Biology subjects

Guttal, V.

Publications and source records attributed to Guttal, V..

13 recordsLinked to original sources

bioRxiv2026

Tracing information propagation in fish schools with a conditioned escape response

Collective escape responses are widespread in animal groups, yet how threat-related information spreads from a few informed individuals to the collective remains poorly understood because the identities of the individuals who initially detect danger are rarely experimentally controlled. Here, we combined aversive conditioning, behavioural tracking, and computational modelling to investigate how escape information propagates through schools of Puntigrus tetrazona. We conditioned selected fish to associate a green light with an aversive stimulus. Subsequently, we tested the escape response of schools containing one informed and four naive fish to the green light. One informed fish was sufficient to trigger a collective escape response. Upon stimulus onset, conditioned fish accelerated and crossed the hurdle, after which naive fish sequentially increased their speed and followed. Temporal correlations in speed revealed a hierarchical leader-follower structure mostly aligned with hurdle-crossing order. Neither initial distance, viewing angle, nor relative orientation to informed fish predicted the order of the escape sequence. An agent-based model incorporating local alignment and distance regulation reproduced experimental observations and suggested that the informed individual likely remains partly attentive to its neighbours during escape. Overall, our work provides a general framework for experimentally dissecting information transfer in animal groups and identifying the behavioural mechanisms underlying collective escape dynamics.

animal behavior and cognition↗

bioRxiv2026

Speed Synchrony Promotes Collective Motion in Mixed-Species Fish Schools

Principles of collective motion are now well established, though research has largely focused on homogeneous groups. Heterogeneity is widespread in animal groups, e.g. arising from sex, size or even species, raising a central question: can collective behaviour emerge when individuals have distinct behaviours? Here, we combine experiments and modelling to investigate mixed-species collective motion using two closely related fish species, rosy barbs and tiger barbs. In conspecific groups, both species exhibit collective motion, but they differ strikingly in their intrinsic movement: tiger barbs exhibit slowand fast-swimming, whereas rosy barbs display fast swimming only. Despite this difference, these species readily form mixed-species schools where the slow swimming speed of tiger barbs disappears, and the collective motion is dominated by a single fast-swimming mode. We develop an individual-based model incorporating local interactions involving speed matching. Our model demonstrates that bimodal speed in conspecific schools of tiger barbs is an emergent property that is lost in mixed-species groups. Additionally, despite high cohesion, we observe spatial sorting of the two species within the mixed-species groups, which our model explains through differences in inter- and intra-specific interactions. Our results provide experimental evidence that canonical principles of collective motion extend to heterogeneous mixed-species groups.

animal behavior and cognition↗

bioRxiv2025

Experimental evidence of stress-induced critical state in schooling fish

How do animal groups dynamically adjust their collective behavior in response to environmental changes is an open and challenging question. Here, we investigate the mechanisms that allow fish schools to tune their collective state under stress, testing the hypothesis that these systems operate near criticality, a state maximizing sensitivity, responsiveness, and adaptability. We combine experiments and data-driven computational modeling to study how group size and stress influence the collective behavior of rummy-nose tetras (Hemigrammus rhodostomus). We quantify the collective state of fish schools using polarization, milling, and cohesion metrics and use a burst-and-coast model to infer the social interaction parameters that drive these behaviors. Our results indicate that group size modulates stress levels, with smaller groups experiencing higher baseline stress, likely due to a reduced social buffering effect. Under stress, fish adjust the strength of their social interactions in a way that leads the group into a critical state, thus enhancing its sensitivity to perturbations and facilitating rapid adaptation. However, large groups require an external stressor to enter the critical regime, whereas small groups are already near this state. Unlike previous studies suggesting that fish adjust their interaction network structure under risk, our results suggest that the intensity of social interactions, rather than network structure, governs collective state transitions. This simpler mechanism reduces cognitive demands while enabling dynamic adaptation. By revealing how stress and group size drive self-organization toward criticality, our study provides fundamental insights into the adaptability of collective biological systems and the emergent properties in animal groups.

animal behavior and cognition↗

bioRxiv2024

Collective responses of flocking sheep to a herding dog

Across taxa, group-living organisms exhibit collective escape responses to stimuli varying from mild stress to predatory pressures. How exactly does information flow among group members leading to a collective escape remains an open question. Here we study the collective responses of a flock of sheep to a shepherd dog in a driving task between well-defined target points. We collected highresolution spatio-temporal data from 14 sheep and the dog, using Ultra Wide Band tags attached to each individual. Through the time delay analysis of velocity correlations, we identify a hierarchy among sheep in terms of directional influence. Notably, the average spatial position of a sheep along the front-back axis of the groups velocity strongly correlates with its impact on the collective movement. Our findings demonstrate that, counter-intuitively, directional information on shorter time scales propagates from the front of the group towards the rear, and that the dog exhibits adaptive movement adjustments in response to the flocks dynamics. Furthermore, we show that a simple shepherding model can capture key features of the collective response of the sheep flocks. In conclusion, our study reveals novel insights on how directional information propagates in escaping animal groups.

