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Researchers often measure the flight initiation distance (FID) of a selected individual to represent the FID of the whole groups which may vary with their flight order (the sequence to flee) and their spatial position within a group. In this study, we examined FID of all individuals in small bird flocks in urban environments when approached by an investigator. We found that individuals' FID did not vary with their flight orders within these flocks. Moreover, the FID of the nearest individual, often used as a proxy for the entire flock, did not differ from the average FID of all flock members. This suggests that the conventional method of measuring the nearest individual's FID to represent is a valid approximation for representing the behaviour of the entire flock in similar contexts. These results have important implications for understanding anti-predator strategies in group-living animals and can inform future studies in this field. [ABSTRACT FROM AUTHOR]

Novel approaches to quantifying density and distributions could help biologists adaptively manage wildlife populations, particularly if methods are accurate, consistent, cost‐effective, rapid, and sensitive to change. Such approaches may also improve research on interactions between density and processes of interest, such as disease transmission across multiple populations. We assess how satellite imagery, unmanned aerial system (UAS) imagery, and Global Positioning System (GPS) collar data vary in characterizing elk density, distribution, and count patterns across times with and without supplemental feeding at the National Elk Refuge (NER) in the US state of Wyoming. We also present the first comparison of satellite imagery data with traditional counts for ungulates in a temperate system. We further evaluate seven different aggregation metrics to identify the most consistent and sensitive metrics for comparing density and distribution across time and populations. All three data sources detected higher densities and aggregation locations of elk during supplemental feeding than non‐feeding at the NER. Kernel density estimates (KDEs), KDE polygon areas, and the first quantile of interelk distances detected differences with the highest sensitivity and were most highly correlated across data sources. Both UAS and satellite imagery provide snapshots of density and distribution patterns of most animals in the area at lower cost than GPS collars. While satellite‐based counts were lower than traditional counts, aggregation metrics matched those from UAS and GPS data sources when animals appeared in high contrast to the landscape, including brown elk against new snow in open areas. UAS counts of elk were similar to traditional ground‐based counts on feed grounds and are the best data source for assessing changes in small spatial extents. Satellite, UAS, or GPS data can provide appropriate data for assessing density and changes in density from adaptive management actions. For the NER, where high elk densities are beneath controlled airspace, GPS collar data will be most useful for evaluating how management actions, including changes in the dates of supplemental feeding, influence elk density and aggregation across large spatial extents. Using consistent and sensitive measures of density may improve research on the drivers and effects of density within and across a wide range of species. [ABSTRACT FROM AUTHOR]

The spatial organization of populations determines their pathogen dynamics. This is particularly important for communally roosting species, whose aggregations are often driven by the spatial structure of their environment.We develop a spatially explicit model for virus transmission within roosts of Australian tree‐dwelling bats (Pteropus spp.), parameterized to reflect Hendra virus. The spatial structure of roosts mirrors three study sites, and viral transmission between groups of bats in trees was modelled as a function of distance between roost trees. Using three levels of tree density to reflect anthropogenic changes in bat habitats, we investigate the potential effects of recent ecological shifts in Australia on the dynamics of zoonotic viruses in reservoir hosts.We show that simulated infection dynamics in spatially structured roosts differ from that of mean‐field models for equivalently sized populations, highlighting the importance of spatial structure in disease models of gregarious taxa. Under contrasting scenarios of flying‐fox roosting structures, sparse stand structures (with fewer trees but more bats per tree) generate higher probabilities of successful outbreaks, larger and faster epidemics, and shorter virus extinction times, compared to intermediate and dense stand structures with more trees but fewer bats per tree. These observations are consistent with the greater force of infection generated by structured populations with less numerous but larger infected groups, and may flag an increased risk of pathogen spillover from these increasingly abundant roost types.Outputs from our models contribute insights into the spread of viruses in structured animal populations, like communally roosting species, as well as specific insights into Hendra virus infection dynamics and spillover risk in a situation of changing host ecology. These insights will be relevant for modelling other zoonotic viruses in wildlife reservoir hosts in response to habitat modification and changing populations, including coronaviruses like SARS‐CoV‐2. [ABSTRACT FROM AUTHOR]

Many migratory animals regularly travel thousands of kilometers, exactly finding their seasonal destinations. The nature of this ability is still not fully understood. The aggregation of animals in groups and their socially coordinated movement is considered a way to eliminate navigational errors. Orientation accuracy of a group may be significantly higher as the errors caused by a variety of casual factors are averaged due to social interactions. This idea, called the “many wrongs principle,” has been confirmed both in behavioral experiments and numerical simulations. However, little is known about the dependence of this effect on the number N of individuals. Until now, there were no analytical models considering this effect and its limitations. In this article, a stochastic dynamic model of group navigation is presented in terms of the course deviation angle and its variance. The N -dependence of the variance of deviations is found. The variance first decreases with N , however the decrease then slows down thus showing disagreement with the “many wrongs principle.” This can be interpreted as meaning that the growth in the accuracy of migration due to the aggregation of individuals into groups is limited. The limit depends on the individual sensitivity of the animal compass, the power of the herd instinct, and the level of random noise. [ABSTRACT FROM AUTHOR]

