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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. 

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. 

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 

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. 

Abstract New graduate students in biology programs may lack the quantitative skills necessary for their research and professional careers. The acquisition of these skills may be impeded by teaching and mentoring experiences that decrease rather than increase students’ beliefs in their ability to learn and apply quantitative approaches. In this opinion piece, we argue that revising instructional experiences to ensure that both student confidence and quantitative skills are enhanced may improve both educational outcomes and professional success. A few studies suggest that explicitly addressing productive failure in an instructional setting and ensuring effective mentoring may be the most effective routes to simultaneously increasing both quantitative self-efficacy and quantitative skills. However, there is little work that specifically addresses graduate student needs, and more research is required to reach evidence-backed conclusions. 

Animal care is a critical component underlying successful behavioral and cognition research. Technological solutions for documentation and verification of care can aid in monitoring that activities are completed according to standard operating procedures and ensure that no individuals are overlooked. Here, I summarize a low-cost, flexible, and easy to use system that I developed to document and monitor care of animals for our research group. The system enables real-time and remote-enabled verification that critical daily tasks have been completed for every cage and helps us monitor our longer-term tasks to make sure that our care team is adhering to our set schedule. The main materials and components needed to implement this system are QR codes, a thermal laminator, a QR scanner, a computer to manage data input, and a database into which the data are scanned and summarized. There are six steps to setting up our system: (1) purchase a QR scanner, (2) generate and print QR codes, (3) set up the central hub for data input, (4) input data from QR scans, (5) filter and collate the raw data, and (6) summarize filtered data to verify and track care. Paired with simple scripts in a cloud-based spreadsheet, scanned QR code data can then be easily summarized in real time to provide verification of care. The flexibility of the system allows it to be customized to a large range of species. 

Human-wildlife interactions continue to increase due to anthropogenic disturbances, with some interactions resulting in conflict. Leveraging a taxa’s bias for a particular sensory cue is a promising management avenue for reducing the potential and realized negative consequences of human-wildlife conflict. For instance, many avian species heavily depend on acoustic communication, and acoustic cues can provide opportunities to reduce conflict with various avian species. The monk parakeet (Myiopsitta monachus) is a gregarious parrot native to South America that has established populations worldwide and is considered an urban and agricultural pest in parts of its native and introduced ranges. We conducted playback experiments with a captive population of monk parakeets to evaluate auditory cues that may be useful for designing management protocols. Our experiment evaluated the efficacy of two stimuli: predator vocalizations as potential repulsion and conspecific vocalizations as potential attraction stimuli for parakeets. We measured two responses: (1) categorical group-level behavioral responses and (2) time to cease vigilance and return to behavior prior to playback. In the repulsion playbacks, monk parakeets were repelled by predator vocalizations in 80% of trials and took longer to cease vigilance and return to baseline behavior compared to attraction playbacks. In the attraction playbacks, monk parakeets exhibited vigilant behavior and weak or no attraction to the stimulus, with attraction only being observed in 10% of trials. Our results demonstrate that predator playbacks may be particularly useful for completing management objectives, such as temporary removal from a location.