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207 results for “spatial response”
Stability of rocky intertidal communities in response to species removal varies across spatial scales
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Data from: Restoring marine ecosystems: spatial reef configuration triggers taxon-specific responses among early colonizers
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Spatial distribution of benthic algae in the South China Sea: responses to gradually changing environmental factors and ecological impacts on coral communities
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Data from: A species' response to spatial climatic variation does not predict its response to climate change
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A new approach to geostatistical synthesis of historical records reveals capuchin spatial responses to climate and demographic change
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Foraging in nature: contrasting responses to resource heterogeneity at small and large spatial scales.
<p>A key problem faced by foragers is how to forage when resources are distributed heterogeneously in space. This heterogeneity and associated trade-offs may change with spatial scale. Furthermore, foragers may also have to optimize acquiring multiple resources. Such complexity of decision-making while foraging is poorly understood. We studied the butterfly <i>Ypthima huebneri</i> to examine how foraging decisions of adults are influenced by spatial scale and multiple resources. We predicted that, at a small-spatial scale, the time spent foraging in a patch should be proportional to resources in the patch, but at large-spatial scales, due to limitations arising from large travel costs, this relationship should turn negative. We also predicted that both adult and larval resources should jointly affect foraging butterflies. To test these predictions, we laid eleven plots, and sub-divided them into patches. We mapped nectar and larval resources and measured butterfly behavior in these patches and plots. We found that adult foraging behavior showed contrasting relationships with adult resource density at small versus large-spatial scales. At the smaller-spatial scale, butterflies spent more time feeding in resource-rich patches, whereas at the large-scale, butterflies spent more time feeding in resource-poor plots. Furthermore, both adult and larval resources appeared to affect foraging decisions, suggesting that individuals may optimize search costs for different resources. Overall, our findings suggest that the variation in foraging behavior seen in foragers might result from animals responding to complex ecological conditions, such as, resource heterogeneity at multiple spatial scales and the challenges of tracking multiple resources.</p>
Spatial modulation of visual responses arises in cortex with active navigation
<p>During navigation, the visual responses of neurons in mouse primary visual cortex (V1) are modulated by the animal's spatial position. Here we show that this spatial modulation is similarly present across multiple higher visual areas but negligible in the main thalamic pathway into V1. Similar to hippocampus, spatial modulation in visual cortex strengthens with experience and with active behavior. Active navigation in a familiar environment, therefore, enhances the spatial modulation of visual signals starting in the cortex.</p>
Data from: Repeatable patterns of small-scale spatial variation in intertidal mussel beds and their implications for responses to climate change
The interaction of ocean conditions and weather with small-scale physical features of a habitat can have profound effects on the experiences of individual organisms. On topographically complex shorelines, and particularly within dense aggregations of organisms such as mussel beds, a mosaic of environmental conditions can develop, and the resulting variation in conditions within the aggregation could drastically alter the performance of neighboring individuals. Using a suite of sensors mounted to individual Mytilus californianus mussels over two summer field deployments, we have characterized the temperature variation and valve gaping behavior differences found at two spatial scales: within a group separated by centimeters, and between groups of mussels located at the upper and lower extents of the natural mussel zone separated by meters. While temperature conditions near the lower edge of the mussel bed were generally more benign, temperature extremes were similar at both heights in the bed, and variation in body temperature among neighbors increased as the daily mean temperature increased. These patterns were similar across years despite a 3.8°C difference in mean air and seawater temperatures between years. Gaping behavior was also highly variable among individuals, though that variability diminished at the high end of the mussel bed where the total time mussels spent submerged was much more constrained. These data indicate that an individual mussel's physiological status and past history can be drastically different than those of its nearby neighbors, complicating our ability to characterize representative conditions within a habitat. These observations also provide for the possibility that the impacts of future climate change will be highly specific to certain individuals based on their relative exposure or protection within the mosaic. To address such possibilities, future work must examine the correlation between genotypic and physiological traits that determine performance and individuals' unique experiences in their disparate micro-environments.
