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FIGURE 1 in Taxonomic status of the Lesser Woolly Horseshoe bats (Chiroptera, Rhinolophidae Rhinolophus beddomei) in peninsular India and Sri Lanka
FIGURE 1. PCA plot based on external morphometric characters of Rhinolophus species of the R. trifoliatus group.
Figure 1 in Effects of roost specialization on extinction risk in bats
Figure 1. Proportion of roost types used by bats aeithin each International Union for Conseroation of Nature Red List category (IUCN 2013) (CR, critically endangered; EN, endangered; VU, oulnerable; NT, near threatened; LC, least concern). Bars shoae the relatioe importance of a particular roost type for bats aeithin a gioen red-list category. For example, aehereas 2 critically endangered and 224 least concern bats use caoes and creoices, the bar for the former is larger because only 3 bats ooerall are considered critically endangered, aehereas 304 are considered of least concern. Only the most commonly used roost types (i.e., those aeith the largest number of data points) are included.
Stuck on a small tropical island: wide in-situ diversification of an urban-dwelling bat
<p>Datasets for the manuscript "<strong>Stuck on a small tropical island: wide <em>in-situ</em> diversification of an urban-dwelling bat </strong>", including information of the bat samples and microsatellites and D-loop data.</p>
Renewable energies and biodiversity: impact of ground-mounted solar photovoltaic sites on bat activity
<ol> <li>Renewable energy is growing at a rapid pace globally, but as yet there has been little research on the effects of ground-mounted solar photovoltaic (PV) developments on bats, many species of which are threatened or protected.</li> <li>We conducted a paired study at 19 ground-mounted solar PV developments in southwest England. We used static detectors to record bat echolocation calls from boundaries (i.e., hedgerows) and central locations (open areas) at fields with solar PV development, and simultaneously at matched sites without solar PV developments (control fields). We used generalized linear mixed-effect models to assess how solar PV developments and boundary habitat affected bat activity and species richness.</li> <li>The activity of six of eight species/species groups analysed was negatively affected by solar PV panels, suggesting that loss and/or fragmentation of foraging/commuting habitat is caused by ground-mounted solar PV panels. <em>Pipistrellus</em> <em>pipistrellus</em> and <em>Nyctalus</em> spp. activity was lower at solar PV sites regardless of the habitat type considered. Negative impacts of solar PV panels at field boundaries were apparent for the activity of <em>Myotis</em> spp. and <em>Eptesicus</em> <em>serotinus</em>, and in open fields for <em>Pipistrellus</em> <em>pygmaeus</em> and <em>Plecotus</em> spp.</li> <li>Bat species richness was greater along field boundaries compared with open fields, but there was no effect of solar PV panels on species richness.</li> <li> <em>Policy Implications</em>: Ground-mounted solar PV developments have a significant negative effect on bat activity, and should be considered in appropriate planning legislation and policy. Solar PV developments should be screened in Environmental Impact Assessments for ecological impacts, and appropriate mitigation (e.g., maintaining boundaries, planting vegetation to network with surrounding foraging habitat) and monitoring should be implemented to highlight potential negative effects.</li> </ol>
Supplementary material 2 from: Hubancheva A, Bozicevic V, Morinière J, Goerlitz HR (2023) DNA metabarcoding data from faecal samples of the lesser (Myotis blythii) and the greater (Myotis myotis) mouse-eared bats from Bulgaria. Metabarcoding and Metagenomics 7: e106844. https://doi.org/10.3897/mbmg.7.106844
Taxonomic relationships and relative abundance of prey and parasite species in faecal samples from M. myotis and M. blythii from Bulgaria
Fig. 4 in Geographic variation in the relationship between large-scale environmental determinants and bat species richness
Fig. 4. Spatial variation in the partial coefficients of determination (R2) for three environmental hypotheses and their shared effects. The coefficients are: energy (E), heterogeneity (H), seasonality (S), shared contribution between energy and heterogeneity (E: H), energy and seasonality (E: S), heterogeneity and seasonality (H: S), and the contribution shared among energy, heterogeneity and seasonality (E: H: S).
Fig. 2 in Geographic variation in the relationship between large-scale environmental determinants and bat species richness
Fig. 2. Distribution of coefficients of determination (R2) of GWR for the analysis of bat species richness regressed on the predictors of the three environmental hypotheses combined.
Fig. 1 in Geographic variation in the relationship between large-scale environmental determinants and bat species richness
Fig. 1. Global pattern of bat species richness. Legend corresponds to the number of bat species per 2◦ × 2◦ grid cell. Letters represent the zoogeographic realms (Holt et al. 2013): Na = Nearctic, P = Panamanian, Nt = Neotropical, Pa = Palearctic, Sa = Saharo-Arabian, At = Afrotropical, M = Madagascan, Or = Oriental, Au = Australian, Sj = Sino-Japanese, Oc = Oceanian.
Fig. 3 in Geographic variation in the relationship between large-scale environmental determinants and bat species richness
Fig. 3. Spatial non-stationarity in the effect (R2) of the predictors of the three environmental hypotheses combined to explain the global pattern of bat species richness.
