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641 results for “data validation”
Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation
<p>This excel file (DOI: <a href="https://doi.org/10.5281/zenodo.3755675">https://doi.org/10.5281/zenodo.3755675</a>) provides the collection of raw data used for developing the first integrative Quantitative Structure-Activity Relationship (QSAR) model using EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase i) to predict acute contact toxicity (LD<sub>50</sub>) and ii) to profile the Mode of Action (MoA) of pesticides active substances in honey bees (<em>Apis mellifera</em>)<em>. </em>Chemical identifiers (e.g. SMILES, CAS n., InChI) and acute contact toxicity data (LD<sub>50</sub>) on honey bees were used to develop and validate i) a two-category QSAR model (toxic/non-toxic; n=411) (sensitivity =0.93), specificity =0.85), balanced accuracy =0.90), Matthews correlation coefficient MCC=0.78), and ii) a regression-based model (n=113) (R2=0.74; MAE=0.52). Similarly, current study proposes the first MoA profiling for 113 pesticides active substances and the first harmonised MoA classification scheme for acute contact toxicity in honey bees, including LD<sub>50s</sub> data points from three different databases such as EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase. Such classification allows to further define MoAs and the target site of Plant Protection Products (PPPs) active substances, thus enabling regulators and scientists to refine chemical grouping and toxicity extrapolations for single chemicals and component-based mixture risk assessment of multiple chemicals.</p> <p>The full data collection and analysis of QSAR models, toxicity data (LD<sub>50</sub>) and Mode of Action (Moa) data are described in Carnesecchi et al., 2020 (DOI: doi.org/10.1016/j.scitotenv.2020.139243).</p> <p>This work was supported by the European Food Safety Authority (EFSA) [contract number: OC/EFSA/SCER/2018/01 and NP/EFSA/AFSCO/2016/02 (Edoardo Carnesecchi)].</p>
Data from: On the objectivity, reliability, and validity of deep learning enabled bioimage analyses
<p>Bioimage analysis of fluorescent labels is widely used in the life sciences. Recent advances in deep learning (DL) allow automating time-consuming manual image analysis processes based on annotated training data. However, manual annotation of fluorescent features with a low signal-to-noise ratio is somewhat subjective. Training DL models on subjective annotations may be instable or yield biased models. In turn, these models may be unable to reliably detect biological effects. An analysis pipeline integrating data annotation, ground truth estimation, and model training can mitigate this risk. To evaluate this integrated process, we compared different DL-based analysis approaches. With data from two model organisms (mice, zebrafish) and five laboratories, we show that ground truth estimation from multiple human annotators helps to establish objectivity in fluorescent feature annotations. Furthermore, ensembles of multiple models trained on the estimated ground truth establish reliability and validity. Our research provides guidelines for reproducible DL-based bioimage analyses.</p>
Data set for validation of a Python script for computation of Protein-Ligand Interaction Fingerprints
<p><strong>1. Data set for for validation of the Protein-Ligand Interaction Fingerprints, which includes examples of protein structures (original PDB and equilibrated) and molecular dynamics trajectories (equilibration and ligand dissociation generated using Random Acceleration MD simulations, RAMD)</strong></p> <p><strong>mdifp_validation_data.tar.gz - </strong>archive that contains benchmark dataset for evaluation of the protein-ligand IFP protocol (PDB structures of protonated complexes, ligands, and MOL2 files of ligands) published in D. B. Kokha, B. Doser, S. Richter, F. Ormersbach, X. Cheng, R. C. Wade "A Workflow for Exploring Ligand Dissociation from a Macromolecule: Efficient Random Acceleration Molecular Dynamics Simulation and Interaction Fingerprints Analysis of Ligand Trajectories" J. Chem. Phys. <strong>153</strong>, 125102 (2020); <a href="https://doi.org/10.1063/5.0019088">https://doi.org/10.1063/5.0019088</a></p> <p>(2020) <a