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9,302 results for “DETECT”
Data from: Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes
The spatial structure of the environment (e.g., the configuration of habitat patches) may play an important role in determining the strength of local adaptation. However, previous studies of habitat heterogeneity and local adaptation have largely been limited to simple landscapes, which poorly represent the multi-scale habitat structure common in nature. Here, we use simulations to pursue two goals: (1) we explore how landscape heterogeneity, dispersal ability, and selection affect the strength of local adaptation, and (2) we evaluate the performance of several genotype-environment association (GEA) methods for detecting loci involved in local adaptation. We found that the strength of local adaptation increased in spatially aggregated selection regimes, but remained strong in patchy landscapes when selection was moderate to strong. Weak selection resulted in weak local adaptation that was relatively unaffected by landscape heterogeneity. In general, the power of detection methods closely reflected levels of local adaptation. False positive rates (FPRs), however, showed distinct differences across GEA methods based on levels of population structure. The univariate GEA approach had high FPRs (up to 55%) under limited dispersal scenarios, due to strong isolation by distance. By contrast, multivariate, ordination-based methods had uniformly low FPRs (0-2%), suggesting these approaches can effectively control for population structure. Specifically, constrained ordinations had the best balance of high detection and low FPRs, and will be a useful addition to the GEA toolkit. Our results provide both theoretical and practical insights into the conditions that shape local adaptation and how these conditions impact our ability to detect selection.
Selection and exploitation of prevalent, tandemly repeated genomic targets for improved real-time PCR-based detection of Wuchereria bancrofti and Plasmodium falciparum in mosquitoes
<p><b>Background</b></p> <p>Optimization of polymerase chain reaction (PCR)-based diagnostics requires the careful selection of molecular targets that are both highly repetitive and pathogen-specific. Advances in both next-generation sequencing (NGS) technologies and bioinformatics-based analysis tools are facilitating this selection process, informing target choices and reducing labor. Once developed, such assays provide disease control and elimination programs with an additional set of tools capable of evaluating and monitoring intervention successes. The importance of such tools is heightened as intervention efforts approach their endpoints, as accurate and complete information is an essential component of the informed decision-making process. As global efforts for the control and elimination of both lymphatic filariasis and malaria continue to make significant gains, the benefits of diagnostics with improved analytical and clinical/field-based sensitivities and specificities will become increasingly apparent.</p> <p><b>Methodology/Principal Findings </b></p> <p>Coupling Illumina-based NGS with informatics approaches, we have successfully identified the tandemly repeated elements in both the <i>Wuchereria bancrofti </i>and <i>Plasmodium falciparum </i>genomes of putatively greatest copy number. Utilizing these sequences as quantitative real-time PCR (qPCR)-based targets, we have developed assays capable of exploiting the most abundant tandem repeats for both organisms. For the detection of <i>P. falciparum</i>, analysis and development resulted in an assay with improved analytical and field-based sensitivity vs. an established ribosomal sequence-targeting assay. Surprisingly, analysis of the <i>W. bancrofti </i>genome predicted a ribosomal sequence to be the genome's most abundant tandem repeat. While resulting cycle quantification values comparing a qPCR assay targeting this ribosomal sequence and a commonly targeted repetitive DNA sequence from the literature supported our finding that this ribosomal sequence was the most prevalent tandemly repeated target in the <i>W. bancrofti </i>genome, the resulting assay did not significantly improve detection sensitivity in conjunction with field sample testing. </p> <p><b>Conclusions/Significance </b></p> <p>Examination of pathogen genomes facilitates the development of PCR-based diagnostics targeting the most abundant and specific genomic elements. While in some instances currently available tools may deliver equal or superior performance, systematic analysis of potential targets provides confidence that the selected assays represent the most advantageous options available and that informed assay selection is occurring in the context of a particular study's objectives.</p>
Data from: Experimental evidence that primate trichromacy is well suited for detecting primate social colour signals
Primate trichromatic colour vision has been hypothesized to be well tuned for detecting variation in facial coloration, which could be due to selection on either signal wavelengths or the sensitivities of the photoreceptors themselves. We provide one of the first empirical tests of this idea by asking whether, when compared with other visual systems, the information obtained through primate trichromatic vision confers an improved ability to detect the changes in facial colour that female macaque monkeys exhibit when they are proceptive. We presented pairs of digital images of faces of the same monkey to human observers and asked them to select the proceptive face. We tested images that simulated what would be seen by common catarrhine trichromatic vision, two additional trichromatic conditions and three dichromatic conditions. Performance under conditions of common catarrhine trichromacy, and trichromacy with narrowly separated LM cone pigments (common in female platyrrhines), was better than for evenly spaced trichromacy or for any of the dichromatic conditions. These results suggest that primate trichromatic colour vision confers excellent ability to detect meaningful variation in primate face colour. This is consistent with the hypothesis that social information detection has acted on either primate signal spectral reflectance or photoreceptor spectral tuning, or both.
