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3,105 results for “Vegetation =”
Bird and vegetation diversity in Chaco forests under different cattle management practices
<p>Birds play key roles in forest dynamics, favoring the resilience of forests to cattle ranching. Generalist species may benefit from changes in the vegetation structure caused by cattle grazing while specialist species are usually negatively affected. Therefore, studying changes in taxonomic and functional bird diversity can provide valuable information in order to propose sustainable cattle management practices. Here, we study the influence of different cattle management practices (Cattle Exclusion: FCE; Continuous grazing: FCG; Rotational grazing: FRG; Seasonal grazing: FSG) on the understory vegetation structure, and the bird taxonomic and functional diversity in humid Chaco forests of Argentina. Forests under grazing had an open (FCG) or semi-open (FRG and FSG) understory, whereas FCE presented a closed understory with higher plant richness. Generalized linear models revealed a decrease in bird richness and abundance under most cattle management practices compared to FCE. However, FRG was most similar to FCE in terms of vegetation structure and showed no differences in bird richness. FCE presented the lowest values of functional diversity while FCG and FRG showed the highest values. A fourth-corner analysis showed that FCE was associated with insectivorous species which forage and nest in the shrub layer, reflecting a possible loss of ecosystem services in forests with cattle grazing. We propose FRG as the most appropriate cattle management option and the least detrimental to bird assemblages. However, maintaining protected areas that exclude cattle ranching activities will be key to striking a balance between the conservation of most specialist species and beef production.</p>
Data from: Spatiotemporal dynamics of Nektonic biodiversity and vegetation shifts during the Smithian–Spathian Transition: Conodont and Palynomorph insights from Svalbard
<p>The dataset includes an Excel file with sporomorph counting of samples from the Stensiöfjellet section, Svalbard. Furthermore, it includes additional taxonomic notes on conodonts from the Stensiöfjellet section, Svalbard.</p>
A 10 m resolution land cover map of the Tibetan Plateau with detailed vegetation types
<p>A 10 m resolution land cover map of the Tibetan Plateau with 12 vegetation types and 3 non-vegetation types for the year 2022 (TP_LC10-2022) by leveraging state-of-the-art remote sensing approaches including the Sentinel-1 and Sentinel-2 imagery, environmental and topographic datasets, and Random Forest model using Google Earth Engine platform.</p>
Soil microbiome dataset from the University of Wisconsin Arlington and Lancaster agricultural research stations and cheese maker and vegetable processor wastewater land application sites
<p>Cheese making and vegetable processing are trillion-dollar industries globally. However, they generate immense volumes of high nitrogen wastewater that must be processed safely and cost effectively. Land application systems are frequently used by rural medium and smaller processing facilities that lack ready access to wastewater resource recovery facilities. This study utilized soil microbial data to determine system differences leading to high denitrification rates observed in incubation studies in agricultural soil collected from University of Wisconsin Agricultural Research Stations (ARS), Arlington and Lancaster stations, compared to industry cheese making and vegetable processing land application water treatment facilities. It was hypothesized that decade long frequent treatment with facility wastewater would alter the microbial communities in the system soils, but this is not the case. No clear correlations were found between soil denitrification rates and biotic or abiotic system factors and the microbial communities observed in the industry systems are similar to the ARS soils under agricultural production and to literature reported denitrifying systems such as wetlands and wastewater resource recovery facilities. Knowing that land application system management does not alter the microbial biome will allow any management advances that increase denitrification efficiency in other denitrifying systems to be readily applied to industry wastewater land application facilities. </p>
Map of the Vegetation of the Natural Reserve of Monte Catillo (central Italy)
