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1,048 results for “interpreting”
Data from: Density-dependent space use affects interpretation of camera trap detection rates
<p>Camera-traps (CTs) are an increasingly popular tool for wildlife survey and monitoring. Estimating relative abundance in unmarked species is often done using detection rate as an index of relative abundance, which assumes a positive linear relationship with true abundance. This assumption may be violated if movement behavior varies with density, but the degree to which movement is density-dependent across taxa is unclear. The potential confounding of population-level relative abundance indices by movement depends on how regularly, and by what magnitude, movement rate and home-range size vary with density. We conducted a systematic review and meta-analysis to quantify relationships between movement rate, home range size, and density, across terrestrial mammalian taxa. We then simulated animal movements and CT sampling to test the effect of contrasting movement scenarios on CT detection rates. Overall, movement rate and home range size were negatively correlated with density and positively correlated with one another. The strength of the relationships varied significantly between taxa and populations. In simulations, detection rates were related to true abundance but underestimated change, particularly for slower moving species with small home ranges. In situations where animal space use changes markedly with density, we estimate that up to thirty percent of a true change in abundance may be missed due to the confounding effect of movement, making trend estimation more difficult. The common assumption that movement remains constant across densities is therefore violated across a wide range of mammal species. When studying unmarked species using CT detection rates, researchers and managers should consider that such indices of relative abundance reflect both density and movement. Practitioners interpreting changes in detection rates should be aware that observed differences may be biased low relative to true changes in abundance, and that further information on animal movement may be required to make robust inferences on population trends.</p>
Data from: The importance of individual heterogeneity for interpreting faecal glucocorticoid metabolite levels in wildlife studies
1. Being a non-invasive and inexpensive method, the analysis of faecal corticosteroid metabolites (FCM) is increasingly being applied in wildlife research. Various environmental factors have been shown to influence FCM levels, but most studies did not account for inter-individual variance, which we hypothesized may substantially affect the results. 2. We combined FCM analysis with genetic analysis to identify the sex and individual of samples collected in three consecutive winters, with repeated samples per individual, across the entire range of an endangered population of capercaillie (Tetrao urogallus) in Southwestern Germany. Using generalized additive mixed models, we modelled FCM levels as a function of sex, season and environmental covariates at two spatial scales (sampling location and home range scales). We compared two models: one including information on the individual animal, and the other excluding this information (i.e. naïve model) to assess the influence of individual effects on the results obtained. 3. Most of the variance (44.0% and 45.1% at the sampling and home range scale, respectively) was explained by the inter-individual differences, and only very little (4.0% and 5.1%, respectively) by the environmental predictors. When ignoring individual effects, the model results changed considerably, with other, previously non-informative predictors, becoming significant. 4. In the full models, accounting for inter-individual variance, no effect was found of weather conditions, at either scale. FCM levels were negatively correlated with habitat quality, at the sampling location, whereas human recreation at the home range scale led to elevated FCM levels. In the naïve models, two additional predictors appeared significant: one weather variable at local scales and two at home range scale. In all models, seasonal FCM patterns differed significantly between males and females. 5. Synthesis and applications. Our results highlight the importance of considering the effects of individual heterogeneity when studying FCM in wildlife research, as ignoring information on the individual might lead to erroneous conclusions. Combining FCM analyses with genetic analyses can be an efficient approach to adequately address this issue.21-Feb-2018
Data from: A novel nonparametric measure of explained variation for survival data with an easy graphical interpretation
Introduction: For survival data the coefficient of determination cannot be used to describe how good a model fits to the data. Therefore, several measures of explained variation for survival data have been proposed in recent years. Methods: We analyse an existing measure of explained variation with regard to minimisation aspects and demonstrate that these are not fulfilled for the measure. Results: In analogy to the least squares method from linear regression analysis we develop a novel measure for categorical covariates which is based only on the Kaplan-Meier estimator. Hence, the novel measure is a completely nonparametric measure with an easy graphical interpretation. For the novel measure different weighting possibilities are available and a statistical test of significance can be performed. Eventually, we apply the novel measure and further measures of explained variation to a dataset comprising persons with a histopathological papillary thyroid carcinoma. Conclusion: We propose a novel measure of explained variation with a comprehensible derivation as well as a graphical interpretation, which may be used in further analyses with survival data.
Data from: Interpreting the evolutionary regression: the interplay between observational and biological errors in phylogenetic comparative studies
Regressions of biological variables across species are rarely perfect. Usually there are residual deviations from the estimated model relationship, and such deviations commonly show a pattern of phylogenetic correlations indicating that they have biological causes. We discuss the origins and effects of phylogenetically correlated biological variation in regression studies. In particular, we discuss the interplay of biological deviations with deviations due to observational or measurement errors, which are also important in comparative studies based on estimated species means. We show how bias in estimated evolutionary regressions can arise from several sources, including phylogenetic inertia and either observational or biological error in the predictor variables. We show how all these biases can be estimated and corrected for in the presence of phylogenetic correlations. We present general formulas for incorporating measurement error in linear models with correlated data. We also show how alternative regression models, such as major-axis and reduced major-axis regression, which are often recommended when there is error in predictor variables, are strongly biased when there is biological variation in any part of the model. We argue that such methods should never be used to estimate evolutionary or allometric regression slopes.
