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4,753 results for “SHAPE”
Data from: Additive genetic and environmental variation interact to shape the dynamics of seasonal migration in a wild bird population
<p><span>Dissecting joint micro-evolutionary and plastic responses to environmental perturbations requires quantifying interacting components of genetic and environmental variation underlying expression of key traits. This ambition is particularly challenging for phenotypically discrete traits where multiscale decompositions are required to reveal non-linear transformations of underlying genetic and environmental variation into phenotypic variation, and when effects must be estimated from incomplete field observations. We devised a joint multistate capture-recapture and quantitative genetic animal model and fitted this model to full-annual-cycle resighting data from partially-migratory European shags (<em>Gulosus</em> <em>aristotelis</em>) to estimate key components of genetic, environmental and phenotypic variance in the ecologically critical discrete trait of seasonal migration versus residence. We demonstrate non-negligible additive genetic variance in latent liability for migration, resulting in detectable micro-evolutionary responses following two episodes of strong survival selection. Further, liability-scale additive genetic effects interacted with substantial permanent individual and temporary environmental effects to generate complex non-additive effects on expressed phenotypes, causing substantial intrinsic gene-by-environment interaction variance on the phenotypic scale. Our analyses therefore reveal how temporal dynamics of partial seasonal migration arise from combinations of instantaneous micro-evolution and within-individual phenotypic consistency, and highlight how intrinsic phenotypic plasticity could expose genetic variation underlying discrete traits to complex forms of selection.</span></p>
Data for "Beyond Magic Numbers: Atomic Scale Equilibrium Nanoparticle Shapes for Any Size"
<p>This record contains <a href="https://wiki.fysik.dtu.dk/ase/ase/db/db.html">ASE databases</a> in sqlite format that contain the minimum energy structures of nanoparticles of Ag, Au, Cu, and Pd as determined in the paper "<em>Beyond Magic Numbers: Atomic Scale Equilibrium Nanoparticle Shapes for Any Size</em>", Rahm and Erhart, Nano Letters <strong>17</strong>, 5775 (2017); <a href="http://doi.org/10.1021/acs.nanolett.7b02761">doi: 10.1021/acs.nanolett.7b02761</a></p> <p>The <code>access-database.py</code> script included in this record demonstrates how to access the databases. It requires the <a href="https://wiki.fysik.dtu.dk/ase/">ASE package</a> to be installed.</p>
What Darwin couldn't see: Island formation and historical sea levels shape genetic divergence and island biogeography in a coastal marine species
<p>Oceanic islands play a central role in the study of evolution and island biogeography. The Galapagos Islands are one of the most studied oceanic archipelagos but research has almost exclusively focused on terrestrial organisms compared to marine species. Here we used the Galapagos bullhead shark (<em>Heterodontus quoyi</em>) and single nucleotide polymorphisms (SNPs) to examine evolutionary processes and their consequences for genetic divergence and island biogeography in a shallow-water marine species without larval dispersal. The sequential separation of individual islands from a central island cluster gradually established different ocean depths between islands that pose barriers to dispersal in <em>H. quoyi</em>. Isolation-by-resistance analysis suggested that ocean bathymetry and historical sea level fluctuations modified genetic connectivity. These processes resulted in at least three genetic clusters that exhibit low genetic diversity and effective population sizes that scale with island size and the level of geographic isolation. Our results exemplify that island formation and climatic cycles shape genetic divergence and biogeography of coastal marine organisms with limited dispersal comparable to terrestrial taxa. Because similar scenarios exist in oceanic islands around the globe our research provides a new perspective on marine evolution and biogeography with implications for the conservation of island biodiversity.</p>
Herbivory and nutrients shape grassland soil seed banks