animal behavior and cognition↗

bioRxiv2024

Spatial structure could explain the maintenance of alternative reproductive tactics in tree cricket males

Trait polymorphisms are widespread in nature, and explaining their stable co-existence is a central problem in ecology and evolution. Alternative reproductive tactics, in which individuals of one or more sex exhibit discrete, discontinuous traits in response to reproductive competition, represent a special case of trait polymorphism in which the traits are often complex, behavioural, and dynamic. Thus, studying how alternative reproductive tactics are maintained may provide general insights into how complex trait polymorphisms are maintained in populations. We construct a spatially explicit individual-based model inspired from extensively collected empirical data to address the mechanisms behind the co-existence of three behavioural alternative reproductive tactics in males of a tree cricket (Oecanthus henryi). Our results show that the co-existence of these tactics over ecological time scales is facilitated by the spatial structure of the landscape they inhabit, which serves to equalize the otherwise unequal mating benefits of the three tactics. We also show that this co-existence is unlikely if spatial aspects of the system are not considered. Our findings highlight the importance of spatial dynamics in understanding ecological and evolutionary processes and underscore the power of integrative approaches that combine models inspired from empirical data.

animal behavior and cognition↗

bioRxiv2024

Eco-evolutionary dynamics for finite populations and the noise-induced reversal of selection

Theoretical studies from diverse areas of population biology have shown that demographic stochasticity can substantially impact evolutionary dynamics in finite populations, including scenarios where traits that are disfavored by natural selection can nevertheless increase in frequency through the course of evolution. Here, we analytically describe the eco-evolutionary dynamics of finite populations from demographic first principles to investigate how noise-induced effects can alter the evolutionary fate of populations in which total population size may vary stochastically over time. Starting from a generic birth-death process describing a finite population of individuals with discrete traits, we derive a set of stochastic differential equations (SDEs) that recover well-known descriptions of evolutionary dynamics such as the replicator-mutator equation, the Price equation, and Fishers fundamental theorem in the infinite population limit. For finite populations, our SDEs reveal how stochasticity can predictably bias evolutionary trajectories to favour certain traits, a phenomenon we call noise-induced biasing. We show that noise-induced biasing acts through two distinct mechanisms that we call the direct and indirect mechanisms. While the direct mechanism can be identified with classic bet-hedging theory, the indirect mechanism is a more subtle consequence of frequency and density-dependent demographic stochasticity. Our equations reveal that noise-induced biasing may lead to evolution proceeding in a direction opposite to that predicted by natural selection in the infinite population limit. By extending and generalizing some standard equations of population genetics, we thus describe how demographic stochasticity appears alongside and interacts with the more well-understood forces of natural selection and neutral drift to determine the eco-evolutionary dynamics of finite populations of non-constant size.

evolutionary biology↗

bioRxiv2023

Leadership and information transfer in groups escaping a (simulated) threat in the wild

Collective motion in many species is believed to be driven by predation, which is considered a crucial evolutionary force. However, there are limited studies on how information about predators spreads through a group, particularly in natural settings. Here, using high-resolution drone-based recordings, we analyze the collective escape dynamics of a group living species - blackbuck (Antilope cervicara), an Indian antelope - under the conditions of a simulated threat in their natural grassland habitat. Our analysis reveals that, in response to the simulated threat, group cohesion first increases, followed by a simultaneous increase in median speed and polarization. We also observe the emergence of a temporary leader-follower relationship during the collective escape. Interestingly, we found that the distance of individuals from the "predator" affected only their response time, not their influence on the group movement. The individuals furthest from the threat were the least likely to initiate movement and were uncoordinated with the groups speed. Additionally, we discovered that the most influential individuals during the collective escape were the least likely to occupy front positions during group movement. Instead, our results indicate that individuals at the rear "push" individuals at the front during collective escape events. This contrasts with the typical notion of individuals at the front determining group movement and those at the back trying to catch up.

animal behavior and cognition↗

bioRxiv2021

Characterising the vegetation-rainfall relationship in the Northeast Himalaya, India

