Intrusion experiments to measure territory size: development of the method, tests through simulations, and application in the frog Allobates femoralis

Territoriality is a widespread behaviour in animals and its analysis is crucial in several areas of behavioural, ecological and evolutionary research. Commonly, territory size is assessed through territory mapping and the application of simple area estimators such as minimum convex polygons. In the present study we demonstrate that territory size can be determined adequately with an active approach through intrusion experiments, a technique that is commonly used in behavioural research in other contexts. Tests with simulated data indicate that a minimum of twelve trials needs to be performed to establish reliable orders of relative territory size. To estimate absolute territory size, detailed hull techniques are most appropriate when analyzing point patterns of intrusion experiments, while the local convex hull estimator enables the construction of internal utilization distributions based on such point patterns. Additionally we suggest a 'stretch the centre' approach to emphasize the actual process of intrusion experiments in the construction of internal utilization distributions. To demonstrate the utility of the method, we apply all findings from the simulations to data from fieldwork with the model species Allobates femoralis, a territorial aromobatid frog from the lowland rainforest of French Guiana.     

Quantifying deficits in the 3D force capabilities of a digit caused by selective paralysis: application to the thumb with simulated low ulnar nerve palsy

We present the development of a vision-feedback method to characterize how selective paralysis distorts the three-dimensional (3D) volume representing digit-tip force production capability and its application to healthy individuals producing thumb-tip force with and without simulated low ulnar nerve palsy (LUNP). Subjects produced maximal static voluntary force spanning the transverse, sagittal and frontal planes of the thumb (16, 15 and 10 subjects, respectively). Subjects produced thumb-tip force tasks in guided and self-selected directions. The envelope (convex hull) of extreme forces in each plane approximated that cross-section of the 3D volume of force capability. Some subjects repeated the tasks with a temporary ulnar nerve block applied at the wrist to simulate complete acute LUNP. Three geometric properties of the force convex hull characterized each cross-section's shape: the ratios of its principal moments of inertia (RPMIs), the orientation of its principal axis (OPA), and its centroid location. Our results show that force production in the thumb's sagittal plane may be a reproducible and objective test to grade motor impairment in LUNP: paired t-tests of the larger RPMI in this plane best distinguished the nerve-blocked cases from the control cases in the guided task (p = 0.012), and Discriminant Analysis of the centroid location for the self-selected task in this plane correctly classified 94.7% of subjects into the control and ulnar nerve-blocked groups. We show that our method measures and detects changes in a digit's force production capabilities. Towards a clinical test of motor impairment in LUNP, this biomechanical study dictates which 3D thumb-tip forces to measure (those in the sagittal plane) and how to measure them (using the self-selected task).     

Correlational selection does not explain the evolution of a behavioural syndrome

Correlated suites of behaviours, or behavioural syndromes, appear to be widespread, and yet few studies have explored how they arise and are maintained. One possibility holds that correlational selection can generate and maintain behavioural syndrome if certain behavioural combinations enjoy greater fitness than other combinations. Here we test this correlational selection hypothesis by comparing behavioural syndrome structure with a multivariate fitness surface based on reproductive success of male water striders. We measured the structure of a behavioural syndrome including dispersal ability, exploration behaviour, latency to remount and sex recognition sensitivity in males. We then measured the relationship between these behaviours and mating success in a range of sex ratio environments. Despite the presence of some significant correlational selection, behavioural syndrome structure was not associated with correlational selection on behaviours. Although we cannot conclusively reject the correlational selection hypothesis, our evidence suggests that correlational selection and resulting linkage disequilibrium might not be responsible for maintaining the strong correlations between behaviours. Instead, we suggest alternative ways in which this behavioural syndrome may have arisen and outline the need for physiological and quantitative genetic tests of these suggestions.     