<p>Anthropogenic nutrient enrichment and shifts in herbivory can lead to dramatic changes in the composition and diversity of aboveground plant communities which, in turn, can alter cryptic biodiversity storage, seed banks, in the soil. We used data from seven Nutrient Network grassland sites on four continents, encompassing a range of climatic and environmental conditions, to test the joint effects of fertilization and aboveground mammalian herbivory on seed banks and on the similarity between aboveground plant communities and seed banks. Fertilization decreased plant species richness and diversity in seed banks, and homogenized composition between aboveground and seed bank communities. Fertilization increased seed bank abundance especially in the presence of herbivores, while this effect was smaller in the absence of herbivores. Our findings highlight that nutrient enrichment can weaken the temporal storage effect as a diversity maintaining mechanism and that herbivory needs to be considered when assessing nutrient enrichment effects on seed bank abundance. </p>
Data and code for "Turning tables: food availability shapes dynamic aggressive behaviour among asynchronously hatching siblings in red kites Milvus milvus"
<p><strong>Abstract</strong></p> <p>Aggression represents the backbone of dominance acquisition in several animal societies, where the decision to interact is dictated by its relative cost. Among siblings, such costs are weighted in the light of inclusive fitness, but how this translates to aggression patterns in response to changing external and internal conditions remains unclear. Using a null-model-based approach, we investigate how day-to-day changes in food provisioning affect aggression networks and food allocation in growing red kite (<em>Milvus milvus</em>) nestlings, whose dominance rank is largely dictated by age. We show that older siblings, irrespective of age, change from targeting only close-aged peers (close-competitor pattern) when food provisioning is low, to uniformly attacking all other peers (downward heuristic pattern) as food conditions improve. While food allocation was generally skewed towards the older siblings, the youngest sibling in the nest increased its probability of accessing food as more was provisioned and as downward heuristic patterns became more prominent, suggesting that different aggression patterns allow for catch-up growth after periods of low food. Our results indicate that dynamic aggression patterns within the nest modulate environmental effects on juvenile development by influencing the process of dominance acquisition and potentially impacting the fledging body condition, with far-reaching fitness consequences.</p>
Soil temperature profiles, measured using a coil-shaped fiber-optic distributed temperature sensor
<p>Measurements of soil temperature temperature profile, by reference sensors and a coil-shaped fiber optic distributed temperature sensor.</p> <p>Retrieved at the Speulderbos measurement site, 52.251048 N, 5.690061 E.</p> <p> </p> <p>A full description can be found in:</p> <p>Schilperoort, B. (2022). <em>Heat Exchange in a Conifer Canopy: A Deep Look using Fiber Optic Sensors</em> [Delft University of Technology]. https://doi.org/10.4233/uuid:6d18abba-a418-4870-ab19-c195364b654b</p>
Simple Shapes Dataset
<p>This dataset is used in the paper <a href="https://arxiv.org/abs/2306.15711"><em>Semi-supervised Multimodal Representation Learning through a Global Workspace, </em>Devillers et al., 2023 (under review)</a>.</p> <p>To use this dataset, use the code provided here: <a href="https://github.com/bdvllrs/bimGW">https://github.com/bdvllrs/bimGW</a>.</p> <p>It consists of 32x32 pixel images of shapes with multiple attributes (size, location, rotation, color). Each image is also paired with its ground truth information (attributes), and a natural language description (English) of the image.</p> <p>The dataset is composed of:</p> <ul> <li>a train set of 500,000 samples,</li> <li>a val and a test set of 1000 samples each.</li> </ul> <p>It also contains already processed 12-dimensional visual features (from a VAE), and presaved BERT features of the text descriptions.</p>
Dataset on Article: River ecological status is shaped by agricultural land use intensity across Europe: Establishing a typology of farming-driven freshwater impacts
<p>This repository contains raw data from the article "River ecological status is shaped by agricultural land use intensity across Europe: Establishing a typology of farming-driven freshwater impacts" which is currently under review.</p> <p>It contains data to allocate the pressures (<strong>Data_pressure_allocation.csv</strong>) and calculate the Pressure Index (<strong>Data_pressure_index.csv</strong>) for Table 1, and for the Spearman correlations for Figure 2 (<strong>Data_Spearman_correlations.csv</strong>).</p> <p> </p> <p>Also available is the Shapefile used for the different agricultural maps (Figure 1 and Figure S1-S4):</p> <p><strong>Shapefile Schürings_et_al._2023</strong> (Coordinate system: ETRS 1989 UTM Zone 32N)</p> <p><strong>Attribute description</strong></p> <p>Id - Identifier of polygons</p> <p>gridcode - Code of agricultural archetypes of Levers et al., (2018)</p> <p>M_ZHYD: Unique identifier of corresponding FEC</p> <p>mars_bt12: River types</p> <p>eco_stat_2: Ecological status</p> <p>Biogeoregi: Biogeographical Regions - AN = Northern and Highland, Temp = Temperate, Mediterranean = Mediterranean</p> <p>Cum_pressu: Agricultural pressure index</p> <p>Nitrogen: Agricultural nitrogen pressure</p> <p>Pesticides: Agricultural pesticide pressure</p> <p>Hydromorph: Agricultural hydromorphological pressure</p> <p>Water_abst: Agricultural water abstraction</p>