Ecosystems are complex systems and are characterised by positive and negative feedbacks between the abiotic and biotic components. The response of an ecosystem to its environment can be determined by examining state diagrams, which are plots of the state variable as a function of the environmental driver. For instance, tree cover as a function of rainfall is widely used to characterise vegetation patterns. Previous studies have shown that tree cover shows bimodal distributions for intermediate rainfall regimes in Africa and South America. In this study, we construct a vegetation state diagram - by plotting vegetation cover as a function of mean annual rainfall - for Northeast India, which is part of the Eastern Himalaya and the Indo-Burma biodiversity hotspot. We use remotely sensed satellite data of Enhanced Vegetation Index (EVI) as a proxy for vegetation cover. We obtain Mean Annual Precipitation (MAP) from the CHIRPS data (Climate Hazards Group InfraRed Precipitation with Station data). We find that EVI increases monotonically as a function of MAP in the range 1000-2000 mm, after which it plateaus. The 1000 to 2000 mm MAP corresponds to the vegetation transitional zone (1200-3700 m), whereas >2000 MAP region covers the greater extent of the tropical forest (<1200 m) of NEI. In other words, we find no evidence for bimodality in tree cover or vegetation states at coarser scales in North Eastern India. Our characterisation of the state diagram for vegetation in northeast India is important to understand response to ongoing change in rainfall patterns.

ecology↗

bioRxiv2020

Multi-Object Tracking in Heterogeneous environments (MOTHe) for animal space-use studies

O_LIVideo recordings of animals are used for many areas of research such as collective movement, animal space-use, animal censuses and behavioural neuroscience. They provide us with behavioural data at scales and resolutions not possible with manual observations. Many automated methods are being developed to extract data from these high-resolution videos. However, the task of animal detection and tracking for videos taken in natural settings remains challenging due to heterogeneous environments. C_LIO_LIWe present an open-source end-to-end pipeline called Multi-Object Tracking in Heterogenous environments (MOTHe), a python-based application that uses a basic convolutional neural network for object detection. MOTHe allows researchers with minimal coding experience to track multiple animals in their natural habitats. It identifies animals even when individuals are stationary or partially camouflaged. C_LIO_LIMOTHe has a command-line-based interface with one command for each action, for example, finding animals in an image and tracking each individual. Parameters used by the algorithm are well described in a configuration file along with example values for different types of tracking scenario. MOTHe doesnt require any sophisticated infrastructure and can be run on basic desktop computing units. C_LIO_LIWe demonstrate MOTHe on six video clips from two species in their natural habitat - wasp colonies on their nests (up to 12 individuals per colony) and antelope herds in four different types of habitats (up to 156 individuals in a herd). Using MOTHe, we are able to detect and track all individuals in these animal group videos. MOTHes computing time on a personal computer with 4 GB RAM and i5 processor is 5 minutes for a 30-second long ultra-HD (4K resolution) video recorded at 30 frames per second. C_LIO_LIMOTHe is available as an open-source repository with a detailed user guide and demonstrations at Github (https://github.com/tee-lab/MOTHe). C_LI

ecology↗

bioRxiv2018

Dispersal increases the resilience of tropical savanna and forest distributions

Global change may induce changes in savanna and forest distributions, but the dynamics of these changes remain unclear. Classical biome theory suggests that climate is predictive of biome distributions, such that shifts will be continuous and reversible. This view, however, cannot explain a widely observed mismatch between climate and tree cover, which some argue results from fire-vegetation feedbacks maintaining savanna and forest as bistable states, such that, instead, shifts will be discontinuous and irreversible. This bistable model, however, cannot reproduce the spatial aggregation of biomes. Here, we suggest that both models are limited in that they ignore spatial processes, such as dispersal. We examine the contributions of dispersal to determining savanna and forest distributions using a reaction-diffusion model, comparing results qualitatively to empirical savanna and forest distributions in Africa. The diffusion model induces spatially aggregated distributions, separated by a stable savanna-forest boundary. The equilibrium position of that boundary depends not only on precipitation but also on the curvature of precipitation contours with some history dependence (although less than in the bistable model). This model predicts different dynamics in response to global change: the boundary continuously tracks climate, recovering following disturbances, unless remnant biome patches are too small.

ecology↗

bioRxiv2017

Patchiness and scale-free correlations: characterising criticality in ecosystems.

