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Pollinator and host sharing lead to hybridization and introgression in Panamanian free-standing figs, but not in their pollinator wasps
<p>Obligate pollination mutualisms, in which plant and pollinator lineages depend on each other for reproduction, often exhibit high levels of species-specificity. However, cases in which two or more pollinator species share a single host species (host sharing), or two or more host species share a single pollinator species (pollinator sharing), are known to occur in the current ecological time. Further, evidence for host switching in evolutionary time is increasingly being recognized in these systems. The degree to which departures from strict specificity differentially affect the potential for hybridization and introgression in the associated host or pollinator is unclear. We addressed this question using genome-wide sequence data from five sympatric Panamanian free-standing fig species (<em>Ficus</em> subgenus <em>Pharmacosycea</em>, section <em>Pharmacosycea</em>) and their six associated fig pollinator wasp species (<em>Tetrapus</em>). Two of the five fig species, <em>F. glabrata</em> and <em>F. maxima</em>, were found to regularly share pollinators. In these species, ongoing hybridization was demonstrated by the detection of several first-generation (F1) hybrid individuals, and historical introgression was indicated by phylogenetic network analysis. In contrast, although two of the pollinator species regularly share hosts, all six species were genetically distinct and deeply divergent, with no evidence for either hybridization or introgression. This pattern is consistent with results from other obligate pollination mutualisms, suggesting that, in contrast to their host plants, pollinators appear to be reproductively isolated, even when different species of pollinators mate in shared hosts.</p>
Data for: Inadequate sampling of the soundscape leads to overoptimistic estimates of recogniser performance: A case study of two sympatric macaw species
<p><span></span></p> <p>Passive acoustic monitoring (PAM) offers the potential to dramatically increase the scale and robustness of species monitoring in rainforest ecosystems. PAM generates large volumes of data that require automated methods of target species detection. Species-specific recognisers, which often use supervised machine learning, can achieve this goal. However, they require a large training dataset of both target and non-target signals, which is time-consuming and challenging to create. Unfortunately, very little information about creating training datasets for supervised machine learning recognisers is available, especially for tropical ecosystems. Here we show an iterative approach to creating a training dataset that improved recogniser precision from 0.12 to 0.55. By sampling background noise using an initial small recogniser, we addressed one of the significant challenges of training dataset creation in acoustically diverse environments. Our work demonstrates that recognisers will likely fail in real-world settings unless the training dataset size is large enough and sufficiently representative of the ambient soundscape. We outline a simple workflow that can provide users with an accessible way to create a species-specific PAM recogniser that addresses these issues for tropical rainforest environments. Our work provides important lessons for PAM practitioners wanting to develop species-specific recognisers for acoustically diverse ecosystems.</p>
Global warming leads to habitat loss and genetic erosion of alpine biodiversity
<p><span><strong>Aim</strong>:</span><span> Species living on steep environmental gradients are expected to be especially sensitive to global climate change. Here, we combined genetic, ecological niche modelling and climatic niche comparisons to investigate the influence of climate on the biogeography of three alpine species with overlapping ranges.</span></p> <p><span><strong>Location</strong>:</span><span> Te Waipounamu (South Island) Aotearoa</span>–<span>New Zealand.</span></p> <p><span><strong>Taxon</strong>:</span><span> Endemic alpine-adapted Cataontopinae grasshoppers.</span></p> <p><span><strong>Methods</strong>:</span><span> We used niche modelling to estimate and project the potential niche of three focal species under past and future climate scenarios.</span><span> Vulnerability assessments were</span><span> performed using </span><span>niche factor analyses. Demographic trends and phylogeographic structure were investigated using samples from 15 mountain tops to generate mitochondrial DNA haplotype networks and population genetic statistics.