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4,753 results for “SHAPE”
Abiotic and biotic contexts shape the effect of disturbance on non-native plant invasion
<p>Making predictions about when and where a given mechanism of invasion will be weak or strong is crucial for the effective management of non-native species. Despite the importance of disturbance on invasion, our understanding of how variation in abiotic and/or biotic conditions may modify the disturbance-invasion relationship is scarce. Here, we aimed to evaluate how abiotic (soil type) and biotic (tree and shrub cover) contexts affect the disturbance-invasion relationship in disturbed and nearby non-disturbed communities in the semi-arid open forest of central Argentina (ca. 36° S) using field sampling. We found that abiotic context modulated non-native species success in disturbed communities, whereas both abiotic and biotic context modulated success in nearby non-disturbed communities. These findings suggest that the plant invasion-disturbance relationship is context-dependent. Our results hint at the possibility that the significance of disturbance in predicting invasion might diminish as the importance of abiotic filters increases.</p>
Factors shaping the abundance of two butterflies sharing resources and enemies across a biogeographic region
<p><strong>Aim: </strong>Intra-specific variation in species relative abundance is shaped by a complex interplay of abiotic and biotic factors, making it both necessary and challenging to assess their combined relative importance in explaining variations across space and time. We used two congeneric butterfly species for which extensive count data and a deep understanding of their natural history are available to test three hypotheses explaining intra-specific variation in their abundance: (H1) seasonal dispersal behaviour driven by climate, (H2) resource availability, and (H3) apparent competition mediated via shared parasitoids.</p> <p><strong>Taxon</strong>: <em>Gonepteryx rhamni </em>(Brimstone) and <em>G. cleopatra</em> (Cleopatra).</p> <p><strong>Location</strong>: NE Iberian Peninsula, where both species coexist, and a nearby archipelago (Balearic Islands), where only Cleopatra occurs.</p> <p><strong>Methods</strong>: We analyzed spatial abundance variations for both species in the mainland and island-mainland differences in the abundance of Cleopatra. Abiotic and biotic factors, including temperature, host plant and overwintering habitat availability, larval parasitism and density dependence were tested to explain the observed variations.</p> <p><strong>Results</strong>: H1 can explain variation in butterfly abundance between mainland regions since in warmer summers populations increased in cooler areas but decreased in warmer areas. H2 explains the variation within mainland climate regions with a strong positive relationship between resource availability and abundance but is unlikely to explain the island-mainland variation in the abundance of Cleopatra. H3 could neither explain biogeographical variation in abundance because although richer parasitoid communities were found on the mainland, larval mortality rates were similar or lower on the mainland than in the islands.</p> <p><strong>Main conclusions</strong>: Climate and resource availability jointly account for variation in butterfly abundance across the mainland, but neither these factors nor parasitism can explain island-mailand differences. Both coexisting butterfly species and their larval parasitoids may have undergone evolutionary processes, resulting in spatial segregation that promotes the coexistence of the two butterfly species on the mainland.</p>
Population size shapes trade-off dilution and adaptation to a marginal niche unconstrained by sympatric habitual conditions
<p>How does niche expansion occur when the habitual (high-productivity) and marginal (low-productivity) niches are simultaneously available? Without spatial structuring, such conditions should impose fitness maintenance in the former while adapting to the latter. Hence, adaptation to a given marginal niche should be influenced by the identity of the simultaneously available habitual niche. This hypothesis remains untested. Similarly, it is unknown if larger populations, which can access greater variation and undergo more efficient selection, are generally better at niche expansion. We tested these hypotheses using a large-scale evolution experiment with <em>Escherichia</em> <em>coli</em>. While