<p><strong>Map of the Vegetation of the Natural Reserve of Monte Catillo (central Italy)</strong></p> <p><strong> </strong></p> <p><strong>Authors:</strong></p> <p>Sabina Burrascano, Lorenzo Caucci, Giulio Ferrante </p> <p>Department of Environmental Biology, Sapienza University of Rome</p> <p>Maria Vinci </p> <p>Città metropolitana di Roma Capitale</p> <p><strong> </strong></p> <p><strong>Reference system:</strong> EPSG 3004 - Monte Mario / Italy zone 2</p> <p><strong>Spatial scale: </strong>the map was drawn at 1:2,000</p> <p><strong>Minimum Mapping Unit:</strong> 0.2 hectares</p> <p><strong>Provided styles:</strong> use "Carta_vegetazione_aprile2024.qml" for Italian legend; or "Carta_veg_ENG.qml" for English legend</p> <p><strong> </strong></p> <p>Vegetation maps has the purpose of assessing and representing the distribution and extension of the types of vegetation of an area, after investigating it through field surveys, both physiognomic (annotation of the dominant species) and floristic-vegetational (complete censuses of all vascular plant species). Vegetation maps have gained a major role in the Geographical Information Systems of national and local administrations, especially in protected areas, due to their usefulness as tools for environmental monitoring, management and territorial planning.</p> <p>The collaboration between the Metropolitan City of Rome Capital and the Department of Environmental Biology of Sapienza University of Rome has led, among other results, to the creation of a novel map of the vegetation of the Monte Catillo Natural Reserve, updating the pre-existing one which dates back to 20051. The new map was created at a spatial scale of 1:2,000 based on both orthophotos, i.e. aerial photos taken in 2014, and satellite images taken in 2022 and 2023. The minimum mapping unit was set at 0.2 hectares.</p> <p>A EUNIS code was assigned to each type of vegetation based on the correspondence between field observations and the habitat descriptions reported in the EUNIS database. </p> <p>EUNIS (European Nature Information System) is a classification system developed by the European Environmental Agency for the description and categorization of habitat types in Europe to facilitate the exchange of information between European countries and improve the management and conservation of biodiversity at the continental level.</p> <p>The EUNIS system is used to classify natural, semi-natural and artificial habitats, as well as species and habitat types of importance for biodiversity conservation. It follows a hierarchical scheme that allows different levels of detail to be applied to each habitat based on the degree of correspondence between what was detected in the field and what is described in the classification. For example, a Mediterranean scrub habitat dominated by Pistacia lentiscus and Phyllyrea sp. could be classified as follows:</p> <p>Level 1: S - Heaths, scrub and tundra;</p> <p>Level 2: S5 - Maquis, shrub matorral and thermo-Mediterranean maquis;</p> <p>Level 3: S51 - Mediterranean scrub and shrubby matorral;</p> <p>Level 4: S512 - Shrubby matorral of Olea europaea and Pistacia lentiscus;</p> <p>Level 5: S5123 - Pistacia lentiscus and Phillyrea shrub matorral.</p> <p>If the species composition detected in the field does not reflect any of the features described at level 5, the habitat will be reported with a level 4 and so on.</p> <p>Due to the biogeographical peculiarities of the Monte Catillo Natural Reserve, a lack of correspondence was found for several vegetation types occurring within the Reserve. For example, no code has been found in the EUNIS system that identifies the cork oak (Quercus suber) forest with Styrax officinalis understorey. The same was true for the pseudomaquis vegetation for which the most detailed code indicates strictly Balkan species not present in Italy.</p> <p>The map was developed in a GIS (Geographic Information System) environment, and allows the extraction and analysis of quantitative data, such as the extension of the main types of vegetation which can thus be monitored over time even in response to specific disturbances such as intense fires that periodically affect the Reserve.</p> <p> </p> <p><strong>References</strong></p> <p>1. Provincia di Roma, (2006). Piano di assetto della Riserva Naturale di Monte Catillo. Approvato con Deliberazione del Commissario ad acta del 26 Novembre 2015 pubblicato sul BURL del 19 Gennaio 2016, n. 5, supplemento 2.</p> <p> </p>
Diel greenhouse gas emissions demonstrate a strong response to vegetation patch types in a freshwater wetland
<p>Data supporting submitted research paper. "All_flux_variables.csv" includes all plot data and is organized by the date/time of sample, campaign number, and sample location in rows and data collected as headers in the columns. "Flux_tower_data.csv" are meterological variables include in data analysis collected by a flux tower on sight. All other files are time series data for water pH (n = 2), water temperature (n = 3-5). Refer to the "metadata.csv" for units and descriptions of data in files. </p>
Fig. 1 in Riqueza e estrutura de uma comunidade vegetal de dunas na planície costeira de Santa Catarina
Fig. 1. Localização da restinga do Parque Estadual do Acaraí, São Francisco do Sul, Santa Catarina.