Data from: Pre‐adaptation and adaptation shape trait‐environment matching in the Neotropics
<p>The resources provided here comprise raw and final data sets used in our study 'Preadaptation and adaptation shape trait-environment matching in the Neotropics'<span> (DOI: 10.1111/geb</span>.<span>13730) comprising curated g</span>eographic occurrences for 156 Swartzia taxa across its distribution in the Neotropics, phylogenetic information for 69 Swartzia taxa, and corresponding climatic (i.e. 19 climatic variables taken from WorldClim), soil (<span>i.e. </span>sand, nitrogen, and clay content taken from the SoilGrids 2.0 portal), and trait data <span>for each taxa</span> (i.e. <span>trait measurements for </span>lefleats, petals, and fruits)<span>. We also provide t</span>he script used to extract trait information from botanical monographs<span>.</span></p>
Changes in vertical and horizontal diversities mediated by the size structure of introduced fish collectively shape food-web stability
<p><span>Species introductions can alter local food-web structure by changing the vertical or horizontal diversity within communities, largely driven by their body size distributions. Increasing vertical and horizontal diversities is predicted to have opposing effects on stability. However, their interactive effects remain largely overlooked. We investigated the independent and collective effects of vertical and horizontal diversities on food-web stability</span><span> in alpine lakes stocked with variable body size distributions of introduced fish species. I</span><span>ntroduced predators destabilize food-webs by </span><span>increasing vertical diversity through food chain lengthening</span><span>. Alternatively, increasing horizontal diversity results in more </span><span>stable food-web topologies. A non-linear interaction between vertical and horizontal diversities </span>suggests that increasing vertical diversity is most destabilizing when horizontal diversity is low<span>.</span> <span>Our findings suggest </span>that the size structure of introduced predators drives their impacts on stability by modifying the structure of food-webs, and highlights the <span>interactive effects of vertical and horizontal diversities on stability.</span></p>
Data and codes from "How does dispersal shape the genetic structure of animal populations in European cities? A simulation approach"
<p>Codes and data used for "Savary et al. How does dispersal shape the genetic structure of animal populations in European cities? A simulation approach".</p> <p> </p>
Disruption of an ant-plant mutualism shapes interactions between lions and their primary prey
<p><strong>Data and file overview:</strong></p> <ol> <li>Kamaru_Path_Analysis_Data.csv</li> <li>Kamaru_Path_Analysis.R</li> <li>Kamaru_Zebra_RSF_Data.csv</li> <li>Kamaru_Zebra_RSF.R</li> </ol> <p><strong>Layers used to build Zebra RSF:</strong></p> <ol> <li>Kamaru_DWater: distance to water</li> <li>Kamaru_DGlade: distance to glade</li> <li>Kamaru_DSettlement: distance to human settlement</li> <li>Kamaru_OPC_Veg: vegetation layer (classes: <em>V. drepanolobium</em>, <em>E. divinorum, </em>others)</li> </ol> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Path_Analysis_Data.csv</strong></p> <ol> <li>Number of variables: 11</li> <li>Description: This data file includes 105 zebra kill sites and paired random locations from June 2019 to August 2020. It also includes: (A) monthly utilization distributions of lion prides associated with each kill site and paired point; and (B) zebra densities estimated from resource selection functions, associated with each kill site, and paired random location. Please see our supplementary materials for more details on data and methods.</li> <li>Variable list:</li> </ol> <p>(A) rsf.block: Resource Selection Function blocks (block 1: Jan-Apr 2019, block 2: May-Sep 2019, block 3: Oct 2019 – Jan 2020, block 4: Feb-May 2020, block 5: Jun-Sep 2020)</p> <p>(B) Kill_ID: kill identifier.</p> <p>(C) Lion_ID: individual lion pride identifier.</p> <p>(D) Date (Day, Month, Year) when a specific kill occurred.</p> <p>(E) Zebra_kill (1 = kill site, 0 = paired random location).</p> <p>(F). Species: Zebra.</p> <p>(G) Visibility: openness measurement using a rangefinder in (m).</p> <p>(H) Lion_activity: Utilization distributions (UD) of lions.</p> <p>(I) Invasion (1 = invaded by big-headed ants, 0 = uninvaded by big-headed ants).</p> <p>(J) zeb.rsf: resource selection function value.