Miniaturized orb-weaving spiders: behavioural precision is not limited by small size

The special problems confronted by very small animals in nervous system design that may impose limitations on their behaviour and evolution are reviewed. Previous attempts to test for such behavioural limitations have suffered from lack of detail in behavioural observations of tiny species and unsatisfactory measurements of their behavioural capacities. This study presents partial solutions to both problems. The orb-web construction behaviour of spiders provided data on the comparative behavioural capabilities of tiny animals in heretofore unparalleled detail; species ranged about five orders of magnitude in weight, from approximately 50-100mg down to some of the smallest spiders known (less than 0.005mg), whose small size is a derived trait. Previous attempts to quantify the 'complexity' of behaviour were abandoned in favour of using comparisons of behavioural imprecision in performing the same task. The prediction of the size limitation hypothesis that very small spiders would have a reduced ability to repeat one particular behaviour pattern precisely was not confirmed. The anatomical and physiological mechanisms by which these tiny animals achieve this precision and the possibility that they are more limited in the performance of higher-order behaviour patterns await further investigation.     

Herding,social influence and economic decision-making: socio-psychological and neuroscientific analyses

Typically, modern economics has steered away from the analysis of sociological and psychological factors and has focused on narrow behavioural assumptions in which expectations are formed on the basis of mathematical algorithms. Blending together ideas from the social and behavioural sciences, this paper argues that the behavioural approach adopted in most economic analysis, in its neglect of sociological and psychological forces and its simplistically dichotomous categorization of behaviour as either rational or not rational, is too narrow and stark. Behaviour may reflect an interaction of cognitive and emotional factors and this can be captured more effectively using an approach that focuses on the interplay of different decision-making systems. In understanding the mechanisms affecting economic and financial decision-making, an interdisciplinary approach is needed which incorporates ideas from a range of disciplines including sociology, economic psychology, evolutionary biology and neuroeconomics.     

Classifying movement behaviour in relation to environmental conditions using hidden Markov models

1.  Linking the movement and behaviour of animals to their environment is a central problem in ecology. Through the use of electronic tagging and tracking (ETT), collection of in situ data from free-roaming animals is now commonplace, yet statistical approaches enabling direct relation of movement observations to environmental conditions are still in development.
2.  In this study, we examine the hidden Markov model (HMM) for behavioural analysis of tracking data. HMMs allow for prediction of latent behavioural states while directly accounting for the serial dependence prevalent in ETT data. Updating the probability of behavioural switches with tag or remote-sensing data provides a statistical method that links environmental data to behaviour in a direct and integrated manner.
3.  It is important to assess the reliability of state categorization over the range of time-series lengths typically collected from field instruments and when movement behaviours are similar between movement states. Simulation with varying lengths of times series data and contrast between average movements within each state was used to test the HMMs ability to estimate movement parameters.
4.  To demonstrate the methods in a realistic setting, the HMMs were used to categorize resident and migratory phases and the relationship between movement behaviour and ocean temperature using electronic tagging data from southern bluefin tuna ( Thunnus maccoyii ). Diagnostic tools to evaluate the suitability of different models and inferential methods for investigating differences in behaviour between individuals are also demonstrated.     

A temporal precedence based clustering method for gene expression microarray data

Background  

Time-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in microarray data analysis where the data is grouped together according to certain characteristics. The majority of clustering techniques are based on distance or visual similarity measures which may not be suitable for clustering of temporal microarray data where the sequential nature of time is important. We present a Granger causality based technique to cluster temporal microarray gene expression data, which measures the interdependence between two time-series by statistically testing if one time-series can be used for forecasting the other time-series or not.     

Preconditioning 2D Integer Data for Fast Convex Hull Computations

In order to accelerate computing the convex hull on a set of n points, a heuristic procedure is often applied to reduce the number of points to a set of s points, s ≤ n, which also contains the same hull. We present an algorithm to precondition 2D data with integer coordinates bounded by a box of size p × q before building a 2D convex hull, with three distinct advantages. First, we prove that under the condition min(p, q) ≤ n the algorithm executes in time within O(n); second, no explicit sorting of data is required; and third, the reduced set of s points forms a simple polygonal chain and thus can be directly pipelined into an O(n) time convex hull algorithm. This paper empirically evaluates and quantifies the speed up gained by preconditioning a set of points by a method based on the proposed algorithm before using common convex hull algorithms to build the final hull. A speedup factor of at least four is consistently found from experiments on various datasets when the condition min(p, q) ≤ n holds; the smaller the ratio min(p, q)/n is in the dataset, the greater the speedup factor achieved.     