Data from: A method for detecting characteristic patterns in social interactions with an application to handover interactions
Social interactions are a defining behavioural trait of social animals. Discovering characteristic patterns in the display of such behaviour is one of the fundamental endeavours in behavioural biology and psychology, as this promises to facilitate the general understanding, classification, prediction and even automation of social interactions. We present a novel approach to study characteristic patterns, including both sequential and synchronous actions in social interactions. The key concept in our analysis is to represent social interactions as sequences of behavioural states and to focus on changes in behavioural states shown by individuals rather than on the duration for which they are displayed. We extend techniques from data mining and bioinformatics to detect frequent patterns in these sequences and to assess how these patterns vary across individuals or changes in interaction tasks. To illustrate our approach and to demonstrate its potential, we apply it to novel data on a simple physical interaction, where one person hands a cup to another person. Our findings advance the understanding of handover interactions, a benchmark scenario for social interactions. More generally, we suggest that our approach permits a general perspective for studying social interactions.
Data from: Distinguishing migration from isolation using genes with intragenic recombination: detecting introgression in the Drosophila simulans species complex
Background: Determining the presence or absence of gene flow between populations is the target of some statistical methods in population genetics. Until recently, these methods either avoided the use of recombining genes, or treated recombination as a nuisance parameter. However, genes with recombination contribute additional information for the detection of gene flow (i.e. through linkage disequilibrium). Methods: We present three summary statistics based on the spatial arrangement of fixed differences, and shared and exclusive polymorphisms that are sensitive to the presence and direction of gene flow. Power and false positive rate for tests based on these statistics are studied by simulation. Results: The application of these tests to populations from the Drosophila simulans species complex yielded results consistent with migration between D. simulans and its two endemic sister species D. mauritiana and D. sechellia, and between populations D. mauritiana on the islands of the Mauritius and Rodrigues. Conclusions: We demonstrate the sensitivity of the developed statistics to the presence and direction of gene flow, and characterize their power as a function of differentiation level and recombination rate. The properties of these statistics make them especially suitable for analyzing high-throughput sequencing data or for their integration within the approximate Bayesian computation framework.
Data from: DNA extraction method affects the detection of a fungal pathogen in formalin-fixed specimens using qPCR
Museum collections provide indispensable repositories for obtaining information about the historical presence of disease in wildlife populations. The pathogenic amphibian chytrid fungus Batrachochytrium dendrobatidis (Bd) has played a significant role in global amphibian declines, and examining preserved specimens for Bd can improve our understanding of its emergence and spread. Quantitative PCR (qPCR) enables Bd detection with minimal disturbance to amphibian skin and is significantly more sensitive to detecting Bd than histology; therefore, developing effective qPCR methodologies for detecting Bd DNA in formalin-fixed specimens can provide an efficient and effective approach to examining historical Bd emergence and prevalence. Techniques for detecting Bd in museum specimens have not been evaluated for their effectiveness in control specimens that mimic the conditions of animals most likely to be encountered in museums, including those with low pathogen loads. We used American bullfrogs (Lithobates catesbeianus) of known infection status to evaluate the success of qPCR to detect Bd in formalin-fixed specimens after three years of ethanol storage. Our objectives were to compare the most commonly used DNA extraction method for Bd (PrepMan, PM) to Macherey-Nagel DNA FFPE (MN), test optimizations for Bd detection with PM, and provide recommendations for maximizing Bd detection. We found that successful detection is relatively high (80–90%) when Bd loads before formalin fixation are high, regardless of the extraction method used; however, at lower infection levels, detection probabilities were significantly reduced. The MN DNA extraction method increased Bd detection by as much as 50% at moderate infection levels. Our results indicate that, for animals characterized by lower pathogen loads (i.e., those most commonly encountered in museum collections), current methods may underestimate the proportion of Bd-infected amphibians. Those extracting DNA from archived museum specimens should ensure that the techniques they are using are known to provide high-quality throughput DNA for later analysis.