</p> <p>(K) zeb.density: zebra density estimated from resource selection functions.</p> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Zebra_RSF_Data.csv</strong></p> <ol> <li>Number of variables: 10</li> <li>Description: This data file includes 182 zebra sightings, paired with 10 random points created for each sighting/used point. Also, the data includes actual GPS locations of each sighting and the total number of zebras in each sighting. Please see our supplementary materials for more details on data and methods.</li> <li>Variable list:</li> </ol> <p>(A) Species: Zebra.</p> <p>(B) Date (Day, Month, Year) for that sighting.</p> <p>(C) Survey: count identifier (Survey 2 to 21).</p> <p>(D) GPS location (X and Y), longitude and latitude of that sighting location.</p> <p>(E) Transect: Transect number.</p> <p>(F) Used: (1= zebra sighting, 0 = paired point).</p> <p>(G) zebra.ct: total number of zebras in each sighting.</p> <p> </p> <p><strong>R CODE</strong></p> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Path_Analysis.R</strong></p> <ol> <li>Description: Apply this code to Kamaru_Path_Analysis_Data.csv to build nested path models.</li> </ol> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Zebra_RSF.R</strong></p> <ol> <li>Description: Apply this code to Kamaru_Zebra_RSF_Data.csv to build resource selection functions for zebra. Use the following layers: Kamaru_DWater, Kamaru_DGlade, Kamaru_DSettlement and Kamaru_OPC_Veg to build the Zebra RSF.</li> </ol>
Data from: Multispecies interactions shape the transition to multicellularity
<p>This data was collected during the study entitled "Multispecies interactions shape the transition to multicellularity."</p> <p>We explored how interspecific competition shapes the emergence of multicellularity in an experimental system with two yeast species, <em>Saccharomyces cerevisiae</em> and <em>Kluyveromyces lactis</em>, where multicellularity evolves in response to selection for faster settling ability. The experimental design consisted of a 19-day evolution experiment, where cocultures and monocultures of unicellular <em>S. cerevisiae </em>and <em>K. lactis</em> were propagated daily with and without selection for settling (6 replicates each). Cocultures included initial frequencies of 25%, 50%, and 75% <em>K. lactis</em>. Differential growth rates and selection coefficients during the sweep of multicellular <em>K. lactis</em> were calculated, and species frequencies (%) were visualized.</p> <p>We also completed a 20-day evolution experiment with cocultures of multicellular <em>S. cerevisaie</em> and <em>K. lacti</em>s, with each species beginning as rare, transferred with and without settling (6 replicates each). Differential growth rates during the invasion-from-rare experiment were calculated and visualized.</p> <p>To assess the competitive dynamics of the two species before and after the transition to multicellularity, competition assays were performed, with species’ frequencies determined before and after either 24 hours of growth or 7 minutes of settling when each species began as rare. These assays were repeated for unicellular and multicellular isolates. We also obtained 48-hour growth curves for monocultures and 50% cocultures (10 replicates each) and determined the intrinsic growth rate (r) and carrying capacity (K) for each population. Population densities of each species were also measured before and after 24 hours of growth on each species’ spent media.</p>
The first arriving virus shapes within-host viral diversity during natural epidemics
Viral diversity has been discovered across scales from host individuals to populations. However, the drivers of viral community assembly are still largely unknown. Within-host viral communities are formed through coinfections, where the interval between the arrival times of viruses may vary. Priority effects describe the timing and order in which species arrive in an environment, and how early colonizers impact subsequent community assembly. To study the effect of the first-arriving virus on subsequent infection patterns of five focal viruses, we set up a field experiment using naïve Plantago lanceolata plants as sentinels during a seasonal virus epidemic. Using joint species distribution modelling, we find both positive and negative effects of early season viral infection on late season viral colonization patterns. The direction of the effect depends on both the host genotype and which virus colonized the host early in the season. It is well-established that co-occurring viruses may change the virulence and transmission of viral infections. However, our results show that priority effects may also play an important, previously unquantified role in viral community assembly. The assessment of these temporal dynamics within a community ecological framework will improve our ability to understand and predict viral diversity in natural systems.
