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44 results for “Animal groups”
Supplementary material 2 from: Höbart R, Schindler S, Essl F (2020) Perceptions of alien plants and animals and acceptance of control methods among different societal groups. NeoBiota 58: 33-54. https://doi.org/10.3897/neobiota.58.51522
Table S1. Overview on demographic data of survey respondents
Supplementary material 1 from: Höbart R, Schindler S, Essl F (2020) Perceptions of alien plants and animals and acceptance of control methods among different societal groups. NeoBiota 58: 33-54. https://doi.org/10.3897/neobiota.58.51522
Text S1, S2
Data from: Accounting for genetic differences among unknown parents in microevolutionary studies: how to include genetic groups in quantitative genetic animal models
Quantifying and predicting microevolutionary responses to environmental change requires unbiased estimation of quantitative genetic parameters in wild populations. 'Animal models', which utilize pedigree data to separate genetic and environmental effects on phenotypes, provide powerful means to estimate key parameters and have revolutionized quantitative genetic analyses of wild populations. However, pedigrees collected in wild populations commonly contain many individuals with unknown parents. When unknown parents are non-randomly associated with genetic values for focal traits, animal model parameter estimates can be severely biased. Yet, such bias has not previously been highlighted and statistical methods designed to minimize such biases have not been implemented in evolutionary ecology. We first illustrate how the occurrence of non-random unknown parents in population pedigrees can substantially bias animal model predictions of breeding values and estimates of additive genetic variance, and create spurious temporal trends in predicted breeding values in the absence of local selection. We then introduce 'genetic group' methods, which were developed in agricultural science, and explain how these methods can minimize bias in quantitative genetic parameter estimates stemming from genetic heterogeneity among individuals with unknown parents. We summarize the conceptual foundations of genetic group animal models and provide extensive, step-by-step tutorials that demonstrate how to fit such models in a variety of software programs. Furthermore, we provide new functions in r that extend current software capabilities and provide a standardized approach across software programs to implement genetic group methods. Beyond simply alleviating bias, genetic group animal models can directly estimate new parameters pertaining to key biological processes. We discuss one such example, where genetic group methods potentially allow the microevolutionary consequences of local selection to be distinguished from effects of immigration and resulting gene flow. We highlight some remaining limitations of genetic group models and discuss opportunities for further development and application in evolutionary ecology. We suggest that genetic group methods should no longer be overlooked by evolutionary ecologists, but should become standard components of the toolkit for animal model analyses of wild population data sets.
Data from: Distinguishing social from nonsocial navigation in moving animal groups
Many animals, such as migrating shoals of fish, navigate in groups. Knowing the mechanisms involved in animal navigation is important when it comes to explaining navigation accuracy, dispersal patterns, population and evolutionary dynamics and consequently the design of conservation strategies. When navigating towards a common target, animals could interact socially by sharing available information directly or indirectly, or each individual could navigate by itself and aggregations may not disperse because all animals are moving towards the same target. Here, we present an analysis technique that uses individual movement trajectories to determine the extent to which individuals in navigating groups interact socially, given knowledge of their target. The basic idea of our approach is that the movement direction of individuals arises from a combination of responses to the environment and to other individuals. We estimate the relative importance of these responses, distinguishing between social and non-social interactions. We develop and test our method using simulated groups and demonstrate its applicability to empirical data in a case study on groups of guppies moving towards shelter in a tank. Our approach is generic and can be extended to different scenarios of animal group movement.
Data from: Do animals living in larger groups experience greater parasitism? A meta-analysis
Parasitism is widely viewed as the primary cost of sociality and a constraint on group size, yet studies report varied associations between group size and parasitism. Using the largest database of its kind, we performed a meta-analysis of 69 studies of the relationship between group size and parasite risk, as measured by parasitism and immune defenses. We predicted a positive correlation between group size and parasitism with organisms that show contagious and environmental transmission, and a negative correlation for searching parasites, parasitoids, and possibly vector-borne parasites (based on the encounter-dilution effect). Overall, we found a positive effect of group size (r=0.187) that varied in magnitude across transmission modes and measures of parasite risk, with only weak indications of publication bias. Among different groups of hosts, we found a stronger relationship between group size and parasite risk in birds than in mammals, which may be driven by ecological and social factors. A meta-regression showed that effect sizes increased with maximum group size. Phylogenetic meta-analyses revealed no evidence for phylogenetic signal in the strength of the group size-parasitism relationship. We conclude that group size is a weak predictor of parasite risk except in species that live in large aggregations, such as colonial birds, where effect sizes are larger.
