Inferring the rules of social interaction in migrating caribou

Social interactions are a significant factor that influence the decision-making of species ranging from humans to bacteria. In the context of animal migration, social interactions may lead to improved decision-making, greater ability to respond to environmental cues, and the cultural transmission of optimal routes. Despite their significance, the precise nature of social interactions in migrating species remains largely unknown. Here we deploy unmanned aerial systems to collect aerial footage of caribou as they undertake their migration from Victoria Island to mainland Canada. Through a Bayesian analysis of trajectories we reveal the fine-scale interaction rules of migrating caribou and show they are attracted to one another and copy directional choices of neighbours, but do not interact through clearly defined metric or topological interaction ranges. By explicitly considering the role of social information on movement decisions we construct a map of near neighbour influence that quantifies the nature of information flow in these herds. These results will inform more realistic, mechanism-based models of migration in caribou and other social ungulates, leading to better predictions of spatial use patterns and responses to changing environmental conditions. Moreover, we anticipate that the protocol we developed here will be broadly applicable to study social behaviour in a wide range of migratory and non-migratory taxa. This article is part of the theme issue ‘Collective movement ecology’

From single steps to mass migration: the problem of scale in the movement ecology of the Serengeti wildebeest

A central question in ecology is how to link processes that occur over different scales. The daily interactions of individual organisms ultimately determine community dynamics, population fluctuations and the functioning of entire ecosystems. Observations of these multiscale ecological processes are constrained by various technological, biological or logistical issues, and there are often vast discrepancies between the scale at which observation is possible and the scale of the question of interest. Animal movement is characterized by processes that act over multiple spatial and temporal scales. Second-by-second decisions accumulate to produce annual movement patterns. Individuals influence, and are influenced by, collective movement decisions, which then govern the spatial distribution of populations and the connectivity of meta-populations. While the field of movement ecology is experiencing unprecedented growth in the availability of movement data, there remain challenges in integrating observations with questions of ecological interest. In this article, we present the major challenges of addressing these issues within the context of the Serengeti wildebeest migration, a keystone ecological phenomena that crosses multiple scales of space, time and biological complexity. This article is part of the theme issue ’Collective movement ecology’

High-predation habitats affect the social dynamics of collective exploration in a shoaling fish

Collective decisions play a major role in the benefits that animals gain from living in groups. Although the mechanisms of how groups collectively make decisions have been extensively researched, the response of within-group dynamics to ecological conditions is virtually unknown, despite adaptation to the environment being a cornerstone in biology. We investigate how within-group interactions during exploration of a novel environment are shaped by predation, a major influence on the behavior of prey species. We tested guppies (Poecilia reticulata) from rivers varying in predation risk under controlled laboratory conditions and find the first evidence of differences in group interactions between animals adapted to different levels of predation. Fish from high-predation habitats showed the strongest negative relationship between initiating movements and following others, which resulted in less variability in the total number of movements made between individuals. This relationship between initiating movements and following others was associated with differentiation into initiators and followers, which was only observed in fish from high-predation rivers. The differentiation occurred rapidly, as trials lasted 5 min, and was related to shoal cohesion, where more diverse groups from high-predation habitats were more cohesive. Our results show that even within a single species over a small geographical range, decision-making in a social context can vary with local ecological factors

PENGARUH TINGKAT PENDIDIKAN DAN PEKERJAAN TERHADAP ORAL HYGIENE PADA IBU HAMIL DI RSUD MEURAXA BANDA ACEH

ABSTRAKNama: Muhammad AdriansyahProgram Studi: Kedokteran GigiJudul:Pengaruh Tingkat Pendidikan dan Pekerjaan Terhadap Oral Hygiene pada Ibu Hamil di RSUD Meuraxa Banda AcehPendidikan adalah faktor sosial ekonomi yang mempengaruhi status kesehatan. Pekerjaan dihubungkan dengan tingkat pendidikan dan penghasilan, dimana pekerjaan membutuhkan latar belakang pendidikan yang tinggi, dan penghasilan dimana seseorang mempunyai penghasilan lebih besar maka akan mampu memenuhi kebutuhan dalam menjaga kesehatan gigi dan mulut. Selama masa kehamilan, wanita mengalami beberapa perubahan fisiologis yang menyebabkan terjadinya perubahan hormonal. Perubahan fisiologis juga berdampak pada perubahan menjaga kesehatan gigi dan mulut, sehingga wanita hamil lebih rentan terkena masalah gigi dan mulut. Tujuan penelitian ini adalah untuk mengetahui pengaruh tingkat pendidikan dan pekerjaan terhadap oral hygiene pada ibu hamil di RSUD Meuraxa Banda Aceh. Jenis penelitian ini bersifat analitik dengan metode cross sectional untuk melihat hubungan antar dua variabel. Penelitian ini melibatkan 48 subjek yang memenuhi kriteria inklusi. Subjek penelitian mengisi kuisioner yang diberikan serta diperiksa tingkat kebersihan rongga mulutnya. Data dianalisis dengan SPSS menggunakan Korelasi Spearman. Hasil uji menunjukkan ada pengaruh yang signifikan antara tingkat pendidikan (