data for Shaping magnetization dynamics in a planar square dot by adjusting its surface anisotropy
<p>Upload contains data needed to create Figures 2 and 4 in the publication Shaping magnetization dynamics in a planar square dot by adjusting its surface anisotropy (<a href="https://arxiv.org/abs/2309.02984">https://arxiv.org/abs/2309.02984</a>)</p>
Inferring shape transformations in a drawing task
<p>Data repository relative to the following publication:</p> <p>Schmidt, F., Tiedemann, H., Fleming, R. W., & Morgenstern, Y. (in press). Inferring shape transformations in a drawing task. <em>Memory & Cognition</em>. <a href="https://doi.org/10.3758/s13421-023-01452-0">https://doi.org/10.3758/s13421-023-01452-0</a></p> <p>The repository includes several MATLAB demos that show how to load the drawings, test, and sample shapes (Figure4_showdrawpercond.m), and scripts that plot Figure 5 and 6 from the manuscript and compute the statistics (Figure5or6_overall_results.m). The latter demo (Figure5or6_overall_results.m) has the option of running the analysis pipeline on the drawing (through analysisScript1.m) or using precomputed analysis. Note that running the analysis pipeline will require that you have ShapeComp, which you can download here: <a href="https://sites.google.com/view/yanivmorgenstern/home/tools/shapecomp">https://sites.google.com/view/yanivmorgenstern/home/tools/shapecomp</a></p> <p>If you use this data or code, please cite our paper.</p> <p>For questions or suggestions, please contact Filipp Schmidt (filipp.schmidt@psychol.uni-giessen.de) and/or Yaniv Morgenstern (Yaniv.Morgenstern@gmail.com).</p>
Stage-mediated priority effects and season lengths shape long-term competition dynamics
<p>The relative arrival time of species can affect their interactions and thus determine which species persist in a community. Although this phenomenon, called priority effect, is widespread in natural communities, it is unclear how it depends on the length of growing season. Using a seasonal stage-structured model, we show that differences in stages of interacting species could generate priority effects by altering the strength of stabilizing and equalizing coexistence mechanisms, changing outcomes between exclusion, coexistence, and positive frequency dependence. However, these priority effects are strongest in systems with just one or a few generations per season and diminish in systems where many overlapping generations per season dilute the importance of stage-specific interactions. Our model reveals a novel link between the number of generations in a season and the consequences of priority effects, suggesting that consequences of phenological shifts driven by climate change should depend on specific life histories of organisms.</p>
Figure 8 Shells. A–C. Taphrenalla diadema. A. specimen CUMZ 7175. B. specimen CUMZ 7177. C. specimen CUMZ 7171 showing channel-shaped suture with wide bottom. D, E. T in Integrative systematics reveals the new land-snail genus Taphrenalla (Eupulmonata: Ariophantidae) with a description of nine new species from Thailand
Figure 8 Shells. A–C. Taphrenalla diadema. A. specimen CUMZ 7175. B. specimen CUMZ 7177. C. specimen CUMZ 7171 showing channel-shaped suture with wide bottom. D, E. T. dalli sp. nov. D. holotype CUMZ 7183. E. specimen CUMZ 7189 showing channel-shaped suture with very wide bottom. Inset SEM image showing shell sculpture of each species.
Microhabitat conditions drive uncertainty of risk and shape neophobic responses in Trinidadian guppies, Poecilia reticulata
<p>In response to uncertain risks, prey may rely on neophobic phenotypes to reduce the costs associated with the lack of information regarding local conditions. Neophobia has been shown to be driven by information reliability, ambient risk, and predator diversity, all of which shape uncertainty of risk. We similarly expect environmental conditions to shape uncertainty by interfering with information availability. In order to test how environmental variables might shape neophobic responses in Trinidadian guppies (<em>Poecilia</em> <em>reticulata</em>), we conducted an in situ field experiment of two high-predation risk guppy populations designed to determine how the "average" and "variance" of several environmental factors might influence the neophobic response to novel predator models and/or novel foraging patches. Our results suggest neophobia is shaped by water velocity, microhabitat complexity, pool width and depth, as well as substrate diversity and heterogeneity. Moreover, we found differential effects of the "average" and "variance" environmental variables on food- and predator-related neophobia. Our study highlights that assessment of neophobic drivers should consider predation risk, various microhabitat conditions, and neophobia being tested. Neophobic phenotypes are expected to increase the probability of prey survival and reproductive success (i.e. fitness), and are therefore likely linked to population health and species survival. Understanding the drivers and consequences of uncertainty of risk is an increasingly pressing issue, as ecological uncertainty increases with the combined effects of climate change, anthropogenic disturbances, and invasive species. </p>
Experimental wind pressure database of low-rise and H-shaped buildings
<p>A database of wind pressure coefficient of three H-shaped and low-rise buildings. The data was derived from experiments performed in an open-circuit wind tunnel located at the Structures Department in the National Laboratory of Civil Engineering (LNEC), in Lisbon, Portugal. The tunnel was configured as a suburban Atmospheric Boundary Layer (ABL) and 20 wind attack angles were tested. The models’ scales were configured to respect the limit of 5% obstruction allowed for wind tunnel experiments. Contents:</p> <p>Test models (JPEG):</p> <p>- Geometry characterization of H-shaped models. (a) floor-plan model parameterization, quadrant divisions, and surface names (no scale); (b) 3D model; (c) models’ dimensions in meters.</p> <p>- Pictures of the three physical models (M1, M2, M3).</p> <p>H-Shape_Database (CSV files):</p> <p>- Mean point Cp values for all tested surfaces (20 wind attack angles).</p> <p>- Tap coordinates (tap, x, y, z, surface).</p>
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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.