we observed widespread niche expansion, larger populations consistently adapted to a greater extent to both marginal and habitual niches. Owing to diverse selection pressures in different habitual niches (constant versus fluctuating environments; environmental fluctuations varying in both predictability and speed), fitness in habitual niches was significantly shaped by their identities. Surprisingly, despite this diversity in habitual selection pressures, adaptation to the marginal niche was unconstrained by the habitual niche's identity. We show that in terms of fitness, two negatively correlated habitual niches can still have positive correlations with the marginal niche. This allows the marginal niche to dilute fitness trade-offs across habitual niches, thereby allowing costless niche expansion. Our results provide fundamental insights into sympatric niche expansion.</p>
Data, stimuli, and analyses for "High-level aftereffects reveal the role of statistical features in visual shape encoding"
<h2><strong>Data and code share.</strong></h2><p>This record contains data and code (written in MATLAB) to reproduce the results shown in:</p><p>Morgenstern, Y. , Storrs, K., R., Schmidt, F., Hartmann, F., Tiedemann, H., Tiedemann, H, ., Wagemans, J., & Fleming, R. (<i>in press</i>) . High-level aftereffects reveal the role of statistical features in visual shape coding. Current Biology</p><p>Below is a summary of shared scripts that load data, run the analysis (including options for fitting model parameters or loading pre-computed fitted parameters), and plotting the results.</p><h3><strong>Figure 1</strong></h3><ul><li><i>Fig1C_shapespace.m</i>: draw shape space (as in Figure 1C)</li><li><i>Fig1EFG_plotpsychometricdata.m</i>: fit psychometric model to pooled data and plot (as in Figure 1EFG)</li><li><i>getExptShapesHbias.m</i>: saves a data structure (which we call 'package') with adaptor, test, and human biases from experiment 1. (Used to fit models; e.g., see <i>fitGabPyr2Hbais.m</i> or <i>fitTAEGANfit2Hbais.m</i>)</li></ul><h3><strong>Figure 2 and 3A</strong></h3><ul><li><i>fig3A_modeval_expt1.m</i>: generate figure that evaluates models in Figure 3A on how well they predict aftereffects in Experiment 1. (script located in the 'Figures 2 and 3A/models' directory).</li></ul><p>Code to fit the models, and figures that show examples of model predictions are in the model directories, and summarized below:</p><h4>Model: <strong>GabPyrAE</strong> </h4><ul><li><i><strong>note: </strong></i>To run GabPyr, you will likely need to recompile the .mex files in 'matlabPyrTools/mex'. Then move the recompiled files into the 'matlabPyrToos' directory</li><li><i>fig2B_GabPyrAEVisFigs.m</i>: produce GabPyrAE model images for example adaptor and test image ( as in Figure 2B )</li><li><i>figS2BC_GabPyrAEExp</i>.m: get GabPyrAE model responses to simulated tilt aftereffect experiment using anisotropic noise, and plot model responses as in Figures S2BC.</li><li><i>getGabPyrAEMod.m: </i>given an adaptor and test image, this function produces the unfit GabPyrAE prediction</li><li><i>fitGabPyr2Hbias</i>.m: fit GabPyrAE model to best predict human baises in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitGabPyrNormMod2Stims</i>.m</li><li><i>evalGabPyrAE_fitmod_aic.m</i>: evaluate GabPyrAE fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalGabPyrAE_unfitmod_aic.m</i>: evaluate GabPyrAE fitted model on how well it predicts human biases from experiment 1</li></ul><h4>Model: <strong>TAE</strong></h4><ul><li><i>fig2CD_TAEmod.m</i>: produce TAE model images for example adaptor and test image (as in Figure 2CD)</li><li><i>getTAEModonShape.m: </i>given an adaptor and test image, this function produces TAE Original prediction. Input to function is adaptor and test shapes, and TAE model parameters alpha and sigma.</li><li><i>getTAEGAN_spwt_onShape.m: </i>given an adaptor and test image, this function produces TAEGAN prediction. Input to function is adaptor and test shapes, and TAEGAN model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much TAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>getTAE_spwt_onShape_nn.m: </i>given an adaptor and test image, this function produces TAE nearest neighbour prediction. Input to function is adaptor and test shapes, and TAE nearest neighbour model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much TAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>fitTAEGAN2Hbias</i>.m: fit TAEGAN model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitTAEGANMod2Stims</i>.m</li><li><i>fitTAENN2Hbias</i>.m: fit TAE nearest neighbour model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitTAENNMod2Stims</i>.m.