Correction: Long-term trends in the honeybee 'whooping signal' revealed by automated detection.
Ontology highlight
ABSTRACT:
SUBMITTER: Ramsey M
PROVIDER: S-EPMC5509340 | biostudies-literature | 2017
REPOSITORIES: biostudies-literature
Publications
Correction: Long-term trends in the honeybee 'whooping signal' revealed by automated detection.
Ramsey Michael M Bencsik Martin M Newton Michael I MI
PloS one 20170713 7
[This corrects the article DOI: 10.1371/journal.pone.0171162.]. ...[more]
Similar Datasets
Project description:It is known that honeybees use vibrational communication pathways to transfer information. One honeybee signal that has been previously investigated is the short vibrational pulse named the 'stop signal', because its inhibitory effect is generally the most accepted interpretation. The present study demonstrates long term (over 9 months) automated in-situ non-invasive monitoring of a honeybee vibrational pulse with the same characteristics of what has previously been described as a stop signal using ultra-sensitive accelerometers embedded in the honeycomb located at the heart of honeybee colonies. We show that the signal is very common and highly repeatable, occurring mainly at night with a distinct decrease in instances towards midday, and that it can be elicited en masse from bees following the gentle shaking or knocking of their hive with distinct evidence of habituation. The results of our study suggest that this vibrational pulse is generated under many different circumstances, thereby unifying previous publication's conflicting definitions, and we demonstrate that this pulse can be generated in response to a surprise stimulus. This work suggests that, using an artificial stimulus and monitoring the changes in the features of this signal could provide a sensitive tool to assess colony status.
| S-EPMC5298260 | biostudies-literature
Project description:Defining and identifying changes to seasonal ranges of migratory species is required for effective conservation. Historic sightings of migrating whooping cranes (Grus americana) have served as sole source of information to define a migration corridor in the Great Plains of North America (i.e., Canadian Prairies and United States Great Plains) for this endangered species. We updated this effort using past opportunistic sightings from 1942-2016 (n = 5,055) and more recent (2010-2016) location data from 58 telemetered birds (n = 4,423) to delineate migration corridors that included 50%, 75%, and 95% core areas. All migration corridors were well defined and relatively compact, with the 95% core corridor averaging 294 km wide, although it varied approximately ±40% in width from 170 km in central Texas to 407 km at the international border of the United States and Canada. Based on historic sightings and telemetry locations, we detected easterly movements in locations over time, primarily due to locations west of the median shifting east. This shift occurred from northern Oklahoma to central Saskatchewan at an average rate of 1.2 km/year (0.3-2.8 km/year). Associated with this directional shift was a decrease in distance of locations from the median in the same region averaging -0.7 km/year (-0.3--1.3 km/year), suggesting a modest narrowing of the migration corridor. Changes in the corridor over the past 8 decades suggest that agencies and organizations interested in recovery of this species may need to modify where conservation and recovery actions occur. Whooping cranes showed apparent plasticity in their migratory behavior, which likely has been necessary for persistence of a wetland-dependent species migrating through the drought-prone Great Plains. Behavioral flexibility will be useful for whooping cranes to continue recovery in a future of uncertain climate and land use changes throughout their annual range.
| S-EPMC5813961 | biostudies-literature
Project description:SummaryThe reliable and timely recognition of outbreaks is a key component of public health surveillance for foodborne diseases. Whole genome sequencing (WGS) offers high resolution typing of foodborne bacterial pathogens and facilitates the accurate detection of outbreaks. This detection relies on grouping WGS data into clusters at an appropriate genetic threshold. However, methods and tools for selecting and adjusting such thresholds according to the required resolution of surveillance and epidemiological context are lacking. Here we present DODGE (Dynamic Outbreak Detection for Genomic Epidemiology), an algorithm to dynamically select and compare these genetic thresholds. DODGE can analyse expanding datasets over time and clusters that are predicted to correspond to outbreaks (or "investigation clusters") can be named with established genomic nomenclature systems to facilitate integrated analysis across jurisdictions. DODGE was tested in two real-world Salmonella genomic surveillance datasets of different duration, 2 months from Australia and 9 years from the United Kingdom. In both cases only a minority of isolates were identified as investigation clusters. Two known outbreaks in the United Kingdom dataset were detected by DODGE and were recognized at an earlier timepoint than the outbreaks were reported. These findings demonstrated the potential of the DODGE approach to improve the effectiveness and timeliness of genomic surveillance for foodborne diseases and the effectiveness of the algorithm developed.Availability and implementationDODGE is freely available at https://github.com/LanLab/dodge and can easily be installed using Conda.
| S-EPMC11244691 | biostudies-literature
Project description:We combined participatory science data and museum records to understand long-term changes in occupancy for 29 ant species in Denmark over 119 years. Bayesian occupancy modelling indicated change in occupancy for 15 species: five increased, four declined and six showed fluctuating trends. We consider how trends may have been influenced by life-history and habitat changes. Our results build on an emerging picture that biodiversity change in insects is more complex than implied by the simple insect decline narrative.
| S-EPMC8526175 | biostudies-literature
Project description:Modern breeding structures are emerging for European honeybee populations. However, while genetic evaluations of honeybees are becoming increasingly well understood, little is known about how selection decisions shape the populations' genetic structures. We performed simulations evaluating 100 different selection schemes, defined by selection rates for dams and sires, in populations of 200, 500, or 1000 colonies per year and considering four different quantitative traits, reflecting different genetic parameters and numbers of influential loci. Focusing on sustainability, we evaluated genetic progress over 100 years and related it to inbreeding developments. While all populations allowed for sustainable breeding with generational inbreeding rates below 1% per generation, optimal selection rates differed and sustainable selection was harder to achieve in smaller populations and for stronger negative correlations of maternal and direct effects in the selection trait. In small populations, a third or a fourth of all candidate queens should be selected as dams, whereas this number declined to a sixth for larger population sizes. Furthermore, our simulations indicated that, particularly in small populations, as many sires as possible should be provided. We conclude that carefully applied breeding provides good prospects for currently endangered honeybee subspecies, since sustainable genetic progress improves their attractiveness to beekeepers.