href="https://arxiv.org/abs/2006.11066">arXiv:2006.11066</a> </p> <p><strong>2YKI </strong>- protein-ligand complex , PDB ID 2YKI<br> - 2yki_MOE.pdb complex with hydrogen added and energy minimized using MOE software (https://www.chemcomp.com/)<br> - ligand_2yki_MOE.mol2 and ligand_2yki_MOE.pdb - ligand structure with hydrogens prepered by MOE software (https://www.chemcomp.com/)</p> <p><strong>6EI5</strong> - MD trajectory of the protein-ligand complex generated from PDB ID 6EI5<br> - ref-min.pdb minimized structure<br> - ref.prmtop topology file<br> - moe.mol2 - ligand structure in mol2 format<br> - amber2namd2.dcd generated MD trajectory </p> <p><strong>SAD_3-RAMD-03-2020.pkl </strong>- a pkl dataset with IFPs generated from RAMD dissociation trajectory of the complex PDB ID: 5LQ9 (trajectories from the paper Front. Mol. Biosci., 2019 DOI:10.3389/fmolb.2019.00036)</p> <p><strong>HSP90_Gromacs.zip </strong>- an archive that contains three pkl data sets of protein-ligand IFPs (for three HSP90 complexes; PDB ID: 5J64, 5J86, 5LQ9) generated from RAMD dissociation trajectories simulated using new Gromacs-RAMD engine (https://github.com/HITS-MCM/gromacs-ramd)</p> <p>The rest of the files contains data obtained from simulation of the complex of <strong>GPCR muscarinic receptor M2 (PDB ID:4MQT);</strong> immersed in a mixed membrane: 50% CHL, 30% POPC, 20% POPE) with a small molecule agonist iperoxo. <br> - <strong>IXO.pdb and moe.mol2 </strong>- PDBand MOL2 structure of iperoxo<br> - <strong>AMBER_eq.tar.gz</strong> - structure of the equilibrated complex generated using AMBER software<br> -<strong> NAMD_eq.tar.gz </strong>- two equilibration trajectories in dcd format generated using NAMD software <br> - <strong>RAMD_eq.tar.gz </strong>- dissociation tarjectoris of iprtoxo from the M2 protein generated from the last snapshot of two NAMD equilibration trajectories (for each case 2 RAMD dissociaiton trajectories are available) </p> <p>( *csv files were added erroneously and do not belong to the project)</p>
Manuscript data for "PASV: Automatic protein partitioning and validation using conserved residues"
<p>Manuscript data for "PASV: Automatic protein partitioning and validation using conserved residues"</p>
Data from: Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data
Implantable-cardioverter defibrillators (ICD) detect and terminate life-threatening ventricular tachyarrhythmia with electric shocks after they occur. This puts patients at risk if they are driving or in a situation where they can fall. ICD's shocks are also very painful and affect a patient's quality of life. It would be ideal if ICDs can accurately predict the occurrence of ventricular tachyarrhythmia and then issue a warning or provide preventive therapy. Our study explores the use of ICD data to automatically predict ventricular arrhythmia using heart rate variability (HRV). A 5 minute and a 10 second warning system are both developed and compared. The participants for this study consist of 788 patients who were enrolled in the ICD arm of the Sudden Cardiac Death – Heart Failure Trial (SCD-HeFT). Two groups of patient rhythms, regular heart rhythms and pre-ventricular-tachyarrhythmic rhythms, are analyzed and different HRV features are extracted. Machine learning algorithms, including random forests (RF) and support vector machines (SVM), are trained on these features to classify the two groups of rhythms in a subset of the data comprising the training set. These algorithms are then used to classify rhythms in a separate test set. This performance is quantified by the area under the curve (AUC) of the ROC curve. Both RF and SVM methods achieve a mean AUC of 0.81 for 5-minute prediction and mean AUC of 0.87-0.88 for 10-second prediction; an AUC over 0.8 typically warrants further clinical investigation. Our work shows that moderate classification accuracy can be achieved to predict ventricular tachyarrhythmia with machine learning algorithms using HRV features from ICD data. These results provide a realistic view of the practical challenges facing implementation of machine learning algorithms to predict ventricular tachyarrhythmia using HRV data, motivating continued research on improved algorithms and additional features with higher predictive power.