Data from: Effect of detection heterogeneity in occupancy-detection models: an experimental test of time-to-first-detection methods
Imperfect detection can bias estimates of site occupancy in ecological surveys but can be corrected by estimating detection probability. Time-to-first-detection (TTD) occupancy models have been proposed as a cost-effective survey method that allows detection probability to be estimated from single site visits. Nevertheless, few studies have validated the performance of occupancy-detection models by creating a situation where occupancy is known, and model outputs can be compared with the truth. We tested the performance of TTD occupancy models in the face of detection heterogeneity using an experiment based on standard survey methods to monitor koala (Phascolarctos cinereus) populations in Australia. Known numbers of koala faecal pellets were placed under trees, and observers, uninformed as to which trees had pellets under them, carried out a TTD survey. We fitted five TTD occupancy models to the survey data, each making different assumptions about detectability, to evaluate how well each estimated the true occupancy status. Relative to the truth, all five models produced strongly biased estimates, overestimating detection probability and underestimating the number of occupied trees. Despite this, goodness-of-fit tests indicated that some models fitted the data well, with no evidence of model misfit. Hence, TTD occupancy models that appear to perform well with respect to the available data may be performing poorly. The reason for poor model performance was unaccounted for heterogeneity in detection probability, which is known to bias occupancy-detection models. This poses a problem because unaccounted for heterogeneity could not be detected using goodness-of-fit tests and was only revealed because we knew the experimentally determined outcome. A challenge for occupancy-detection models is to find ways to identify and mitigate the impacts of unobserved heterogeneity, which could unknowingly bias many models.
Data from: Failure to reproduce period-dependent song cycles in Drosophila is due to poor automated pulse-detection and low-intensity courtship
Stern has criticized a body of work from several groups that have independently studied the so-called "Kyriacou and Hall" courtship song rhythms of male Drosophila melanogaster, claiming that these ultradian ∼60-s cycles in the interpulse interval (IPI) are statistical artifacts that are not modulated by mutations at the period (per) locus [Stern DL (2014) BMC Biol 12:38]. We have scrutinized Stern's raw data and observe that his automated song pulse-detection method identifies only ∼50% of the IPIs found by manual (visual and acoustic) monitoring. This critical error is further compounded by Stern's use of recordings with very little song, the large majority of which do not meet the minimal song intensity criteria which Kyriacou and Hall used in their studies. Consequently most of Stern's recordings only contribute noise to the analyses. Of the data presented by Stern, only perL and a small fraction of wild-type males sing vigorously, so we limited our reanalyses to these genotypes. We manually reexamined Stern's raw song recordings and analyzed IPI rhythms using several independent time-series analyses. We observe that perL songs show significantly longer song periods than wild-type songs, with values for both genotypes close to those found in previous studies. These per-dependent differences disappear when the song data are randomized. We conclude that Stern's negative findings are artifacts of his inadequate pulse-detection methodology coupled to his use of low-intensity courtship song records.
Data from: A single microphone noise reduction algorithm based on the detection and reconstruction of spectro-temporal features
Animals throughout the animal kingdom excel at extracting individual sounds from competing background sounds, yet current state-of-the-art signal processing algorithms struggle to process speech in the presence of even modest background noise. Recent psychophysical experiments in humans and electrophysiological recordings in animal models suggest that the brain is adapted to process sounds within the restricted domain of spectro-temporal modulations found in natural sounds. Here, we describe a novel single microphone noise reduction algorithm called spectro-temporal detection–reconstruction (STDR) that relies on an artificial neural network trained to detect, extract and reconstruct the spectro-temporal features found in speech. STDR can significantly reduce the level of the background noise while preserving the foreground speech quality and improving estimates of speech intelligibility. In addition, by leveraging the strong temporal correlations present in speech, the STDR algorithm can also operate on predictions of upcoming speech features, retaining similar performance levels while minimizing inherent throughput delays. STDR performs better than a competing state-of-the-art algorithm for a wide range of signal-to-noise ratios and has the potential for real-time applications such as hearing aids and automatic speech recognition.
