The effect of changing topography on the coordinated marching of locust nymphs

Locust behavior and city topology: A biodynamic approach for assessing urban flows

A city's economic growth and the inhabitants' wellbeing are highly affected by its topology and connecting networks, which, in turn, influence movement and flows in the city. Flow relates to how a city is developed, organized, managed, and built. The analysis of flow in cities is challenging but essential. In this study, the fields of urban design and animal science are combined, and a new approach for exploring the relationships between urban topology and physical flow is developed. Specifically, we establish an interdisciplinary methodology to evaluate mobility performance in various urban settings, utilizing experimental observations of the dynamic behavior of natural-biological agents, i.e., locusts, within physical city models. Our novel approach enriches the currently available toolbox by using living organisms as indicators for flow in physical city models. Our findings improve our understanding of the intricate flow interactions in urban settings.

Revealing the mechanism and function underlying pairwise temporal coupling in collective motion

Coordinated motion in animal groups has predominantly been studied with a focus on spatial interactions, such as how individuals position and orient themselves relative to one another. Temporal aspects have, by contrast, received much less attention. Here, by studying pairwise interactions in juvenile zebrafish (Danio rerio)-including using immersive volumetric virtual reality (VR) with which we can directly test models of social interactions in situ-we reveal that there exists a rhythmic out-of-phase (i.e., an alternating) temporal coordination dynamic. We find that reciprocal (bi-directional) feedback is both necessary and sufficient to explain this emergent coupling. Beyond a mechanistic understanding, we find, both from VR experiments and analysis of freely swimming pairs, that temporal coordination considerably improves spatial responsiveness, such as to changes in the direction of motion of a partner. Our findings highlight the synergistic role of spatial and temporal coupling in facilitating effective communication between individuals on the move.

An effective hydrodynamic description of marching locusts

A fundamental question in complex systems is how to relate interactions between individual components ('microscopic description') to the global properties of the system ('macroscopic description'). Furthermore, it is unclear whether such a macroscopic description exists and if such a description can capture large-scale properties. Here, we address the validity of a macroscopic description of a complex biological system using the collective motion of desert locusts as a canonical example. One of the world's most devastating insect plagues begins when flightless juvenile locusts form 'marching bands'. These bands display remarkable coordinated motion, moving through semiarid habitats in search of food. We investigated how well macroscopic physical models can describe the flow of locusts within a band. For this, we filmed locusts within marching bands during an outbreak in Kenya and automatically tracked all individuals passing through the camera frame. We first analyzed the spatial topology of nearest neighbors and found individuals to be isotropically distributed. Despite this apparent randomness, a local order was observed in regions of high density in the radial distribution function, akin to an ordered fluid. Furthermore, reconstructing individual locust trajectories revealed a highly aligned movement, consistent with the one-dimensional version of the Toner-Tu equations, a generalization of the Navier-Stokes equations for fluids, used to describe the equivalent macroscopic fluid properties of active particles. Using this effective Toner-Tu equation, which relates the gradient of the pressure to the acceleration, we show that the effective 'pressure' of locusts increases as a linear function of density in segments with the highest polarization (for which the one-dimensional approximation is most appropriate). Our study thus demonstrates an effective hydrodynamic description of flow dynamics in plague locust swarms.

Vision-based collective motion: A locust-inspired reductionist model

Naturally occurring collective motion is a fascinating phenomenon in which swarming individuals aggregate and coordinate their motion. Many theoretical models of swarming assume idealized, perfect perceptual capabilities, and ignore the underlying perception processes, particularly for agents relying on visual perception. Specifically, biological vision in many swarming animals, such as locusts, utilizes monocular non-stereoscopic vision, which prevents perfect acquisition of distances and velocities. Moreover, swarming peers can visually occlude each other, further introducing estimation errors. In this study, we explore necessary conditions for the emergence of ordered collective motion under restricted conditions, using non-stereoscopic, monocular vision. We present a model of vision-based collective motion for locust-like agents: elongated shape, omni-directional visual sensor parallel to the horizontal plane, and lacking stereoscopic depth perception. The model addresses (i) the non-stereoscopic estimation of distance and velocity, (ii) the presence of occlusions in the visual field. We consider and compare three strategies that an agent may use to interpret partially-occluded visual information at the cost of the computational complexity required for the visual perception processes. Computer-simulated experiments conducted in various geometrical environments (toroidal, corridor, and ring-shaped arenas) demonstrate that the models can result in an ordered or near-ordered state. At the same time, they differ in the rate at which order is achieved. Moreover, the results are sensitive to the elongation of the agents. Experiments in geometrically constrained environments reveal differences between the models and elucidate possible tradeoffs in using them to control swarming agents. These suggest avenues for further study in biology and robotics.

