between attention, memory, and foraging success were influenced by preference values, with the highest foraging success achieved at low to intermediate values of preference. Our results highlight the necessity of certain cognitive skills for animals to take advantage of the foraging success of their group mates, and scenarios in which rigid following behavior may lead to less beneficial results for foragers. This model lays the groundwork for further investigations into the cognitive and environmental factors expected to influence a feedback process between social connections and the value provided and received by group members. 

Abstract In many group‐living animals, survival and reproductive success depend on the formation of long‐term social bonds, yet it remains largely unclear why particular pairs of groupmates form social bonds and not others. Can social bond formation be reliably predicted from each individual's immediately observable traits and behaviors at first encounter? Or is social bond formation hard to predict due to the impacts of shifting social preferences on social network dynamics? To begin to address these questions, we asked how well long‐term cooperative relationships among vampire bats were predicted by how they interacted during their first encounter as introduced strangers. In Study 1, we found that the first 6 h of observed interactions among unfamiliar bats co‐housed in small cages did not clearly predict the formation of allogrooming or food‐sharing relationships over the next 10 months. In Study 2, we found that biologger‐tracked first contacts during the first 4–24 h together in a flight cage did not strongly predict allogrooming rates over the next 4 months. These results corroborate past evidence that social bonding in vampire bats is not reducible to the individual traits or behaviors observed at first encounter. Put simply, first impressions are overshadowed by future social interactions. 

There is increasing awareness that data science and computational thinking are critical skills for undergraduates to develop but these can be difficult to integrate into undergraduate Biology classes. In this module, we describe how we have used a system for learning the programming language R that focuses on building students? skills and confidence in data exploration, management, and visualization. This activity pairs a hands-on virtual experiment where students simulate animal movements and social interactions to provide a friendly introduction to basic data science for biologists. During the activity, students play the ?Bat Game?, an online game which students access via an internet browser. Each student controls the movement decisions of one bat within a social group. The bats must search for cows they can bite to get a meal of blood. Students take the roles of bats in a series of foraging tasks. Students must follow ?rules? and attempt to match their overall actions to those of their group members under different scenarios. The game platform collects all the locations of all bats in the game. After playing the game, students export the data they just created and analyze it to learn how to detect known patterns through basic summaries and plotting in R. All analyses and programming skills are presented in one cohesive R Markdown file, where students can read about the goals of each coding chunk, can run each chunk, and then answer questions about the biology of the social system as well as basic questions about the code used in the analyses. This approach decouples coding from statistics, assumes no prior knowledge, and uses a charismatic species to incentivize student participation. This module can be used in many courses including lab sections of large-enrollment introductory biology courses as well as smaller upper-level courses 

Social structure can emerge fromhierarchically embedded scales of movement, where movement at one scale is constrained within a larger scale (e.g. among branches, trees, forests). In most studies of animal social networks, some scales of movement are not observed, and the relative importance of the observed scales of movement is unclear. Here, we asked: how does individual variation in movement, at multiple nested spatial scales, influence each individual's social connectedness? Using existing data from common vampire bats (Desmodus rotundus), we created an agent-based model of how three nested scales of movement—among roosts, clusters and grooming partners—each influence a bat's grooming network centrality. In each of 10 simulations, virtual bats lacking social and spatial preferences moved at each scale at empirically derived rates that were either fixed or individually variable and either independent or correlated across scales. We found that numbers of partners groomed per bat were driven more by within-roost movements than by roost switching, highlighting that co-roosting networks do not fully capture bat social structure. Simulations revealed how individual variation in movement at nested spatial scales can cause false discovery and misidentification of preferred social relationships. Our model provides several insights into how nonsocial factors shape social networks. 

Sexual selection by mate choice is a powerful force that can lead to evolutionary change, and models of why females choose particular mates are central to understanding its effects. Predominant mate choice theories assume preferences are determined solely by genetic inheritance, an assumption still lacking widespread support. Moreover, preferences often vary among individuals or populations, fail to correspond with conspicuous male traits, or change with context, patterns not predicted by dominant models. Here, we propose a new model that explains this mate choice complexity with one general hypothesized mechanism, “Inferred Attractiveness.” In this model, females acquire mating preferences by observing others’ choices and use context-dependent information to infer which traits are attractive. They learn to prefer the feature of a chosen male that most distinguishes him from other available males. Over generations, this process produces repeated population-level switches in preference and maintains male trait variation. When viability selection is strong, Inferred Attractiveness produces population-wide adaptive preferences superficially resembling “good genes.” However, it results in widespread preference variation or nonadaptive preferences under other predictable circumstances. By casting the female brain as the central selective agent, Inferred Attractiveness captures novel and dynamic aspects of sexual selection and reconciles inconsistencies between mate choice theory and observed behavior. 

Animals can actively encode different types of identity information in learned communication signals, such as group membership or individual identity. The social environments in which animals interact may favor different types of information, but whether identity information conveyed in learned signals is robust or responsive to social disruption over short evolutionary timescales is not well understood. We inferred the type of identity information that was most salient in vocal signals by combining computational tools, including supervised machine learning, with a conceptual framework of “hierarchical mapping”, or patterns of relative acoustic convergence across social scales. We used populations of a vocal learning species as a natural experiment to test whether the type of identity information emphasized in learned vocalizations changed in populations that experienced the social disruption of introduction into new parts of the world. We compared the social scales with the most salient identity information among native and introduced range monk parakeet (Myiopsitta monachus) calls recorded in Uruguay and the United States, respectively. We also evaluated whether the identity information emphasized in introduced range calls changed over time. To place our findings in an evolutionary context, we compared our results with another parrot species that exhibits well-established and distinctive regional vocal dialects that are consistent with signaling group identity. We found that both native and introduced range monk parakeet calls displayed the strongest convergence at the individual scale and minimal convergence within sites. We did not identify changes in the strength of acoustic convergence within sites over time in the introduced range calls. These results indicate that the individual identity information in learned vocalizations did not change over short evolutionary timescales in populations that experienced the social disruption of introduction. Our findings point to exciting new research directions about the robustness or responsiveness of communication systems over different evolutionary timescales.