Data from: Soil carbon response to woody plant encroachment: Importance of spatial heterogeneity and deep soil storage
1. Recent global trends of increasing woody plant abundance in grass-dominated ecosystems may substantially enhance soil organic carbon (SOC) storage and could represent a strong carbon (C) sink in the terrestrial environment. However, few studies have quantitatively addressed the influence of spatial heterogeneity of vegetation and soil properties on SOC storage at the landscape scale. In addition, most studies assessing SOC response to woody encroachment consider only surface soils, and have not explicitly assessed the extent to which deeper portions of the soil profile may be sequestering C. 2. We quantified the direction, magnitude, and pattern of spatial heterogeneity of SOC in the upper 1.2 m of the profile following woody encroachment via spatially-specific intensive soil sampling across a landscape in a subtropical savanna in the Rio Grande Plains, USA, that has undergone woody proliferation during the past century. 3. Increased SOC accumulation following woody encroachment was observed to considerable depth, albeit at reduced magnitudes in deeper portions of the profile. Overall, woody clusters and groves accumulated 12.87 and 18.67 Mg C ha-1 more SOC compared to grasslands to a depth of 1.2 m. 4. Woody encroachment significantly altered the pattern of spatial heterogeneity of SOC to a depth of 5 cm, with marginal effect at 5-15 cm, and no significant impact on soils below 15 cm. Fine root density explained greater variability of SOC in the upper 15 cm, while a combination of fine root density and soil clay content accounted for more of the variation in SOC in soils below 15 cm across this landscape. 5. Synthesis: Substantial SOC sequestration can occur in deeper portions of the soil profile following woody encroachment. Furthermore, vegetation patterns and soil properties influenced the spatial heterogeneity and uncertainty of SOC in this landscape, highlighting the need for spatially specific sampling that can characterize this variability and enable scaling and modeling. Given the geographic extent of woody encroachment on a global scale, this undocumented deep soil C sequestration suggests this vegetation change may play a more significant role in regional and global C sequestration than previously thought.
Sea or summit? Wild reindeer spatial responses to changing high-arctic winters
<p>Data for the article “Sea or summit? Wild reindeer spatial responses to changing high-arctic winters”</p> <p>By ÅØ Pedersen, LT Beumer, R Aanes, BB Hansen</p> <p>The data set includes four files: A readme file describing the data files, and three data files accompanying the above publication.</p> <p>For further queries please contact Åshild Ø. Pedersen: Ashild.Pedersen@npolar.no</p>
Data and code from: Traits and phylogenies modulate the environmental responses of wood-inhabiting fungal communities across spatial scales
<p>Identifying the spatial scales at which community assembly processes operate is fundamental for gaining a mechanistic understanding of the drivers shaping ecological communities. In this study, we examined whether and how traits and phylogenetic relationships structure fungal community assembly across spatial scales.</p> <p>We applied joint species distribution modelling to a European-scale dataset on 215 wood-inhabiting fungal species, which includes data on traits, phylogeny and environmental variables measured at the local (log-level) and regional (site-level) scales.</p> <p>At the local scale, wood-inhabiting fungal communities were mostly structured by deadwood decay stage, and the trait and phylogenetic patterns along this environmental gradient suggested the lack of diversifying selection.</p> <p>At regional scales, fungal communities and their trait distributions were influenced by climatic and connectivity-related variables. The fungal climatic niches were not phylogenetically structured, suggesting that diversifying selection or stabilizing selection for climatic niches has played a strong role in wood-inhabiting communities. In contrast, we found a strong phylogenetic signal in the responses to connectivity-related variables, revealing phylogenetic homogenization in small and isolated forests.</p> <p>Altogether, our results show that species-level traits and phylogenies modulate the responses of wood-inhabiting fungi to environmental processes acting at different scales. This result suggests that the evolutionary histories of fungal traits diverge along different environmental axes.</p>
