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427 results for “OWL”
Characterizing juvenile dispersal dynamics of invasive barred owls: implications for management
<p>Characterizing natal dispersal can help manage the spread of invasive species expanding their ranges in response to land use and climate change. The Barred Owl (<em>Strix varia</em>) is a prominent example of an apex predator undergoing a rapid range expansion, having spread from eastern to western North America where it is now hyperabundant—threatening the Northern Spotted Owl (<em>S. occidentalis caurina</em>) with extinction and potentially endangering many other native species. We attached satellite tags to 31 Barred Owl juveniles at the southern leading edge of the Barred Owl's expanding range in California to characterize natal dispersal patterns and inform management. Juveniles traveled up to 100km from natal territories and experienced high mortality (annual survival = 0.204). At landscape scales, juveniles preferentially used forests, shrublands, and lower elevations during dispersal and avoided grasslands and burned areas. At finer scales, juveniles preferred shorter (younger) forests, lower elevations, and drainages, and avoided unforested areas. Our results suggest the Barred Owl range expansion is being driven primarily by high reproductive rates and densities despite low juvenile survival rates and dispersal through putatively suboptimal younger forests as a result of exclusion from high-quality habitat by territorial individuals. These findings also point to several strategies for conserving Spotted Owls and other native species in the Barred Owl's expanded range, including: (1) creating and maintaining Barred Owl-free reserves bounded by open or high-elevation areas; (2) creating reserves large enough to reduce immigration by long distance dispersers; and (3) removing Barred Owls from large riparian corridors. </p>
Demonstration of semantic and inter-input constraints on software in OWL 2 and SPARQL for fulfilling the M1 Machine FAIR Use Case
<p>This video demonstrates using hypothetical examples how to (1) find a valid dataset for input into a software using OWL 2 classification inference, (2) validly combine two software using OWL 2 subsumption inference to infer that the output of software 1 is valid input to software 2, and (3) combine OWL 2 inference with a SPARQL query to find two datasets that satisfy a software's inter-input constraints.</p>
"How much OWL do you need to know to make sense of building ontologies?" supplementary material
<p>This records contains the ontologies analized in the “How much OWL do you need to know to make sense of building ontologies?” paper presented at “LDAC2024 - Linked Data in Architecture and Construction” workshop. It also includes the resulting estructures and patterns identified as well as a library of graphical pattersn generated with the Chowlk notation (https://chowlk.linkeddata.es/).</p>
Fig. 4 in New Data On Phylogeography Of The Boreal Owl, Aegolius Funereus (Strigiformes, Strigidae), In Eurasia
Fig. 4. Boreal Owl mtDNA CR1 haplotype distribution across it's Eurasian range. Colored dots indicate approximate regions of sampling for individuals possessing the corresponding haplotype. White dots with numbers reflect the total number of unique haplotypes for this region.
Fig. 2 in New Data On Phylogeography Of The Boreal Owl, Aegolius Funereus (Strigiformes, Strigidae), In Eurasia
Fig. 2. Mismatch distribution graph for the pairwise comparison of mtDNA CR1 sequences of Eurasian Boreal Owl population. X axis reflects pairwise difference, Y axis reflects frequency of the difference across sequences; Freq. Obs. is the studied sample's observed mismatch frequency graph, Freq. Exp. is the expected frequency for the sudden expansion model.
Fig. 3 in New Data On Phylogeography Of The Boreal Owl, Aegolius Funereus (Strigiformes, Strigidae), In Eurasia
Fig. 3. Median joining network of mtDNA CR1 haplotypes for the studied Boreal Owl sample. Each circle reflects a mtDNA CR1 haplotype. Circle sizes reflect the number of studied individuals possessing the haplotype; circle colors reflect geographic origin of individuals possessing the haplotype. Bars connect related haplotypes, with notches on bars reflecting the number of nucleotide differences between them. Black dots indicate implied haplotypes not present in the sample.
Fig. 1 in New Data On Phylogeography Of The Boreal Owl, Aegolius Funereus (Strigiformes, Strigidae), In Eurasia
Fig. 1. The source regions of Boreal Owl mtDNA CR1 sequences used in the present study. Numbered yellow circles indicate sampling regions for utilized Boreal Owl mtDNA CR1 sequences, as well as the number of sequences per each region. Green coloration indicates boreal owl's range.
