Group navigation and the "many-wrongs principle" in models of animal movement.

Journal ArticleResearch Support, Non-U.S. Gov'tTraditional studies of animal navigation over both long and short distances have usually considered the orientation ability of the individual only, without reference to the implications of group membership. However, recent work has suggested that being in a group can significantly improve the ability of an individual to align toward and reach a target direction or point, even when all group members have limited navigational ability and there are no leaders. This effect is known as the "many-wrongs principle" since the large number of individual navigational errors across the group are suppressed by interactions and group cohesion. In this paper, we simulate the many-wrongs principle using a simple individual-based model of movement based on a biased random walk that includes group interactions. We study the ability of the group as a whole to reach a target given different levels of individual navigation error, group size, interaction radius, and environmental turbulence. In scenarios with low levels of environmental turbulence, simulation results demonstrate a navigational benefit from group membership, particularly for small group sizes. In contrast, when movement takes place in a highly turbulent environment, simulation results suggest that the best strategy is to navigate as individuals rather than as a group.Marine Institute and the Marine RTDI Measure, Productive Sector Operational Programme, National Development Plan 2000–2006

Space-use patterns highlight behavioural differences linked to lameness, parity, and days in milk in barn-housed dairy cows

This is the author accepted manuscript. The final version is available from Public Library of Science (PLoS) via the DOI in this record.Lameness is a key health and welfare issue affecting commercial herds of dairy cattle, with potentially significant economic impacts due to the expense of treatment and lost milk production. Existing lameness detection methods can be time-intensive, and under-detection remains a significant problem leading to delayed or missed treatment. Hence, there is a need for automated monitoring systems that can quickly and accurately detect lameness in individual cows within commercial dairy herds. Recent advances in sensor tracking technology have made it possible to observe the movement, behaviour and space-use of a range of animal species over extended time-scales. However, little is known about how observed movement behaviour and space-use patterns in individual dairy cattle relate to lameness, or to other possible confounding factors such as parity or number of days in milk. In this cross-sectional study, ten lame and ten non-lame barn-housed dairy cows were classified through mobility scoring and subsequently 55 tracked using a wireless local positioning system. Nearly 900,000 spatial locations were recorded in total, allowing a range of movement and space-use measures to be determined for each individual cow. Using linear models, we highlight where lameness, parity, and the number of days in milk have a significant effect on the observed space-use patterns. Non-lame cows spent more time, and had higher site fidelity (on a day-to-day basis they were more likely to revisit areas they had visited previously), in the feeding area. Non-lame cows also had a larger full range size within the barn. In contrast, lame cows spent more time, and had a higher site-fidelity, in the cubicle (resting) areas of the barn than non-lame cows. Higher parity cows were found to spend more time in the right-hand-side area of the barn, closer to the passageway to the milking parlour. The number of days in milk was found to positively affect the core range size, but with a negative interaction effect with lameness. Using a simple predictive model, we demonstrate how it is possible to accurately determine the lameness status of all individual cows within the study using only two observed space-use measures, the proportion of time spent in the feeding area and the full range size. Our findings suggest that differences in individual movement and space-use behaviour could be used as indicators of health status for automated monitoring within a Precision Livestock Farming approach, potentially leading to faster diagnosis and treatment, and improved animal welfare for dairy cattle and other managed animal species

Copycat dynamics in leaderless animal group navigation

Background: Many animals are known to have improved navigational efficiency when moving together as a social group. One potential mechanism for social group navigation is known as the 'many wrongs principle', where information from many inaccurate compasses is pooled across the group. In order to understand how animal groups may use the many wrongs principle to navigate, it is important to consider how directional information is transferred and shared within the group. Methods: Here we use an individual-based model to explore the information-sharing and copying dynamics of a leaderless animal group navigating towards a target in a virtual environment. We assume that communication and information-sharing is indirect and arises through individuals partially copying the movement direction of their neighbours and weighting this information relative to their individual navigational knowledge. Results: We find that the best group navigation performance occurs when individuals directly copy the direction of movement of a subset of their neighbours while only giving a small (6%) weighting to their individual navigational knowledge. Surprisingly, such a strategy is shown to be highly efficient regardless of the level of individual navigational error. We find there is little relative improvement in navigational efficiency when individuals copy from more than 7 influential neighbours. Conclusions: Our findings suggest that we would expect navigating group-living animals to predominantly copy the movement of others rather than relying on their own navigational knowledge. We discuss our results in the context of individual and group navigation behaviour in animals