Data from: Multivariate ratio analysis reveals Trigonoderus pedicellaris Thomson (Hymenoptera, Chalcidoidea, Pteromalidae) as a valid species
We demonstrate by multivariate ratio analysis (MRA) the validity of two female colour morphs as separate species in what was previously regarded as a single species, Trigonoderus cyanescens (Förster, 1841) (Hymenoptera: Pteromalidae). As a result, T. pedicellaris Thomson, 1878 stat.r., is resurrected from synonymy under T. cyanescens and T. filatus binubilatus Erdős, 1960 syn.n. is synonymized with T. cyanescens. More than 20 characters were measured as part of two MRA dataset analyses. The first analysis excluded all measurements related to the gaster, whereas the second included gaster length, gaster breadth and seventh gaster tergite breadth. The first analysis revealed that the best separating morphometric ratios for the two species are head breadth:metatibia length and OOL:parastigma length, whereas the second analysis revealed OOL:gaster length as the second best separating ratio. The measurement error of all characters was below the admissible level of 30%. Gaster length proved to be a good character for separating the two groups, showed the lowest measurement error, and its percentage coefficient of variation was not greater than for other characters. This indicates that gaster length should not be discarded out-of-hand as a morphometric character in Pteromalidae. The variables that gave the best separating ratios included different body parts; therefore we suggest that the body of a specimen should be taken as a whole for use in MRA analyses, where each distance measurement can interact freely with any other. A key, figures and re-descriptions of T. cyanescens and T. pedicellaris are provided.
Data from: Exploitation of a turbot (Scophthalmus maximus L.) immune-related expressed sequence tag (EST) database for microsatellite screening and validation
In this study, we identified and characterized 160 microsatellite loci from an expressed sequence tag (EST) database generated from immune-related organs of turbot (Scophthalmus maximus). A final set of 83 new polymorphic microsatellites were validated after the analysis of 40 individuals from Atlantic origin including both wild and farmed individuals. The allele number and the expected heterozygosity ranged from 2 to 18 and from 0.021 to 0.951, respectively. Evidences of null alleles at moderate-high frequencies were detected at six loci using population data. None of the analyzed loci showed deviations from Mendelian segregation after analysis of five full-sib families including ~92 individuals/family. The markers are used to consolidate the turbot genetic map and, since they are mostly EST-derived, they will be very useful for comparative genomic studies within flatfishes and with model fish species. Using an in silico approach, we detected significant homologies of microsatellite sequences with the EST databases of the flatfish species with highest genomic resources (Senegalese sole, Atlantic halibut, bastard halibut) at 31% of these turbot markers. The conservation of these microsatellites within Pleuronectiformes will pave the way for anchoring genetic maps of different species and identifying genomic regions related to productive traits.
Data from: A from-benchtop-to-desktop workflow for validating HTS data and for taxonomic identification in diet metabarcoding studies
The main objective of this work was to develop and validate a robust and reliable 'from benchtop-to-desktop' metabarcoding workflow to investigate the diet of invertebrate-eaters. We applied our workflow to fecal DNA samples of an invertebrate-eating fish species. A fragment of the COI gene was amplified by combining two minibarcoding primer sets to maximize the taxonomic coverage. Amplicons were sequenced by an Illumina MiSeq platform. We developed a filtering approach based on a series of non-arbitrary thresholds established from control samples and from molecular replicates in order to address the elimination of cross-contamination, PCR/sequencing errors and mistagging artifacts. This resulted in a conservative and informative metabarcoding dataset. We developed a taxonomic assignment procedure that combines different approaches and that allowed the identification of ~75% of invertebrate COI variants to the species level. Moreover, based on the diversity of the variants, we introduced a semi-quantitative statistic in our diet study, the Minimum Number of Individuals (MNI), which is based on the number of distinct variants in each sample. The metabarcoding approach described in this paper may guide future diet studies that aim to produce robust datasets associated with a fine and accurate identification of prey items.
Data from: Development, validation, and evaluation of an assay for the detection of wood frogs (Rana sylvatica) in environmental DNA
We developed and describe a qPCR assay for the detection of wood frogs (Rana sylvatica) using environmental DNA (eDNA) sampling. A single primer set was designed to amplify a 115-bp region of the wood frog cytochrome B gene and assessed for target specificity. There was no evidence of amplification in 11 non-target species. We evaluated the utility of the primer set in qPCR assay by conducting geo-referenced eDNA field surveys in Interior Alaska. Results indicate that the assay consistently detects wood frog DNA in the environment to 1.83 × 10−3 pg/μL. The assay provides a complement to traditional survey methods and can be readily applied in a wider conservation and management context.
