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234 results for “small body”
Statistical analysis and dataset for: Three-dimensional body reconstruction enables quantification of liquid consumption in small invertebrates
<p>Linked to the journal article published in bioRxiv (https://doi.org/10.1101/2024.06.14.599002).</p> <p><em><strong>Abstract</strong></em></p> <p>Quantifying feeding patterns provides valuable insights into animal behaviour. However, small invertebrates often consume incredibly small amounts of food. This renders traditional methods, such as weighing individuals before and after food acquisition, either inaccurate or prohibitively expensive. Here, we present a non-invasive method to quantify food consumption of small invertebrates whose body expands during feeding. Using the markerless pose estimation software DeepLabCut, we three-dimensionally track the body of Argentine ants, <em>Linepithema humile</em>. Using these extracted markers, we developed an algorithm which computationally reconstructs the ant’s body, directly measuring volumetric change over time. Moreover, we provide measures of accuracy and quantify the ant’s feeding response to a range of sucrose concentrations, as well as a gradient of caffeine-laced sucrose solutions. Small invertebrates are often prolific invasive species and disease vectors, causing significant ecological and economical damage. Understanding their feeding behaviour could be an important step towards effective control strategies.</p> <p> </p> <ul> <li><strong>VolEst_C1_volume_calculation_multiprocessing.py</strong>: Takes as input H5 3D DeepLabCut files, calculates the gaster volume at every frame using seven different methods and outputs these as CSV files.</li> <li><strong>VolEst_C2_interactive_GUI.py</strong>: Given a folder with Volume CSV files, interactively plots the volume over time, 3D coordinates tracked by DeepLabCut and the frame of interest for both cameras.</li> <li><strong>VolEst_C3_linear_regression.py</strong>: Applies a linear regression to each feeding event tracked and provides measures of interest such as crop load and consumption rate.</li> <li><strong>VolEst_C4_statistical_analysis</strong>: Complete statistical analysis and code for the manuscript.</li> <li><strong>VolEst_D1_sucrose_density.csv</strong>: Data obtained to quantify the density of sucrose solutions of varying molarity.</li> <li><strong>VolEst_D2_accuracy_weight_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the weight-volume accuracy measurements.</li> <li><strong>VolEst_D3_accuracy_weight.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the weight-volume accuracy measurements.</li> <li><strong>VolEst_D4_accuracy_nanoliter_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the volume-volume accuracy measurements.</li> <li><strong>VolEst_D5_accuracy_nanoliter.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the volume-volume accuracy measurements.</li> <li><strong>VolEst_D6_sucrose_caffeine_consumption_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the sucrose and caffeine dilutions application measurements.</li> <li><strong>VolEst_D7_sucrose_caffeine_consumption.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the sucrose and caffeine dilutions application measurements.</li> <li><strong>VolEst_Camera_A-Henrique-2023-09-20.zip</strong>: DeepLabCut labels and trained network for camera A.</li> <li><strong>VolEst_Camera_B-Henrique-2023-09-20.zip</strong>: DeepLabCut labels and trained network for camera B.</li> <li><strong>VolEst_base.stl</strong>: 3D file for the resin platform used in the experimental validation of the setup.</li> <li><strong>VolEst_platform.stl</strong>: 3D file for the resin platform used in the experimental validation of the setup.</li> </ul>
Environmental, community and trait data of small water bodies in Zijin Mountain, Nanjing, Jiangsu, China, 2022
Small water bodies (SWBs) are vulnerable to drought and play a vital role in the conservation of aquatic biodiversity. Currently climate change is intensifying the seasonal drought of SWBs in monsoonal east Asia. However, little is known about the response of benthic macroinvertebrates of small ponds and streams that simultaneously suffer from climate-induced extreme drought. This study aimed to explore the taxonomic and functional response of macroinvertebrates in ponds and streams, either respectively or jointly, to extreme summer drought. We calculated taxonomic and functional diversity indices of communities in 11 streams and 12 ponds across three seasons: spring, summer and winter in 2022. We performed a permutational multivariate analysis of variance, Moran’s eigenvector maps, Moran Spectral Randomization based variation partitioning and convex hull analysis of trait space to examine temporal compositional and functional, as well as trait changes, and the contributions of environmental and spatial factors in shaping communities. The responses of taxonomic and functional diversity in ponds and streams were contrasting