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32 results for “Animal selection”
Fig. 1 in A Review Of Major Impact Factors Of Hostilities Influencing Biodiversity In The Eastern Ukraine (Modeled On Selected Animal Species)
Fig. 1. Spatial distribution of ignitions in 2010–2014 on studied area (dotted line is ATO zone's limits in 1.06– 30.09.2014).
Fig. 5 in A Review Of Major Impact Factors Of Hostilities Influencing Biodiversity In The Eastern Ukraine (Modeled On Selected Animal Species)
Fig. 5. Distribution of two snake species, H. caspius and E. dione, in Ukrainian East (ATO zone is indicated by dotted line, burnt area marked inside zone).
Fig. 3 in A Review Of Major Impact Factors Of Hostilities Influencing Biodiversity In The Eastern Ukraine (Modeled On Selected Animal Species)
Fig. 3. Spatial local distribution of ignitions in 2010–2014 in the outskirts of Slavyanoserbsk, Luhansk Region.
Code and data for: Manipulating actions: a selective two-option device for cognitive experiments in wild animals
<p>Code and data for recreating survival analyses in 'Manipulating actions: a selective two-option device for cognitive experiments in wild animals'</p>
Data from: Forecasting animal distribution through individual habitat selection: Insights for population inference and transferable predictions
<p>Habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance to unobserved areas or periods of time. However, these types of models often poorly predict how animals will use habitat beyond the place and time of data collection because space-use behaviors vary between individuals and are context-dependent. Here, we present a modelling workflow to advance predictive distribution performance by explicitly accounting for individual variability in habitat selection behavior and dependence on environmental context. Using global positioning system (GPS) data collected from 238 individual pronghorn, (<em>Antilocapra americana</em>), across 3 years in Utah, we combine individual-year-season-specific exponential habitat-selection models with weighted mixed-effects regressions to both draw inference about the drivers of habitat selection and predict space-use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter- and intra-individual components. We then used the results to predict population-level, spatially and temporally dynamic, habitat-selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30x30m resolution but an extent of 220,000km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional habitat selection models - variability in habitat selection - into a tool to understand and predict species-habitat associations across space and time.</p>
Data from: Forecasting animal distribution through individual habitat selection: Insights for population inference and transferable predictions
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Fig. 2 in A Review Of Major Impact Factors Of Hostilities Influencing Biodiversity In The Eastern Ukraine (Modeled On Selected Animal Species)
Fig. 2. Temporal distribution of numbers of ignitions in ATO zone in 2010–2014.
Sexual selection and sexual size dimorphism in animals
<p>Sexual selection is often considered as a critical evolutionary force promoting sexual size dimorphism (SSD) in animals. However, empirical evidence for a positive relationship between sexual selection on males and male-biased SSD received mixed support depending on the studied taxonomic group and on the method used to quantify sexual selection. Here, we present a meta-analytic approach accounting for phylogenetic non-independence to test how standardized metrics of the opportunity and strength of pre-copulatory sexual selection relate to SSD across a broad range of animal taxa comprising up to 95 effect sizes from 59 species. We found that SSD based on length measurements was correlated with the sex difference in the opportunity for sexual selection but showed a weak and statistically non-significant relationship with the sex difference in the Bateman gradient. These findings suggest that pre-copulatory sexual selection plays a limited role for the evolution of sexual size dimorphism in a broad phylogenetic context.</p>
Sexual selection and sexual size dimorphism in animals
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Data from: Can interbreeding of wild and artificially propagated animals be prevented by using broodstock selected for a divergent life history?
Two strategies have been proposed to avoid negative genetic effects of artificially propagated individuals on wild populations: (i) integration of wild and captive populations to minimize domestication selection, and (ii) segregation of released individuals from the wild population to minimize interbreeding. We tested the efficacy of the strategy of segregation by divergent life history in a steelhead trout, Oncorhynchus mykiss system, where hatchery fish were selected to spawn months earlier than the indigenous wild population. Up to 80% of the naturally produced steelhead in any given year were hatchery-wild hybrids. The proportion of wild ancestry smolts and adults declined by 10 to 20% over the three generations since the beginning of the program, due to an increase in hybrids. Regression model selection analysis suggested that proportions of hybrid smolts and adults were positively correlated with the number of naturally spawning hatchery-produced adults. Furthermore, the proportion of smolt hybrids was higher in years with high stream discharge allowing hatchery fish to bypass the weir protecting wild spawning habitat. Divergent life history failed to prevent interbreeding when physical isolation was compromised following changes in environmental conditions, an inadequacy of the segregation strategy that is likely to prevail in many other situations.