TAU Spatial Room Impulse Response Database (TAU-SRIR DB)
<p><strong>DESCRIPTION</strong></p> <p>The <strong>TAU Spatial Room Impulse Response Database (TAU-SRIR DB)</strong> database contains spatial room impulse responses (SRIRs) captured in various spaces of Tampere University (TAU), Finland, for a fixed receiver position and multiple source positions per room, along with separate recordings of spatial ambient noise captured at the same recording point. The dataset is intended for emulation of spatial multichannel recordings for evaluation and/or training of multichannel processing algorithms in realistic reverberant conditions and over multiple rooms. The major distinct properties of the database compared to other databases of room impulse responses are:</p> <ul> <li>Capturing in a high resolution multichannel format (32 channels) from which multiple more limited application-specific formats can be derived (e.g. tetrahedral array, circular array, first-order Ambisonics, higher-order Ambisonics, binaural).</li> <li>Extraction of densely spaced SRIRs along measurement trajectories, allowing emulation of moving source scenarios.</li> <li>Multiple source distances, azimuths, and elevations from the receiver per room, allowing emulation of complex configurations for multi-source methods.</li> <li>Multiple rooms, allowing evaluation of methods at various acoustic conditions, and training of methods with the aim of generalization on different rooms.</li> </ul> <p>The RIRs were collected by staff of TAU between 12/2017 - 06/2018, and between 11/2019 - 1/2020. The data collection received funding from the European Research Council, grant agreement 637422 <a href="https://cordis.europa.eu/project/id/637422">EVERYSOUND</a>.</p> <p><strong><em>NOTE</em></strong><em>: This database is a work-in-progress. We intend to publish additional rooms, additional formats, and potentially higher-fidelity versions of the captured responses in the near future, as new versions of the database in this repository.</em></p> <p> </p> <p><strong>REPORT AND REFERENCE</strong></p> <p>A compact description of the dataset, recording setup, recording procedure, and extraction can be found in:</p> <p>Politis., Archontis, Adavanne, Sharath, & Virtanen, Tuomas (2020). <strong>A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection</strong>. In <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020)</em>, Tokyo, Japan.</p> <p>available <a href="https://dcase.community/documents/workshop2020/proceedings/DCASE2020Workshop_Politis_88.pdf">here</a>. A more detailed report specifically focusing on the dataset collection and properties will follow.</p> <p> </p> <p><strong>AIM</strong></p> <p>The dataset can be used for generating multichannel or monophonic mixtures for testing or training of methods under realistic reverberation conditions, related to e.g. multichannel speech enhancement, acoustic scene analysis, and machine listening, among others. It is especially suitable for the follow application scenarios:</p> <ul> <li>monophonic and multichannal reverberant single- or multi-source speech in multi-room reverberant conditions</li> <li>monophonic and multichannel polyphonic sound events in multi-room reverberant conditions </li> <li>single-source and multi-source localization in multi-room reverberant conditions, in static or dynamic scenarios</li> <li>single-source and multi-source tracking in multi-room reverberant conditions, in static or dynamic scenarios</li> <li>sound event localization and detection in multi-room reverberant conditions, in static or dynamic scenarios</li> </ul> <p> </p> <p><strong>SPECIFICATIONS</strong></p> <p>The SRIRs were captured using an [Eigenmike](https://mhacoustics.com/products) spherical microphone array. A [Genelec G Three loudspeaker](https://www.genelec.com/g-three) was used to playback a maximum length sequence (MLS) around the Eigenmike. The SRIRs were obtained in the STFT domain using a least-squares regression between the known measurement signal (MLS) and far-field recording independently at each frequency. In this version of the dataset the SRIRs and ambient noise are downsampled to 24kHz for compactness.