The Highs and Lows of Grazing: Effects of Ungulates and Elevation on Grassland and Sagebrush Steppe Vegetation Composition in Yellowstone National Park
<p>Yellowstone National Park’s Northern Range is emblematic for the wolves, bison and elk that inhabit its grassland and sagebrush-steppe habitats. Their interactions and populations have been the focus of considerable conservation debate. Recent changes in ungulate populations provide an opportunity to examine their influence on the vegetation communities. We conducted expansive vegetation surveys along an elevation gradient and at exclosure sites where ungulates were unable to graze. We collected a large database (n = 620 quadrats), which we analyzed using classic community ecology approaches. We found that non-native species have higher abundances at low elevations, and that grazing by ungulates reduces beta diversity and native cover, while promoting non-native cover. The magnitude of these changes are greater in bison-dominated than elk-dominated areas of Yellowstone, suggesting that bison may be overgrazing. These results provide valuable insights into major factors shaping vegetation communities, while also contextualizing management practices in a world-renowned ecosystem.</p> <p> </p> <p>Please respect licensing, and contact the corresponding author for permission to use this dataset in any research or publications. </p>
FIGURE 7 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 7 (caption on next page).
Vegetation changes with climate change in the Grandes Rousses mountain range
<p><strong><em>Questions:</em></strong> We assessed interactions between climate change, bedrock types and snow cover duration on the trajectories of taxonomic and functional composition of subalpine plant communities. We predict (i) an increase in species richness on siliceous bedrock due to a reduced competition and a decrease in richness on calcareous bedrock due to increasing drought stress, (ii) decreasing snow cover duration should induce a higher shrub encroachment in hollows as compared to ridges (iii) increasing growing season temperature should induce taller sizes and more conservative growth traits, in particular in hollows.</p> <p><strong><em>Location: </em></strong>Subalpine belt of the Grandes Rousses mountain range, south western Alps (France).</p> <p><strong><em>Methods:</em></strong> 189 vegetation plots were sampled in 1997 and 2017-2018. The duration of snow cover was assessed during two years in 1995-1997 and five functional traits were measured on 108 species in 2021. We performed multivariate analyses, quantified community weighted-means (CWM) of traits and used ANOVAs to detect responses to local-scale factors and changes in snow cover, temperature and precipitation since 1997 according to a nearby meteorological station.</p> <p><strong><em>Results:</em></strong> Overall, taxonomic composition weakly changed and changes were more dependent on the position of communities along the snow cover duration gradient than on their bedrock type. The abundance of drought-tolerant species increased at the border of hollows and there was, over all communities, a slight increase in the abundance of dwarf shrubs and tall herbaceous species, a strong decrease in short herbaceous species and, thus, an overall decrease in species richness. There were important overall changes in CWM of size traits, in particular leaf area which increased the most in hollows irrespective of bedrock types.</p> <p><strong><em>Conclusion:</em></strong> In this subalpine site the effects of decreasing snow cover duration overwhelmed the effects of bedrocks, which may explain the overall increase in competitive species and decrease in species richness.</p>
Data on soil variables (with plot IDs) and grassland species traits used for the analysis of grassland vegetation data by Pillar, V.D. (2024) Trait divergence in plant community assembly is generated by environmental factor interactions. Journal of Vegetation Science, 35, e13259. Available from: https://doi.org/10.1111/jvs.13259
<p>File <a href="../api/records/10983049/draft/files/Plot_IDs_990ua.txt/content" target="_blank" rel="noopener noreferrer">Plot_IDs_990ua.txt</a> contains the IDs of the 1-m2 plots used for the analysis of grassland vegetation data by Pillar, V.D. (2024) Trait divergence in plant community assembly is generated by environmental factor interactions. The plot data are stored in the sPlot database (PPBio South Brazilian Grassland Database).</p> <p>File <a href="../api/records/10983049/draft/files/E_990ua_21SoilVar.txt/content" target="_blank" rel="noopener noreferrer">E_990ua_21SoilVar.txt</a> contains data on soil variables evaluated in the 250 m transects, but here expanded to the 990 1-m2 plots (each transect was sampled using 10 1-m2 pots).</p> <p>File <a href="../api/records/10983049/draft/files/B_769spp_4t.txt/content" target="_blank" rel="noopener noreferrer">B_769spp_4t.txt</a> is the species trait database collected in the framework of several research projects in the Quantitative Ecology Lab (EcoQua) and Grassland Vegetation Studies Lab (LevCamp) of Universidade Federal do Rio Grande do Sul (UFRGS). Data gaps were filled by compiled from the TRY database and data imputation.</p> <p> </p> <p> </p>