Constructing a Stochastic Model of Bumblebee Flights from Experimental Data

PMCID: PMC3592844This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Using animal-mounted sensor technology and machine learning to predict time-to-calving in beef and dairy cows

Worldwide, there is a trend towards increased herd sizes, and the animal-to-stockman ratio is increasing within the beef and dairy sectors; thus, the time available to monitoring individual animals is reducing. The behaviour of cows is known to change in the hours prior to parturition, for example, less time ruminating and eating and increased activity level and tail-raise events. These behaviours can be monitored non-invasively using animal-mounted sensors. Thus, behavioural traits are ideal variables for the prediction of calving. This study explored the potential of two sensor technologies for their capabilities in predicting when calf expulsion should be expected. Two trials were conducted at separate locations: (i) beef cows (n = 144) and (ii) dairy cows (n = 110). Two sensors were deployed on each cow: (1) Afimilk Silent Herdsman (SHM) collars monitoring time spent ruminating (RUM), eating (EAT) and the relative activity level (ACT) of the cow, and (2) tail-mounted Axivity accelerometers to detect tail-raise events (TAIL). The exact time the calf was expelled from the cow was determined by viewing closed-circuit television camera footage. Machine learning random forest algorithms were developed to predict when calf expulsion should be expected using single-sensor variables and by integrating multiple-sensor data-streams. The performance of the models was tested using the Matthew’s correlation coefficient (MCC), the area under the curve, and the sensitivity and specificity of predictions. The TAIL model was slightly better at predicting calving within a 5-h window for beef cows (MCC = 0.31) than for dairy cows (MCC = 0.29). The TAIL + RUM + EAT models were equally as good at predicting calving within a 5-h window for beef and dairy cows (MCC = 0.32 for both models). Combining data-streams from SHM and tail sensors did not substantially improve model performance over tail sensors alone; therefore, hour-by-hour algorithms for the prediction of time of calf expulsion were developed using tail sensor data. Optimal classification occurred at 2 h prior to calving for both beef (MCC = 0.29) and dairy cows (MCC = 0.25). This study showed that tail sensors alone are adequate for the prediction of parturition and that the optimal time for prediction is 2 h before expulsion of the calf

Multiscale models for movement in oriented environments and their application to hilltopping in butterflies

Hilltopping butterflies direct their movement in response to topography, facilitating mating encounters via accumulation at summits. In this paper, we take hilltopping as a case study to explore the impact of complex orienteering cues on population dynamics. The modelling employs a standard multiscale framework, in which an individual's movement path is described as a stochastic 'velocity-jump' process and scaling applied to generate a macroscopic model capable of simulating large populations in landscapes. In this manner, the terms and parameters of the macroscopic model directly relate to statistical inputs of the individual-level model (mean speeds, turning rates and turning distributions). Applied to hilltopping in butterflies, we demonstrate how hilltopping acts to aggregate populations at summits, optimising mating for low-density species. However, for abundant populations, hilltopping is not only less effective but also possibly disadvantageous, with hilltopping males recording a lower mating rate than their non-hilltopping competitors. © 2013 Springer Science+Business Media Dordrecht

Universality, limits and predictability of gold-medal performances at the Olympic Games