</p> <p>The currently published SRIR set was recorded at nine different indoor locations inside the Tampere University campus at Hervanta, Finland. Additionally, 30 minutes of ambient noise recordings were collected at the same locations with the IR recording setup unchanged. SRIR directions and distances differ with the room. Possible azimuths span the whole range of $\phi\in[-180,180)$, while the elevations span approximately a range between $\theta\in[-45,45]$ degrees. The currently shared measured spaces are as follows:</p> <ol> <li>Large open space in underground bomb shelter, with plastic-coated floor and rock walls. Ventilation noise. Circular source trajectory.</li> <li>Large open gym space. Ambience of people using weights and gym equipment in adjacent rooms. Circular source trajectory.</li> <li>Small classroom (PB132) with group work tables and carpet flooring. Ventilation noise. Circular source trajectory.</li> <li>Meeting room (PC226) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory.</li> <li>Lecture hall (SA203) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory.</li> <li>Small classroom (SC203) with group work tables and carpet flooring. Ventilation noise. Linear source trajectory.</li> <li>Large classroom (SE203) with hard floor and rows of desks. Ventilation noise. Linear source trajectory.</li> <li>Lecture hall (TB103) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory.</li> <li>Meeting room (TC352) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory.</li> </ol> <p>The measurement trajectories were organised in groups, with each group being specified by a circular or linear trace at the floor at a certain distance from the z-axis of the microphone. For circular trajectories two ranges were measured, a <em>close</em> and a <em>far</em> one, except room TC352, where the same range was measured twice, but with different furniture configuration and open or closed doors. For linear trajectories also two ranges were measured, <em>close</em> and <em>far</em>, but with linear paths at either side of the array, resulting in 4 unique trajectory groups, with the exception of room SA203 where 3 ranges were measured resulting on 6 trajectory groups. Linear trajectory groups are always parallel to each other, in the same room.</p> <p>Each trajectory group had multiple measurement trajectories, following the same floor path, but with the source at different heights. </p> <p>The SRIRs are extracted from the noise recordings of the slowly moving source across those trajectories, at an angular spacing of approximately every 1 degree from the microphone. Instead of extracting SRIRs at equally spaced points along the path (e.g. every 20cm), this extraction scheme was found more practical for synthesis purposes, making emulation of moving sources at an approximately constant angular speed easier.</p> <p>More details on the trajectory geometries can be found in the <strong>README</strong> file and the <strong>measinfo.mat</strong> file.</p> <p> </p> <p><strong>RECORDING FORMATS</strong></p> <p>As with the DCASE2019-2021 datasets, currently the database is provided in two formats, first-order Ambisonics, and a tetrahedral microphone array - both derived from the Eigenmike 32-channel recordings. For more details on the format specifications, check the README. </p> <p>We intend to add additional formats of the database, of both higher resolution (e.g. higher-order Ambisonics), or lower resolution (e.g. binaural).</p> <p> </p> <p><strong>REFERENCE DOAs</strong></p> <p>For each extracted RIR across a measurement trajectory there is a direction-of-arrival (DOA) associated with it, which can be used as the reference direction for sound source spatialized using this RIR, for training or evaluation purposes. The DOAs were determined acoustically from the extracted RIRs, by windowing the direct sound part and applying a broadband version of the MUSIC localization algorithm on the windowed multichannel signal.</p> <p>The DOAs are provided as Cartesian components [x, y, z] of unit length vectors.</p> <p> </p> <p><strong>SCENE GENERATOR</strong></p> <p>A set of routines is shared, here termed <em>scene generator</em>, that can spatialize a bank of sound samples using the SRIRs and noise recordings of this library, to emulate scenes for the two target formats. The code is similar to the one used to generate the <a href="https://doi.org/10.5281/zenodo.5476980"><strong>TAU-NIGENS Spatial Sound Events 2021</strong></a> dataset, and has been ported to Python from the original version written in Matlab.