Social information use and collective foraging in a pursuit diving seabird

This is the final version. Available on open access from Public Library of Science via the DOI in this recordData Availability: Data files are available from an online data repository (DOI: 10.6084/m9.figshare.9798491).Individuals of many species utilise social information whilst making decisions. While many studies have examined social information in making large scale decisions, there is increasing interest in the use of fine scale social cues in groups. By examining the use of these cues and how they alter behaviour, we can gain insights into the adaptive value of group behaviours. We investigated the role of social information in choosing when and where to dive in groups of socially foraging European shags. From this we aimed to determine the importance of social information in the formation of these groups. We extracted individuals’ surface trajectories and dive locations from video footage of collective foraging and used computational Bayesian methods to infer how social interactions influence diving. Examination of group spatial structure shows birds form structured aggregations with higher densities of conspecifics directly in front of and behind focal individuals. Analysis of diving behaviour reveals two distinct rates of diving, with birds over twice as likely to dive if a conspecific dived within their visual field in the immediate past. These results suggest that shag group foraging behaviour allows individuals to sense and respond to their environment more effectively by making use of social cues

Assessing rotation-invariant feature classification for automated wildebeest population counts

Accurate and on-demand animal population counts are the holy grail for wildlife conservation organizations throughout the world because they enable fast and responsive adaptive management policies. While the collection of image data from camera traps, satellites, and manned or unmanned aircraft has advanced significantly, the detection and identification of animals within images remains a major bottleneck since counting is primarily conducted by dedicated enumerators or citizen scientists. Recent developments in the field of computer vision suggest a potential resolution to this issue through the use of rotation-invariant object descriptors combined with machine learning algorithms. Here we implement an algorithm to detect and count wildebeest from aerial images collected in the Serengeti National Park in 2009 as part of the biennial wildebeest count. We find that the per image error rates are greater than, but comparable to, two separate human counts. For the total count, the algorithm is more accurate than both manual counts, suggesting that human counters have a tendency to systematically over or under count images. While the accuracy of the algorithm is not yet at an acceptable level for fully automatic counts, our results show this method is a promising avenue for further research and we highlight specific areas where future research should focus in order to develop fast and accurate enumeration of aerial count data. If combined with a bespoke image collection protocol, this approach may yield a fully automated wildebeest count in the near future.CJT is supported by a Complex Systems Scholar Award from the James S. McDonnell Foundation. JGCH is supported by a Lord Kelvin Adam Smith Fellowship, funding from the British Ecological Society and the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641918 AfricanBioServices. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Smart Swarms of Bacteria-Inspired Agents with Performance Adaptable Interactions

Collective navigation and swarming have been studied in animal groups, such as fish schools, bird flocks, bacteria, and slime molds. Computer modeling has shown that collective behavior of simple agents can result from simple interactions between the agents, which include short range repulsion, intermediate range alignment, and long range attraction. Here we study collective navigation of bacteria-inspired smart agents in complex terrains, with adaptive interactions that depend on performance. More specifically, each agent adjusts its interactions with the other agents according to its local environment – by decreasing the peers' influence while navigating in a beneficial direction, and increasing it otherwise. We show that inclusion of such performance dependent adaptable interactions significantly improves the collective swarming performance, leading to highly efficient navigation, especially in complex terrains. Notably, to afford such adaptable interactions, each modeled agent requires only simple computational capabilities with short-term memory, which can easily be implemented in simple swarming robots

Leadership, collective motion and the evolution of migratory strategies

Migration is a hallmark life history strategy of a diverse range of organisms, and also ubiquitous in ontogenic processes including normal embryonic development as well as tumor progression. In such scenarios, individual organisms/cells typically respond to long range (and often noisy) environmental cues. In addition, individuals may interact socially with one another leading to emergent group-level navigational abilities. Although much progress has been made in understanding the mechanisms of taxis, there is a lack of theoretical and quantitative understanding of how individuals trade-off information obtained through their own migratory ability and that via social interactions. Here, we discuss results and insights from a recent computational model developed to investigate the evolution of leadership and collective motion in migratory populations. It is shown that, for a broad range of parameter values, only a small proportion of the population gather directional information while the majority employ social cues alone. More generally, ecological conditions for the evolution of resident, solitary and collective migratory strategies are obtained. We discuss how consideration of both proximate and ultimate factors within the same framework may provide insights into preserving migratory patterns that are in grave danger due to anthropogenic pressures

Human Rights Attitude and Civic Engagement Behavior Among University Students

Although civic engagement and human rights are critical values in social work education, few empirical studies have explored the association between civic engagement and human rights exposure and attitudes. This study aims to examine the relationship between the exposure to human rights information, human rights attitudes, normative beliefs, and civic engagement behaviors among university students. A total of 214 students at a public university in the Midwest of the United States responded to the study survey. Findings indicate that students with more exposure to human rights issues showed more civic engagement. This relationship between human rights exposure and civic engagement was mediated by students’ attitudes toward human rights, but not moderated by normative beliefs. In addition, students majoring in social work revealed higher civic engagement and more positive attitudes toward human rights issues than those in other disciplines