Inspired by the Games held in ancient Greece, modern Olympics represent the world's largest pageant of athletic skill and competitive spirit. Performances of athletes at the Olympic Games mirror, since 1896, human potentialities in sports, and thus provide an optimal source of information for studying the evolution of sport achievements and predicting the limits that athletes can reach. Unfortunately, the models introduced so far for the description of athlete performances at the Olympics are either sophisticated or unrealistic, and more importantly, do not provide a unified theory for sport performances. Here, we address this issue by showing that relative performance improvements of medal winners at the Olympics are normally distributed, implying that the evolution of performance values can be described in good approximation as an exponential approach to an a priori unknown limiting performance value. This law holds for all specialties in athletics-including running, jumping, and throwing-and swimming. We present a self-consistent method, based on normality hypothesis testing, able to predict limiting performance values in all specialties. We further quantify the most likely years in which athletes will breach challenging performance walls in running, jumping, throwing, and swimming events, as well as the probability that new world records will be established at the next edition of the Olympic Games.Comment: 8 pages, 3 figures, 1 table. Supporting information files and data are available at filrad.homelinux.or

Food Searching Strategy of Amoeboid Cells by Starvation Induced Run Length Extension

Food searching strategies of animals are key to their success in heterogeneous environments. The optimal search strategy may include specialized random walks such as Levy walks with heavy power-law tail distributions, or persistent walks with preferred movement in a similar direction. We have investigated the movement of the soil amoebae Dictyostelium searching for food. Dictyostelium cells move by extending pseudopodia, either in the direction of the previous pseudopod (persistent step) or in a different direction (turn). The analysis of ∼4000 pseudopodia reveals that step and turn pseudopodia are drawn from a probability distribution that is determined by cGMP/PLA2 signaling pathways. Starvation activates these pathways thereby suppressing turns and inducing steps. As a consequence, starved cells make very long nearly straight runs and disperse over ∼30-fold larger areas, without extending more or larger pseudopodia than vegetative cells. This ‘win-stay/lose-shift’ strategy for food searching is called Starvation Induced Run-length Extension. The SIRE walk explains very well the observed differences in search behavior between fed and starving organisms such as bumble-bees, flower bug, hoverfly and zooplankton

The Ordered Extension of Pseudopodia by Amoeboid Cells in the Absence of External Cues

Eukaryotic cells extend pseudopodia for movement. In the absence of external cues, cells move in random directions, but with a strong element of persistence that keeps them moving in the same direction Persistence allows cells to disperse over larger areas and is instrumental to enter new environments where spatial cues can lead the cell. Here we explore cell movement by analyzing the direction, size and timing of ∼2000 pseudopodia that are extended by Dictyostelium cells. The results show that pseudpopod are extended perpendicular to the surface curvature at the place where they emerge. The location of new pseudopods is not random but highly ordered. Two types of pseudopodia may be formed: frequent splitting of an existing pseudopod, or the occasional extension of a de novo pseudopod at regions devoid of recent pseudopod activity. Split-pseudopodia are extended at ∼60 degrees relative to the previous pseudopod, mostly as alternating Right/Left/Right steps leading to relatively straight zigzag runs. De novo pseudopodia are extended in nearly random directions thereby interrupting the zigzag runs. Persistence of cell movement is based on the ratio of split versus de novo pseudopodia. We identify PLA2 and cGMP signaling pathways that modulate this ratio of splitting and de novo pseudopodia, and thereby regulate the dispersal of cells. The observed ordered extension of pseudopodia in the absence of external cues provides a fundamental insight into the coordinated movement of cells, and might form the basis for movement that is directed by internal or external cues

Somersault of Paramecium in extremely confined environments

We investigate various swimming modes of Paramecium in geometric confinements and a non-swimming self-bending behavior like a somersault, which is quite different from the previously reported behaviors. We observe that Paramecia execute directional sinusoidal trajectories in thick fluid films, whereas Paramecia meander around a localized region and execute frequent turns due to collisions with adjacent walls in thin fluid films. When Paramecia are further constrained in rectangular channels narrower than the length of the cell body, a fraction of meandering Paramecia buckle their body by pushing on the channel walls. The bucking (self-bending) of the cell body allows the Paramecium to reorient its anterior end and explore a completely new direction in extremely confined spaces. Using force deflection method, we quantify the Young’s modulus of the cell and estimate the swimming and bending powers exerted by Paramecium. The analysis shows that Paramecia can utilize a fraction of its swimming power to execute the self-bending maneuver within the confined channel and no extra power may be required for this new kind of self-bending behavior. This investigation sheds light on how micro-organisms can use the flexibility of the body to actively navigate within confined spaces