</p> <p>The generator can be found [**<strong>here</strong>**](https://github.com/danielkrause/DCASE2022-data-generator), along with more details on its use. </p> <p>The generator at the moment is set to work with the <a href="https://zenodo.org/record/2535878">NIGENS</a> sound event sample database, and the <a href="https://zenodo.org/record/4060432">FSD50K</a> sound event database, but additional sample banks can be added with small modifications.</p> <p>The dataset together with the generator has been used by the authors in the following public challenges:</p> <p>- <a href="https://dcase.community/challenge2019/task-sound-event-localization-and-detection">DCASE 2019 Challenge Task 3</a>, to generate the <strong>TAU Spatial Sound Events 2019</strong> dataset (<a href="https://doi.org/10.5281/zenodo.2599196">development</a>/<a href="https://doi.org/10.5281/zenodo.3377088">evaluation</a>)</p> <p>- <a href="https://dcase.community/challenge2020/task-sound-event-localization-and-detection">DCASE 2020 Challenge Task 3</a>, to generate the <a href="https://doi.org/10.5281/zenodo.4064792"><strong>TAU-NIGENS Spatial Sound Events 2020</strong></a> dataset</p> <p>- <a href="https://dcase.community/challenge2021/task-sound-event-localization-and-detection">DCASE2021 Challenge Task 3</a>, to generate the <a href="https://doi.org/10.5281/zenodo.5476980"><strong>TAU-NIGENS Spatial Sound Events 2021</strong></a> dataset</p> <p>- <a href="https://dcase.community/challenge2022/task-sound-event-localization-and-detection">DCASE2022 Challenge Task 3</a>, to generate additional <a href="https://doi.org/10.5281/zenodo.6406873"><strong>SELD synthetic mixtures for training the task baseline</strong></a></p> <p><em><strong>NOTE</strong>: The current version of the generator is work-in-progress, with some code being quite "rough". If something does not work as intended or it is not clear what certain parts do, please contact us.</em></p> <p> </p> <p><strong>DATASET STRUCTURE</strong></p> <p>The dataset contains a folder of the SRIRs (<strong>TAU-SRIR_DB</strong>), with all the SRIRs per room in a single MAT file. The file <strong>rirdata.mat</strong> contains some general information such as sample rate, format specifications, and most importantly the DOAs of every extracted SRIR. The file <strong>measinfo.mat</strong> contains measurement and recording information in each room. Finally, the dataset contains a folder of spatial ambient noise recordings (<strong>TAU-SNoise_DB</strong>), with one subfolder per room having two audio recordings fo the spatial ambience, one for each format, FOA or MIC. For more information on how to SRIRs and DOAs are organized, check the README.</p> <p> </p> <p><strong>DOWNLOAD</strong></p> <p>The files <em>TAU-SRIR_DB.z01</em>, ..., <em>TAU-SRIR_DB.zip</em> contain the SRIRs and measurement info files.</p> <p>The files <em>TAU-SNoise_DB.z01</em>, ..., <em>TAU-SNoise_DB.zip</em> contain the ambient noise recordings.</p> <p>Download the zip files and use your preferred compression tool to unzip these split zip files. To extract a split zip archive (named as zip, z01, z02, ...), you could use, for example, the following syntax in Linux or OSX terminal:</p> <p>Combine the split archive to a single archive:</p> <pre><code>zip -s 0 split.zip --out single.zip</code></pre> <p>Extract the single archive using unzip:</p> <pre><code>unzip single.zip</code></pre> <p> </p> <p><strong>LICENSE</strong></p> <p>The database is published under a custom **<strong>open non-commercial with attribution</strong>** license. It can be found in the `LICENSE.txt` file that accompanies the data.</p>
Spatial dynamics of CD39⁺CD8⁺ exhausted T cells reveal tertiary lymphoid structures-mediated response to PD-1 blockade in esophageal cancer
<p><strong>Data related to the paper</strong>: <em>"Spatial dynamics of CD39+CD8+ exhausted T cells reveal tertiary lymphoid structures-mediated response to PD-1 blockade in esophageal cancer,”</em> <em>Nature Communications</em> (2024)</p> <p>The repository data consists of two main folders: <strong>IMC_dataset</strong> and <strong>MC_normalized_dataset</strong>.</p> <p><strong>IMC_dataset</strong> includes:</p> <ol> <li> <p><strong>IMC_denoised_dataset</strong>: This folder contains cell mask images and noise-reduced images for each sample.</p> </li> <li> <p><strong>IMC_raw_dataset</strong>: This folder contains raw, unprocessed data.</p> </li> <li> <p><strong>IMC_processed_data</strong>: This folder contains standardized single-cell information and spillover-corrected FCS files, along with the compensation matrix.</p> </li> </ol> <p>The <strong>MC_normalized dataset</strong> includes FCS files that have been sorted by barcode.</p> <p><strong>Please note</strong> that in the IMC dataset, the following mass channels are blank:</p> <ul> <li><strong>Tumor-ROI</strong>: 80Ar, 127I, 131Xe, 145Nd, 146Nd, 149Sm, 160Gd, 171Yb, 174Yb, 176Yb, 190Os</li> <li><strong>SLO-ROI</strong>: 80Ar, 127I, 131Xe, 145Nd, 146Nd, 149Sm, 160Gd, 176Yb, 190Os</li> </ul> <p>The names attached to the file names are IDs.</p>
