Transport and aggregation of self-propelled particles in fluid flows

An analysis of the dynamics of prolate swimming particles in laminar flow is presented. It is shown that the particles concentrate around flow regions with chaotic trajectories. When the swimming velocity is larger than a threshold, dependent on the aspect ratio of the particles, all particles escape from regular elliptic regions. For thin rodlike particles the threshold velocity vanishes; thus, the arbitrarily small swimming velocity destroys all transport boundaries. We derive an expression for the minimum swimming velocity required for escape based on a circularly symmetric flow approximation of the regular elliptic regions

Phototactic clustering of swimming microorganisms in a turbulent velocity field

We study the distribution of swimming microorganisms advected by a two-dimensional smooth turbulent flow and attracted towards a light source through phototaxis. It is shown that particles aggregate along a dynamical attractor with fractal measure whose dimension depends on the strength of the phototaxis. Using an effective diffusion approximation for the flow, we derive an analytic expression for the increase in light exposure over the aggregate and by extension an accurate prediction for the fractal dimension based on the properties of the advection and the statistics of the attracting field

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’

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

On the evolutionary interplay between dispersal and local adaptation in heterogeneous environments

Journal ArticleCopyright © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.Dispersal, whether in the form of a dandelion seed drifting on the breeze, or a salmon migrating upstream to breed in a nonnatal stream, transports genes between locations. At these locations, local adaptation modifies the gene frequencies so their carriers are better suited to particular conditions, be those of newly disturbed soil or a quiet river pool. Both dispersal and local adaptation are major drivers of population structure; however, in general, their respective roles are not independent and the two may often be at odds with one another evolutionarily, each one exhibiting negative feedback on the evolution of the other. Here, we investigate their joint evolution within a simple, discrete-time, metapopulation model. Depending on environmental conditions, their evolutionary interplay leads to either a monomorphic population of highly dispersing generalists or a collection of rarely dispersing, locally adapted, polymorphic sub-populations, each adapted to a particular habitat type. A critical value of environmental heterogeneity divides these two selection regimes and the nature of the transition between them is determined by the level of kin competition. When kin competition is low, at the transition we observe discontinuities, bistability, and hysteresis in the evolved strategies; however, when high, kin competition moderates the evolutionary feedback and the transition is smooth.Natural Sciences and Engineering Research Council of CanadaYukon FoundationArmy Research Offic

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

A Study on Discrete-Time Movement Models

Understanding animal movement is an important challenge in ecology, with improvement in tagging technology permitting the collection of data on an increasingly wide range of species. Consequently, methodologies for statistical analysis of such data has received considerable attention in recent years. Discrete-time random walks are the foundation of the movement data models. The advantages of the discrete-time movement models are that they are intuitive and easily implemented, however the specification of the discretization step is often problematic and must be done in advance. Misspecification of the discretization step might lead to a model mismatch and thus choosing an appropriate test statistic to capture the model mismatch is essential

Context-dependent interaction leads to emergent search behavior in social aggregates

Locating the source of an advected chemical signal is a common challenge facing many living organisms. When the advecting medium is characterized by either high Reynolds number or high Peclet number the task becomes highly non-trivial due to the generation of heterogenous, dynamically changing filamental concentrations which do not decrease monotonically with distance to the source. Defining search strategies which are effective in these environments has important implications for the understanding of animal behavior and for the design of biologically inspired technology. Here we present a strategy which is able to solve this task without the higher intelligence required to assess spatial gradient direction, measure the diffusive properties of the flow field or perform complex calculations. Instead our method is based on the collective behavior of autonomous individuals following simple social interaction rules which are modified according to the local conditions they are experiencing. Through these context-dependent interactions the group is able to locate the source of a chemical signal and in doing so displays an awareness of the environment not present at the individual level. Our model demonstrates the ability of decentralized information processing systems to solve real world problems and also illustrates an alternative pathway to the evolution of higher cognitive capacity via the emergent, group level intelligence which can result from local interactions.Comment: 3 figure

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