| S-EPMC7412524 | biostudies-literature
Project description:Automated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the algorithm on a cohort of ten adult patients with focal refractory epilepsy indicates sensitivity, specificity, and accuracy of 90%, along with a true alarm rate of 95% and less than four false alarms per day. The proposed approach emphasizes ictal patterns against noisy background without the need of data preprocessing. Finally, we benchmark our approach against previous studies on two publicly available datasets, demonstrating the good performance of our algorithm.
| S-EPMC7811137 | biostudies-literature
Project description:The Kramers escape problem is a paradigmatic model for the kinetics of rare events, which are usually characterized by Arrhenius law. So far, analytical approaches have failed to capture the kinetics of rare events in the important case of non-Markovian processes with long-term memory, as occurs in the context of reactions involving proteins, long polymers, or strongly viscoelastic fluids. Here, based on a minimal model of non-Markovian Gaussian process with long-term memory, we determine quantitatively the mean FPT to a rare configuration and provide its asymptotics in the limit of a large energy barrier E. Our analysis unveils a correction to Arrhenius law, induced by long-term memory, which we determine analytically. This correction, which we show can be quantitatively significant, takes the form of a second effective energy barrier E'<E and captures the dependence of rare event kinetics on initial conditions, which is a hallmark of long-term memory. Altogether, our results quantify the impact of long-term memory on rare event kinetics, beyond Arrhenius law.
| S-EPMC11358423 | biostudies-literature
Project description:Gut microbiome disequilibrium is increasingly implicated in host fitness reductions, including for the economically important and disease-challenged western honey bee Apis mellifera. In laboratory experiments, the antibiotic tetracycline, which is used to prevent American Foulbrood Disease in countries including the US, elevates honey bee mortality by disturbing the microbiome. It is unclear, however, how elevated individual mortality affects colony-level fitness.We used an agent-based model (BEEHAVE) and empirical data to assess colony-level effects of antibiotic-induced worker bee mortality, by measuring colony size. We investigated the relationship between the duration that the antibiotic-induced mortality probability is imposed for and colony size.We found that when simulating antibiotic-induced mortality of worker bees from just 60 days per year, up to a permanent effect, the colony is reduced such that tetracycline treatment would not meet the European Food Safety Authority's (EFSA) honey bee protection goals. When antibiotic mortality was imposed for the hypothetical minimal exposure time, which assumes that antibiotics only impact the bee's fitness during the recommended treatment period of 15 days in both spring and autumn, the colony fitness reduction was only marginally under the EFSA's threshold. Synthesis and Applications. Modelling colony-level impacts of antibiotic treatment shows that individual honey bee worker mortality can lead to colony mortality. To assess the full impact, the persistence of antibiotic-induced mortality in honey bees must be determined experimentally, in vivo. We caution that as the domestication of new insect species increases, maintaining healthy gut microbiomes is of paramount importance to insect health and commercial productivity. The recommendation from this work is to limit prophylactic use of antibiotics and to not exceed recommended treatment strategies for domesticated insects. This is especially important for highly social insects as excess antibiotic use will likely decrease colony growth and an increase in colony mortality.
| S-EPMC7839786 | biostudies-literature
Project description:A very common honeybee signal is the dorso-ventral abdominal vibration (DVAV) signal, widely accepted as a modulatory signal meaning: "prepare for greater activity". In this study, using ultra-sensitive accelerometer technology embedded in the honeycomb, we visually confirm the one-to-one relationship between a DVAV signal being produced and the resulting accelerometer waveform, allowing the measurement of DVAV signals without relying on any visual inspection. We then demonstrate a novel method for the continuous in-situ non-invasive automated monitoring of this honeybee signal, not previously known to induce any vibration into the honeycomb, and most often inaudible to human hearing. We monitored a total of three hives in the UK and France, showing that the signal is very common, highly repeatable and occurs more frequently at night, exhibiting a distinct decrease in instances and increase in amplitude towards mid-afternoon. We also show an unprecedented increase in the cumulative amplitude of DVAV signals occurring in the hours preceding and following a primary swarm. We conclude that DVAV signals may have additional functions beyond solely being a foraging activation signal, and that the amplitude of the signal might be indicative of the switching of its purpose.
| S-EPMC6167329 | biostudies-literature
Project description:Epilepsy causes when the repeated seizure occurs in the brain. Electroencephalogram (EEG) test provides valuable information about the brain functions and can be useful to detect brain disorder, especially for epilepsy. In this study, application for an automated seizure detection model has been introduced successfully.The EEG signals are decomposed into sub-bands by discrete wavelet transform using db2 (daubechies) wavelet. The eight statistical features, the four gray level co-occurrence matrix and Renyi entropy estimation with four different degrees of order, are extracted from the raw EEG and its sub-bands. Genetic algorithm (GA) is used to select eight relevant features from the 16 dimension features. The model has been trained and tested using support vector machine (SVM) classifier successfully for EEG signals. The performance of the SVM classifier is evaluated for two different databases.The study has been experimented through two different analyses and achieved satisfactory performance for automated seizure detection using relevant features as the input to the SVM classifier.Relevant features using GA give better accuracy performance for seizure detection.
| S-EPMC5523521 | biostudies-other