</span></p> <p><span><strong>Results</strong>:</span><span> Niche models and genetic data suggest suitable habitat for all three alpine species was more widespread and contiguous in the past than today. Demographic analyses indicate in situ survival rather than post-Pleistocene colonisation of current habitat. Population structuring and genetic divergence suggest that mountain uplift during the Pliocene and environmental barriers during Pleistocene glacial and interglacial stages shaped contemporary population structure of each species. Though geographically overlapping, niche analyses suggest these alpine species are not ecologically identical, and each shows similar but distinct responses to environmental change, but all will lose intraspecific diversity through population extinction.</span></p> <p><span><strong>Main</strong> <strong>conclusions</strong>:</span><span> Climatic, biological and geophysical factors controlled population structuring of three cold-adapted species during the Pleistocene with a legacy of spatially separate intraspecific lineages. Ecological niche models for each species emphasise distinct combinations of environmental proxies, but all are expected to experience severe habitat reduction during climate warming. Increased global temperatures drive available habitat to higher elevation resulting in population contractions, range shifts, habitat fragmentation, local extinctions, and genetic impoverishment. Despite alpine species not being ecologically identical, we predict all mountain biota will lose significant genetic diversity due to global warming.</span></p>
To disperse or compete? Coevolution of traits leads to a limited number of reproductive strategies
<p><span><span>Reproductive strategies are defined by a combination of behavioural, morphological, and life-history traits. Reproductive investment and offspring propagule size are two key traits defining reproductive strategies. While a substantial amount of work has been devoted to understanding the independent fitness effects of each of these traits, it remains unclear how coevolution between them ultimately affects the evolution of reproductive strategies, and how this might influence the relationship between dispersal and environmental factors. In this study, we explore how the evolution of reproductive strategies defined by these two coevolving traits is influenced by resource availability and spatial structuring of the environment using a simulation model. We find three possible equilibrium strategies across all scenarios: a competitor strategy with high reproductive investment (producing large propagules which disperse short distances), and two coloniser strategies differing in reproductive investment (both producing small propagules which disperse long distances). The possible equilibrium strategies for each scenario depended on starting conditions, spatial structure and resource availability. Evolutionary transitions between these equilibrium strategies were more likely in heterogeneous than homogeneous landscapes and at higher resource levels. Transition from coloniser strategy to competitor strategy was usually a two-step process, with changes in propagule size following initial evolution in investment. This highlights how the interaction between the two trait axes affects the evolution of reproductive strategies, particularly where fitness valleys preclude the simultaneous evolution of traits. Our results highlight the need to incorporate trait coevolution into evolutionary models to help develop a more integrative understanding of the structure of natural populations and how the interaction between traits constrains or hinders evolutionary processes.</span></span></p>
Rising surface temperatures lead to more frequent and longer burrow retreats in males of the fiddler crab, Minuca pugnax
<p><span>The fiddler crab <em>Minuca</em> <em>pugnax</em> occupies thermally unstable mudflat habitats along the eastern United States coastline, where it uses behavioral thermoregulation, including burrow retreats, to manage body temperature (T<sub>b</sub>). We explored the relationship between frequency of burrow use and environmental conditions, including burrow and surface temperatures, relative tidal height, and time of day, by twenty male <em>M. pugnax</em> in breeding areas around Flax Pond, New York, USA. We found a highly significant positive correlation between burrow use and surface temperature, with a clear shift to longer times underground above 32°C degrees. We also experimentally heated live crabs in the laboratory and allowed them to retreat into cooled artificial burrows while continuously measuring body temperatures (T<sub>b</sub>). Laboratory data on cooling times were compared to field observations of burrow retreat durations. The median burrow stay in the field of 2.74 min was enough time for our laboratory crabs to capture over 70% of the cooling potential of artificial burrows 10 or 15 degrees below T<sub>b</sub>. Because crab bodies in burrows experience exponential declines in T<sub>b</sub> due to Newton's law of cooling, there are diminishing returns to remaining in a burrow, and many crabs probably leave before coming to equilibrium. For <em>M. pugnax</em>, burrow retreats reduce time spent feeding and courting, activities that only occur on the surface. Current concerns about the impacts of climate change on animals include whether compensatory mechanisms, like more frequent and longer burrow retreats, will come at the cost of other behaviors necessary for survival and reproduction.</span></p>
MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs obtained through electrophysiological simulations
<p>Mechanistic cardiac electrophysiology models allow for personalized simulations of the electrical activity in the heart and the ensuing electrocardiogram (ECG) on the body surface. As such, synthetic signals possess precisely known ground truth labels of the underlying disease (model parameterization) and can be employed for validation of machine learning ECG analysis tools in addition to clinical signals. Recently, synthetic ECG signals were used to enrich sparse clinical data for machine learning or even replace them completely during training leading to good performance on real-world clinical test data.<br> <br> We thus generated a large synthetic database comprising a total of 16,900 12 lead ECGs based on multi-scale electrophysiological simulations equally distributed into 1 normal healthy control and 7 pathology classes. The pathological case of myocardial infraction had 6 sub-classes. A comparison of extracted timing and amplitude features between the virtual cohort and a large publicly available clinical ECG database demonstrated that the synthetic signals represent clinical ECGs for healthy and pathological subpopulations with high fidelity. The novel dataset of simulated ECG signals is split into training, validation and test data folds for development of novel machine learning algorithms and their objective assessment. </p> <p>This folder WP2_largeDataset_Noise contains the 12 lead ECGs of 10 seconds length. Each ECG is stored in a separate CSV file with one row per lead (lead order: I, II, III, aVR, aVL, aVF, V1-V6) and one sample per column (sampling rate: 500Hz). Data are split by pathologies (avblock = AV block, lbbb = left bundle branch block, rbbb = right bundle branch block, sinus = normal sinus rhythm, lae = left atrial enlargement, fam = fibrotic atrial cardiomyopathy, iab = interatrial conduction block, mi = myocardial infarction). MI data are further split into subclasses depending on the occlusion site (LAD, LCX, RCA) and transmurality (0.3 or 1.0). Each pathology subclass contains training, validation and testing data (~ 70/15/15 split). Training, validation and testing datasets were defined according to the model with which QRST complexes were simulated, i.e., ECGs calculated with the same anatomical model but different electrophysiological parameters are only present in one of the test, validation and training datasets but never in multiple. Each subfolder also contains a "siginfo.csv" file specifying the respective simulation run for the P wave and the QRST segment that was used to synthesize the 10 second ECG segment. Each signal is available in three variations:</p> <ul> <li>*_raw.csv contains the synthesized ECG without added noise and without filtering</li> <li>*_noise.csv contains the synthesized ECG (unfiltered) with superimposed noise</li> <li>*_filtered.csv contains the filtered synthesized ECG (fiter settings: highpass cutoff frequency 0.5Hz, lowpass cutoff frequency 150Hz, butterworth filters of order 3).</li> </ul> <p>The folder WP2_largeDataset_ParameterFiles contains the parameter files used to simulate the 12 lead ECGs. Parameters are split for atrial and ventricular simulations, which were run independently from one another. <br> See <a href="https://doi.org/10.48550/arXiv.2211.15997">Gillette*, Gsell*, Nagel* et al. "MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs obtained through electrophysiological simulations"</a> for a description of the model parameters.</p>
Data set for the ensemble postprocessing of 2m surface temperature forecasts in Germany for 24 hours lead time
<p>Full data set for the ensemble postprocessing of 2m surface temperature forecasts at 462 observation stations in Germany for 24 hours lead time in the years 2015-2020. The data set is provided in .Rdata format supported by the statistical software <a href="https://www.r-project.org">R</a>. The ensemble forecasts are retrieved from <a href="https://www.ecmwf.int">ECMWF</a> and the observation data from the <a href="https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/hourly/air_temperature/historical/BESCHREIBUNG_obsgermany_climate_hourly_tu_historical_de.pdf">German Weather Service</a> (<a href="https://www.dwd.de/">DWD</a>). <br> <br> For more information about the data set see: <a href="https://github.com/jobstdavid/paper_gamvinereg">https://github.com/jobstdavid/paper_gamvinereg</a></p>