during the summer drought. Ponds showed increased taxonomic richness (TR), functional richness (FRic), functional richness (FRed) and trait space volume, while streams experienced decreased TR, FRic and trait space volume but increased FRed. The taxonomic and functional increases of ponds were driven by an influx of generalist taxa from streams, while the increase of FRed in streams resulted from the loss of species with strong dispersal and lentic adaptation traits. Dispersal played a more significant role than environmental filtering in shaping community structure during the drought, especially for streams lacking hydrological connectivity. This study provides the first insights into the complex response of macroinvertebrates to summer drought of SWBs in east Asia monsoonal region. Our results underscore the refuge effect of ponds during summer
ARTICLE DATASET - ANTHROPOGENIC MICROPARTICLES ACCUMULATION IN SMALL-BODIED SEAGRASS MEADOWS: THE CASE OF TROPICAL ESTUARINE SPECIES IN BRAZIL
<p>This dataset with the data analysis script refers to the publication <a href="https://www.sciencedirect.com/science/article/abs/pii/S0025326X24007768?via%3Dihub"><strong>https://doi.org/10.1016/j.marpolbul.2024.116799</strong></a>.</p> <p>The dataset consists of a spreadsheet containing 9 tabs with survey data described below and their respective captions, found in the first line of each tab.</p> <p>The script for statistical analysis in the R language contains descriptive and statistical analyses, in addition to the functions for creating the graphs displayed in the article.</p> <p><strong>Description of the spreadsheet dataset tabs --------------------------------------------------------------------------------</strong></p> <ol> <li>description - Contains general information about the article</li> <li>meadows - Contains properties related to the characteristics of the multispecific grassland.</li> <li>ap_samples - Contains properties related to the abundances of anthropogenic microparticles, as to classifications by shape, size (mm) and color in units, kg and frequency of occurrence.</li> <li>ap_categories - Contains raw data related to the classifications of anthropogenic microparticles, in terms of shape, size (mm) and color.</li> <li>sediment - Contains raw data related to the classifications of sediment particles.</li> <li>granulometry - Contains properties related to sediment particles in µm, and their classifications by predominance, frequency and kg.</li> <li>shape - Contains raw data related to the classification of the shapes of anthropogenic microparticles in units and kg.</li> <li>size - Contains raw data related to the classification of the sizes of anthropogenic microparticles in units and kg.</li> <li>color - Contains raw data related to the classification of the colors of anthropogenic microparticles in units and kg.</li> </ol>
Dataset of Detection Distances to Small Bodies using Spacecraft Cameras
<p>The dataset contains the detection distances to small bodies (in kilometres) considering three different spacecraft camera setups for the full list of known objects by the Minor Planet Center catalogue (https://www.minorplanetcenter.net). A separate ASCII file has been created per each considered phase angle.</p> <p>The generation of the dataset as well as the simulation settings are detailed in the following paper:</p> <p>Franzese, Hein, Modelling Detection Distances to Small Bodies Using Spacecraft Cameras, <em>Modelling</em> <strong>2023</strong>, <em>4</em>(4), 600-610; <a href="https://doi.org/10.3390/modelling4040034">https://doi.org/10.3390/modelling4040034</a></p> <p>The columns of the dataset are as follows:</p> <ol> <li>Object: The object's numerical identifier.</li> <li>MPC Designation: The object designation of the Minor Planet Center.</li> <li>Name: The object name, if available.</li> <li>HP Cam & rp: Object detection distance in km considering the high-performance camera and the object at perihelion</li> <li>HP Cam & ra: Object detection distance in km considering the high-performance camera and the object at aphelion</li> <li>MP Cam & rp: Object detection distance in km considering the medium performance camera and the object at perihelion</li> <li>MP Cam & ra: Object detection distance in km considering the medium performance camera and the object at aphelion</li> <li>LP Cam & rp: Object detection distance in km considering the low-performance camera and the object at perihelion</li> <li>LP Cam & ra: Object detection distance in km considering the low-performance camera and the object at aphelion.</li> </ol> <p>Note that the detection distances refer to the following phase angles: 0 deg, 15 deg, 30 deg, 60 deg, and 90 deg.</p>
A multi-scale labeled dataset for boulder segmentation and navigation on small bodies