Data from: Validation of an algorithm for identifying MS cases in administrative health claims datasets
Objective: To develop a valid algorithm for identifying multiple sclerosis (MS) cases in administrative health claims (AHC) datasets. Methods: We used 4 AHC datasets from the Veterans Administration (VA), Kaiser Permanente Southern California (KPSC), Manitoba (Canada), and Saskatchewan (Canada). In the VA, KPSC, and Manitoba, we tested the performance of candidate algorithms based on inpatient, outpatient, and disease-modifying therapy (DMT) claims compared to medical records review using sensitivity, specificity, positive and negative predictive values, and interrater reliability (Youden J statistic) both overall and stratified by sex and age. In Saskatchewan, we tested the algorithms in a cohort randomly selected from the general population. Results: The preferred algorithm required ≥3 MS-related claims from any combination of inpatient, outpatient, or DMT claims within a 1-year time period; a 2-year time period provided little gain in performance. Algorithms including DMT claims performed better than those that did not. Sensitivity (86.6%–96.0%), specificity (66.7%–99.0%), positive predictive value (95.4%–99.0%), and interrater reliability (Youden J = 0.60–0.92) were generally stable across datasets and across strata. Some variation in performance in the stratified analyses was observed but largely reflected changes in the composition of the strata. In Saskatchewan, the preferred algorithm had a sensitivity of 96%, specificity of 99%, positive predictive value of 99%, and negative predictive value of 96%. Conclusions: The performance of each algorithm was remarkably consistent across datasets. The preferred algorithm required ≥3 MS-related claims from any combination of inpatient, outpatient, or DMT use within 1 year. We recommend this algorithm as the standard AHC case definition for MS.
Data from: Validation of the hospital frailty risk score in a tertiary care hospital in Switzerland: results of a prospective, observational study
Objectives: Recently, the Hospital Frailty Risk Score based on a derivation and validation study in the United Kingdom has been proposed as a low-cost, systematic screening tool to identify older, frail patients who are at greater risk of adverse outcomes and for whom a frailty-attuned approach might be useful. We aimed to validate this Score in an independent cohort in Switzerland. Design: Secondary analysis of a prospective, observational study (TRIAGE study). Setting: One 600-bed tertiary care hospital in Aarau, Switzerland Participants: Consecutive medical inpatients aged 75 years or older that presented to the emergency department or were electively admitted between October 2015 and April 2018. Primary and secondary outcome measures: The primary endpoint was all-cause 30-day mortality. Secondary endpoints were length of hospital stay, hospital readmission, functional impairment, and quality of life measures. We used multivariate regression analyses. Results: Of 4957 included patients, 3150 (63.5%) were classified as low risk, 1663 (33.5%) intermediate risk, and 144 (2.9%) high risk for frailty. Compared to the low-risk group, patients in the moderate risk and high-risk groups had increased risk for 30-day mortality (odds ratio [OR] 2.53, 95%CI 2.09 to 3.06, P<0.001 and OR 4.40, 95%CI 2.94 to 6.57, P<0.001) with overall moderate discrimination (area under the ROC curve 0.66). The results remained robust after adjustment for important confounders. Similarly, we found longer length of hospital stay, more severe functional impairment and a lower quality of life in higher risk group patients. Conclusion: Our data confirms the prognostic value of the Hospital Frailty Risk Score to identify older, frail people at risk for mortality and adverse outcomes in an independent patient population. Trial registration number: ClinicalTrials.gov; Identifier: NCT01768494
Data from: Bearding the scorpion in his den: desert isopods take risks to validate their 'landscape of fear' assessment
Animals balance the risk of predation against other vital needs by adjusting their spatial behavior to match spatiotemporal variation in predation risk. To map this 'landscape of fear', prey use evolutionary rules of thumbs that are associated with the activity and hunting efficiency of predators. In addition, prey acquire perceptual information about the presence, identity, and state of potential predators and use these cues to focus their acute anti-predatory responses. Our goal was to explore if and how prey also use such perceptual information that decays with time to update their spatiotemporal risk assessment. We placed scorpions in freshly dug burrows and recorded the isopods' spatial activity and their defense behavior upon encountering the burrows straight after settling the scorpions and seven days later. To corroborate our understanding, we also examined the isopods' detailed reactions towards deserted scorpion burrows. The isopods reacted defensively to scorpion burrows during their first encounter. After seven days, proportionally more isopods approached the scorpion burrows on their way out for foraging and fewer isopods encountered it on their way back. No changes in the spatial activity were observed towards deserted burrows. In addition, on the eighth day, more isopods entered the risky area near the scorpion burrows when leaving their own burrow than on the first encounter. The results suggest that isopods used predator cues to readjust the 'landscape of fear'. Yet, rather than avoiding the dangerous areas altogether, the isopods implemented risky inspection behavior, validating whether the danger is actual. Our findings imply that inspection behavior toward predators can be used for future planning of prey spatial activity, offsetting possible 'information decay costs'.
