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77 results for “Selection bias”
Data to "Object visibility, not energy expenditure, accounts for spatial biases in human grasp selection"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p><strong>Maiello, G</strong>.<sup> †</sup>, Paulun, V. C.<sup> †</sup>, Klein, L. K. , & Fleming, R. W. (2018) Object visibility, not energy expenditure, accounts for spatial biases in human grasp selection. <em>i-Perception,10</em>(1), 1–5. doi:10.1177/2041669519827608.</p> <p><sup>†</sup>co-first authors</p>
Code and data for: Is habitat selection in the wild shaped by individual-level cognitive biases in orientation strategy?
<p>This repository is a companion to the manuscript "<em>Is habitat selection in the wild shaped by individual-level cognitive biases in orientation strategy?</em>" and is linked to <a href="https://github.com/CBeardsworth/Pheasant_OrientStrat_Habitat">Github</a>.</p> <p>For any questions about the code please contact Christine at <a href="mailto:c.e.beardsworth@gmail.com">c.e.beardsworth@gmail.com</a></p> <p>To use any data contained in this repository contact Joah at <a href="mailto:j.r.madden@exeter.ac.uk">j.r.madden@exeter.ac.uk</a> for permission.</p> <p>In this repository, we have included a run-through of the R analysis <a href="https://cbeardsworth.github.io/Pheasant_OrientStrat_Habitat/">here</a> to show the outputs of the analysis without the need to run the code. For those that might want to run the code themselves, we have included three R scripts (<a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/R">/R</a>) and their accompanying datasets (<a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/Data">/Data</a>). A description of the code and the data needed to run them is below:</p> <p><em>Cognition analysis and figs.R</em> = Run the cognition analysis for the first section of the manuscript and create the figures. For this, the datasets mazeData.csv (the learning trials) and mazeRotationResults.csv (the probe trial) are required. </p> <p><em>iSSA analysis and bootstrapping.R</em> = Run iSSA models and bootstrapping. This produces the datasets required for the next stage of analysis. For this code, the datasets habitat.grd (habitat information), atlas2018-strategy.csv (atlas data + id and strategy data for each bird) and FeederCoords2017_27700.csv (coordinates of feeder locations from 2017-2018) are required. The produced datasets are included in <a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/Data">/Data</a> therefore to run subsequent analyses, this code does not need to be run. To develop this code we relied heavily on the code included in the supplementary material of <a href="https://doi.org/10.1002/ece3.4823">Signer et al. (2019)</a> as well as an <a href="https://bsmity13.github.io/log_rss">online tutorial</a> from Brian J. Smith for calculating log-RSS.</p> <p><em>Habitat analysis and Figs.R</em> = Run the statistical models for the final section of the manuscript and create the figures. For this code, the datasets produced in the previous R script are required (habitatOrientation_coefs.csv and habitatOrientation_avail.csv). We have included <a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/Data">these datasets</a> so users do not need to run the iSSA analysis and bootstrapping.R script themselves. </p>
Population size differences can lead to biases in phylogenetic inference and introgression detection in the presence of purifying selection
<p>Phylogenetic reconstruction and introgression detection rely on an assumption about the probability distribution of gene tree topologies. Recently, evidence has emerged that population size differences can affect the probability distribution of gene tree topologies in the presence of purifying selection. Here, using the population genetic simulator SLiM, we provide evidence that in the presence of purifying selection, population size differences can lead to biases in phylogenetic inference. We also provide evidence that in the presence of purifying selection, population size differences can cause statistics used for introgression detection to exhibit patterns resembling those caused by introgression. In addition, we present a theoretical analysis showing that the occurrence of population size–dependent gene tree distributions is an inherent consequence of purifying selection. Our work underscores the importance of considering the potential confounding effect of purifying selection on phylogenetic inference and introgression detection.</p>
Figure 3 in Addressing biases in replacement series: the importance of reference density selection for interpretation of competition outcomes
Figure 3. Aboveground biomass (g m−2) across densities (plants m−2) for maize and Setorio foberi. Vertical arrows indicate the maize (black numbers) and S. foberi (gray numbers) densities at which S. foberi reaches inflection point and 90% of maximum biomass. Horizontal arrows indicate S. foberi biomass at inflection point and 90% of maximum biomass.
Figure 4 in Addressing biases in replacement series: the importance of reference density selection for interpretation of competition outcomes
Figure 4. Relative biomass of Amoronthus hybridus:maize replacement series from experiment 1 (left) and experiment 2 (right) with densities based in inflection point (A and B), maximum biomass (C and D), and equal N uptake (E and F). Black circles represent maize relative biomass,white circles represent A. hybridus relative biomass,and gray diamonds represent relative yield total biomass (RYT). Dotted line represents a relative biomass of 1 for all the proportions. The points and error bars represent data means and standard errors.