</li><li><i>evalTAEGAN_fitmod_aic.m</i>: evaluate TAEGAN fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalTAENN_fitmod_aic.m</i>: evaluate TAE nearest neighbour fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalTAE_unfitmod_aic.m</i>: evaluate TAE Original model on how well it predicts human biases from experiment 1</li></ul><h4>Model: <strong>PSAE</strong></h4><ul><li><i>fig2EF_PSAEmod.m</i>: produce PSAE model images for example adaptor and test image</li><li><i>getPos_spwt_ShiftononShape_io.m: </i>given an adaptor and test image, this function produces PSAEGAN prediction. Input to function is adaptor and test shapes, and PSAEGAN model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much PSAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>getPos_spwt_ShiftonShape_io_nn.m: </i>given an adaptor and test image, this function produces PSAE nearest neighbour prediction. Input to function is adaptor and test shapes, and PSAE nearest neighbour model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much PSAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>fitPSAEGAN2Hbias</i>.m: fit PSAEGAN model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitPSAEGANMod2Stims</i>.m</li><li><i>fitPSAENN2Hbias</i>.m: fit PSAE nearest neighbour model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitPSAENNMod2Stims</i>.m.</li><li><i>evalPSAEGAN_fitmod_aic.m</i>: evaluate PSAEGAN fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalPSAENN_fitmod_aic.m</i>: evaluate PSAE nearest neighbour fitted model on how well it predicts human biases from experiment 1.</li></ul><h4>Model: <strong>ShapeComp </strong>and <strong>No Adaptation</strong></h4><ul><li><i>eval_ShapeComp _aic.m</i>: evaluate ShapeComp 1 parameter fitted model on how well it predicts human biases from experiment 1.</li><li><i>eval_NoAdaptation _aic.m</i>: evaluate model that predicts no adaptation on how well it predicts human biases from experiment 1.</li></ul><h3><strong>Figure 3BC</strong></h3><ul><li><i>fig3BC_Experiment2.m</i>: load, analyze, and plot experiment 2 data (as in Figure 3B and C).</li><li><i>figS4_Expt2_stimuli.m</i>: show adaptors (in black) and test shapes for ShapeComp (purple), PSAE fit GAN (green), and no adaptation model (white) (as in Figure S4)</li></ul><p> </p>
Foot shape-function model data
<p><span>The human foot is a complex structure that plays an important role in our capacity for upright locomotion. Comparisons of our feet to those of our closest extinct and extant relatives have linked shape features (e.g., the longitudinal and transverse arches, heel size and toe length) to specific mechanical functions. However, foot shape varies widely across the human population, so it remains unclear if and how specific shape variants are related to locomotor mechanics. Here we construct a statistical shape-function model (SFM) from 100 healthy participants to directly explore the relationship between the shape and function of our feet. We also examined if we could predict the joint motion and moments occurring within a person's foot during locomotion based purely on shape features. The SFM revealed that the longitudinal and transverse arches, relative foot proportions and toe shape along with their associated joint mechanics were the most variable. However, each of these only accounted for small proportions of the overall variation in shape, deformation, and joint mechanics, most likely due to the high structural complexity of the foot. Nevertheless, a leave-one-out analysis showed that the SFM can accurately predict joint mechanics of a novel foot, based on its shape and deformation.</span></p>
Divergent selection in low recombination regions shapes the genomic islands in two incipient shorebird species