Data set for the ensemble postprocessing of 2m surface temperature forecasts in Germany for five different lead times
<p>Full data set for the ensemble postprocessing of 2m surface temperature forecasts at 462 observation stations in Germany for the lead times 24, 48, 72, 96 and 120 hours in the years 2015-2020. The data set is provided in .RData format supported by the statistical software <a href="https://www.r-project.org">R</a>. The ensemble forecasts are retrieved from <a href="https://www.ecmwf.int">ECMWF</a> and the observation data from the <a href="https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/hourly/air_temperature/historical/BESCHREIBUNG_obsgermany_climate_hourly_tu_historical_de.pdf">German Weather Service</a> (<a href="https://www.dwd.de/">DWD</a>). <br> <br> For more information about the data set see: <a href="https://github.com/jobstdavid/paper_tsEMOS">https://github.com/jobstdavid/paper_tsEMOS</a></p>
Mechanisms leading to exceptional niobium concentration during lateritic weathering: the key role of secondary oxides
<p>This dataset supports the publication entitled: 'Mechanisms leading to exceptional niobium concentration during lateritic weathering: the key role of secondary oxides' submitted to <em>Chemical Geology</em> journal.</p> <p>Data include:</p> <p>- EPMA data: 'Fe_ox_EPMA', ...</p> <p>- XRD data: 'SL_02.dat', ...</p> <p>- XAS data on bulk samples 'XAS_SL_12_meascan3'...</p> <p>- EXAFS data: 'EXAFS_nbbrookite_R_space.csv', ...</p> <p><strong>SL02: Fragmented laterite/SL04: Fragmented laterite/SL12: Upper purple laterite/SL14: manganiferous laterite/SL23: lower purple laterite/SL31: brown laterite/SL_CARB: Core siderite carbonatite</strong></p>
Datasets for 2D Vertical Convection: Base States and Leading Linear Modes using Snek5000-cbox
<p>This repository contains two types of datasets related to 2D vertical convection analysis, generated using the snek5000-cbox simulation framework. The first dataset includes base states computed with the Selective Frequency Damping (SFD) method, considering various aspect ratios and Prandtl numbers. The second dataset provides the decomposed amplitude, phase, frequency, and omega of the leading linear mode, accompanied by the corresponding base states for different aspect ratios and Prandtl numbers. All datasets are stored in the .h5 file format for easy access and analysis. The scripts used to produce the datasets are provided in the repository https://github.com/snek5000/snek5000-cbox/tree/main/doc/scripts/2022sidewall_conv_instabilities.</p>
Distribution System Environmental and Sequencing Datasets for Assessing the Impacts of Lead Corrosion Control on the Microbial Ecology and Abundance of Drinking Water Associated Pathogens in a Full-Scale Drinking Water Distribution System
<p>The dataset of environmental parameters and sequence fastqs used to create figures and do analysis in the paper <strong>Assessing the Impacts of Lead Corrosion Control on the Microbial Ecology and Abundance of Drinking Water Associated Pathogens in a Full-Scale Drinking Water Distribution System </strong>submitted to Environmental Science & Technology</p>
Impact on heated simplified leading edge
<p>These images present an impact configuration of a simulated fragment on an heated simplified landing edge. This test was performed as part of the Clean Sky 2 PANTHER project (Grant Agreement #820819), and implemented by Thiot Ingenierie Shock Physics Laboratory.</p><p>It consists in evaluating the impact resistance of a TiAl leading edge when heated at 800°C and impacted by a 3mm fragment at 150 m/s.</p>
Molecular signatures of resource competition: Clonal interference favors ecological diversification and can lead to incipient speciation
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VIIRS Sea ice leads detections using a U-Net
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Data for: Selection on the joint action of pairs leads to divergent adaptation and coadaptation of care-giving parents during pre-hatching care
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Population size differences can lead to biases in phylogenetic inference and introgression detection in the presence of purifying selection
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Rising surface temperatures lead to more frequent and longer burrow retreats in males of the fiddler crab, Minuca pugnax
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Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system
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To disperse or compete? Coevolution of traits leads to a limited number of reproductive strategies
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Data for: Inadequate sampling of the soundscape leads to overoptimistic estimates of recogniser performance: A case study of two sympatric macaw species
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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.