Data and code for paper "Stealth echolocation in aerial hawking bats reflects a substrate gleaning ancestry"
<p>- Bat_gleaning_ancestral_recon.R (in folder "AncestralStateReconstruction") reads shi_rabosky_2015_spp1A.csv and does the ancestral state reconstruction</p> <p>- Lewanzik.et.al_Lombard.effect.R (in folder "LombardEffect") reads Lewanzik.et.al_Lombard.data.csv to produce Fig. 2 and to model source levels as a function of noise treatment (silence vs white noise playback)</p> <p>- figure3.m and figure4.m (in folder "EcholocationParameters") is the Matlab code to produce Fig. 3 and Fig. 4, respectively. These .m-files use code in the "private" folder and data (.mat files) in the "Barbastella" and "Pygmaeus" folders.</p>
Acoustic camouflage increases with body size and bat echolocation frequency range in a community of nocturnally-active Lepidoptera
<ol> <li>Body size is an important trait in predator-prey dynamics as it is often linked to detection, as well as the success of capture or escape. Larger prey, for example, often runs higher risk of detection by their predators, which imposes stronger selection on their anti-predator traits compared to smaller prey.</li> <li>Nocturnal Lepidoptera (moths) vary strongly in body size, which has consequences for their predation risk, as bigger moths return stronger echoes for echolocating bats. To compensate for increased predation risk, larger moths are therefore expected to have improved anti-predator defences. Moths are covered by different types of scales, which for a few species are known to absorb ultrasound, thus providing acoustic camouflage. Here we assessed whether moths differ in their acoustic camouflage in a size-dependent way by focusing on their body scales and the different frequency ranges used by bats.</li> <li>We used a sonar head to measure 3D echo scans of a total of 111 moth specimens across 58 species, from eight different families of Lepidoptera. We scanned all the specimens and related their echo-acoustic target strength to various body size measurements. Next, we removed the scales covering the thorax and abdomen and scanned a subset of specimens again to assess the sound-absorptive properties of these scales.</li> <li>Comparing intact specimens with descaled specimens we found almost all species to absorb ultrasound, reducing detection risk on average by 8%. Furthermore, the sound absorptive capacities of body scales increased with body size suggesting that larger species benefit more from acoustic camouflage. The size-dependent effect of camouflage was in particular pronounced for the higher frequencies (above 29 kHz), with moth species belonging to large-bodied families consequently demonstrating similar target strengths compared to species from small-bodied families. Finally, we found the families to differ in frequency range that provided the largest reduction in detection risk, which may be related to differences in predation pressure and predator communities of these families.</li> <li>In general, our findings have important implications for predator-prey interactions across eco-evolutionary timescales and may suggest that acoustic camouflage played a role in body size evolution of nocturnally-active Lepidoptera. </li> </ol>
Figure 4 in The mitochondrial genome of the endemic and endangered trumpet-nosed bat Musonycteris harrisoni (Chiroptera: Phyllostomidae)
Figure 4. Selective pressure in protein-coding genes of Musonycteris harrisoni vs Ectophylla alba and vs Anoura caudifer.
Figure 1 in The mitochondrial genome of the endemic and endangered trumpet-nosed bat Musonycteris harrisoni (Chiroptera: Phyllostomidae)
Figure 1. Circular map of the mitochondrial genome in Musonycteris harrisoni (Photo credit: Lorena Orozco-Lugo).
Figure 3 in The mitochondrial genome of the endemic and endangered trumpet-nosed bat Musonycteris harrisoni (Chiroptera: Phyllostomidae)
Figure 3. Secondary structure of the 22 tRNAs in the mitochondrial genome of Musonycteris harrisoni.
Figure 5 in The mitochondrial genome of the endemic and endangered trumpet-nosed bat Musonycteris harrisoni (Chiroptera: Phyllostomidae)
Figure 5. (A) Schematic organisation of the control region (CR) in the mitochondrial genome of Musonycteris harrisoni. CR is composed of three functional domains: extended termination associated sequence (ETAS), central domain, and conserved sequence block (CSB). (B) Secondary structure of the tandem repeat located in the CSB domain. (C) Domains and features in the CR sequence of Musonycteris harrisoni. Conserved blocks within each domain are highlighted.
Bermuda Atlantic Time-Series Study (BATS) Pigment Data Validation
<p>This dataset is published on Zenodo by the Simons CMAP curators for long-term care. All credits go to the data producers at the Bermuda Atlantic Time-series Study (BATS): https://bats.bios.asu.edu/bats-data/ </p><p>The BATS (Bermuda Atlantic Time-series Study) discrete HPLC pigment validation dataset is time-series spanning from 1996 to 2022. The dataset contains the 21 separate in-situ pigment measurements along with sampling depth and the BATS Cruise ID.</p><p>This description has been reproduced using https://www.dropbox.com/s/6ajl545hyua8ot8/bval_pigments.txt?dl=0</p>
Phase I EGFR BATs in Newly Diagnosed Glioblastoma
ClinicalTrials.gov study NCT03344250. IPD Sharing: NO. Countries: 1. Publications: 3.
Randomized Evaluation of Beta Blocker and ACEI/ARB Treatment in MINOCA Patients - MINOCA-BAT
ClinicalTrials.gov study NCT03686696. IPD Sharing: YES. Countries: 5. Publications: 1.
RCT Cefiderocol vs BAT for Treatment of Gram Negative BSI
ClinicalTrials.gov study NCT03869437. IPD Sharing: Not stated. Countries: 6. Publications: 2.
BATs Treatment for Pancreatic Cancer, Phase Ib/II
ClinicalTrials.gov study NCT03269526. IPD Sharing: NO. Countries: 1. Publications: 2.
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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.