Density-dependent selection and the maintenance of colour polymorphism in barn owls
<p>The capacity of natural selection to generate adaptive changes is according to the Fundamental Theorem of Natural Selection proportional to the additive genetic variance in fitness. In spite of its importance for development of new adaptations to a changing environment, processes affecting the magnitude of the genetic variance in fitness-related traits are poorly understood. Here we show that the red-white colour polymorphism in female barn owls is subject to density-dependent selection at the phenotypic and genotypic level. The diallelic melanocortin-1 receptor (MC1R) gene explained a large amount of the phenotypic variance in reddish colouration in the females (R^2 = 59.8 %). Red individuals (RR genotype) were selected for at low densities, while white individuals (WW genotype) were favoured at high densities and were less sensitive to changes in density. We show that this density-dependent selection favours white individuals and predicts fixation of the white allele in this population at longer time scales without immigration or other selective forces. Still, fluctuating population density will cause selection to fluctuate and periodically favour red individuals. These results suggest how balancing selection caused by fluctuations in population density can be a general mechanism affecting the level of additive genetic variance in natural populations.</p>
Fig. 2 in Difference In Small Mammal Assemblages In The Diet Of The Common Barn-Owl Tyto Alba Between Two Landscapes
Fig. 2. Difference of estimated species richness of the Common Barn-owl's food composition between two landscape categories, based on individual rarefaction analysis
Fig. 1 in Difference In Small Mammal Assemblages In The Diet Of The Common Barn-Owl Tyto Alba Between Two Landscapes
Fig. 1. Study area in the South-Transdanubian region, Hungary, showing the location of sampled nesting sites (settlements) and the two separated landscape types, indicated by different symbols
Fig. 3 in Difference In Small Mammal Assemblages In The Diet Of The Common Barn-Owl Tyto Alba Between Two Landscapes
Fig. 3. Variables factor maps at land-use level (A), species level (B) and guild level (C) in case of the agricultural lands (D-AL) and the semi-natural habitats (D-SNH)
Figure 1 in Monitoring the feeding and parental care behavior of a pair of free-living owls (Tyto furcata) in the nest during the reproductive period in Rio de Janeiro, Brazil
Figure 1. Couple of Tyto furcata image captured by the security camera positioned opposite from the nest.Campos dos Goytacazes, RJ.
Figure 5 in Monitoring the feeding and parental care behavior of a pair of free-living owls (Tyto furcata) in the nest during the reproductive period in Rio de Janeiro, Brazil
Figure 5. Day frequency that the Tyto furcata family brought food to the nest. Campos dos Goytacazes, RJ.
Figure 4 in Monitoring the feeding and parental care behavior of a pair of free-living owls (Tyto furcata) in the nest during the reproductive period in Rio de Janeiro, Brazil
Figure 4. Frequency that the Tyto furcata parents bring the chicks near themselves (July and August, 2017). Campos dos Goytacazes, RJ.
Figure 6 in Monitoring the feeding and parental care behavior of a pair of free-living owls (Tyto furcata) in the nest during the reproductive period in Rio de Janeiro, Brazil
Figure 6. Day frequency that the Tyto furcata family brought food to the nest, from laying the eggs until the chicks left the nest. Campos dos Goytacazes, RJ.
Figure 3 in Monitoring the feeding and parental care behavior of a pair of free-living owls (Tyto furcata) in the nest during the reproductive period in Rio de Janeiro, Brazil
Figure 3. Observation of the Tyto furcata family in the nest. (A) Female sitting on eggs in the artificial nest; (B) 25-day-old chicks; (C) Adult owl bringing food to the chicks; (D) Chicks feeding alone in the nest. Campos dos Goytacazes, RJ.
Figure 7 in Diet and reproductive outputs of common barn-owl (Tyto alba) during the common vole (Microtus arvalis) outbreak and crash
Figure 7. GLMM diagrams illustrating the effect of the main and alternative prey taxa and the two derived indices on the number of fledglings (A: Common vole, B: Apodemus genus, C: Microtinae/Murinae ratio, D: Trophic level index).
Figure 5 in Diet and reproductive outputs of common barn-owl (Tyto alba) during the common vole (Microtus arvalis) outbreak and crash
Figure 5. GLMM diagrams illustrating the effect of the main and alternative prey taxa and the two derived indices on clutch size (A: Common vole, B–C: Apodemus genus, D–E: Microtinae/Murinae ratio, F: Trophic level index).
Figure 4 in Diet and reproductive outputs of common barn-owl (Tyto alba) during the common vole (Microtus arvalis) outbreak and crash
Figure 4. Box plots of the relative frequency of the main and alternative prey taxa. The bottom and top limits of each box are the lower and upper quartiles; error bars equal ±1.5 times the interquartile range; the horizontal black band within each box is the median; and the red triangle is the mean.
Figure 6 in Diet and reproductive outputs of common barn-owl (Tyto alba) during the common vole (Microtus arvalis) outbreak and crash
Figure 6. GLMM diagrams illustrating the effect of the main and alternative prey taxa and the two derived indices on the number of hatchlings (A: Common vole, B: Apodemus genus, C: Microtinae/Murinae ratio, D: Trophic level index).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.