Pattern formation in self-organized biological aggregation is a phenomenon that has been studied intensively over the past 20 years. In general, the studies on pattern formation focus mainly on identifying the biological mechanisms that generate these patterns. However, identifying the mathematical mechanisms behind these patterns is equally important, since it can offer information on the biological parameters that could contribute to the persistence of some patterns and the disappearance of other patterns. Also, it can offer information on the mechanisms that trigger transitions between different patterns (associated with different group behaviors). In this article, we focus on a class of nonlocal hyperbolic models for self-organized aggregations and show that these models are O(2)-equivariant. We then use group-theoretic methods, linear analysis, weakly nonlinear analysis, and numerical simulations to investigate the large variety of patterns that arise through O(2)-symmetric codimension-two bifurcations (i.e., Hopf/Hopf, steady-state/Hopf, and steady-state/steady-state mode interactions). We classify the bifurcating solutions according to their isotropy types (subgroups), and we determine the criticality and stability of primary branches of solutions. We numerically show the existence of these solutions and determine scenarios of secondary bifurcations. Also, we discuss the secondary bifurcating solutions from the biological perspective of transitions between different group behaviors. [ABSTRACT FROM AUTHOR]

We study the cumulative effect of successive predator attacks on the disturbance of a prey aggregation using a modelling approach. Our model intends to represent fish schools attacked by both aerial and underwater predators. This individual-based model uses long-distance attraction and short-distance repulsion between prey, which leads to prey aggregation and swarming in the absence of predators. When intermediate-distance alignment is added to the model, the prey aggregation displays a cohesive displacement, i.e., schooling, instead of swarming. Including predators, i.e. with repulsion behaviour for prey to predators in the model, leads to flash expansion of the prey aggregation after a predator attack. When several predators attack successively, the prey aggregation dynamics is a succession of expanding-grouping-swarming/schooling phases. We quantify this dynamics by recording the changes in the simulated prey aggregation radius over time. This radius is computed as the longest distance of individual prey to the aggregation centroid, and it is assumed to increase along with prey disturbance. The prey aggregation radius generally increases during flash expansion, then decreases during grouping until reaching a constant lowest level during swarming/schooling. This general dynamics is modulated by several parameters: the frequency, direction (vertical vs. horizontal) and target (centroid of the prey aggregation vs. random prey) of predator attacks; the distance at which prey detect predators; the number of prey and predators. Our results suggest that both aerial and underwater predators are more efficient at disturbing fish schools by increasing their attack frequency at such level that the fish cannot return to swarming/schooling. We find that a mix between aerial and underwater predators is more efficient at disturbing a fish school than a single type of attack, suggesting that aerial and underwater foragers may gain mutual benefits in forming foraging groups. [ABSTRACT FROM AUTHOR]

The origin of regular spatial patterns in ecological systems has long fascinated researchers. Turing's activator-inhibitor principle is considered the central paradigm to explain such patterns. According to this principle, local activation combined with long-range inhibition of growth and survival is an essential prerequisite for pattern formation. Here, we show that the physical principle of phase separation, solely based on density-dependent movement by organisms, represents an alternative class of self-organized pattern formation in ecology. Using experiments with self-organizing mussel beds, we derive an empirical relation between the speed of animal movement and local animal density. By incorporating this relation in a partial differential equation, we demonstrate that this model corresponds mathematically to the well-known Cahn-Hilliard equation for phase separation in physics. Finally, we show that the predicted patterns match those found both in field observations and in our experiments. Our results reveal a principle for ecological self-organization, where phase separation rather than activation and inhibition processes drives spatial pattern formation. [ABSTRACT FROM AUTHOR]

Many animals aggregate into organized temporary or stable groups under the influence of biotic and abiotic factors, and some studies have shown the influence of habitat features on animal aggregation. This study, conducted from 2002 to 2004 in the Dzanga-Ndoki National Park, Central African Republic, studied a herd of forest buffaloes ( Syncerus caffer nanus) to determine whether spatial aggregation patterns varied by season and habitat. Our results show that both habitat structure and season influenced spatial aggregation patterns. In particular, in open habitats such as clearings, the group covered a larger area when resting and was more rounded in shape compared to group properties noted in forest during the wet season. Moreover, forest buffaloes had a more aggregated spatial distribution when resting in clearings than when in the forest, and individual positions within the herd in the clearing habitat varied with age and sex. In the clearings, the adult male ( n = 24) was generally, on most occasions, located in the centre of the herd ( n = 20), and he was observed at the border only four times. In contrast, females ( n = 80) occupied intermediate ( n = 57), peripheral ( n = 14) and central positions ( n = 9) within the group. Juveniles ( n = 77) also occurred in intermediate ( n = 64) and peripheral positions ( n = 13). Based on these results, we concluded that habitat characteristics and social behaviour can have relevant effects on the spatial distribution of animals within a group. [ABSTRACT FROM AUTHOR]

