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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]