Data from: Spatial heterogeneity in species composition constrains plant community responses to herbivory and fertilization
Environmental change can result in substantial shifts in community composition. The associated immigration and extinction events are likely constrained by the spatial distribution of species. Still, studies on environmental change typically quantify biotic responses at single spatial (time series within a single plot) or temporal (spatial beta-diversity at single time points) scales, ignoring their potential interdependence. Here, we use data from a global network of grassland experiments to determine how turnover responses to two major forms of environmental change – fertilization and herbivore loss – are affected by species pool size and spatial compositional heterogeneity. Fertilization led to higher rates of local extinction whereas turnover in herbivore exclusion plots was driven by species replacement. Overall, sites with more spatially heterogeneous composition showed significantly higher rates of annual turnover, independent of species pool size and treatment. Taking into account spatial biodiversity aspects will therefore improve our understanding of consequences of global and anthropogenic change on community dynamics.
Response of flow and saltating particle characteristics to bed roughness and particle spatial density
<p>The data were used in the paper "Response of flow and saltating particle characteristics to bed roughness and particle spatial density" which was submitted to "<em>Water Resources Research</em>". In this paper, The numerical model combining LES method and point-particle method is applied for tracking particle trajectories. The effects of bed roughness and particle spatial density on the bedload transport are investigated by numerical simulations. The distributions of key parameters for saltation, including their changes, are assessed using the PDF curves. </p>
Distinct Roles of Land Cover in Regulating Spatial Variabilities of Temperature Responses to Radiative Effects of Aerosols and Clouds
<p>Dataset of drawing figures in work 'Distinct Roles of Land Cover in Regulating Spatial Variabilities of Temperature Responses to Radiative Effects of Aerosols and Clouds'.</p> <p>Dataset File List </p> <p>1. Present_Day_Variables.nc : global spatial distributions of radiative forcing, climate sensitivity and temperature response </p> <p>2. Regional_Variables.xlsx : regional data of radiative forcing, climate sensitivity and temperature response</p> <p>3. Aerosol_Burdens.nc : monthly aerosol burdens (atmospheric column concentration) of ten simulation cases </p> <p>4. Aerosol_Burdens_README.txt : dimensional descriptions of Aerosol_Burdens.nc</p> <p>5. Monthly_Radiation.nc : monthly radiation variables of PD simulation for testing model performance</p> <p>6. Monthly_Radiation_README.txt : dimensional descriptions of Monthly_Radiation.nc</p> <p> </p>
Spatial and temporal variation in phenotypes and fitness in response to developmental thermal environments
<p>1) Phenotypic variation within populations is influenced by the environment via plasticity and natural selection. How phenotypes respond to the environment can vary among traits, populations, and life stages in ways that can influence fitness.</p> <p>2) Plastic responses during early development are particularly important because they can affect components of fitness throughout an individual's life. Consequently, how natural selection shapes developmental plasticity could be influenced by fitness consequences across different life stages. Moreover, spatial variation in selection pressures could generate differences in plastic responses among populations.</p> <p>3) To gain insight into sources of variation in phenotypes and survival, we used a laboratory egg incubation experiment using brown anole lizards (Anolis sagrei) from mainland (ancestral) and island (descendent) populations, combined with a mark-release-recapture experiment in the field. Our study was designed to (i) quantify the effects developmental temperature on embryo development and offspring morphology, (ii) assess how developmental temperature influences offspring survival across different life stages, and (iii) quantify how thermal reaction norms vary among ancestral and descendant populations.