Modelling multiscale collective behavior with Gaussian processes

Collective behavior is characterized by the emergence of large-scale phenomena from local interactions. It is found in many contexts, including political movements, fads and fashions, and animal grouping. In this paper, we aim to elucidate the mechanisms that underlie observed collective behavior by developing a novel mathematical framework based on equation-free modelling procedures and Gaussian process regression. This allows us to circumvent the possible lack of formal mathematical links between scales and instead use statistical emulation to learn an empirical Fokker-Planck equation. Our approach advances our ability to understand how complex systems function at both the individual and collective level when a formal mathematical description of macroscale dynamics is unavailable

Approximate Bayesian inference for individual-based models with emergent dynamics

Individual-based models are used in a variety of scientific domains to study systems composed of multiple agents that interact with one another and lead to complex emergent dynamics at the macroscale. A standard approach in the analysis of these systems is to specify the microscale interaction rules in a simulation model, run simulations, and then qualitatively compare outputs to empirical observations. Recently, more robust methods for inference for these types of models have been introduced, notably approximate Bayesian computation, however major challenges remain due to the computational cost of simulations and the nonlinear nature of many complex systems. Here, we compare two methods of approximate inference in a classic individual-based model of group dynamics with well-studied nonlinear macroscale behaviour; we employ a Gaussian process accelerated ABC method with an approximated likelihood and with a synthetic likelihood. We compare the accuracy of results when re-inferring parameters using a measure of macro-scale disorder (the order parameter) as a summary statistic. Our findings reveal that for a canonical simple model of animal collective movement, parameter inference is accurate and computationally efficient, even when the model is poised at the critical transition between order and disorder

Social information use and collective foraging in a pursuit diving seabird

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

A hierarchical machine learning framework for the analysis of large scale animal movement data

Background: In recent years the field of movement ecology has been revolutionized by our ability to collect high-accuracy, fine scale telemetry data from individual animals and groups. This growth in our data collection capacity has led to the development of statistical techniques that integrate telemetry data with random walk models to infer key parameters of the movement dynamics. While much progress has been made in the use of these models, several challenges remain. Notably robust and scalable methods are required for quantifying parameter uncertainty, coping with intermittent location fixes, and analysing the very large volumes of data being generated. Methods: In this work we implement a novel approach to movement modelling through the use of multilevel Gaussian processes. The hierarchical structure of the method enables the inference of continuous latent behavioural states underlying movement processes. For efficient inference on large data sets, we approximate the full likelihood using trajectory segmentation and sample from posterior distributions using gradient-based Markov chain Monte Carlo methods. Results: While formally equivalent to many continuous-time movement models, our Gaussian process approach provides flexible, powerful models that can detect multiscale patterns and trends in movement trajectory data. We illustrate a further advantage to our approach in that inference can be performed using highly efficient, GPU-accelerated machine learning libraries. Conclusions: Multilevel Gaussian process models offer efficient inference for large-volume movement data sets, along with the fitting of complex flexible models. Applications of this approach include inferring the mean location of a migration route and quantifying significant changes, detecting diurnal activity patterns, or identifying the onset of directed persistent movements

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

The Social Context of Cannibalism in Migratory Bands of the Mormon Cricket

Cannibalism has been shown to be important to the collective motion of mass migratory bands of insects, such as locusts and Mormon crickets. These mobile groups consist of millions of individuals and are highly destructive to vegetation. Individuals move in response to attacks from approaching conspecifics and bite those ahead, resulting in further movement and encounters with others. Despite the importance of cannibalism, the way in which individuals make attack decisions and how the social context affects these cannibalistic interactions is unknown. This can be understood by examining the decisions made by individuals in response to others. We performed a field investigation which shows that adult Mormon crickets were more likely to approach and attack a stationary cricket that was side-on to the flow than either head- or abdomen-on, suggesting that individuals could reduce their risk of an attack by aligning with neighbours. We found strong social effects on cannibalistic behaviour: encounters lasted longer, were more likely to result in an attack, and attacks were more likely to be successful if other individuals were present around a stationary individual. This local aggregation appears to be driven by positive feedback whereby the presence of individuals attracts others, which can lead to further crowding. This work improves our understanding of the local social dynamics driving migratory band formation, maintenance and movement at the population level