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70 results for “measuring selection”
Data from: When field experiments yield unexpected results: lessons learned from measuring selection in White Sands lizards
Determining the adaptive significance of phenotypic traits is key for understanding evolution and diversification in natural populations. However, evolutionary biologists have an incomplete understanding of how specific traits affect fitness in most populations. The White Sands system provides an opportunity to study the adaptive significance of traits in an experimental context. Blanched color evolved recently in three species of lizards inhabiting the gypsum dunes of White Sands and is likely an adaptation to avoid predation. To determine whether there is a relationship between color and susceptibility to predation in White Sands lizards, we conducted enclosure experiments, quantifying survivorship of Holbrookia maculate exhibiting substrate-matched and substrate-mismatched phenotypes. Lizards in our study experienced strong predation. Color did not have a significant effect on survival, but we found several unexpected relationships including variation in predation over small spatial and temporal scales. In addition, we detected a marginally significant interaction between sex and color, suggesting selection for substrate matching may be stronger for males than females. We use our results as a case study to examine six major challenges frequently encountered in field-based studies of natural selection, and suggest that insight into the complexities of selection often results when experiments turn out differently than expected.
Data from: Combined measurements of prey availability explain habitat selection in foraging seabirds
Understanding links between habitat characteristics and foraging efficiency help to predict how environmental change could influence populations of top-predators. This study examines whether measurements of prey (clupeids) availability varied over stratification gradients, and determined if any of those measurements coincided with aggregations of foraging seabirds (common guillemot Uria aalge, Manx shearwater Puffinus puffinus) in the Celtic Sea, UK. The probability of encountering foraging seabirds was highest around fronts between mixed and stratified water. Prey were denser and shallower in mixed water, and encounters with prey most frequent in stratified water. Therefore, no single measurement of increased prey availability coincided with the location of fronts. However, when considered in combination, overall prey availability was highest in these areas. These results show that top-predators may select foraging habitats by trading-off several elements of prey availability. By showing that top-predators select areas where prey are switching between behaviours, these results also identify a mechanism that could explain the wider importance of edge habitats for these taxa. As offshore developments (e.g. marine renewable energy installations) change patterns of stratification, their construction may have consequences on the foraging efficiency of seabirds.
Data from: Natural selection on a measure of parasite resistance varies across ages and environmental conditions in a wild mammal
Parasites detrimentally affect host fitness, leading to expectations of positive selection on host parasite resistance. However, since immunity is costly, host fitness may be maximized at low, but non-zero, parasite infection intensities. These hypotheses are rarely tested on natural variation in free-living populations. We investigated selection on a measure of host parasite resistance in a naturally-regulated Soay sheep population using a longitudinal data set, and found negative correlations between parasite infection intensity and annual fitness in lambs, male yearlings, and adult females. However, having accounted for confounding effects of body weight, the effect was only significant in lambs. Associations between fitness and parasite resistance were environment-dependent, being strong during low-mortality winters, but negligible during harsher high-mortality winters. There was no evidence for stabilising selection. Our findings reveal processes that may shape variation in parasite resistance in natural populations, and illustrate the importance of accounting for correlated traits in selection analysis.
Data from: Multilocus approaches for the measurement of selection on correlated genetic loci
The study of ecological speciation is inherently linked to the study of selection. Methods for estimating phenotypic selection within a generation based on associations between trait values and fitness (e.g. survival) of individuals are established. These methods attempt to disentangle selection acting directly on a trait from indirect selection caused by correlations with other traits via multivariate statistical approaches (i.e. inference of selection gradients). The estimation of selection on genotypic or genomic variation could also benefit from disentangling direct and indirect selection on genetic loci. However, achieving this goal is difficult with genomic data because the number of potentially correlated genetic loci (p) is very large relative to the number of individuals sampled (n). In other words, the number of model parameters exceeds the number of observations (p ≫ n). We present simulations examining the utility of whole-genome regression approaches (i.e. Bayesian sparse linear mixed models) for quantifying direct selection in cases where p ≫ n. Such models have been used for genome-wide association mapping and are common in artificial breeding. Our results show they hold promise for studies of natural selection in the wild and thus of ecological speciation. But we also demonstrate important limitations to the approach and discuss study designs required for more robust inferences.