Data from: Development and validation of an expert-based habitat suitability model to support boreal caribou conservation
The long-term persistence of boreal caribou (Rangifer tarandus caribou) is threatened by the negative impacts of human activities, including industrial development. The vast geographical distribution and behavioural variability of caribou warranted the development and validation of an appropriate conservation tool for managers in eastern Canada. We developed a habitat suitability model for boreal caribou in Québec by integrating expert knowledge into a hierarchical analysis. We elicited responses from 14 experts on caribou ecology to determine the best spatiotemporal scales of application, the relative importance of habitat variables, the zone of influence of human infrastructure, and the parameterization of the model. Based on this input, we built the model using 8 habitat categories and 3 human infrastructure variables. Experts identified mature conifer-dominated forests and open lichen woodlands as the most important vegetation categories for boreal caribou, whereas density of and proximity to paved roads, forest roads, and mines decreased habitat quality. We mapped the resulting model over the entire province of Québec (up to the northern forest allocation limit), and validated it using independent GPS telemetry datasets acquired in 3 distinct regions. Our model predicted that habitat suitability was highest in the northeastern part of our study area, where timber harvest activities and roads were virtually absent. Conversely, southern parts of Québec were generally unsuitable for boreal caribou. Our habitat suitability model is among the first tools for boreal caribou conservation available to wildlife and land managers in Québec.
Data from: Landscape connectivity for wildlife: development and validation of multi-species linkage maps
The ability to identify regions of high functional connectivity for multiple wildlife species is of conservation interest with respect to forest management and corridor planning. We present a method that does not require independent, field-collected data, is insensitive to the placement of source and destination sites (nodes) for modeling connectivity, and does not require the selection of a focal species. In the first step of our approach, we created a cost surface that represented permeability of the landscape to movement for a suite of species. We randomly selected nodes around the perimeter of the buffered study area and used circuit theory to connect pairs of nodes. When the buffer was removed, the resulting current density map represented, for each grid cell, the probability of use by moving animals. We found that using nodes that were randomly located around the perimeter of the buffered study area was less biased by node placement than randomly selecting nodes within the study area. We also found that a buffer of ≥ 20% of the study area width was sufficient to remove the effects of node placement on current density. We tested our method by creating a map of connectivity in the Algonquin to Adirondack region in eastern North America, and we validated the map with independently collected data. We found that amphibians and reptiles were more likely to cross roads in areas of high current density, and fishers (Pekania [Martes] pennanti) used areas with high current density within their home ranges. Our approach provides an efficient and cost-effective method of predicting areas with relatively high functional connectivity.
Data from: DNA metabarcoding multiplexing and validation of data accuracy for diet assessment: application to omnivorous diet
Ecological understanding of the role of consumer-resource interactions in natural food webs is limited by the difficulty of accurately and efficiently determining the complex variety of food types animals have eaten in the field. We developed a method based on DNA metabarcoding multiplexing and next-generation sequencing to uncover different taxonomic groups of organisms from complex diet samples. We validated this approach on 91 faeces of a large omnivorous mammal, the brown bear, using DNA metabarcoding markers targeting the plant, vertebrate, and invertebrate components of the diet. We included internal controls in the experiments and performed PCR replication for accuracy validation in post-sequencing data analysis. Using our multiplexing strategy, we significantly simplified the experimental procedure and accurately and concurrently identified different prey DNA corresponding to the targeted taxonomic groups, with ≥60% of taxa of all diet components identified to genus/species level. The systematic application of internal controls and replication was a useful and simple way to evaluate the performance of our experimental procedure, standardize the selection of sequence filtering parameters for each marker data, and validate the accuracy of the results. Our general approach can be adapted to the analysis of dietary samples of various predator species in different ecosystems, for a number of conservation and ecological applications entailing large-scale population level diet assessment through cost effective screening of multiple DNA metabarcodes, and the detection of fine dietary variation among samples or individuals and of rare food items.