Predicting protein-ligand binding sites based on an improved geometric algorithm

Knowledge of protein-ligand binding sites is very important for structure-based drug designs. To get information on the binding site of a targeted protein with its ligand in a timely way, many scientists tried to resort to computational methods. Although several methods have been released in the past few years, their accuracy needs to be improved. In this study, based on the combination of incremental convex hull, traditional geometric algorithm, and solvent accessible surface of proteins, we developed a novel approach for predicting the protein-ligand binding sites. Using PDBbind database as a benchmark dataset and comparing the new approach with the existing methods such as POCKET, Q-SiteFinder, MOE-SiteFinder, and PASS, we found that the new method has the highest accuracy for the Top 2 and Top 3 predictions. Furthermore, our approach can not only successfully predict the protein-ligand binding sites but also provide more detailed information for the interactions between proteins and ligands. It is anticipated that the new method may become a useful tool for drug development, or at least play a complementary role to the other existing methods in this area.     

Predation mediated population divergence in complex behaviour of nine‐spined stickleback (Pungitius pungitius)

The proximate and ultimate explanations for behavioural syndromes (correlated behaviours – a population trait) are poorly understood, and the evolution of behavioural types (configuration of behaviours – an individual trait) has been rarely studied. We investigated population divergence in behavioural syndromes and types using individually reared, completely predator‐ or conspecific‐naïve adult nine‐spined sticklebacks (Pungitius pungitius) from two marine and two predatory fish free, isolated pond populations. We found little evidence for the existence of behavioural syndromes, but population divergence in behavioural types was profound: individuals from ponds were quicker in feeding, bolder and more aggressive than individuals from marine environments. Our data reject the hypothesis that behavioural syndromes exist as a result of genetic correlations between behavioural traits, and support the contention that different behavioural types can be predominant in populations differing in predation pressure, most probably as a result of repeated independent evolution of separate behavioural traits.     

Analysis of breathing via optoelectronic systems: comparison of four methods for computing breathing volumes and thoraco-abdominal motion pattern

Breathing parameters can be measured by motion capture systems by placing photo-reflective markers on the chest wall. A computational model is mandatory to compute the breathing volume and to calculate temporal and kinematical features by the gathered markers trajectories. Despite different methods based on different geometrical approaches can be adopted to compute volumes, no information about their differences in the respiratory evaluation are available. This study investigated the performances of four methods (conventional, prism-based, convex hull with boundary condition, based on Delaunay triangulation) using an optoelectronic motion capture system, on twelve healthy participants during 30 s of breathing. Temporal trends of volume traces, tidal volume values, and breathing durations were compared between methods and spirometry (used as reference instrument). Additionally, thoraco-abdominal motion patterns were compared between methods by analysing the compartmental contributions and their variability. Results shows comparable similarities between the volume traces obtained using spirometry, prism-based and conventional methods. Prism-based and convex hull with boundary condition methods show lower bias in tidal volumes estimation up to 0.06 L, compared to the conventional and Delaunay triangulation methods. Prism-based method shows maximum differences of 30 mL in the comparison of compartmental contributions to the total volume, by resulting in a maximum deviation of 1.6% in the percentage contribution analysis. In conclusion, our finding demonstrated the accuracy of the non-invasive MoCap-based breathing analysis with the prism-based method tested. Data provided in this study will lead researchers and clinicians in the computational method choice for temporal and volumetric breathing analysis.     