<p>Divergent selection in the face of gene <span>flow is usually associated with</span> a heterogeneous genomic landscape of divergence in nascent species pairs. However, multiple factors, such as divergent selection and local recombination rate variation, can influence the formation of these genomic island. This conundrum can be solved through examination of the genomic landscapes of species pairs that are still in the early stages of evolution. In this study, population genomics analyses were undertaken using a wide range of sampling and whole-genome resequencing data from 96 unrelated individuals of Kentish plover (<em>Charadrius alexandrinus</em>) and white-faced plover (<em>C. dealbatus</em>). We suggest that the two species exhibit varying levels of population admixture along the Chinese coast and on Taiwan Island<span>.</span> <span>Genome-wide analyses for introgression indicate that ancient introgression had occurred in Taiwan population, and recurrent gene flow is still ongoing in mainland coastal populations</span>. Furthermore, we identified a few genomic regions with significant levels of interspecific differentiation and local recombination suppression, which<span> contain several genes potentially associated with disease resistance, coloration, and regulation of plumage moulting, thus may be connected to the phenotypic and ecological divergence of</span> the two nascent species<span>. Overall, our findings suggest that divergent selection in low recombination regions may be the main force in shaping the genomic islands in two incipient shorebird species.</span></p>
Involvement vs Commitment L-shaped matrix diagram
<p>Involvement vs Commitment L-shaped matrix diagram of volunteering work in non-profit organizations.</p>
Data from: Lunar synchrony, geography, and individual clocks shape autumn migration timing in an avian migrant
<p>Timing programs in animal migrants have been selected to synchronize movements that coincide with predictable resources on the breeding and nonbreeding grounds. Migrants face potential temporal conflicts if their migration schedules benefit from synchrony to conflicting rhythms associated with annual biogeographical (circannual) cues, lunar (circalunar) cues, or individually-repeatable internal clocks. We repeat-tracked individuals of an avian lunaphilic species, Eastern Whip-poor-will (<em>Antrostomus vociferus</em>), for 2–3 successive autumn migrations to determine the influence of the lunar cycle, breeding location, and individual repeatability on migration timing. Almost all birds avoided departing for migration during a full moon, likely to take advantage of the bright moonlight to facilitate visual foraging and enhance pre-migration fattening. However, groups from two latitudinally-distant sampling areas adjusted their autumn departure timing differently relative to the timing of the September full moon, presumably due to differences in seasonal prey availability. Individual repeatability increased throughout autumn migration, suggesting that the factors responsible for shaping migration timing may differ for different migration stages. Our results, that lunar synchrony, local climate, and individual internal clocks appeared to account for much of the variation in migration timing in whip-poor-wills, underscore the value of measuring potentially interacting factors that shape migratory behavior at species, group, and individual levels. It remains unclear if, or how, maintaining individually-repeatable annual migration schedules provides an adaptive benefit for whip-poor-wills or other lunaphilic migrants. Further clarifying the reasons for phenotypic variation in whip-poor-will migration timing will improve predictions of their abilities to adjust migratory movements under changing environmental conditions.</p>
Trough-shaped dolmen Kokai-3 (cross-section)
The dolmen is located under Kokai cliffs (Kokai group of monuments), Mezetsu range, Tuapse region, Caucasus, Russia, Middle Bronze Age (4000-2000 BC), Dolmen Culture of Caucasus. Sepulcher chamber is carved out of large block of stone. Trapezoidal in shape chamber is sealed with a flat capstone. This construction imitate plan and elements of the plinth type of stone slabs. The distinctive element of this version is its access hole placed on side wall. So, the largest wall of that construction is the side of false portal. Another unique element is protruding shelf on rare side of this chamber. The third strange element is the box-shaped roof inside camera. Дольмен расположен под скалами Кокай, хребет Мезецу, Туапсинский район. Относится к типу ложнопортальных корытообразных сооружений с отверстием в боковой стене. Особенности: на задней стене камеры выступающая полка, ребро жесткости коробчатого сечения в основании перекрытия. Source: Objaverse 1.0 / Sketchfab
Fish-shaped besamin box
ID no. MNS/3733/S "Fish-shaped besamin box" Museum: Nowy Sącz District Museum http://muzea.malopolska.pl/en/obiekty/-/a/26897/1130640 Digitalisation: RDW MIC, Małopolska's Virtual Museums project Source: Objaverse 1.0 / Sketchfab