Data from: The impact of phylogenetic dating method on interpreting trait evolution: a case study of Cretaceous–Palaeogene eutherian body-size evolution
The fossil record of the earliest Cenozoic contains the first large-bodied placental mammals. Several evolutionary models have been invoked to explain the transition from small to large body sizes, but methods for determining evolutionary mode of trait change depend on input from tree topology and divergence dates. Different dating methods may therefore affect inference of evolutionary model. Here, we fit models of body mass evolution onto dated phylogenies of Cretaceous and Palaeogene mammals, comparing the effect of dating method on interpretation of evolutionary model. Among traditional palaeontological dating approaches, an Ornstein–Uhlenbeck model with high alpha parameters is recovered as best-fitting when minimum-age dating is used, while branch-sharing methods are highly sensitive to topology. Release or release–radiate models are preferred when Bayesian fossilized birth–death method is used, but when using stochastic cal3 dating of trees, a model of increased evolutionary rate without a release in constraint at the Cretaceous–Palaeogene boundary has highest support. These results demonstrate unambiguously that choice of dating method is critical for interpretation of continuous trait evolution, and that care must therefore be taken to consider these effects in macroevolutionary studies.
Data from: Fundamental activity constraints lead to specific interpretations of the connectome
The continuous integration of experimental data into coherent models of the brain is an increasing challenge of modern neuroscience. Such models provide a bridge between structure and activity, and identify the mechanisms giving rise to experimental observations. Nevertheless, structurally realistic network models of spiking neurons are necessarily underconstrained even if experimental data on brain connectivity are incorporated to the best of our knowledge. Guided by physiological observations, any model must therefore explore the parameter ranges within the uncertainty of the data. Based on simulation results alone, however, the mechanisms underlying stable and physiologically realistic activity often remain obscure. We here employ a mean-field reduction of the dynamics, which allows us to include activity constraints into the process of model construction. We shape the phase space of a multi-scale network model of the vision-related areas of macaque cortex by systematically refining its connectivity. Fundamental constraints on the activity, i.e., prohibiting quiescence and requiring global stability, prove sufficient to obtain realistic layer- and area-specific activity. Only small adaptations of the structure are required, showing that the network operates close to an instability. The procedure identifies components of the network critical to its collective dynamics and creates hypotheses for structural data and future experiments. The method can be applied to networks involving any neuron model with a known gain function.
Data from: Interpreting ELISA analyses from wild animal samples: some recurrent issues and solutions
1. Many studies in disease and immunological ecology rely on the use of assays that quantify the amount of specific antibodies (immunoglobulin) in samples. Enzyme-Linked Immuno Sorbent Assays (ELISAs) are increasingly used in ecology due to their availability for a broad array of antigens and the limited amount of sampling material they require. Two recurrent methodological issues are nevertheless faced by researchers: (i) the limited availability of immunological assays and reagents developed for non-model species, and (ii) the statistical determination of the cut-off threshold used to distinguish individual samples that are likely to have or not to have antibodies against a specific antigen. 2. Here, we outline two solutions to deal with these issues. First, we show that implementing two assays with differing detection methods can help validate the use of reagents, such as antibodies, in species different from their intended target. We illustrate this by comparing the quantification of specific vaccinal antibodies against Newcastle Disease Virus (NDV) using two ELISA approaches in four seabird species (Cory's shearwater, European shag, European storm petrel, and Southern rockhopper penguin). 3. Second, we provide a simple way to determine from the distribution of ELISA values whether the assayed samples are likely to be made of a single group of individuals (likely negative) or of two groups of individuals (negative and positive). We illustrate the use of this approach with two independent datasets: NDV antibody levels following vaccination and anti-Borrelia antibody levels following natural exposure. 4. The practical implementation of these methodological approaches could provide a way to efficiently apply ELISAs and other immune-based assays to address questions in the growing fields of ecological immunology and disease ecology.
Data from: Bias and sensitivity in the placement of fossil taxa resulting from interpretations of missing data
The utility of fossils in evolutionary contexts is dependent on their accurate placement in phylogenetic frameworks, yet intrinsic and widespread missing data make this problematic. The complex taphonomic processes occurring during fossilization can make it difficult to distinguish absence from non-preservation, especially in the case of exceptionally preserved soft-tissue fossils: is a particular morphological character (e.g. appendage, tentacle or nerve) missing from a fossil because it was never there (phylogenetic absence), or just happened to not be preserved (taphonomic loss)? Missing data has not been tested in the context of interpretation of non-present anatomy nor in the context of directional shifts and biases in affinity. Here, complete taxa, both simulated and empirical, are subjected to data loss through the replacement of present entries (1s) with either missing (?s) or absent (0s) entries. Both cause taxa to drift down trees, from their original position, toward the root. Absolute thresholds at which downshift is significant are extremely low for introduced absences (2 entries replaced, 6 % of present characters). The opposite threshold in empirical fossil taxa is also found to be low; two absent entries replaced with presences causes fossil taxa to drift up trees. As such, only a few instances of non-preserved characters interpreted as absences will cause fossil organisms to be erroneously interpreted as more primitive than they were in life. This observed sensitivity to coding non-present morphology presents a problem for all evolutionary studies that attempt to use fossils to reconstruct rates of evolution or unlock sequences of morphological change. Stem-ward slippage, whereby fossilization processes cause organisms to appear artificially primitive, appears to be a ubiquitous and problematic phenomenon inherent to missing data, even when no decay biases exist. Absent characters therefore require explicit justification and taphonomic frameworks to support their interpretation.