A web based tool to merge geometric morphometric data from multiple characters

Geometric morphometrics (GM) is an important method of shape analysis and increasingly used in a wide range of scientific disciplines.Presently,a single character comparison system of geometric morphometric data is used in almost all empirical studies,and this approach is sufficient for many scientific problems.However,the estimation of overall similarity among taxa or objects based on multiple characters is crucial in a variety of contexts (e.g.(semi-)automated identification,phenetic relationships,tracing of character evolution,phylogenetic reconstruction).Here we propose a new web-based tool for merging several geometric morphometrics data files from multiple characters into a single data file.Using this approach information from multiple characters can be compared in combination and an overall similarity estimate can be obtained in a convenient and geometrically rigorous manner.To illustrate our method,we provide an example analysis of 25 dung beetle species with seven Procrustes superimposed landmark data files representing the morphological variation of body features:the epipharynx,right mandible,pronotum,elytra,hindwing,and the metendosternite in dorsal and lateral view.All seven files were merged into a single one containing information on 649 landmark locations.The possible applications of such merged data files in different fields of science are discussed.     

Matching the forecast horizon with the relevant spatial and temporal processes and data sources

Most phenomenological, statistical models used to generate ecological forecasts take either a time-series approach, based on long-term data from one location, or a space-for-time approach, based on data describing spatial patterns across environmental gradients. However, the magnitude and even the sign of environment–response relationships detected using these two approaches often differs, leading to contrasting predictions about responses to future environmental change. Here we consider how the forecast horizon determines whether more accurate predictions come from the time-series approach, the space-for-time approach or a combination of the two. As proof of concept, we use simulated case studies to show that forecasts for short and long forecast horizons need to focus on different ecological processes, which are reflected in different kinds of data. First, we simulated population or community dynamics under stationary temperature using two simple, mechanistic models. Second, we fit statistical models to the simulated data using a time-series approach, a space-for-time approach or a weighted average. We then forecast the response to a temperature increase using the statistical models, and compared these forecasts to temperature effects simulated by the mechanistic models. We found that the time-series approach made accurate short-term predictions because it captured initial conditions and effects of fast processes such as birth and death. The space-for-time approach made more accurate long-term predictions because it better captured the influence of slower processes such as evolutionary and ecological selection. The weighted average made accurate predictions at all time scales, including intermediate time-scales where the other two approaches performed poorly. A weighted average of time-series and space-for-time approaches shows promise, but making this weighted model operational will require new research to predict the rate at which slow processes begin to influence dynamics.     

CNV-CH: A Convex Hull Based Segmentation Approach to Detect Copy Number Variations (CNV) Using Next-Generation Sequencing Data

Copy number variation (CNV) is a form of structural alteration in the mammalian DNA sequence, which are associated with many complex neurological diseases as well as cancer. The development of next generation sequencing (NGS) technology provides us a new dimension towards detection of genomic locations with copy number variations. Here we develop an algorithm for detecting CNVs, which is based on depth of coverage data generated by NGS technology. In this work, we have used a novel way to represent the read count data as a two dimensional geometrical point. A key aspect of detecting the regions with CNVs, is to devise a proper segmentation algorithm that will distinguish the genomic locations having a significant difference in read count data. We have designed a new segmentation approach in this context, using convex hull algorithm on the geometrical representation of read count data. To our knowledge, most algorithms have used a single distribution model of read count data, but here in our approach, we have considered the read count data to follow two different distribution models independently, which adds to the robustness of detection of CNVs. In addition, our algorithm calls CNVs based on the multiple sample analysis approach resulting in a low false discovery rate with high precision.     

Interpreting 16S metagenomic data without clustering to achieve sub-OTU resolution

The standard approach to analyzing 16S tag sequence data, which relies on clustering reads by sequence similarity into Operational Taxonomic Units (OTUs), underexploits the accuracy of modern sequencing technology. We present a clustering-free approach to multi-sample Illumina data sets that can identify independent bacterial subpopulations regardless of the similarity of their 16S tag sequences. Using published data from a longitudinal time-series study of human tongue microbiota, we are able to resolve within standard 97% similarity OTUs up to 20 distinct subpopulations, all ecologically distinct but with 16S tags differing by as little as one nucleotide (99.2% similarity). A comparative analysis of oral communities of two cohabiting individuals reveals that most such subpopulations are shared between the two communities at 100% sequence identity, and that dynamical similarity between subpopulations in one host is strongly predictive of dynamical similarity between the same subpopulations in the other host. Our method can also be applied to samples collected in cross-sectional studies and can be used with the 454 sequencing platform. We discuss how the sub-OTU resolution of our approach can provide new insight into factors shaping community assembly.     