<p>The capability to detect boulders on the surface of small bodies is beneficial for vision-based applications such as hazard detection during critical operations, safety quantification, autonomous planning of scientific operations, and autonomous navigation. This task, however, is challenging due to the wide assortment of irregular shapes, the characteristics of the boulders population, and the rapid variability in the illumination conditions. Moreover, the lack of publicly available labeled datasets damps the research about data-driven algorithms. The following dataset has been designed and made publicly available to tackle these challenges. Its purpose is twofold. First, from the lessons learned from previous datasets, to develop a multi-purpose, high-fidelity dataset with boulders scattered across the surface of a small body. Second, to exploit domain randomization, artificial noise addition, scaling, and post-processing, enabling the design of data-driven pipelines. </p> <p>The methodology used to generate the dataset is illustrated in the work "A multi-scale labeled dataset for boulder segmentation and navigation on small bodies" by Mattia Pugliatti and Michele Maestrini, presented at the 74th IAC (International Astronautical Congress), 2024, Baku, Azerbaijan.</p> <p>The dataset contains the image-label pairs of 47502 samples, organized with the following structure: </p> <p>Dataset_PugliattiMaestrini_2023IAC<br> --img<br> --labels<br> --masks</p> <p>The dataset is comprised of 47502 samples. The "img" folder contains the input, 512x 512 grayscale images. The "labels" folder includes the .txt segmentation labels of the 15 most prominent boulders for each image detected with the methodology illustrated in the IAC paper. The "masks" dataset contains the segmentation masks for all image layers, with the values being encoded between 0 and 17 as uint8. The samples are named as XXXXXX_YYY. XXXXXX stands for the image's original ID during rendering. YYY corresponds to the sub-splits of the original image obtained at rendering: </p> <p> 001 - Top-Left crop<br> 002 - Top-Right crop<br> 003 - Bottom-Left crop<br> 004 - Bottom-right crop<br> 005 - Whole, resized</p> <p>The file "10000_ub_2023-01-18 00.09.43.txt" contains all the values of the rendering inputs detailed in the IAC paper.</p>
High-precision body mass estimators for small mammals: A case study in the Mesozoic
<p>Body mass is a pivotal quantity in palaeobiology but must be estimated from an imperfect fossil record. We analyse the precision of skeletal predictors of mammalian body mass as a mean to inform the Mesozoic mammal record, including a new eutriconodont from North America. We focus on the critical small end of the size spectrum – critical because the earliest mammals were small, because small size persisted onto the stems of the major extant radiations, and because small mammals compose a large proportion of crown diversity. Linear regressions based on extant small mammals indicate a universal correlation of body mass with observed measurements, but with clear differences in precision. Postcranial predictors outperform jaw and dental metrics, with certain femoral joint dimensions providing surprisingly precise estimations. Overall, our data indicate small-mammal evolution during the Mesozoic unfolded in patterns of underappreciated complexity. Studying these dynamics is only possible when estimating body mass within a strict, highly focused phylogenetic context. The heuristic value of the estimators we provide here are not limited to the Mesozoic but are phylogenetically justified for any small-bodied mammal regardless of age.</p>
Text-fig. 10. Platanoxylon cf. haydenii, a, e, h: UF 279-34470; b, c, d, f g: UF 279-34469. a, b: Diffuse porous wood with vessels solitary and in small multiples, which are mostly tangential or oblique, diffuse and diffuse-in-aggregates axial parenchyma., TS. c–e: Scalariform perforation plates. f, g: Opposite intervessel pits, TLS. h: Two size classes of rays, TLS. Platanus sp., UF 279- 24552. i: Predominantly solitary vessels, diffuse and diffuse-in-aggregates parenchyma, growth ring boundary distinct, noded rays, TS. j: Simple perforation plates (PP), RLS. k: Body of ray with procumbent ray cells, RLS. l: Scalariform perforation plate, RLS. m: Rays of two sizes, wide rays>10-seriate, TLS. Scale bars: 200 µm in a, b, h, i, m; 100 µm in j, k: 50 µm in c, d, e, f, l. in A Diverse Assemblage Of Late Eocene Woods From Oregon, Western Usa
Text-fig. 10. Platanoxylon cf. haydenii, a, e, h: UF 279-34470; b, c, d, f g: UF 279-34469. a, b: Diffuse porous wood with vessels solitary and in small multiples, which are mostly tangential or oblique, diffuse and diffuse-in-aggregates axial parenchyma., TS. c–e: Scalariform perforation plates. f, g: Opposite intervessel pits, TLS. h: Two size classes of rays, TLS. Platanus sp., UF 279- 24552. i: Predominantly solitary vessels, diffuse and diffuse-in-aggregates parenchyma, growth ring boundary distinct, noded rays, TS. j: Simple perforation plates (PP), RLS. k: Body of ray with procumbent ray cells, RLS. l: Scalariform perforation plate, RLS. m: Rays of two sizes, wide rays>10-seriate, TLS. Scale bars: 200 µm in a, b, h, i, m; 100 µm in j, k: 50 µm in c, d, e, f, l.