Vegetation pictures, Alpine grasslands at the Nivolet Plain, Gran Paradiso National Park, Italy 2017-2023
<p>This dataset presents vegetation pictures collected at the Nivolet Plain in Gran Paradiso National Park (GPNP), western Italian Alps, between 2017 and 2023.</p> <p>This collection is linked to the dataset named "Net Ecosystem Exchange, Ecosystem Respiration and meteoclimatic data of Alpine grasslands at Nivolet Plain, Gran Paradiso National Park, Italy 2017-2023" (https://zenodo.org/doi/10.5281/zenodo.7590917) already published in the Zenodo Community "IGG-CNR Critical Zone Observatories".</p> <p>For each measurement, a picture was taken from a nadir perspective, aiming at monitoring the vegetation within the measurement area.</p> <p>The collection is organised as follows.</p> <ul> <li>Main folder: NIVOLET_YEAR (e.g., NIVOLET_2023) <ul> <li>Subfolder: SITE-NAME_YEAR(yyyy format)_MONTH(mm format)_DAY(dd format) (e.g., AL_2023_06_15) <ul> <li>Images: SITE-NAME_YEAR(yyyy format)_MONTH(mm format)_DAY(dd format)_NEE-time(hhmmss format) (e.g., AL_2023_06_15_101400.jpg)</li> </ul> </li> </ul> </li> </ul>
Restoration of vegetation and soil in the Horqin Sandy Land, Northern China
<p>These data aimed to clarify the response of vegetation and soil properties to different restoration measures and dune topography, and to quantify dynamics over time. Vegetation and soil characteristics were evaluated at different topographic positions on dunes in the Horqin Sandy Land, with 5-, 25- and 35-year monitoring periods for grazing exclusion and tree planting, and 5- and 25-year monitoring periods for shrub planting.</p>
Assessment of Vegetation Indices for Mapping Burned Areas Using a Deep Learning Method and a Comprehensive Forest Fire Dataset from Landsat Collection.
<p>This repository contains a dataset focused on the delineation of burned areas (BA) in forests, created from Landsat satellite images covering the period from 1985 to 2021. The study also explores the integration of vegetation spectral indices (VIs) within a Convolutional Neural Network (CNN) detector, utilizing U-Net architecture. Along with the dataset of historical BA in Galicia from 1985, we provide the necessary images and code to facilitate the analysis and application of these methods. This repository aims to serve as a valuable resource for researchers and professionals in the field of forest fire management and remote sensing, highlighting the potential advantages of using VIs for improved burned area detection and analysis.</p> <p>DOI for published article: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.asr.2024.12.001" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.asr.2024.12.001</span></span></a></p>
A probabilistic map of Costa Rican peatlands based on vegetation, ecosystem, and soil inventories
<p>There are the GIS files that accompany a peer-reviewed article.</p> <p>This is the first published effort aimed at developing a peatland map for Costa Rica. A probabilistic approach using vegetation, ecosystem, and soil datasets was used to predict the distribution and extent of peatlands below 700 m in elevation. High-elevation sites found in the Talamanca Mountains were visually identified using satellite imagery; those peatlands are small in size (< 0.05 km<sup>2</sup>). Our analysis produced an estimated low-elevation peatland extent of 1433 km<sup>2</sup> and a high-elevation peatland extent of 23.08 km<sup>2</sup>, yielding an estimated total extent of 1456 km<sup>2</sup> for Costa Rica. This figure is in line with previously published extent estimates for this country (577-2670 km<sup>2</sup>). As for all maps, we stress that the accuracy of this product is ultimately limited by data availability and quality, as well as ground-referencing information. Still, the new map can provide guidance for land management, policymaking, and future science endeavors.</p>
The lag and cumulative response of vegetation WUE to water vapor pressure deficit on the Shiyang River Basin, northwest of China_data description
<p>Data description</p> <p>This document provides details about the data description in the manuscript. The variables are described below along with their collection methods and calculation processes:</p> <p>the lag effect: The zip file of“The lag effect” contains three files: "rET_lag_result_z", "rGPP_lag_result"_, and" rWUE_lag_result_z".</p> <p>rET_lag_result_z: "rET_lag_result_z" database is a dataset used to analyze the lag effect of water vapor pressure deficit (VPD) on Evapotranspiration (ET)</p> <p>rGPP_lag_result_z: "rGPP_lag_result_z" database is a dataset used to analyze the lag effect of water vapor pressure deficit (VPD) on Gross Primary Productivity (GPP) </p> <p>rWUE_lag_result_z: "rWUE_lag_result_z" database is a dataset used to analyze the lag effect of water vapor pressure deficit (VPD) on Water Use Efficiency (WUE)</p> <p> </p> <p><br>the Cumulative effect: The zip file of“The Cumulative effect” contains three files: "rET_acc_result_z", "rGPP_acc_result"_, and" rWUE_acc_result_z".