Data from: Visual modelling supports the potential for prey detection by means of diurnal active photolocation in a small cryptobenthic fish
Active sensing has been well documented in animals that use echolocation and electrolocation. Active photolocation, or active sensing using light, has received much less attention, and only in bioluminescent nocturnal species. However, evidence has suggested the diurnal triplefin Tripterygion delaisi uses controlled iris radiance, termed ocular sparks, for prey detection. While this form of diurnal active photolocation was behaviourally described, a study exploring the physical process would provide compelling support for this mechanism. In this paper, we investigate the conditions under which diurnal active photolocation could assist T. delaisi in detecting potential prey. In the field, we sampled gammarids (genus Cheirocratus) and characterized the spectral properties of their eyes, which possess strong directional reflectors. In the laboratory, we quantified ocular sparks size and their angle-dependent radiance. Combined with environmental light measurements and known properties of the visual system of T. delaisi, we modeled diurnal active photolocation under various scenarios. Our results corroborate that diurnal active photolocation should help T. delaisi detect gammarids at distances relevant to foraging, 4.5 cm under favourable conditions and up to 2.5 cm under average conditions. To determine the prevalence of diurnal active photolocation for micro-prey, we encourage further theoretical and empirical work.
Data from: Can microstructural MRI detect subclinical tissue injury in subjects with asymptomatic cervical spinal cord compression? a prospective cohort study
Objectives: Degenerative cervical myelopathy (DCM) involves extrinsic spinal cord compression causing tissue injury and neurological dysfunction. Asymptomatic spinal cord compression (ASCC) is more common but its significance is poorly defined. This study investigates if: 1) ASCC can be automatically diagnosed using spinal cord shape analysis; 2) multiparametric quantitative MRI can detect similar spinal cord tissue injury as previously observed in DCM. Design: Prospective observational longitudinal cohort study. Setting: Single centre, tertiary care and research institution. Participants: 40 neurologically intact subjects (19 female, 21 male) divided into groups with and without ASCC. Interventions: None. Outcome Measures: Clinical assessments: modified Japanese Orthopedic Association (mJOA) score and physical examination. 3T MRI assessments: automated morphometric analysis compared with consensus ratings of spinal cord compression, and measures of tissue injury: cross-sectional area (CSA), diffusion fractional anisotropy (FA), magnetization transfer ratio (MTR), and T2-weighted imaging white to grey matter signal intensity ratio (T2WI WM/GM) extracted from rostral (C1-3), caudal (C6-7), and maximally compressed levels (MCL). Results: ASCC was present in 20/40 subjects. Diagnosis with automated shape analysis showed area under the curve > 97%. Five MRI metrics showed differences suggestive of tissue injury in ASCC compared with uncompressed subjects (p<0.05), while a composite of all 10 measures (average of z scores) showed highly significant differences (p=0.002). At follow-up (median 21 months), two ASCC subjects developed DCM. Conclusions: ASCC appears to be common and can be accurately and objectively diagnosed with automated morphometric analysis. Quantitative MRI appears to detect subclinical tissue injury in ASCC prior to the onset of neurological symptoms and signs. These findings require further validation, but offer the intriguing possibility of pre-symptomatic diagnosis and treatment of DCM and other spinal pathologies. Registration: Not registered.
Data from: Predictive Bayesian selection of multistep Markov chains, applied to the detection of the hot hand and other statistical dependencies in free throws
When extended to data from the 2016--2017 NBA season specifically for LeBron James, a model depending on the previous shot (single-step Markovian) does not clearly beat a model with independent outcomes. An error-correcting variable length model of two parameters, where James shoots a higher percentage after a missed free throw than otherwise, is more predictive than either model.
Data from: An examination of the accuracy of a sequential PCR and sequencing test used to detect the incursion of an invasive species: the case of the red fox in Tasmania
1. Polymerase Chain Reaction (PCR) diagnostic tests are increasingly applied to the identification of wildlife. Yet rigorous verification is rare and the estimation of test accuracy (the probability that true positive and true negative samples are correctly identified – test sensitivity and specificity, respectively), particularly in combination with sequencing, is uncommon. This is important because PCR-based tests are prone to contamination in sampling and the laboratory. 2. Here, we use an experimental case–control approach to estimate the sensitivity and specificity of a sequential PCR-based wildlife detection test used to identify incursions of red foxes into Tasmania from predator faeces (scats). 3. Our results show that the sensitivity of the fox test is high (~94%) for the PCR-based test on its own, but this decreases to ~84% when combined with the DNA sequencing step. In contrast, the specificity increases from ~96% in the PCR only test to ~99.6% after inclusion of the DNA sequencing step. 4. The intense public scrutiny of the fox eradication program in Tasmania, has undoubtedly influenced the application of a sequential PCR test that maximises specificity at the expense of sensitivity and so increases the risk that scats containing fox DNA would not be detected. This could lead to the establishment of foxes in Tasmania as a consequence. 5. Synthesis and applications. Importantly, the estimation of the sensitivity and specificity of sequential tests enables decisions about the risk associated with mistaken identification (i.e. false negatives vs false positives) to be quantified for decision makers. The cost of false negative errors should be balanced against the costs of false positive errors, which could include the expenditure incurred in the application of unnecessary management actions were foxes not in fact present. Understanding the risks and costs associated with both false negative and false positive errors is therefore a key component to the decision making process for the management of the Tasmanian fox incursion.