Data from: Validating genome-wide association candidates controlling quantitative variation in nodulation
Genome-wide association (GWA) studies offer the opportunity to identify genes that contribute to naturally occurring variation in quantitative traits. However, GWA relies exclusively on statistical association, so functional validation is necessary to make strong claims about gene function. We used a combination of gene-disruption platforms (Tnt1 retrotransposons, hairpin RNA-interference constructs, and CRISPR/Cas9 nucleases) together with randomized, well-replicated experiments to evaluate the function of genes that an earlier GWA study in Medicago truncatula had identified as candidates contributing to variation in the symbiosis between legumes and rhizobia. We evaluated ten candidate genes found in six clusters of strongly associated single nucleotide polymorphisms, selected on the basis of their strength of statistical association, proximity to annotated gene models, and root or nodule expression. We found statistically significant effects on nodule production for three candidate genes, each validated in two independent mutants. Annotated functions of these three genes suggest their contributions to quantitative variation in nodule production occur through processes not previously connected to nodulation, including phosphorous supply and salicylic acid-related defense response. These results demonstrate the utility of GWA combined with reverse mutagenesis technologies to discover and validate genes contributing to naturally occurring variation in quantitative traits. The results highlight the potential for GWA to complement forward genetics in identifying the genetic basis of ecologically and economically important traits.
Data from: Proxies in practice: calibration and validation of multiple indices of animal abundance
The abundance of individuals in a population is a fundamental metric in basic and applied ecology, but sampling protocols yielding precise and unbiased estimates of abundance are often cost prohibitive. Proxies of abundance are therefore common, but require calibration and validation. There are many ways to calibrate a proxy, and it is not obvious which will perform best. We use data from eight populations of Chinook salmon (Oncorhynchus tshawytscha) on the Oregon coast where multiple proxies of abundance were obtained contemporaneously with independent mark-recapture estimates. We combined multiple proxy values associated with a single level of abundance into a univariate index and then calibrated that index to mark-recapture estimates using several different techniques. We tested our calibration methods using leave-one-out cross validation and simulation. Our cross-validation analysis did not definitively identify a single best calibration technique for all populations, but we could identify consistently inferior approaches. The simulations suggested that incorporating the known mark-recapture uncertainty into the calibration technique added bias and imprecision. Cross validation techniques should be used to test multiple methods of calibrating multiple proxies to an estimate of abundance. Critical uncertainties with the application of calibrated proxies still exist, and cost-benefit analysis should be performed to help identify optimal monitoring designs.
Data from: An updated global dataset for diet preferences in terrestrial mammals: testing the validity of extrapolation
1. Diet is a key trait of an organism's life history that influences a broad spectrum of ecological and evolutionary processes. Kissling et al. (2014) compiled a species-specific dataset of diet preferences of mammals for 38% of a total of 5364 terrestrial mammalian species assessed for the International Union for Conservation of Nature's Red List, to facilitate future studies. The authors imputed dietary data for the remaining 62% by using extrapolation from phylogenetic relatives. 2. We collected dietary information for 1261 mammalian species for which data were extrapolated by Kissling et al. (2014), in order to evaluate the success with which such extrapolation can predict true diets. 3. The extrapolation method devised by Kissling et al. (2014) performed well for broad dietary categories (consumers of plants and animals). However, the method performed inconsistently, and sometimes poorly, for finer dietary categories, varying in accuracy in both dietary categories and mammalian orders. 4. The results of the extrapolation performance serve as a cautionary tale. Given the large variation in extrapolation performance, we recommend a more conservative approach for inferring mammalian diets, whereby dietary extrapolation is implemented only when there is a high degree of phylogenetic conservatism for dietary traits. Phylogenetic comparative methods can be used to detect and measure phylogenetic signal in diet. If data for species are needed, then only the broadest feeding categories should be used. This would ensure a greater level of accuracy and provide a more robust dataset for further ecological and evolutionary analysis.