Bird-shaped buckle of bronze
Burial finds from the Late Iron Age/Merovingian period. Bird-shaped buckle of bronze, decorated with circles on the back and rib-like lines running down towards the tail. The buckle has a small hole the neck, just below the needle holder on the back. Needle holder and attachment are incompletely preserved. Found in: Hedlebakkane - Ferkingstad - Karmøy - Rogaland - Norway MuseumsID: S4260a Dated to: Late Iron Age/Merovingian period 550-800 AD. Source: Objaverse 1.0 / Sketchfab
Trough-shaped dolmen Kokai-3 (full complex)
The dolmen is located under Kokai cliffs (Kokai group of monuments), Mezetsu range, Tuapse region, Caucasus, Russia, Middle Bronze Age (4000-2000 BC), Dolmen Culture of Caucasus. Sepulcher chamber is carved out of large block of stone. Trapezoidal in shape chamber is sealed with a flat capstone. This construction imitate plan and elements of the plinth type of stone slabs. The distinctive element of this version is its access hole placed on side wall. So, the largest wall of that construction is the side of false portal. Another unique element is protruding shelf on rare side of this chamber. The third strange element is the box-shaped roof inside camera. Дольмен расположен под скалами Кокай, хребет Мезецу, Туапсинский район. Относится к типу ложнопортальных корытообразных сооружений с отверстием в боковой стене. Особенности: на задней стене камеры выступающая полка, ребро жесткости коробчатого сечения в основании перекрытия. Source: Objaverse 1.0 / Sketchfab
Calocedrus Formosana Natural Shape Decoration
Natural wood decoration with Chinese style and Zen The original is located at Sheraton Grand Taipei Hotel Source: Objaverse 1.0 / Sketchfab
Beaker-Shaped Vase, From a Five-Piece Garniture
Smithsonian source data can be found [here](https://ids.si.edu/ids/media_view?id=3d_package:d8c63a70-4ebc-11ea-b77f-2e728ce88125) This media file is in the public domain (free of copyright restrictions). You can copy, modify, and distribute this work without contacting the Smithsonian. For more information and to review the 3D disclaimer, visit the Smithsonian's [Terms of Use](https://www.si.edu/Termsofuse) page. Beaker-shaped vase, from a five-piece garniture (F1980.190-.194) Medium: Porcelain with cobalt pigment under clear colorless glaze Dimensions: H x Diam: 42.5 × 21.2 cm (16 3/4 × 8 3/8 in) Style: Jingdezhen ware Type: Ceramic, Vessel Origin: Jingdezhen, Jiangxi province, China Date: 1662-1722 Period: Qing dynasty Credit Line: Gift of Myron S. Falk, Jr. and his sisters, Mrs. Mildred F. Loew and Mrs. Eleanor F. Lenzner Accession Number: F1980.193a-b EDAN-URL: edanmdm:fsg_F1980.193a-b Source: Objaverse 1.0 / Sketchfab
Vessel in the shape of the Polish Eagle
ID no.: ZKWawel 6033 Museum: Wawel Royal Castle – State Art Collection http://muzea.malopolska.pl/en/obiekty/-/a/4869610/4890386 Digitalisation: RDW MIC, 2014 Source: Objaverse 1.0 / Sketchfab
Bone plaque in the shape of a bull's head
Bone plaque in the shape of a bull's head from Bilche Zolote ID no.: MAK/8475 Museum: Archaeological Museum in Kraków https://muzea.malopolska.pl/en/objects-list/630 Digitalisation: RDW MIC, Małopolska's Virtual Museums project Source: Objaverse 1.0 / Sketchfab
Data from: Competition for iron shapes metabolic antagonism between Bacillus subtilis and Pseudomonas
<p>Siderophores have long been implicated in sociomicrobiology as determinants of bacterial interrelations. For plant-associated genera like <em>Bacillus</em> and <em>Pseudomonas</em>, siderophores are often acclaimed for their function in biocontrol. Here, we set out to determine the functional role of the<em> Bacillus subtilis</em> siderophore bacillibactin in an antagonistic interaction with <em>Pseudomonas marginalis</em>. The presence of bacillibactin strongly influences the outcome of the interaction in an iron-dependent manner. A bacillibactin producer <em>B. subtilis</em> restricts the colony spreading of <em>P. marginalis</em>, repress the transcription of histidine kinase-encoding gene <em>gacS</em>, and thereby abolish production of secondary metabolites such as pyoverdine and viscosin. In contrast, the lack of bacillibactin restricts <em>B. subtilis</em> colony growth in a mechanism reminiscent of a siderophore tug-of-war for iron. Our study identifies a <em>Bacillus-Pseudomonas</em> interaction conserved across fluorescent <em>Pseudomonas spp.</em>, expanding our understanding of the interplay between two genera of the most well-studied soil microbes.</p>