Text-fig. 16. Photomicrographs of thin sections of specimen BP/16/1734, Terminalioxylon mozambicense sp. nov. from Mhengere Hill, Gorongosa, Mozambique. a: TS with round vessels, scanty paratracheal to vasicentric parenchyma, diffuse and narrow terminal or initial bands; b: TS at higher magnification, note the very narrow rays; c: TLS, vessels with small alternate pits, and partly tylosed; d–g: TLS with crystals (small white arrows) in the parenchyma cells, medium to thick-walled fibres and uniseriate, low rays; h: TLS, rays up to 20 cells high; i: RLS rays with procumbent body cells and 1–2 rows of marginal upright cells. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 16. Photomicrographs of thin sections of specimen BP/16/1734, Terminalioxylon mozambicense sp. nov. from Mhengere Hill, Gorongosa, Mozambique. a: TS with round vessels, scanty paratracheal to vasicentric parenchyma, diffuse and narrow terminal or initial bands; b: TS at higher magnification, note the very narrow rays; c: TLS, vessels with small alternate pits, and partly tylosed; d–g: TLS with crystals (small white arrows) in the parenchyma cells, medium to thick-walled fibres and uniseriate, low rays; h: TLS, rays up to 20 cells high; i: RLS rays with procumbent body cells and 1–2 rows of marginal upright cells.
A small body open-source dataset for image processing algorithms
<p>Crater-analog dataset acquired with a drone setup at the RIC-DFKI center. The dataset can be used to bridge the domain gap for image processing applications for lunar and small-body missions. </p>
Figure 1 in Book Review: Small Water Bodies of the Western Balkans Vladimir Pešić, Djuradj Milošević & Marko Miliša (Editors),
Figure 1. Cover page of the book "Small Water Bodies of the Western Balkans" (Springer Water, 2022).
Text-fig. 18. Scanning electron microscope (SEM) images of a fruit of Canrightia sp. with associated pollen; Torres Vedras locality, Portugal. a) Fruit in lateral view showing prominent cavities in the fruit wall formed by the scattered oil bodies and the broad hypanthium fused to the base of the fruit (arrowhead); b) Fruit surface showing epidermal cells and the scattered oil cells embedded in the fruit wall (arrowheads); c) Cluster of monocolpate pollen grains in the probable stigmatic region of the fruit; d) Pollen grains showing the long colpus and semitectate-reticulate pollen wall; e) Pollen wall showing the reticulum with large and small lumina, and scattered, compressed columellae supporting the smooth muri. Specimen, TV142-S170213. Scale bars 300 Μm (a), 100 Μm (b), 30 Μm (c), 6 Μm (d), 1 Μm (e). in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 18. Scanning electron microscope (SEM) images of a fruit of Canrightia sp. with associated pollen; Torres Vedras locality, Portugal. a) Fruit in lateral view showing prominent cavities in the fruit wall formed by the scattered oil bodies and the broad hypanthium fused to the base of the fruit (arrowhead); b) Fruit surface showing epidermal cells and the scattered oil cells embedded in the fruit wall (arrowheads); c) Cluster of monocolpate pollen grains in the probable stigmatic region of the fruit; d) Pollen grains showing the long colpus and semitectate-reticulate pollen wall; e) Pollen wall showing the reticulum with large and small lumina, and scattered, compressed columellae supporting the smooth muri. Specimen, TV142-S170213. Scale bars 300 Μm (a), 100 Μm (b), 30 Μm (c), 6 Μm (d), 1 Μm (e).