Data from: A robust semi-parametric test for detecting trait-dependent diversification
Rates of species diversification vary widely across the tree of life and there is considerable interest in identifying organismal traits that correlate with rates of speciation and extinction. However, it has been challenging to develop methodological frameworks for testing hypotheses about trait-dependent diversification that are robust to phylogenetic pseudoreplication and to directionally biased rates of character change. We describe a semi-parametric test for trait-dependent diversification that explicitly requires replicated associations between character states and diversification rates in order to detect effects. To use the method, diversification rates are reconstructed across a phylogenetic tree with no consideration of character states. A test statistic is then computed to measure the association between species-level traits and the corresponding diversification rate estimates at the tips of the tree. The empirical value of the test statistic is compared to a null distribution that is generated by structured permutations of evolutionary rates across the phylogeny. The test is applicable to binary discrete characters as well as continuous-valued traits and can accommodate extremely sparse sampling of character states at the tips of the tree. We apply the test to several empirical datasets and demonstrate that the method has acceptable Type I error rates.
Data from: Detecting environment-dependent diversification from phylogenies: a simulation study and some empirical illustrations
Understanding the relative influence of various abiotic and biotic variables on diversification dynamics is a major goal of macroevolutionary studies. Recently, phylogenetic approaches have been developed that make it possible to estimate the role of various environmental variables on diversification using time-calibrated species trees, paleoenvironmental data, and maximum-likelihood techniques. These approaches have been effectively employed to estimate how speciation and extinction rates vary with key abiotic variables, such as temperature and sea level, and we can anticipate that they will be increasingly used in the future. Here we compile a series of biotic and abiotic paleodatasets that can be used as explanatory variables in these models and use simulations to assess the statistical properties of the approach when applied to these paleodatasets. We demonstrate that environment-dependent models perform well in recovering environment-dependent speciation and extinction parameters, as well as in correctly identifying the simulated environmental model when speciation is environment-dependent. We explore how the strength of the environment-dependency, tree size, missing taxa, and characteristics of the paleoenvironmental curves influence the performance of the models. Finally, using these models, we infer environment-dependent diversification in two empirical phylogenies: temperature-dependence in Cetacea and δ 13C-dependence in Ruminantia. We illustrate how to evaluate the relative importance of abiotic and biotic variables in these two clades and interpret these results in light of macroevolutionary hypotheses. Given the important role paleoenvironments are presumed to have played in species evolution, our statistical assessment of how environment-dependent models behave is crucial for their utility in macroevolutionary analysis.
Data from: Label-free sensitive detection of influenza virus using PZT discs with a synthetic sialylglycopolymer receptor layer
We describe rapid, label-free detection of Influenza A viruses using the first radial mode of oscillations of lead zirconate titanate (PZT) piezoelectric disks with a 2-mm radius and 100-µm thickness fabricated from a piezoelectric membrane. The disks are modified with a synthetic sialylglycopolymer receptor layer,and the coated disks are inserted in a flowing virus suspension. Label-free detection of the virus is achieved by monitoring the disk radial mode resonance frequency shift. Piezo transducers with sialylglycopolymer sensor layers exhibited a long lifetime, a high sensitivity, and the possibility of regeneration. We demonstrate positive, label-free detection of Influenza A viruses at concentrations below 10^5 virus particles per millilitre. We show that label-free, selective, sensitive detection of Influenza viruses by home appliances is possible in principle.