Dataset and Code for Manuscript "Cell sorting based on pulse shapes from angle resolved detection of scattered light"
<p>Dataset and code for the cell cycle analysis and cluster selection for sorting:</p> <ul> <li>ReadMe file with explanations of the data set and analysis</li> <li>Python scripts for converting the data and to reproduce the sort cluster selection</li> <li>binary data files containing pulse shapes and wavelet transform coefficients</li> <li>FSC data files containing the respective common flow cytometry parameters</li> <li>text files with event indices that represent the gating</li> </ul>
Data for: Phylogenetic structure of body shape in a diverse inland ichthyofauna
<p>Body shape is a fundamental metric of animal diversity affecting critical behavioral and ecological dynamics and conservation status, yet previously available methods <span>capture only a fraction of total body-shape variance</span>. <span>Here we use structure-from-motion (SFM) 3D </span>photogrammetry to generate digital 3D models of adult fishes from the Lower Mississippi Basin, one of the most diverse temperate-zone freshwater faunas on Earth, and 3D geometric morphometrics to capture morphologically distinct shape variables, interpreting <span>principal components as growth fields. The mean body shape in this fauna resembles plesiomorphic teleost fishes, and the major dimensions of body-shape disparity are similar to those of other fish faunas worldwide. Major p</span>atterns of b<span>ody-shape disparity are structured by phylogeny, with nested clades occupying distinct portions of the morphospace, </span>most of the morphospace occupied by multiple distinct clades, and one clade (Acanthomorpha) accounting for over half of the total body shape variance.<span> In contrast to previous studies, variance in </span>body depth (59.4%) structures overall body-shape disparity more than does length (31.1%), while width accounts for a non-trivial (9.5%) amount of the total body-shape disparity.</p>
Micro- and macroclimate interactively shape diversity, niches, and traits of Orthoptera communities along elevational gradients
<p>Temperature is one of the main drivers shaping species diversity and assembly processes. Yet, site-specific effects of the local microclimate on species and trait compositions of insect communities have rarely been assessed along macroclimatic temperature clines. Bavarian Alps, Germany Bayesian joint species distribution models were applied to investigate how ecological and morphological traits drive variation in the climatic niches of 32 Orthoptera species on 93 grassland sites with contrasting microclimatic conditions along a steep elevational macroclimatic gradient in an Alpine region in Central Europe. Species richness and abundance decreased along the elevational macroclimatic gradient, and both benefitted from warm microclimate. Interactive effects of elevation and microclimate on the abundance were, however, species-specific, and partly mediated by traits: Warm microclimatic conditions facilitated the occurrence of demanding xerophilic and late-hatching species, resulting in marked community dissimilarities at mid-elevations where colder sites harboured only a subset of the species. The latter mainly occurred at low elevations together with long-winged species. Abundance peaks of non-xerophilic species were further upslope when microclimate was warm. Intraspecifically, the body sizes and wing lengths of the larger females, but not the males, decreased with elevation akin the community mean, and brown colour morphs were more frequent at sites with warm microclimate. Our nuanced results reveal that trait-dependent responses of species to microclimate play a key role in the assembly and structuring of insect communities along macroclimatic gradients. Since microclimate preferences changed with elevation, we conclude that species temperature niches are narrower than the elevational range suggests and both macro- and microclimatic conditions must be considered when predicting species responses to climate change. Microclimatic contrasts among sites at similar elevations enhanced species turnover mediated by moisture preferences and phenology, highlighting the importance of mountains for conservation as climatic refugia where species with diverging niches can persist in proximity.</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.