I.O_LIIn diverse ecosystems, organisms cluster together in such a manner that the frequency distribution of cluster sizes is a power-law function. Spatially-explicit models of ecosystems suggest that loss of such power-law clustering may indicate loss of ecosystem resilience. Hence, it is hypothesised that spatial clustering properties in ecosystems - which can be readily measured using remotely-sensed high-resolution data - can help infer proximity to ecosystem thresholds and may even provide early warning signals of ecosystem collapse. Recent empirical and simulation studies, however, dont find consistent relationships between spatial clustering and ecosystem resilience. Furthermore, how spatial clustering metrics relate to other well-known early warning signals of ecosystems collapse, specifically the phenomenon of critical slowing down (CSD), remains unclear. C_LIO_LIWe synthesize the literature on cluster sizes in empirical and theoretical studies that show how local interactions (especially, positive feedback) among organisms can cause power-law clustering. In addition, we analyse a minimal spatial model of ecosystem transitions that allows us to disentangle the role of environmental stressor and positive feedback on spatial patterns and ecosystem resilience. C_LIO_LIOur literature synthesis reveals that empirically observed power-law clustering in ecosystems is parsimoniously explained by local positive feedback. Our synthesis together with model analysis demonstrates that, depending on the strength of positive feedback, emergence of power-law clustering can occur at any distance from the critical threshold of ecosystem collapse. In fact, we find that for systems with strong positive feedbacks, which are most likely to exhibit abrupt transitions, loss of power-law clustering may not even occur prior to ecosystem thresholds. We also argue that cluster-size distributions are unrelated to the phenomenon of CSD. C_LIO_LIWe demonstrate that, due to CSD, a power-law feature does occur near critical thresholds but in a different quantity; specifically, a power-law decay of spatial correlations of ecosystem state. C_LIO_LIWe conclude that loss of power-law clustering cannot be used as a reliable indicator of ecosystem resilience. Our synthesis and model analyses highlights links between local positive feedback, emergent spatial properties and how they may be used to interpret ecosystem resilience. C_LI

ecology↗

bioRxiv2017

Inferring critical points of ecosystem transitions from spatial data

Ecosystems can undergo abrupt transitions from one state to an alternative stable state when the driver crosses a threshold or a critical point. Dynamical systems theory suggests that systems take long to recover from perturbations near such transitions. This leads to characteristic changes in the dynamics of the system, which can be used as early warning signals of imminent transitions. However, these signals are qualitative and cannot quantify the critical points. Here, we propose a method to estimate critical points quantitatively from spatial data. We employ a spatial model of vegetation that shows a transition from vegetated to bare state. We show that the critical point can be estimated as the ecosystem state and the driver values at which spatial variance and autocorrelation are maximum. We demonstrate the validity of this method by analysing spatial data from regions of Africa and Australia that exhibit alternative vegetation biomes.

ecology↗

bioRxiv2017

Mobility can promote the evolution of cooperation via emergent self-assortment dynamics

The evolution of costly cooperation, where cooperators pay a personal cost to benefit others, requires that cooperators interact more frequently with other cooperators. This condition, called positive assortment, is known to occur in spatially-structured viscous populations, where individuals typically have low mobility and limited dispersal. However many social organisms across taxa, from cells and bacteria, to birds, fish and ungulates, are mobile, and live in populations with considerable inter-group mixing. In the absence of information regarding others traits or conditional strategies, such mixing may inhibit assortment and limit the potential for cooperation to evolve. Here we employ spatially-explicit individual-based evolutionary simulations to incorporate costs and benefits of two coevolving costly traits: cooperative and local cohesive tendencies. We demonstrate that, despite possessing no information about others traits or payoffs, mobility (via self-propulsion or environmental forcing) facilitates assortment of cooperators via a dynamically evolving difference in the cohesive tendencies of cooperators and defectors. We show analytically that this assortment can also be viewed in a multilevel selection framework, where selection for cooperation among emergent groups can overcome selection against cooperators within the groups. As a result of these dynamics, we find an oscillatory pattern of cooperation and defection that maintains cooperation even in the absence of well known mechanisms such as kin interactions, reciprocity, local dispersal or conditional strategies that require information on others strategies or payoffs. Our results offer insights into differential adhesion based mechanisms for positive assortment and reveal the possibility of cooperative aggregations in dynamic fission-fusion populations.\n\nAuthor SummaryCooperation among animals is ubiquitous. In a cooperative interaction, the cooperator confers a benefit to its partner at a personal cost. How does natural selection favour such a costly behaviour? Classical theories argue that cooperative interactions among genetic relatives, reciprocal cooperators, or among individuals within groups in viscous population structures are necessary to maintain cooperation. However, many organisms are mobile, and live in dynamic (fission-fusion) groups that constantly merge and split. In such populations, the above mechanisms may be inadequate to explain cooperation. Here, we develop a minimal model that explicitly accounts for mobility and cohesion among organisms. We find that mobility can support cooperation via emergent dynamic groups, even in the absence of previously known mechanisms. Our results may offer insights into the evolution of cooperation in animals that live in fission fusion groups, such as birds, fish or mammals, or microbes living in turbulent media, such as in oceans or in the bloodstreams of animal hosts.

evolutionary biology↗