A data-driven method for reconstructing and modelling social interactions in moving animal groups.
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ABSTRACT:
SUBMITTER: Escobedo R
PROVIDER: S-EPMC7423377 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Collective motion phenomena in large groups of social organisms have long fascinated the observer, especially in cases, such as bird flocks or fish schools, where large-scale highly coordinated actions emerge in the absence of obvious leaders. However, the mechanisms involved in this self-organized behavior are still poorly understood, because the individual-level interactions underlying them remain elusive. Here, we demonstrate the power of a bottom-up methodology to build models for animal group motion from data gathered at the individual scale. Using video tracks of fish shoal in a tank, we show how a careful, incremental analysis at the local scale allows for the determination of the stimulus/response function governing an individual's moving decisions. We find in particular that both positional and orientational effects are present, act upon the fish turning speed, and depend on the swimming speed, yielding a novel schooling model whose parameters are all estimated from data. Our approach also leads to identify a density-dependent effect that results in a behavioral change for the largest groups considered. This suggests that, in confined environment, the behavioral state of fish and their reaction patterns change with group size. We debate the applicability, beyond the particular case studied here, of this novel framework for deciphering interactions in moving animal groups.
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Project description:BackgroundThe lack of classification by educational attainment in death and population exposure data at older ages is an important constraint for studying changes and patterns of mortality disparities by education in Denmark and Sweden. The missing educational distribution of population also restricts analyses aiming at estimating contributions of compositional change to the improvements in national longevity. This study proposes a transparent approach to solve the two methodological issues allowing to obtain robust education-specific mortality estimates and population weights.MethodsUsing nonparametric approach, we redistribute the unknown cases and extrapolate the mortality curves of these sub-populations with the help of population-level data on an aggregate level from the Human Mortality Database.ResultsWe present reconstructed and harmonized education-specific abridged and complete life tables for Sweden and Denmark covering 5-year-long periods from 1991-1995 to 2011-2015. The newly estimated life tables are in good agreement with the national life tables and show plausible age- and education-specific patterns. The observed changes in life expectancy by education suggest about the widening longevity gap between the highest and lowest educated for males and females in both countries.ConclusionsThe proposed simple and transparent method can be applied in similar country-specific cases showing large proportions of missing education or other socio-economic characteristics at older ages.
| S-EPMC8393442 | biostudies-literature
Project description:Collective decision-making is a daily occurrence in the lives of many group-living animals, and can have critical consequences for the fitness of individuals. Understanding how decisions are reached, including who has influence and the mechanisms by which information and preferences are integrated, has posed a fundamental challenge. Here, we provide a methodological framework for studying influence and leadership in groups. We propose that individuals have influence if their actions result in some behavioural change among their group-mates, and are leaders if they consistently influence others. We highlight three components of influence (influence instances, total influence and consistency of influence), which can be assessed at two levels (individual-to-individual and individual-to-group). We then review different methods, ranging from individual positioning within groups to information-theoretic approaches, by which influence has been operationally defined in empirical studies, as well as how such observations can be aggregated to give insight into the underlying decision-making process. We focus on the domain of collective movement, with a particular emphasis on methods that have recently been, or are being, developed to take advantage of simultaneous tracking data. We aim to provide a resource bringing together methodological tools currently available for studying leadership in moving animal groups, as well as to discuss the limitations of current methodologies and suggest productive avenues for future research.This article is part of the theme issue 'Collective movement ecology'.
| S-EPMC5882976 | biostudies-literature
Project description:Groups of animals coordinate remarkable, coherent, movement patterns during periods of collective motion. Such movement patterns include the toroidal mills seen in fish shoals, highly aligned parallel motion like that of flocks of migrating birds, and the swarming of insects. Since the 1970's a wide range of collective motion models have been studied that prescribe rules of interaction between individuals, and that are capable of generating emergent patterns that are visually similar to those seen in real animal group. This does not necessarily mean that real animals apply exactly the same interactions as those prescribed in models. In more recent work, researchers have sought to infer the rules of interaction of real animals directly from tracking data, by using a number of techniques, including averaging methods. In one of the simplest formulations, the averaging methods determine the mean changes in the components of the velocity of an individual over time as a function of the relative coordinates of group mates. The averaging methods can also be used to estimate other closely related quantities including the mean relative direction of motion of group mates as a function of their relative coordinates. Since these methods for extracting interaction rules and related quantities from trajectory data are relatively new, the accuracy of these methods has had limited inspection. In this paper, we examine the ability of an averaging method to reveal prescribed rules of interaction from data generated by two individual based models for collective motion. Our work suggests that an averaging method can capture the qualitative features of underlying interactions from trajectory data alone, including repulsion and attraction effects evident in changes in speed and direction of motion, and the presence of a blind zone. However, our work also illustrates that the output from a simple averaging method can be affected by emergent group level patterns of movement, and the sizes of the regions over which repulsion and attraction effects are apparent can be distorted depending on how individuals combine interactions with multiple group mates.