</p> <p>rET_acc_result_z: "rET_acc_result_z" database is a dataset used to analyze the Cumulative effect of water vapor pressure deficit (VPD) on Evapotranspiration (ET)</p> <p>rGPP_acc_result_z: "rGPP_acc_result_z" database is a dataset used to analyze the Cumulative effect of water vapor pressure deficit (VPD) on Gross Primary Productivity (GPP) </p> <p>rWUE_acc_result_z: "rWUE_acc_result_z" database is a dataset used to analyze the Cumulative effect of water vapor pressure deficit (VPD) on Water Use Efficiency (WUE)</p> <p> </p> <p><br>the Slop_Partition: The zip file of“The Slop_Partition”contains three files: "Slop_ET_Partition", "Slop_GPP_Partition"_, and" Slop_WUE_Partition".</p> <p>Slop_ET_Partition: "Slop_ET_Partition" database is a dataset used to calculate the temporal and spatial dynamic change·trend of Evapotranspiration (ET)</p> <p>Slop_GPP_Partition: "Slop_GPP_Partition" database is a dataset used to calculate the temporal and spatial dynamic change·trend of Gross Primary Productivity (GPP)</p> <p>Slop_WUE_Partition: " Slop_WUE_Partition" database is a dataset used to calculate the temporal and spatial dynamic change·trend of Water Use Efficiency (WUE)</p> <p> </p> <p> </p>
A global daily seamless 9-km Vegetation Optical Depth (VOD) product from 2010 to 2021
<p> </p> <p><strong>(I) DESCRIPTION</strong>:</p> <p>· A <strong>global daily seamless 9-km Vegetation Optical Depth (VOD)</strong> product is generated through gap-filling and spatiotemporal fusion model. This daily products start <strong>from Jan 01, 2010 to Jul 31, 2021 </strong>(about 20GB memory after uncompressing all zip files).</p> <p>· To further validate the effectiveness of these products, three verification ways are employed as follow: 1) Time series validation; 2) Simulated missing-region validation; And 3) Data comparison validation.</p> <p>· It is important to note that the original data contain missing dates, and these corresponding gaps are also present in our dataset.</p> <p><strong>(II) DATA FORMATTING AND FILE NAMES </strong></p> <p>For the convenience of our readers, we have two formats of data available for download.</p> <p><strong>1) MAT file (Version v1)</strong></p> <p>Data from 2010 to 2021 are stored separately into folders for the corresponding years, with each folder containing daily `.mat` files. The naming convention for the data is “YYYYXXZZ,” where YYYY is the 4-digit year, XX is the 2-digit month, and ZZ is the 2-digit date. The geographic scope is global and the grid size is 4000*2000.</p> <p>MATFILES (.mat): The folders with matfiles contain individual files for:</p> <p>1. Vegetation Optical Depth: VOD_seamless_9km_ YYYYXXZZ.mat</p> <p>2. Latitude/Longitude: VOD_9km_Coordinates.mat</p> <p><strong>2) NetCDF file (Version v2)</strong></p> <p>The year-by-year daily data from 2010 to 2021 are stored in the ‘.nc’ files for the corresponding years. The daily data within each year into one NetCDF file. <span>The variable names are named as VOD_xxxxyydd, where xxxx represents the year, yy represents the month, and dd represents the day. The longitude variable is named “lon” with a dimension of 4000×1, and the latitude variable is named “lat” with a dimension of 2000×1. </span></p> <p><span>It should be noted that these NetCDF files are saved using the netCDF4 library in Python, with the dimension order being (lat, lon). When reading these NetCDF files in MATLAB, the default data dimension order is (lon, lat). Therefore, it is necessary to transpose the variables to match the correct dimension order.</span></p>
Vegetation survey data to understand drivers of plant rarity
<p>Determining the drivers of plant rarity is a major challenge in ecology. Analysing spatial associations between different plant species can provide an exploratory avenue for understanding the ecological drivers of plant rarity. Here, we examined the different types of spatial associations between rare and common plants to determine if they influence the occurrence patterns of rare species. We completed vegetation surveys at 86 sites in woodland, forest, and heath communities in south-east Australia. We also examined two different rarity measures to quantify how categorisation criteria affected our results. Rare species were more likely to have positive associations with both rare and common species across all three vegetation communities. However, common species had positive or negative associations with rare and other common species, depending on the vegetation community in which they occurred. Rare species were positively associated with species diversity in forest communities. In woodland communities, rare species were associated negatively with species diversity but positively associated with species evenness. Rare species with high habitat specificity were more clustered spatially than expected by chance. Efforts to understand the drivers of plant rarity should use rarity definitions that consider habitat specificity. Our findings suggest that examining spatial associations between plants can help understand the drivers of plant rarity.</p>
Data from: Invasive Impatiens glandulifera: a driver of changes in native vegetation?