Data from: Initiation and spread of escape waves within animal groups
The exceptional reactivity of animal collectives to predatory attacks is thought to be owing to rapid, but local, transfer of information between group members. These groups turn together in unison and produce escape waves. However, it is not clear how escape waves are created from local interactions, nor is it understood how these patterns are shaped by natural selection. By startling schools of fish with a simulated attack in an experimental arena, we demonstrate that changes in the direction and speed by a small percentage of individuals that detect the danger initiate an escape wave. This escape wave consists of a densely packed band of individuals that causes other school members to change direction. In the majority of cases, this wave passes through the entire group. We use a simulation model to demonstrate that this mechanism can, through local interactions alone, produce arbitrarily large escape waves. In the model, when we set the group density to that seen in real fish schools, we find that the risk to the members at the edge of the group is roughly equal to the risk of those within the group. Our experiments and modelling results provide a plausible explanation for how escape waves propagate in nature without centralized control.
Group F: Pendulum_Animation
<p>Group F's model of the pendulum animation in the Euler's model</p>
Figure 2 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 2 - Virtual configurations used for software validation. Virtual configurations used to compile the data presented in the Table 1. Part 2.8 is one of the 10 replicates obtained with a random distribution. All other configurations have been designed in order to reach the desired level of aggregation and affinity between groups. The filled and empty shapes represented two virtual groups in the population.
Figure 1 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 1 - Flow chart of the creation of a new NEIGHBOUR-IN file. This figure presents the different steps in the creation of a new file, from the importation of the snapshot to the calculation of the statistics of dispersion.
Figure 4 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 4 - Spatial distribution in woodlice. Graphic outputs of spatial distribution patterns obtained in three configurations with monospecific or bispecific populations including two groups of eight individuals: a PD-PD: The two groups are Porcellio dilatatus (red and green) b PD-PS: Porcellio dilatatus (red) and Porcellio scaber (green) c PD-AV: Porcellio dilatatus (red) and Armadillidium vulgare (green). The outputs show 64 cells. Each cell is represented with a colour corresponding to the individual(s) in that cell. The colour is mixed using green and red proportional to the number of green and red individuals. If the cell is empty, the colour is black. The intensity of the colour reflects the number of individuals. The position of the individual is determined by its point G (centre-point).
Figure 3 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 3 - Aggregation heterogeneity in woodlice. Aggregation patterns of two groups of woodlice illustrating the Aggregation Heterogenity Index (AHI) and the Spatial Mixed Index (SMI). PD: Porcellio dilatatus, PS: Porcellio scaber, CC: Cylisticus convexus. Values of indexes: PD-PD: AHI=0.93 & SMI=0.80; PD-PS: AHI=0.67 & SMI=0.60; PD-CC: AHI=0.63 & SMI=0.33.
Epidemiology of Resistant Microbial Strains Among Different Groups of People (Healthy, Infected and Exposed to Animals)
ClinicalTrials.gov study NCT03343119. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Data from: Initiation and spread of escape waves within animal groups
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Data from: Accounting for genetic differences among unknown parents in microevolutionary studies: how to include genetic groups in quantitative genetic animal models
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Data from: Distinguishing social from nonsocial navigation in moving animal groups
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Data from: Potential leaders trade off goal-oriented and socially-oriented behavior in mobile animal groups
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Data from: Do animals living in larger groups experience greater parasitism? A meta-analysis
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Gene Expression Analysis in an Animal Model of Cortical Dysplasia-CONTROL GROUP
GEO Series GSE13676. Rattus norvegicus. 9 samples. Type: Expression profiling by array.
Gene Expression Analysis in an Animal Model of Cortical Dysplasia-IRRADIATED GROUP
GEO Series GSE13697. Rattus norvegicus. 9 samples. Type: Expression profiling by array.
Sequencing of Nothobranchius furzeri (strain: MZM-04/10p) brain from Rotenone treated animals in two age groups
GEO Series GSE103804. Nothobranchius furzeri. 20 samples. Type: Expression profiling by high throughput sequencing.
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OpenNeuro
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