Data from: Robust regression and posterior predictive simulation increase power to detect early bursts of trait evolution
A central prediction of much theory on adaptive radiations is that traits should evolve rapidly during the early stages of a clade's history and subsequently slowdown in rate as niches become saturated – a so-called "Early Burst". Although a common pattern in the fossil record, evidence for early bursts of trait evolution in phylogenetic comparative data has been equivocal at best. We show here that this may not necessarily be due to the absence of this pattern in nature. Rather, commonly used methods to infer its presence perform poorly when when the strength of the burst - the rate at which phenotypic evolution declines - is small, and when some morphological convergence is present within the clade. We present two modifications to existing comparative methods that allow greater power to detect early bursts in simulated datasets. First, we develop posterior predictive simulation approaches and show that they outperform maximum likelihood approaches at identifying early bursts at moderate strength. Second, we use a robust regression procedure that allows for the identification and down-weighting of convergent taxa, leading to moderate increases in method performance. We demonstrate the utility and power of these approach by investigating the evolution of body size in cetaceans. Model fitting using maximum likelihood is equivocal with regards the mode of cetacean body size evolution. However, posterior predictive simulation combined with a robust node height test return low support for Brownian motion or rate shift models, but not the early burst model. While the jury is still out on whether early bursts are actually common in nature, our approach will hopefully facilitate more robust testing of this hypothesis. We advocate the adoption of similar posterior predictive approaches to improve the fit and to assess the adequacy of macroevolutionary models in general.
Data from: Detecting mosaic patterns in macroevolutionary disparity
Evolutionary biologists have long sought to understand the full complexity in pattern and process that shapes organismal diversity. Although phylogenetic comparative methods are often used to reconstruct complex evolutionary dynamics, they are typically limited to a single phenotypic trait. Extensions that accommodate multiple traits lack the ability to partition multidimensional datasets into a set of mosaic suites of evolutionarily linked characters. I introduce a comparative framework that identifies heterogeneity in evolutionary patterns across large datasets of continuous traits. Using a model of continuous trait evolution based on the differential accumulation of disparity across lineages in a phylogeny, the approach algorithmically partitions traits into a set of character suites that best explains the data, where each suite displays a distinct pattern in phylogenetic morphological disparity. When applied to empirical data, the approach revealed a mosaic pattern predicted by developmental biology. The evolutionary distinctiveness of individual suites can be investigated in more detail, either by fitting conventional comparative models or by directly studying the phylogenetic patterns in disparity recovered during the analysis. This framework can supplement existing comparative approaches by inferring the complex, integrated patterns that shape evolution across the body plan from disparate developmental, morphometric, and environmental sources of phenotypic data.
Data from: 'True' null allele detection in microsatellite loci: a comparison of methods, assessment of difficulties, and survey of possible improvements
Null alleles are alleles that for various reasons fail to amplify in a PCR assay. The presence of null alleles in microsatellite data is known to bias the genetic parameter estimates. Thus, efficient detection of null alleles is crucial, but the methods available for indirect null allele detection return inconsistent results. Here, our aim was to compare different methods for null allele detection, to explain their respective performance and to provide improvements. We applied several approaches to identify the 'true' null alleles based on the predictions made by five different methods, used either individually or in combination. First, we introduced simulated 'true' null alleles into 240 population data sets and applied the methods to measure their success in detecting the simulated null alleles. The single best-performing method was ML-NullFreq_frequency. Furthermore, we applied different noise reduction approaches to improve the results. For instance, by combining the results of several methods, we obtained more reliable results than using a single one. Rule-based classification was applied to identify population properties linked to the false discovery rate. Rules obtained from the classifier described which population genetic estimates and loci characteristics were linked to the success of each method. We have shown that by simulating 'true' null alleles into a population data set, we may define a null allele frequency threshold, related to a desired true or false discovery rate. Moreover, using such simulated data sets, the expected null allele homozygote frequency may be estimated independently of the equilibrium state of the population.
Data from: An integrative approach to detect subtle trophic niche differentiation in the sympatric trawling bat species Myotis dasycneme and Myotis daubentonii
Bats are well known for species richness and ecological diversity thus they provide a good opportunity to study relationships and interaction between species. To assess interactions we consider distinct traits which are likely to be triggered by niche shape and evolutionary processes. We present data on the trophic niche differentiation between two sympatric European trawling bat species, Myotis dasycneme and M. daubentonii, incorporating a wide spectrum of methodological approaches. We measure morphological traits involved in foraging and prey handling performance including bite force, weight lifting capacity and wing morphology. We then measure resulting prey consumption using both morphological and molecular diet analysis. These species closely resemble each other in morphological traits however, subtle but significant differences were apparent in bite force and lift capacity which are related to differences in basic body and head size. Both morphological and molecular diet analyses show strong niche overlap. We detected subtle differences in less frequent prey items, as well as differences in the exploitation of terrestrial and aquatic-based prey groups. M. dasycneme feeds more on aquatic prey, like Chironomidae and their pupal stages, or the aquatic moth Acentria ephemerella. M. daubentonii feeds more on terrestrial prey, like Brachycera, or Coleoptera. This suggests that these bats use different micro-habitats within the habitat where they co-occur.
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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.