<p class="western"><span>Biological invasions are one of the major threats to biodiversity worldwide and contribute to changing community patterns and ecosystem processes. However, it is often not obvious whether an invader is the "driver" causing ecosystem changes or a "passenger" which is facilitated by previous ecosystem changes. Causality of the impact can be demonstrated by experimental removal of the invader or introduction into a native community. <span>Using such an experimental approach, we tested whether the impact of the invasive plant </span><i>Impatiens glandulifera</i><span> on native vegetation is causal,</span><i> </i><span>and whether the impact is habitat-dependent. We conducted a field study comparing invaded and uninvaded plots with plots from which </span><i>I. glandulifera</i><span> was removed and plots where </span><i>I. glandulifera</i><span> was planted within two riparian habitats, alder forests and meadows. A negative impact of planting</span><i> I. glandulifera</i><span> and a concurrent positive effect of removal</span><i> </i><span>on the native vegetation indicated a causal effect of </span><i>I. glandulifera</i><span> on total native biomass and growth of </span><i>Urtica dioica</i><span>. Species </span><span>α</span><span>-diversity and composition were not affected by </span><i>I. glandulifera</i><span> manipulations. Thus,</span><i> I. glandulifera</i><span> had a causal but low effect on the native vegetation.</span><i> </i><span>The impact depended slightly on habitat as only the effect of </span><i>I. glandulifera</i><span> planting on total biomass was slightly stronger in alder forests than meadows. We suggest that </span><i>I. glandulifera</i><span> is a "back-seat driver" of changes, which is facilitated by previous ecosystem changes but is also a driver of further changes. Small restrictions of growth of the planted </span><i>I. glandulifera</i><span> and general association of </span><i>I. glandulifera</i><span> with disturbances indicate characteristics of a back-seat driver. For management of </span><i>I. glandulifera</i><span> populations this requires habitat restoration along with removal of the invader.</span></span></p>
Supporting data for: Post-fire early successional vegetation buffers surface microclimate and increases survival of planted conifer seedlings in the southwestern United States
<p>Climate change and fire-exclusion have increased the flammability of western US forests, leading to forest cover loss when wildfires occur under severe weather conditions. Increasingly large high-severity burn patches are a limitation to natural regeneration because of dispersal distance, increasing the chance that these areas are converted to non-forest. Post-fire planting can overcome dispersal limitations, yet warmer and drier post-fire conditions can still limit survival. Early successional vegetation can alter surface microclimate; however, it is unclear whether this is enough to increase planted seedling survival in southwestern US forests. Here we examined how two shrub species of different canopy density would affect survival rates of planted tree seedlings following a high-severity fire in northern New Mexico. We expected that shrubs with a higher density canopy (Gambel oak) would have a greater effect on buffering below-shrub climate than shrubs with a lower density canopy (New Mexico locust) and seedlings planted under Gambel oak would have higher survival rates. We found that seedlings planted under Gambel oak had survival rates approximately 10% to 35% greater than those planted under New Mexico locust. The higher light availability beneath New Mexico locust corresponded to higher temperatures, lower humidity, and higher VPD, which impacted the mortality of planted tree seedlings. These results suggest that by waiting for post-fire shrub establishment, shrubs can be leveraged to buffer microclimate and increase post-fire planting success in the southwestern US.</p>
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.