Abstract: Grouping in animals is ubiquitous and thought to provide group members antipredatory advantages and foraging efficiency. However, parasitic foraging strategy often emerges in a group. The optimal parasitic policy has given rise to the producer–scrounger (PS) game model, in which producers search for food patches, and scroungers parasitize the discovered patches. The N-persons PS game model constructed by Vickery et al. (1991. Producers, scroungers, and group foraging. American Naturalist 137, 847–863) predicts the evolutionarily stable strategy (ESS) of frequency of producers that depends on the advantage of producers and the number of foragers in a group. However, the model assumes that the number of discovered patches in one time unit never exceeds one. In reality, multiple patches could be found in one time unit. In the present study, we relax this assumption and assumed that the number of discovered patches depends on the producers’ variable encounter rate with patches (λ). We show that strongly depends on λ within a feasible range, although it still depends on the advantage of producer and the number of foragers in a group. The basic idea of PS game is the same as the information sharing (parasitism), because scroungers are also thought to parasitize informations of locations of food patches. Horn (1968) indicated the role of information-parasitism in animal aggregation (Horn, H.S., 1968. The adaptive significance of colonial nesting in the Brewer''s blackbird (euphagus cyanocephalus). Ecology 49, 682–646). Our modified PS game model shows the same prediction as the Horn''s graphical animal aggregation model; the proportion of scroungers will increase or animals should adopt colonial foraging when resource is spatiotemporally clumped, but scroungers will decrease or animals should adopt territorial foraging if the resource is evenly distributed. [Copyright &y& Elsevier]

Abstract: We develop a genetic algorithm (GA) approach to a well-known model of vigilance behaviour in a group of animals. We first demonstrate that the GA approach can provide a good match to analytic solutions to the original model. We demonstrate that a GA can be used to find the evolutionarily stable strategies in a model relevant to behavioural ecology where the fitness of each strategy is determined by the frequencies of different strategies in the population. We argue that the GA implementation demonstrates the combination of assumptions used to generate analytic solution to the original model can only be simultaneously satisfied under relatively restrictive conditions on the ecology of the species involved; specifically that group membership is very fluid but group size is conserved over timescales of individual foraging bouts. We further explore the sensitivity of model predictions to alternative choices in the implementation of the GA, and present advice for implementation and presentation of similar models. In particular, we emphasise the need for care in measuring the predictions of such models, so as to capture the intrinsic behaviour of the system and not the remnant of often arbitrarily chosen initial conditions. We also emphasise the potential for GA models to be more transparent about model assumptions regarding underlying biology than analytic models. [Copyright &y& Elsevier]

We explore the phenomenon of animal self-organization due to autotaxis, that is, the movement of individuals induced by their own density gradient. There is natural evidence that clustering occurs as a result of the interplay between random and directed movements of individuals due to mutual attraction and repulsion. Classically, it is assumed that taxis velocity is determined by the density gradient of some stimulus. However, it is known that partial differential equation (PDE) diffusion-advection models that rest on this assumption cannot give a realistic representation of a stationary or moving cohesive group of individuals with a uniform interior density and sharp edges. Pioneering work by Okubo and coworkers suggests that the acceleration of individuals (rather than their velocity directly) is proportional to the population density gradient. A PDE model resting on this finding was constructed and investigated. The model demonstrates the formation of steady heterogeneous structures of the required shape. This feature can be interpreted as dynamic self-organization, like fish shoaling or insect swarming. This model is the first to achieve this result while considering an autonomous population in a simple PDE framework. Analytical and numerical studies show that the link between the acceleration and the density gradient is crucial for the appearance of clusters. [ABSTRACT FROM AUTHOR]

The origin of regular spatial patterns in ecological systems has long fascinated researchers. Turing's activator-inhibitor principle is considered the central paradigm to explain such patterns. According to this principle, local activation combined with long-range inhibition of growth and survival is an essential prerequisite for pattern formation. Here, we show that the physical principle of phase separation, solely based on density-dependent movement by organisms, represents an alternative class of self-organized pattern formation in ecology. Using experiments with self-organizing mussel beds, we derive an empirical relation between the speed of animal movement and local animal density. By incorporating this relation in a partial differential equation, we demonstrate that this model corresponds mathematically to the well-known Cahn-Hilliard equation for phase separation in physics. Finally, we show that the predicted patterns match those found both in field observations and in our experiments. Our results reveal a principle for ecological self-organization, where phase separation rather than activation and inhibition processes drives spatial pattern formation.