Polarization dataset - Reflection, emission, and polarization properties of surfaces made of hyperfine grains, and implications for the nature of primitive small bodies
<p>This data are relative to the polarization measurements of the paper </p> <p>"Reflection, emission, and polarization properties of surfaces made of hyperfine grains, and implications for the nature of primitive small bodies"</p> <p>The dataset consists in 8 .txt files that represent the polarization measurements of mixtures of FeS and Olivine (ol) with different mass ratios. In the name of each file it is specified the mass percentage of the two components respect with the total mass of the sample (eg. data_ol_FeS_10-90_530.txt is the sample composed by 10% olivine and 90% FeS, measured at 530 nm). </p> <p>In each file, the data are organized in the following columns: </p> <p>#phase_angles[°] #Q/I #U/I #V/I #DOLP #DC #delta_Q/I #delta_U/I #delta_V/I #delta_DOLP #delta_dc</p> <p>"delta" refers to the standard deviation of the measurement upon rotation of the sample on the azimuthal axis. <br> </p>
Optical Navigation Dataset for Solar System Small Bodies
<p>This dataset has been curated for the purpose of training and evaluating a variety of local feature extractors intended for optical navigation in the proximity of Solar System small bodies (SSSBs). It aims to serve as a resource for researchers in the field and it is referenced in the related article titled "CNN-based local features for navigation near an asteroid" [1]. Additionally, the associated Python code for this dataset can be found in [2].</p> <p>The dataset is a compilation of images obtained from four distinct space missions focused on SSSBs, specifically NEAR Shoemaker (Eros) [3], Hayabusa (Itokawa) [4], Rosetta (67P/Churyumov-Gerasimenko) [5, 6], and OSIRIS-REx (Bennu) [7]. It also incorporates synthetic data generated through the utilization of a Bennu shape model [8] and OpenGL-based rendering software [9, 10]. Access to mission-specific images is available through the NASA Planetary Data System (PDS), and for the Rosetta mission, via the ESA Planetary Science Archive [11].</p> <p>The prefix <code>rot-</code> has been applied to subsets in which images have been pre-rotated to orient the SSSB's rotation axis upwards within the image frame. These subsets are primarily intended for training purposes and encompass image pairs with pixel correspondences that can be found in the <code>aflow</code> directory. Pixel correspondences are stored as 16-bit PNG images, where the G- and B-channels respectively represent the x and y image coordinates. To facilitate data compression and storage, a fixed scaling coefficient of 8 has been employed to convert the pixel correspondence float array into a 16-bit integer array to be used by the PNG compression. These pixel correspondence files can be loaded using the <code>navex.datasets.tools.load_aflow</code> function from [2].</p> <p>On the other hand, subsets designated with a <code>-d</code> postfix include depth information (<code>*.d</code> files) and are exclusively employed during the evaluation of the proposed feature extractors. The depth data is stored as scaled grayscale 16-bit integer arrays using PNG compression. A custom additional header accompanies these images, providing two 32-bit float values, namely the subtracted offset <em>v<sub>0</sub> </em>and the scale multiplier <em>s</em> utilized in the calculation of image pixel values as <em>v</em>' = (<em>v</em> - <em>v<sub>0</sub></em>)·<em>s</em>. To access the depth data as a 32-bit float array, researchers can utilize the <code>navex.datasets.tools.load_mono</code> function from [2].</p> <p>Please note that the file paths in e.g. <code>rot-cg67p-osinac.tar</code> and <code>cg67p-osinac-d.tar</code> archives are the same, so you need to either rename the extracted folder, extract them to different folders, or only extract the archive that you need.</p> <p>For clarity, it should be noted that subsets lacking the aforementioned pre- or postfixes do not contain paired images and consequently lack pixel correspondences. These subsets were exclusively used for feature extractor training in [1].</p> <p>The dataset also includes <code>*.ckpt</code> files, which are the trained feature extractor models referred to in [1]. More details about how to use them can be found in [2].</p>
Unravelling arthropod movement in natural landscapes: small-scale effects of body size and weather conditions
<p>This dataset was used to run the analyses for Logghe et al. (2024). Unravelling arthropod movement in natural landscapes: small-scale effects of body size and weather conditions. Journal of Animal Ecology, 93(9), 1365-1379. <a href="https://doi.org/10.1111/1365-2656.14161">https://doi.org/10.1111/1365-2656.14161</a></p> <p>"Arthropod_movement.csv" contains the results of experimental fieldwork, where movement speed and direction were recorded for individuals belonging to multiple arthropod species. Each row contains data on taxonomy, weather conditions, movement speed and movement direction of an individual measurement. Metadata is provided for the different columns.</p> <p>"Summary_observations.csv" gives an overview of the amount of individuals that were tested for each species. This dataset separates flying and cursorial movement, since several species were able to switch between these two modes.