</p> <p>4) Developmental temperature influenced offspring morphology, but thermal reaction norms of embryos showed little variation among populations. Developmental temperature influenced offspring survival, but the patterns differed between embryo and hatchling stages; the optimal temperature for embryos was about 5ºC lower than that for hatchlings. High temperatures were thermally stressful to embryos, but they reduced incubation duration and led to early hatching. In turn, earlier hatching increased the probability of survival to adulthood. Moreover, the effect of temperature on hatchling survival was most pronounced for offspring that hatched late in the season.</p> <p>5) The difference in optimal developmental temperatures between life stages may be driven by physiological tolerance for embryos and by ecological factors for hatchlings. Moreover, the fitness consequences of the developmental environment depend upon the phenology of hatching. Overall, these results highlight how the developmental environment can differentially effect fitness across life stages and show that temporal thermal heterogeneity can influence survival of embryos, but the consequences on post-hatching stages may vary at different times of the season.</p>
Behavioral responses of a large, heat-sensitive mammal to climatic variation at multiple spatial scales
<p>1. Climate warming creates energetic challenges for endothermic species by increasing metabolic and hydric costs of thermoregulation. Although endotherms can invoke an array of behavioral and physiological strategies for maintaining homeostasis, the relative effectiveness of those strategies in a climate that is becoming both warmer and drier is not well understood.</p> <p>2. In accordance with the heat dissipation limit theory, which suggests that allocation of energy to growth and reproduction by endotherms is constrained by the ability to dissipate heat, we expected that patterns of habitat use by large, heat-sensitive mammals across multiple scales are critical for behavioral thermoregulation during periods of potential heat stress and that they must invest a large portion of time to maintain heat balance.</p> <p>3. To test our predictions, we evaluated mechanisms underpinning the effectiveness of bed sites for ameliorating daytime heat loads and potential heat stress across the landscape while accounting for other factors known to affect behavior. We integrated detailed data on microclimate and animal attributes of moose <em>Alces</em> <em>alces</em>, into a biophysical model to quantify costs of thermoregulation at fine and coarse spatial scales.</p> <p>4. During summer, moose spent an average of 67.8% of daylight hours bedded, and selected bed sites and home ranges that reduced risk of experiencing heat stress. For most of the day, shade could effectively mitigate the risk of experiencing heat stress up to 10°C, but at warmer temperatures (up to 20℃) wet soil was necessary to maintain homeostasis via conductive heat loss. Consistent selection across spatial scales for locations that reduced heat load underscores the importance of the thermal environment as a driver of behavior in this heat-sensitive mammal.</p> <p>5. Moose in North America have long been characterized as riparian-obligate species because of their dependence on woody plant species for food. Nevertheless, the importance of dissipating endogenous heat loads conductively through wet soil suggests riparian habitats also are critical thermal refuges for moose. Such refuges may be especially important in the face of a warming climate in which both high environmental temperatures and drier conditions will likely exacerbate limits to heat dissipation, especially for large, heat-sensitive animals.</p>
Data from: Soil nutrient availability rather than spatial nutrient heterogeneity shapes the intraspecific response of root architectural, morphological, and mycorrhizal traits in <em>Vaccinium myrtillus</em>
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Spatial modulation of visual responses arises in cortex with active navigation
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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.