Figure 2 in Addressing biases in replacement series: the importance of reference density selection for interpretation of competition outcomes
Figure 2. Aboveground biomass (g m−2) across densities (plants m−2) for maize and Amoronthus hybridus. Vertical arrows indicate the maize (black numbers) and A. hybridus (gray numbers) densities at which A. hybridus reaches inflection point and 90% of maximum biomass. Horizontal arrows indicate A. hybridus biomass at the inflection point and 90% of maximum biomass.
Figure 1 in Addressing biases in replacement series: the importance of reference density selection for interpretation of competition outcomes
Figure 1. Theoretical relationship between relative biomass and plant density ratios of (A) two species of the same size and (B) two species with species 1 being larger than species 2. Dashed lines represent the regression lines for both species, and solid lines represent theoretical line of parity (1:1) between species.
Figure 5 in Addressing biases in replacement series: the importance of reference density selection for interpretation of competition outcomes
Figure 5. Relative biomass of Setorio foberi:maize replacement series from experiment 1 (left) and experiment 2 (right) with densities based in inflection point (A and B), maximum biomass (C and D), and equal N uptake (E and F). Black circles represent maize relative biomass, white circles represent S. foberi relative biomass, and gray diamonds represent relative yield total biomass (RYT). Dotted line represents a relative biomass of 1 for all the proportions. The points and error bars represent data means and standard errors.
Ignoring within-flower self-fertilization and inbreeding depression biases estimates of selection on floral traits
<p>Within-flower self-pollination should be the major source of self-fertilization in mixed-mating species that present single or few flowers simultaneously. It is also an often unmeasured source of selfing in species with many flowers open simultaneously. In self-compatible species in which pistil and stamen numbers vary, the rate of within-flower selfing should depend on the number of pistils and stamens, the timing of flowering, and the morphology of subsidiary floral traits. The intensity and direction of selection on these traits should thus also depend on the level of inbreeding depression. Here, we measured the dependence of the within-flower selfing rate on floral sex allocation, phenology, petal length, and floral stalk height in a population of the perennial herb <em>Pulsatilla alpina</em> (Ranunculaceae) in which most individuals had single flowers. We estimated inbreeding depression in the population by comparing inbreeding coefficients between parents and seed progeny using microsatellite markers. We then estimated selection on the measured traits via female reproductive success at the flower level and compared our estimates with a hypothetical scenario in which inbreeding depression was assumed to be absent. Inbreeding depression was estimated to be severe (0.95). The within-flower selfing rate varied widely among flowers and depended positively on stamen number and negatively on pistil number and flowering date, supporting the predictions of a mass-action model. The dependence of the selfing rate on the measured floral traits consistently predicted (non-linear) patterns of selection under high inbreeding depression that were distinct from those under a hypothetical scenario of no inbreeding depression.</p> <p>Synthesis: While previous research has emphasized the importance of mass-action mating on selfing among flowers of plants with large floral displays, our results demonstrate its importance for selfing within individual flowers. They also demonstrate the importance of accounting for both the selfing rate and inbreeding depression when inferring selection on floral and other traits via female fitness.</p>
Pervasive selection biases inferences of the species tree
<p>Supplementary files, scripts, and data of 'Pervasive selection biases inferences of the species tree' by Borges, Boussau, Szöllősi, and Kosiol</p>
Population size differences can lead to biases in phylogenetic inference and introgression detection in the presence of purifying selection
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Ignoring within-flower self-fertilization and inbreeding depression biases estimates of selection on floral traits
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Common field data limitations can substantially bias sexual selection metrics
<p>Sexual selection studies widely estimate several metrics, but values may be inaccurate because standard field methods for studying wild populations produce limited data (e.g., incomplete sampling, inability to observe copulations directly). We compared four selection metrics (Bateman gradient, opportunity for sexual selection, opportunity for selection, and <em>s'<sub>max</sub></em>) estimated with simulated complete and simulated limited data for 15 socially monogamous songbird species with extra-pair paternity (4-54% extra-pair offspring). Inferring copulation success from offspring parentage creates non-independence between these variables and systematically underestimates copulation success. We found that this introduces substantial bias for the Bateman gradient, opportunity for sexual selection, and <em>s'<sub>max</sub></em>. Notably, 47.5% of detected Bateman gradients were significantly positive for females, suggesting selection on females to copulate with multiple males, though the true Bateman gradient was zero. Bias generally increased with the extent of other sources of data limitations tested (nest predation, male infertility, and unsampled floater males). Incomplete offspring sampling introduced bias for all metrics except the Bateman gradient, while incomplete sampling of extra-pair sires did not introduce additional bias when sires were a random subset of breeding males. Overall, our findings demonstrate how biases due to field data limitations can strongly impact the study of sexual selection.</p>
Data from: Sex-biased habitat selection by American marten in the Acadian forest
<p>Differences in spatial ecology between the sexes are generally expected for mammalian carnivores because of physiological (e.g., sexual dimorphism) or behavioral (e.g., uniparental care of offspring) differences, but sex is rarely included in studies of occurrence or occupancy. We evaluated differences in landscape-scale occurrence, as a function of habitat amount and configuration, between male and female American martens in the heterogeneous, commercially-managed forests of northern Maine. Our analysis focused on resident adults; individuals that had survived to >1 year old, dispersed, and successfully established their home range.</p>