</p> <p><strong>Abstract</strong></p> <p><strong>Background: </strong>Movement is a crucial life history component and holds direct significance to population dynamics, thereby influencing population viability. For arthropods in general, larger species achieve greater dispersal distances and large scale movements are influenced by weather conditions. However, many aspects of arthropod movement behaviour remain relatively unexplored, especially on small spatial scales. Studies on this topic are scarce and often limited to a few specific species or laboratory conditions. Consequently, it remains uncertain whether the effects of body size and weather conditions can be generalized across a wide range of arthropod species in natural environments.</p> <p><strong>Methods: </strong>To help address this knowledge gap, we conducted a field study in two nature reserves in Belgium, focusing on both flying and cursorial arthropods. Over 200 different arthropod species were captured and released within a circular setup, allowing quantification of movement speed and direction. By analysing the relationship between these movement variables and morphological (body size) as well as environmental factors (temperature and wind), we aimed to gain insights into the mechanisms driving small-scale arthropod movement under natural conditions.</p> <p><strong>Results: </strong>For flying species, movement speed is positively correlated with both body size and (tail)wind speed. In contrast, movement speed of cursorial individuals was solely positively related with temperature. Notably, movement direction was biased towards the vegetated areas where the arthropods were originally caught, suggesting an internal drive to move towards suitable habitat. This tendency was particularly strong in larger flying individuals, in smaller cursorial species and under tailwind conditions. Furthermore, both flying and cursorial taxa were hindered from moving towards the habitat by strong upwind.</p> <p><strong>Conclusions: </strong>Body size can be used as a useful proxy for not only movement speed, but orientation capacity as well. This movement-size correlation is, at least at small temporal scales, conditional to the prevailing wind conditions.</p>
Data from: Recurrent evolution of small body size and loss of the sword ornament in Northern swordtail fish
Open the record for dataset details and reuse information.
High-precision body mass predictors for small mammals: A case study in the Mesozoic
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Data from: Defaunation increases clustering and fine-scale spatial genetic structure in a small-seeded palm despite remaining small-bodied frugivores
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Weighing the cost: the impact of serial heatwaves on body mass in a small Australian passerine
<p>Rising temperatures pose a grave risk to arid zone birds because they are already living close to their physiological limits and must balance water conservation against the need for evaporative cooling. We assess how extreme temperatures affect a wild population of small passerines by monitoring daily mass change in individual Jacky Winters (a small Australasian robin; <i>Microeca fascinans</i>) across a series of severe heatwaves that afflicted southern Australia in the summer of 2018-19. Daily maximum temperature and duration of heat exposure were negatively related to the birds' ability to maintain body mass. At maximum temperatures ≥42<sup>o</sup>C, birds lost 2.0% of their body mass daily and at ≥45<sup>o</sup>C, 2.6%. Apparent mortality increased almost three-fold, and all breeding birds abandoned their nests. Nevertheless, net daily mass loss was less than might be expected from laboratory-based findings, presumably because wild Jacky Winters undertook behavioural thermoregulation. The birds also regained some mass between heatwave events and suffered no long-term reduction in body condition.</p>
Small increases in ambient temperature reduce offspring body mass in an equatorial mammal
<p class="MsoNormal">Human-induced climate change is leading to temperature rises, along with increases in the frequency and intensity of heatwaves. Many animals respond to high temperatures through behavioural thermoregulation, for example by resting in the shade, but this may impose opportunity costs by reducing foraging time (therefore energy supply), and so may be most effective when food is abundant. However, the heat dissipation limit theory (HDL) proposes that even when energy supply is plentiful, high temperatures can still have negative effects. This is because dissipating excess heat becomes harder, which limits processes that generate heat such as lactation. We tested predictions from HDL on a wild, equatorial population of banded mongooses (<em>Mungos mungo</em>). In support of HDL, higher ambient temperatures led to lighter pups, and increasing food availability made little difference to pup weight under hotter conditions. This suggests that direct physiological constraints rather than opportunity costs of behavioural thermoregulation explain the negative impact of high temperatures on pup growth. Our results indicate that climate change may be particularly important for equatorial species, which often experience high temperatures year-round so cannot time reproduction to coincide with cooler conditions.</p>
Movies associated with "Direct N-body simulations of satellite formation around small asteroids: insights from DART's encounter with the Didymos system"
<pre>All movies are titled based on the figure in the paper they correspond to and the ID number of the simulation. All movies, except for movie_fig1_fig2_spinup.mp4 are rendered in a rotating, primary-centered frame with a period of 10 hours. This is done to make it easier to watch the satellite accumulate. movie_fig1_fig2_spinup.mp4 is rendered in an inertial frame. The movies are quite long, so we recommend fast-forwarding some of the boring parts :)</pre>
ScienceDex guides
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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.