A Locust-Inspired Model of Collective Marching on Rings

We study the collective motion of autonomous mobile agents in a ringlike environment. The agents’ dynamics are inspired by known laboratory experiments on the dynamics of locust swarms. In these experiments, locusts placed at arbitrary locations and initial orientations on a ring-shaped arena are observed to eventually all march in the same direction. In this work we ask whether, and how fast, a similar phenomenon occurs in a stochastic swarm of simple locust-inspired agents. The agents are randomly initiated as marching either clockwise or counterclockwise on a discretized, wide ring-shaped region, which we subdivide into k concentric tracks of length n. Collisions cause agents to change their direction of motion. To avoid this, agents may decide to switch tracks to merge with platoons of agents marching in their direction. We prove that such agents must eventually converge to a local consensus about their direction of motion, meaning that all agents on each narrow track must eventually march in the same direction. We give asymptotic bounds for the expected time it takes for such convergence or “stabilization” to occur, which depends on the number of agents, the length of the tracks, and the number of tracks. We show that when agents also have a small probability of “erratic”, random track-jumping behavior, a global consensus on the direction of motion across all tracks will eventually be reached. Finally, we verify our theoretical findings in numerical simulations.

Dynamic Forecast of Desert Locust Presence Using Machine Learning with a Multivariate Time Lag Sliding Window Technique

Desert locust plagues can easily cause a regional food crisis and thus affect social stability. Preventive control of the disaster highlights the early detection of hopper gregarization before they form devastating swarms. However, the response of hopper band emergence to environmental fluctuation exhibits a time lag. To realize the dynamic forecast of band occurrence with optimal temporal predictors, we proposed an SVM-based model with a temporal sliding window technique by coupling multisource time-series imagery with historical locust ground survey observations from between 2000–2020. The sliding window method was based on a lagging variable importance ranking used to analyze the temporal organization of environmental indicators in band-forming sequences and eventually facilitate the early prediction of band emergence. Statistical results show that hopper bands are more likely to occur within 41–64 days after increased rainfall; soil moisture dynamics increasing by approximately 0.05 m³/m³ then decreasing may enhance the chance of observing bands after 73–80 days. While sparse vegetation areas with NDVI increasing from 0.18 to 0.25 tend to witness bands after 17–40 days. The forecast model combining the optimal time lags of these dynamic indicators with other static indicators allows for a 16-day extended outlook of band presence in Somalia, Ethiopia, and Kenya. Monthly predictions from February to December 2020 display an overall accuracy of 77.46%, with an average ROC-AUC of 0.767 and a mean F-score close to 0.772. The multivariate forecast framework based on the lagging effect can realize the early warning of band presence in different spatiotemporal scenarios, supporting early decisions and response strategies for desert locust preventive management.

Band movement and thermoregulation in Schistocerca cancellata

At high density, juvenile locusts create marching hopper bands. Understanding the roles of temperature and vegetation on the movement of these bands shall allow to better forecast and control them. Following a hopper band in North Argentina in November 2019, we explored the thermoregulation behaviours of the South American locust, Schistocerca cancellata. Gut-content samples informed about the feeding status at different time of the day. Hoppers' body temperature was above cold air temperature in the mornings during basking and group-basking activities and before the onset of marching behaviour. Marching by walking or hopping was dominant at body temperatures close to 40 °C. Jumping, stilting, shading and perching on plants were seen as thermoregulatory behaviours to avoid ground temperatures above 50 °C. Feeding was observed throughout the day with continuous high gut contents despite an intermittent pattern of feeding-resting-marching. Speed and daily travelled distance of the front of the hopper band was depending on the type of encountered vegetation. Daily behavioural patterns, thermoregulatory behaviours, walking speed and daily travelled distances of S. cancellata were similar to the ones observed for the Desert locust, S. gregaria, in Africa. High air temperatures recorded during the observation times could explain the continuous feeding patterns. These species may have evolved behaviours of alternating consuming a bit and marching as a migration strategy to avoid staying where no food is available after the havoc left behind large hopper bands. Recommendations made for the control of Desert locust hopper bands can be extended to South American locust ones.

Intra- versus intergroup variance in collective behavior

Animal collective motion arises from the intricate interactions between the natural variability among individuals, and the homogenizing effect of the group, working to generate synchronization and maintain coherence. Here, these interactions were studied using marching locust nymphs under controlled laboratory settings. A novel experimental approach compared single animals, small groups, and virtual groups composed of randomly shuffled real members. We found that the locust groups developed unique, group-specific behavioral characteristics, reflected in large intergroup and small intragroup variance (compared with the shuffled groups). Behavioral features that differed between single animals and groups, but not between group types, were classified as essential for swarm formation. Comparison with Markov chain models showed that individual tendencies and the interaction network among animals dictate the group characteristics. Deciphering the bidirectional interactions between individual and group properties is essential for understanding the swarm phenomenon and predicting large-scale swarm behaviors.