Selected Electric Field Measurement Campaigns data
<p>This presents the selected electric field measurement data acquired under several measurement campaigns in indoor and outdoor environments. </p> <p>This work was supported by the EU project 5GRFEX entitled – ‘Metrology for RF exposure from Massive MIMO 5G base station: Impact on 5G network deployment’ (this project has received funding from the support for impact (SIP) programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme), under European Association of National Metrology Institutes (EURAMET) Reference 18SIP02.</p> <p> </p>
Selected processed 5G base station RF-EMF measurement data
<p>This presents the selected processed 5G base station (BS) radio frequency electromagnetic field (RF-EMF) measurement data acquired under measurement campaign in outdoor environment. This data links to the findings shown in Section 5 of D1 report at http://empir.npl.co.uk/5grfex/wp-content/uploads/sites/55/2022/01/updated-EMPIR-18SIP02-5GRFEX-Deliverable-Report-D1.pdf. </p> <p>This work was supported by the EU project 5GRFEX entitled – ‘Metrology for RF exposure from Massive MIMO 5G base station: Impact on 5G network deployment’ (this project has received funding from the support for impact (SIP) programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme), under European Association of National Metrology Institutes (EURAMET) Reference 18SIP02.</p>
Selected SSB-based RF-EMF Outdoor Measurement Campaigns data
<p>This presents the selected Synchronisation Signal Block (SSB) based radio frequency electromagnetic field (RF-EMF) measurement data acquired under measurement campaign in indoor environment. This data links to the findings shown in Section 4.2 of D1 report at http://empir.npl.co.uk/5grfex/wp-content/uploads/sites/55/2022/01/updated-EMPIR-18SIP02-5GRFEX-Deliverable-Report-D1.pdf. </p> <p>This work was supported by the EU project 5GRFEX entitled – ‘Metrology for RF exposure from Massive MIMO 5G base station: Impact on 5G network deployment’ (this project has received funding from the support for impact (SIP) programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme), under European Association of National Metrology Institutes (EURAMET) Reference 18SIP02.</p>
Selected SSB-based RF-EMF Indoor Measurement Campaigns data
<p>This presents the selected Synchronisation Signal Block (SSB) based radio frequency electromagnetic field (RF-EMF) measurement data acquired under measurement campaign in indoor environment. This data links to the findings shown in Section 3.3 of D1 report at http://empir.npl.co.uk/5grfex/wp-content/uploads/sites/55/2022/01/updated-EMPIR-18SIP02-5GRFEX-Deliverable-Report-D1.pdf. </p> <p>This work was supported by the EU project 5GRFEX entitled – ‘Metrology for RF exposure from Massive MIMO 5G base station: Impact on 5G network deployment’ (this project has received funding from the support for impact (SIP) programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme), under European Association of National Metrology Institutes (EURAMET) Reference 18SIP02.</p>
in millimeters measurements all, specimens rex Tyrannosaurus other and 2523.8 P RSM of measurements Select. 1 TABLE in An Older and Exceptionally Large Adult Specimen of Tyrannosaurus rex
in millimeters measurements all, specimens rex Tyrannosaurus other and 2523.8 P RSM of measurements Select. 1 TABLE
Data from: Comparing measures of breeding inequality and opportunity for selection with sexual selection on a quantitative character in bighorn rams
The reliability and consistency of the many measures proposed to quantify sexual selection have been questioned for decades. Realized selection on quantitative characters measured by the selection differential i was approximated by metrics based on variance in breeding success, using either the opportunity for sexual selection Is or indices of inequality. There is no consensus about which metric best approximates realized selection on sexual characters. Recently, the opportunity for selection on character mean OSM was proposed to quantify the maximum potential selection on characters. Using 21 years of data on bighorn sheep (Ovis canadensis), we investigated the correlations between seven indices of inequality, Is, OSM and i on horn length of males. Bighorn sheep are ideal for this comparison because they are highly polygynous, sexually dimorphic, ram horn length is under strong sexual selection, and we have detailed knowledge of individual breeding success. Different metrics provided conflicting information, potentially leading to spurious conclusions about selection patterns. Iδ, an index of breeding inequality, and to a lesser extent Is, showed the highest correlation with i on horn length, suggesting that these indices document breeding inequality in a selection context. OSM on horn length was strongly correlated with i, Is, and indices of inequality. By integrating information on both realized sexual selection and breeding inequality, OSM appeared to be the best proxy of sexual selection and may be best suited to explore its ecological bases.
FIGURE 6. Principal component analysis of the nine call measurements for various species selections. A: all small Anthus species except A. cervinus; B: two types of calls of A. r. rubescens and A. [r.] japonicus. C: only common-type calls of A. r. rubescens and A. [r.] japonicus; D: A. petrosus and different A. spinoletta subspecies; E: A. petrosus and A. s. spinoletta. in --Molecular--and--acoustic--evidence--support--the--species--status--of--Anthus rubescens rubescens and--Anthus [rubescens] japonicus--(Passeriformes:--Motacillidae)
FIGURE 6. Principal component analysis of the nine call measurements for various species selections. A: all small Anthus species except A. cervinus; B: two types of calls of A. r. rubescens and A. [r.] japonicus. C: only common-type calls of A. r. rubescens and A. [r.] japonicus; D: A. petrosus and different A. spinoletta subspecies; E: A. petrosus and A. s. spinoletta.
Collection of Blood and Bone Marrow Samples From Select Patients With CML to Measure Minimal Residual Disease
ClinicalTrials.gov study NCT00819143. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Measuring the Impact of Onco4D® Guidance on Chemotherapy Selection and Outcomes
ClinicalTrials.gov study NCT04891952. IPD Sharing: NO. Countries: 1. Publications: 3.
Biomarkers and Validation of Selected Outcome Measures (CMTNSmod)
ClinicalTrials.gov study NCT03386266. IPD Sharing: NO. Countries: 1. Publications: 3.
The Role of Angiographic Measurements and Anatomical Correlations in Intra-Aortic Balloon Pump (IABP) Size Selection
ClinicalTrials.gov study NCT06947174. IPD Sharing: YES. Countries: 1. Publications: 11.
Data from: Natural selection on a measure of parasite resistance varies across ages and environmental conditions in a wild mammal
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Data from: When field experiments yield unexpected results: lessons learned from measuring selection in White Sands lizards
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Data from: Combined measurements of prey availability explain habitat selection in foraging seabirds
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Data from: Measuring viability selection from prospective cohort mortality studies: a case study in Maritime pine
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Data from: The measurement of selection when detection is imperfect: how good are naïve methods?
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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)
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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.