A New Method of Regression of Time-series Based on a Mathematical Connection Between Path and Compartmental Analyses

In the regression analysis of time-series the error terms may be serially correlated with the results. In this case the possibility arises that the expectations and adjustment processes are themselves mis-specifications of the correct behavioural relationships. In this paper an analogy is pointed out between the compartmental-analysis and the path analysis by the author. It will be argued that the time-series of the standardized partial regression coefficients (path coefficients or beta weights) computed from the cross-sectional data, have to be analysed instead of applying ordinary least squares directly to the timeseries. An application of the new method is briefly discussed.     

Biological Time Series Analysis Using a Context Free Language: Applicability to Pulsatile Hormone Data

We present a novel approach for analyzing biological time-series data using a context-free language (CFL) representation that allows the extraction and quantification of important features from the time-series. This representation results in Hierarchically AdaPtive (HAP) analysis, a suite of multiple complementary techniques that enable rapid analysis of data and does not require the user to set parameters. HAP analysis generates hierarchically organized parameter distributions that allow multi-scale components of the time-series to be quantified and includes a data analysis pipeline that applies recursive analyses to generate hierarchically organized results that extend traditional outcome measures such as pharmacokinetics and inter-pulse interval. Pulsicons, a novel text-based time-series representation also derived from the CFL approach, are introduced as an objective qualitative comparison nomenclature. We apply HAP to the analysis of 24 hours of frequently sampled pulsatile cortisol hormone data, which has known analysis challenges, from 14 healthy women. HAP analysis generated results in seconds and produced dozens of figures for each participant. The results quantify the observed qualitative features of cortisol data as a series of pulse clusters, each consisting of one or more embedded pulses, and identify two ultradian phenotypes in this dataset. HAP analysis is designed to be robust to individual differences and to missing data and may be applied to other pulsatile hormones. Future work can extend HAP analysis to other time-series data types, including oscillatory and other periodic physiological signals.     

Behavioural syndromes and social insects: personality at multiple levels

Animal personalities or behavioural syndromes are consistent and/or correlated behaviours across two or more situations within a population. Social insect biologists have measured consistent individual variation in behaviour within and across colonies for decades. The goal of this review is to illustrate the ways in which both the study of social insects and of behavioural syndromes has overlapped, and to highlight ways in which both fields can move forward through the synergy of knowledge from each. Here we, (i) review work to date on behavioural syndromes (though not always referred to as such) in social insects, and discuss mechanisms and fitness effects of maintaining individual behavioural variation within and between colonies; (ii) summarise approaches and principles from studies of behavioural syndromes, such as trade‐offs, feedback, and statistical methods developed specifically to study behavioural consistencies and correlations, and discuss how they might be applied specifically to the study of social insects; (iii) discuss how the study of social insects can enhance our understanding of behavioural syndromes—research in behavioural syndromes is beginning to explore the role of sociality in maintaining or developing behavioural types, and work on social insects can provide new insights in this area; and (iv) suggest future directions for study, with an emphasis on examining behavioural types at multiple levels of organisation (genes, individuals, colonies, or groups of individuals).     

Collaborative Brain-Computer Interface for Aiding Decision-Making

We look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controlled conditions, we asked observers to perform a simple matching task involving the rapid sequential presentation of pairs of visual patterns and the subsequent decision as whether the two patterns in a pair were the same or different. We recorded the response times of observers as well as a neural feature which predicts incorrect decisions and, thus, indirectly indicates the confidence of the decisions made by the observers. We then built a composite neuro-behavioural feature which optimally combines the two measures. For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features. Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule. An analysis of event-related potentials indicates that substantial differences are present in the proximity of the response for correct and incorrect trials, further corroborating the idea of using hybrids of brain-computer interfaces and traditional strategies for improving decision making.