Data from: Incorporating animal spatial memory in step selection functions
Memory is among the most important and neglected forces that shapes animal movement patterns. Research on the movement-memory interface is crucial to understand how animals use spatial learning to navigate across space because memory-biased navigation is directly linked to animals' space use and home range behaviour; however, because memory cannot be measured directly, it is difficult to account for. Here, we incorporated spatial memory into Step Selection Functions (SSF) to understand how resource selection and spatial memory affect space use of feral hogs (Sus scrofa). We used Biased Random Bridge kernel estimates linked to residence time as a surrogate for memory and tested four conceptually different dynamic maps of spatial memory. We applied this memory-based SSF to a data set of hog relocations to evaluate the importance of land cover type, time of day, and spatial memory on the animals' space use. Our approach has shown how the incorporation of spatial memory into animal movement models can improve estimates of habitat selection. Memory-based SSF provided a feasible way to gain insight into how animals use spatial learning to guide their movement decisions. We found that while hogs selected forested areas and water bodies and avoided grasslands during the day (primarily at noon), they had a strong tendency to select previously visited areas, mainly those held in recent memory. Beyond actively updating their memory with recent experiences, hogs were able to discriminate among spatial memories encoded at different circadian phases of their activity. Even though hogs are thought to have long memory retention, they likely relied on recent experiences because the local food resources are quickly depleted and slowly renewed, yielding an uncertain spatial distribution of resources.
Operational sex ratio predicts the opportunity and direction of sexual selection across animals
<p>The operational sex ratio (OSR) has long been assumed to be a key ecological factor determining the opportunity and direction of sexual selection. However, recent theoretical work has challenged this view, arguing that a biased OSR does not necessarily result in greater monopolisation of mates and therefore stronger sexual selection in the mate-limited sex. Hence, the role of the OSR for shaping animal mating systems remains a conundrum in sexual selection research. Here we took a meta-analytic approach to test whether OSR explains interspecific variation in sexual selec- tion metrics across a broad range of animal taxa. Our results demonstrate that the OSR predicts the opportunity for sexual selection in males and the direction of sexual selection in terms of sex differences in both the opportunity for sexual selection and the Bateman gradient (i.e. the selection differential of mating success), as predicted by classic theory.</p>
Figure 1. Best selected tree from a maximum likelihood reconstruction for the cytochrome c oxidase subunit I in Integrating DNA and morphological taxonomy to describe diversity in poorly studied microscopic animals: new species of the genus Abrochtha Bryce, 1910 (Rotifera: Bdelloidea: Philodinavidae)
Figure 1. Best selected tree from a maximum likelihood reconstruction for the cytochrome c oxidase subunit I data set under the general time reversible model with gamma distribution, displaying all compatible groupings and with average branch lengths proportional to numbers of substitutions per site, indicated by the scale bar. Bootstrap support values above 80% are shown below each branch; posterior probabilities above 0.8 from 36 000 sampled trees from the Bayesian analysis are shown above each branch. Support values for within-species relationships are not shown. Filled circles indicate clades (and singlets) identified by the 4¥ rule; open diamonds indicate clades (and singlets) identified by the generalized mixed yule coalescent model. Names refer to the species and the clonal populations.
Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distributions
<p>Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSF), or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from Golden Eagles (Aquila chrysaetos) from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k-fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.</p>
Data from: Can interbreeding of wild and artificially propagated animals be prevented by using broodstock selected for a divergent life history?
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Data from: Predicting the continuum between corridors and barriers to animal movements using Step Selection Functions and Randomized Shortest Paths
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Data from: Harvest selection on multiple traits in the wild revealed by aquatic animal telemetry
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Data from: Temporal plasticity in habitat selection criteria explains patterns of animal dispersal
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Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distributions
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Data from: Continuous time resource selection analysis for moving animals
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.