Data from: Challenges in analysis and interpretation of microsatellite data for population genetic studies
Advancing technologies have facilitated the ever-widening application of genetic markers such as microsatellites into new systems and research questions in biology. In light of the data and experience accumulated from several years of using microsatellites, we present here a literature review that synthesizes the limitations of microsatellites in population genetic studies. With a focus on population structure, we review the widely used fixation (FST) statistics and Bayesian clustering algorithms and find that the former can be confusing and problematic for microsatellites and that the latter may be confounded by complex population models and lack power in certain cases. Clustering, multivariate analyses, and diversity-based statistics are increasingly being applied to infer population structure, but in some instances these methods lack formalization with microsatellites. Migration-specific methods perform well only under narrow constraints. We also examine the use of microsatellites for inferring effective population size, changes in population size, and deeper demographic history, and find that these methods are untested and/or highly context-dependent. Overall, each method possesses important weaknesses for use with microsatellites, and there are significant constraints on inferences commonly made using microsatellite markers in the areas of population structure, admixture, and effective population size. To ameliorate and better understand these constraints, researchers are encouraged to analyze simulated datasets both prior to and following data collection and analysis, the latter of which is formalized within the approximate Bayesian computation framework. We also examine trends in the literature and show that microsatellites continue to be widely used, especially in non-human subject areas. This review assists with study design and molecular marker selection, facilitates sound interpretation of microsatellite data while fostering respect for their practical limitations, and identifies lessons that could be applied toward emerging markers and high-throughput technologies in population genetics.
Data from: Interpreting the estimated timing of migration events between hybridizing species
The question of whether speciation can occur in the presence of gene flow has long been a contentious one. However, measuring the amount and timing of gene flow remains challenging. The computer program IMA2 allows researchers to estimate the timing of migration events for each locus during analyses, and these estimates have been used to infer the timing of introgression and mode of speciation. We use simulated datasets to examine the degree to which gene flow timing estimates can be used for these purposes, and what demographic conditions and datasets may be most amenable to gene flow timing estimation. We find that the 90% highest posterior density (HPD) interval of gene flow timing is almost always substantially wider than the actual window of gene flow, and increasing the information content of the dataset in terms of number of loci, number of sequences sampled, or locus length (and thus number of variable sites) has little impact on the posterior distribution over the range of values we tested. Even when simulated gene flow only occurred over the most recent 0.01% of the species' history, the HPD interval usually encompasses the inferred divergence time. Our results indicate that gene flow timing estimates made using the method currently implemented in IMA2 cannot reliably be used to make inferences about the timing of introgression between diverged species or to distinguish between speciation with gene flow and allopatric speciation followed by one or more episodes of gene flow.
FIGURE 1 in Revision of the Panurgus dargius Warncke, 1972 interpretation (Hymenoptera: Andrenidae), comparison with the morphologically close species
FIGURE 1. Bud shaped supraclypeal area observed in females of Panurgus maroccanus. Scale = 0.17mm.
Spatial Informatics Group (SIG)_CEO Interpretation - Demo Change Events
Demo project forest loss/degradation from different drivers
Spatial Informatics Group (SIG)_CEO Interpretation - Demo - Land Use (South Africa)
Demo project land use identification and land use change detection
Spatial Informatics Group (SIG)_CEO Interpretation - Demo - Land Use
Demo project land use identification and land use change detection
Spatial Informatics Group (SIG)_CEO Interpretation - Demo Change Events
Demo project forest loss/degradation from different drivers
test data for Cardiologist-level interpretable knowledge-fused deep neural network for automatic arrhythmia diagnosis
<p>Companion python scripts are available in: https://github.com/xin-gou/automatic-ecgdiagnosis</p>
Spatial Informatics Group (SIG)_CEO Interpretation - Demo - Land Use
Demo project land use identification and land use change detection
CONTEMPORARY INTERPRETATION OF PATHOGENETIC TREATMENT AND PREVENTION OF HERPETIC STOMATITS IN PATIENTS INFECTED WITH TORCH (LITERATURE REVIEW)
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An Interpretable 3D Multi-hierarchical Representation-based Deep Neural Network for EH&S Properties Prediction
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An Interpretable 3D Multi-Hierarchical Molecular Hybrid Representation for Environmental, Health, and Safety Properties Prediction
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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.