Data from: Establishing macroecological trait datasets: digitalization, extrapolation, and validation of diet preferences in terrestrial mammals worldwide
Ecological trait data are essential for understanding the broad-scale distribution of biodiversity and its response to global change. For animals, diet represents a fundamental aspect of species' evolutionary adaptations, ecological and functional roles, and trophic interactions. However, the importance of diet for macroevolutionary and macroecological dynamics remains little explored, partly because of the lack of comprehensive trait datasets. We compiled and evaluated a comprehensive global dataset of diet preferences of mammals ("MammalDIET"). Diet information was digitized from two global and cladewide data sources and errors of data entry by multiple data recorders were assessed. We then developed a hierarchical extrapolation procedure to fill-in diet information for species with missing information. Missing data were extrapolated with information from other taxonomic levels (genus, other species within the same genus, or family) and this extrapolation was subsequently validated both internally (with a jack-knife approach applied to the compiled species-level diet data) and externally (using independent species-level diet information from a comprehensive continentwide data source). Finally, we grouped mammal species into trophic levels and dietary guilds, and their species richness as well as their proportion of total richness were mapped at a global scale for those diet categories with good validation results. The success rate of correctly digitizing data was 94%, indicating that the consistency in data entry among multiple recorders was high. Data sources provided species-level diet information for a total of 2033 species (38% of all 5364 terrestrial mammal species, based on the IUCN taxonomy). For the remaining 3331 species, diet information was mostly extrapolated from genus-level diet information (48% of all terrestrial mammal species), and only rarely from other species within the same genus (6%) or from family level (8%). Internal and external validation showed that: (1) extrapolations were most reliable for primary food items; (2) several diet categories ("Animal," "Mammal," "Invertebrate," "Plant," "Seed," "Fruit," and "Leaf") had high proportions of correctly predicted diet ranks; and (3) the potential of correctly extrapolating specific diet categories varied both within and among clades. Global maps of species richness and proportion showed congruence among trophic levels, but also substantial discrepancies between dietary guilds. MammalDIET provides a comprehensive, unique and freely available dataset on diet preferences for all terrestrial mammals worldwide. It enables broad-scale analyses for specific trophic levels and dietary guilds, and a first assessment of trait conservatism in mammalian diet preferences at a global scale. The digitalization, extrapolation and validation procedures could be transferable to other trait data and taxa.
Data from: Testing the validity of functional response models using molecular gut content analysis for prey choice in soil predators
Analysis of predator - prey interactions is a core concept of animal ecology, explaining structure and dynamics of animal food webs. Measuring the functional response, i.e. the intake rate of a consumer as a function of prey density, is a powerful method to predict the strength of trophic links and assess motives of prey choice, particularly in arthropod communities. However, due to their reductionist set-up, functional responses, which are based on laboratory feeding experiments, may not display field conditions, possibly leading to skewed results. Here, we tested the validity of functional responses of centipede predators and their prey by comparing them with empirical gut content data from field-collected predators. Our predator - prey system included lithobiid and geophilomorph centipedes, abundant and widespread predators of forest soils and their soil-dwelling prey. First, we calculated the body size-dependent functional responses of centipedes using a published functional response model in which we included natural prey abundances and animal body masses. This allowed us to calculate relative proportions of specific prey taxa in the centipede diet. In a second step, we screened field-collected centipedes for DNA of eight abundant soil-living prey taxa and estimated their body size-dependent proportion of feeding events. We subsequently compared empirical data for each of the eight prey taxa, on proportional feeding events with functional response-derived data on prey proportions expected in the gut, showing that both approaches significantly correlate in five out of eight predator - prey links for lithobiid centipedes but only in one case for geophilomorph centipedes. Our findings suggest that purely allometric functional response models, which are based on predator-prey body size ratios are too simple to explain predator - prey interactions in a complex system such as soil. We therefore stress that specific prey traits, such as defence mechanisms, must be considered for accurate predictions.
ScienceDex guides
Understand access before you commit
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.