Data from: Sex-biased habitat selection by American marten in the Acadian forest
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Common field data limitations can substantially bias sexual selection metrics
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Raw fitness data (offspring number and weight), main dataset for manuscript: Female-specific resource limitation does not make the opportunity for selection more female biased
<p>The present dataset contains the entirety of the raw empirical data used in the manuscript entitled: "Female-specific resource limitation does not make the opportunity for selection more female biased". This data is composed of offspring count and weight, which represent proxys of fitness. Fitness was assessed for males and females independently, and across three different experimental treatments. These fitness measures are used to calculate the opportunity for selection, as the variance in realtive fitness. Detailed methods regarding data collection can be found in the main manuscript.</p>
Male-biased sexual selection, but not sexual dichromatism, predicts speciation in birds
<p>Sexual selection is thought to shape phylogenetic diversity by affecting speciation or extinction rates. However, the net effect of sexual selection on diversification is hard to predict, because many of the hypothesised effects on speciation or extinction have opposing signs and uncertain magnitudes. Theoretical work also suggests that the net effect of sexual selection on diversification should depend strongly on ecological factors, though this prediction has seldom been tested. Here, we test whether variation in sexual selection can predict speciation and extinction rates across passerine birds (up to 5,812 species, covering most genera) and whether this relationship is mediated by environmental factors. Male-biased sexual selection, and specifically sexual size dimorphism, predicted two of the three measures of speciation rates that we examined. The link we observed between sexual selection and speciation was independent of environmental variability, though species with smaller ranges had higher speciation rates. There was no association between any proxies of sexual selection and extinction rate. Our findings support the view that male-biased sexual selection, as measured by frequent predictors of male-male competition, has shaped diversification in the largest radiation of birds.</p>
Data from: A hidden Markov model to identify and adjust for selection bias: an example involving mixed migration strategies
An important assumption in observational studies is that sampled individuals are representative of some larger study population. Yet, this assumption is often unrealistic. Notable examples include online public-opinion polls, publication biases associated with statistically significant results, and in ecology, telemetry studies with significant habitat-induced probabilities of missed locations. This problem can be overcome by modeling selection probabilities simultaneously with other predictor–response relationships or by weighting observations by inverse selection probabilities. We illustrate the problem and a solution when modeling mixed migration strategies of northern white-tailed deer (Odocoileus virginianus). Captures occur on winter yards where deer migrate in response to changing environmental conditions. Yet, not all deer migrate in all years, and captures during mild years are more likely to target deer that migrate every year (i.e., obligate migrators). Characterizing deer as conditional or obligate migrators is also challenging unless deer are observed for many years and under a variety of winter conditions. We developed a hidden Markov model where the probability of capture depends on each individual's migration strategy (conditional versus obligate migrator), a partially latent variable that depends on winter severity in the year of capture. In a 15-year study, involving 168 white-tailed deer, the estimated probability of migrating for conditional migrators increased nonlinearly with an index of winter severity. We estimated a higher proportion of obligates in the study cohort than in the population, except during a span of 3 years surrounding back-to-back severe winters. These results support the hypothesis that selection biases occur as a result of capturing deer on winter yards, with the magnitude of bias depending on the severity of winter weather. Hidden Markov models offer an attractive framework for addressing selection biases due to their ability to incorporate latent variables and model direct and indirect links between state variables and capture probabilities.
Data from: The search for loci under selection: trends, biases and progress
Detecting genetic variants under selection using FST outlier analysis (OA) and environmental association analyses (EAA) are popular approaches that provide insight into the genetic basis of local adaptation. Despite the frequent use of OA and EAA approaches and their increasing attractiveness for detecting signatures of selection, their application to field-based empirical data have not been synthesized. Here, we review 66 empirical studies that use Single Nucleotide Polymorphisms (SNPs) in OA and EAA. We report trends and biases across biological systems, sequencing methods, approaches, parameters, environmental variables and their influence on detecting signatures of selection. We found striking variability in both the use and reporting of environmental data and statistical parameters. For example, linkage disequilibrium among SNPs and numbers of unique SNP associations identified with EAA were rarely reported. The proportion of putatively adaptive SNPs detected varied widely among studies, and decreased with the number of SNPs analyzed. We found that genomic sampling effort had a greater impact than biological sampling effort on the proportion of identified SNPs under selection. OA identified a higher proportion of outliers when more individuals were sampled, but this was not the case for EAA. To facilitate repeatability, interpretation and synthesis of studies detecting selection, we recommend that future studies consistently report geographic coordinates, environmental data, model parameters, linkage disequilibrium, and measures of genetic structure. Identifying standards for how OA and EAA studies are designed and reported will aid future transparency and comparability of SNP-based selection studies and help to progress landscape and evolutionary genomics.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.