| S-EPMC7725364 | biostudies-literature
Project description:BackgroundBiology is rapidly becoming a data intensive, data-driven science. It is essential that data is represented and connected in ways that best represent its full conceptual content and allows both automated integration and data driven decision-making. Recent advancements in distributed multi-relational directed graphs, implemented in the form of the Semantic Web make it possible to deal with complicated heterogeneous data in new and interesting ways.ResultsThis paper presents a new approach, scenario driven data modelling (SDDM), that integrates multi-relational directed graphs with data streams. SDDM can be applied to virtually any data integration challenge with widely divergent types of data and data streams. In this work, we explored integrating genetics data with reports from traditional media. SDDM was applied to the New Delhi metallo-beta-lactamase gene (NDM-1), an emerging global health threat. The SDDM process constructed a scenario, created a RDF multi-relational directed graph that linked diverse types of data to the Semantic Web, implemented RDF conversion tools (RDFizers) to bring content into the Sematic Web, identified data streams and analytical routines to analyse those streams, and identified user requirements and graph traversals to meet end-user requirements.ConclusionsWe provided an example where SDDM was applied to a complex data integration challenge. The process created a model of the emerging NDM-1 health threat, identified and filled gaps in that model, and constructed reliable software that monitored data streams based on the scenario derived multi-relational directed graph. The SDDM process significantly reduced the software requirements phase by letting the scenario and resulting multi-relational directed graph define what is possible and then set the scope of the user requirements. Approaches like SDDM will be critical to the future of data intensive, data-driven science because they automate the process of converting massive data streams into usable knowledge.
| S-EPMC3236839 | biostudies-literature
Project description:Group life involves both advantages and disadvantages, meaning that individuals have to compromise between their nutritional needs and their social links. When a compromise is impossible, the group splits in order to reduce conflict of interests and favour positive social interactions between its members. In this study we built a dynamic model of social networks to represent a succession of temporary fissions involving a change in social relations that could potentially lead to irreversible group fission (i.e. no more group fusion). This is the first study that assesses how a social network changes according to group fission-fusion dynamics. We built a model that was based on different parameters: the group size, the influence of nutritional needs compared to social needs, and the changes in the social network after a temporary fission. The results obtained from this theoretical data indicate how the percentage of social relation transfer, the number of individuals and the relative importance of nutritional requirements and social links influence the average number of days before irreversible fission occurs. The greater the nutritional needs and the higher the transfer of social relations during temporary fission, the fewer days will be observed before an irreversible fission. It is crucial to bridge the gap between the individual and the population level if we hope to understand how simple, local interactions may drive ecological systems.
| S-EPMC4022680 | biostudies-literature
Project description:Social interactions represent a ubiquitous aspect of our everyday life that we acquire by interpreting and responding to visual cues from conspecifics1. However, despite the general acceptance of this view, how visual information is used to guide the decision to cooperate is unknown. Here, we wirelessly recorded the spiking activity of populations of neurons in the visual and prefrontal cortex in conjunction with wireless recordings of oculomotor events while freely moving macaques engaged in social cooperation. As animals learned to cooperate, visual and executive areas refined the representation of social variables, such as the conspecific or reward, by distributing socially relevant information among neurons in each area. Decoding population activity showed that viewing social cues influences the decision to cooperate. Learning social events increased coordinated spiking between visual and prefrontal cortical neurons, which was associated with improved accuracy of neural populations to encode social cues and the decision to cooperate. These results indicate that the visual-frontal cortical network prioritizes relevant sensory information to facilitate learning social interactions while freely moving macaques interact in a naturalistic environment.
| S-EPMC10959748 | biostudies-literature
Project description:Frequently, vital rates are driven by directional, long-term environmental changes. Many of these are of great importance, such as land degradation, climate change, and succession. Traditional demographic methods assume a constant or stationary environment, and thus are inappropriate to analyze populations subject to these changes. They also require repeat surveys of the individuals as change unfolds. Methods for reconstructing such lengthy processes are needed. We present a model that, based on a time series of population size structures and densities, reconstructs the impact of directional environmental changes on vital rates. The model uses integral projection models and maximum likelihood to identify the rates that best reconstructs the time series. The procedure was validated with artificial and real data. The former involved simulated species with widely different demographic behaviors. The latter used a chronosequence of populations of an endangered cactus subject to increasing anthropogenic disturbance. In our simulations, the vital rates and their change were always reconstructed accurately. Nevertheless, the model frequently produced alternative results. The use of coarse knowledge of the species' biology (whether vital rates increase or decrease with size or their plausible values) allowed the correct rates to be identified with a 90% success rate. With real data, the model correctly reconstructed the effects of disturbance on vital rates. These effects were previously known from two populations for which demographic data were available. Our procedure seems robust, as the data violated several of the model's assumptions. Thus, time series of size structures and densities contain the necessary information to reconstruct changing vital rates. However, additional biological knowledge may be required to provide reliable results. Because time series of size structures and densities are available for many species or can be rapidly generated, our model can contribute to understand populations that face highly pressing environmental problems.
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Project description:Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context.
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Project description:In this work, we develop a data-driven modelling framework to reproduce the locomotion of fish in a confined environment. Specifically, we highlight the primary characteristics of the motion of individual zebrafish (Danio rerio), and study how these can be suitably encapsulated within a mathematical framework utilising a limited number of calibrated model parameters. Using data captured from individual zebrafish via automated visual tracking, we develop a model using stochastic differential equations and describe fish as a self propelled particle moving in a plane. Based on recent experimental evidence of the importance of speed regulation in social behaviour, we extend stochastic models of fish locomotion by introducing experimentally-derived processes describing dynamic speed regulation. Salient metrics are defined which are then used to calibrate key parameters of coupled stochastic differential equations, describing both speed and angular speed of swimming fish. The effects of external constraints are also included, based on experimentally observed responses. Understanding the spontaneous dynamics of zebrafish using a bottom-up, purely data-driven approach is expected to yield a modelling framework for quantitative investigation of individual behaviour in the presence of various external constraints or biological assays.
| S-EPMC4598355 | biostudies-literature