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8,375 results for “nationalism”
Mangrove Crab Sampling Data in Dongzhaigang National Nature Reserve, Haikou, Hainan Province, China
<p>This dataset contains the results of a study on mangrove crabs conducted in four seasons (Summer, SU; Autumn, AU; Winter, WI; Spring, SP) of 2020 and 2021. The samples were collected in the Dongzhaigang National Nature Reserve, Haikou, Hainan Province, China, at five sites: Sanjiang (SJ), Tashi (TS), Shanweitou (SWT), Luodou (LD), and Puqian (PQ). The primary focus is on crab species belonging to the superfamilies Ocypodoidea (ghost crabs), Grapsoidea (square crabs), and Portunoidea (swimming crabs).</p> <p>Sampling was conducted using net trapping, with three replicate plots set up for each habitat type at each site. Each plot was sampled continuously for three days. Vegetation information was recorded using dominant species as representatives, and water environmental information was collected using a WTW instrument. The parameters measured include total dissolved solids (TDS) (mg/L), dissolved oxygen (DO) (mg/L), salinity (SAL) (‰), water temperature (T) (℃), and pH. Finally, the longitude and latitude in the WGS84 coordinate system and Cartesian coordinates for each plot were recorded.</p> <p>The dataset fields are as follows:</p> <ul> <li>date: Date of sampling</li> <li>year: Year of sampling</li> <li>month: Month of sampling</li> <li>day: Day of sampling</li> <li>site: Sampling location, including TS, SJ, SWT, LD, PQ</li> <li>habitat: Habitat type, including tidal channels, tidal flats, and several vegetation types represented by mangrove trees such as Avicennia marina, Rhizophora stylosa, Bruguiera sexangular, Sonneratia apetala, and Ceriops tagal.</li> <li>plotname: Plot name</li> <li>species: Species name, as per the World Register of Marine Species (<a href="https://www.marinespecies.org/">https://www.marinespecies.org</a>)</li> <li>superfamily: Superfamily, as per the World Register of Marine Species (<a href="https://www.marinespecies.org/">https://www.marinespecies.org</a>)</li> <li>season: Season, including Summer (SU), Autumn (AU), Winter (WI), and Spring (SP)</li> <li>cname: Plot division by season, site, and habitat</li> <li>fullname: Plot division by season, site, habitat, and plot sequence number</li> <li>pname: Plot division by site, habitat, and plot sequence number</li> <li>TDS: Water total dissolved solids (mg/L)</li> <li>pH: Water pH</li> <li>DO: Water dissolved oxygen (mg/L)</li> <li>T: Water temperature (℃)</li> <li>SAL: Water salinity (‰)</li> <li>longitude: Longitude in WGS84 coordinate system</li> <li>latitude: Latitude in WGS84 coordinate system</li> <li>x: Cartesian coordinate x</li> <li>y: Cartesian coordinate y</li> </ul> <p>We thank Chengpu Jiang, Liangjun Wei and other colleagues for their assistance during the field samplings. Thanks also for the experimental conditions and sampling support provided by Hainan Dongzhaigang National Nature Reserve Authority.</p>
Flight dataSet, madrid to any national destination
<p>This is a dataset that have the flights that departs from Madrid and go to any National destination. This dataset is extracted from google flights.</p>
US National Native Bee Monitoring RCN Data Management Workshop: Public Domain Videos
<p>The US National Native Bee Monitoring Research Coordination Network (RCN) held a two-day workshop on data management best practices for native bee inventory, survey, and monitoring data on March 28 and 30, 2023. Videos in this data set were played at the workshop. These videos are released into the public domain. This data set includes the following videos:</p> <ul> <li>Ecological Metadata Standards to Enable Data Reuse by Julien Brun</li> <li>Useful Photo Management for Bee Species by Sam Droege</li> <li>Trait Data Models and Vocabulary by Jen Hammock</li> <li>Symbiota: open-source community portals for insect data management by Andrew Johnston</li> <li>Moving data from the field to the world by Jonathan Koch</li> <li>Exploring data using Discover Life by Clare Maffei</li> <li>USDA Data Sharing Policies and Opportunities by Cynthia Sims Parr</li> <li>Why Share Species Interaction Data? by Jorrit H. Poelen</li> <li>Big-Bee: Sharing Bee Interactions & Traits by Katja C. Seltmann</li> <li>Let’s talk about data by Katja C. Seltmann</li> <li>Responsible use of museum specimens & their data by Erika M. Tucker</li> </ul>
US National Native Bee Monitoring RCN Data Management Workshop: CC BY Videos
<p>The US National Native Bee Monitoring Research Coordination Network (RCN) held a two-day workshop on data management best practices for native bee inventory, survey, and monitoring data on March 28 and 30, 2023. Videos in this data set were played at the workshop. These videos are released with a CC BY license. Please cite the presenter(s) of the video(s) you use. This data set includes the following videos:</p> <ul> <li>The ABeeCs of Data Attribution: Please use your magic words by David Bloom</li> <li>Darwin Core Geography: How to make your locality data complete and accurate by David Bloom</li> <li>Best Practices for Managing Native Bee Molecular Data by Michael G. Branstetter</li> <li>A Trait Database for Bees by Elizabeth A. Crisfield</li> <li>Biotic interaction data and invasive species assessment by Quentin Groom</li> <li>OpenTraits Network (OTN) & TRY Plant Trait Database by Jens Kattge</li> <li>Semantics modeling of phenotypic trait data with ontologies by Diego S. Porto</li> <li>WorldFAIR: towards making plant-pollinator data FAIR by Maarten Trekels</li> </ul>
Datapackage for national high-resolution conservation prioritisation of boreal forests
<p>This data package concerns the following work:</p> <p>Ninni Mikkonen, Niko Leikola, Joona Lehtomäki, Panu Halme, Atte Moilanen,<br> National high-resolution conservation prioritisation of boreal forests,<br> Forest Ecology and Management, Volume 541, 2023, 121079<br> ISSN 0378-1127</p> <p><a href="https://doi.org/10.1016/j.foreco.2023.121079">https://doi.org/10.1016/j.foreco.2023.121079</a></p> <p>The overall objective of the work was to develop spatial prioritizations that can assist the forest conservation programme METSO (The Finnish Government 2008; 2014) to make well-informed decisions about acquisition of forests for protection. The results are also aimed to be useful for other actors interested in forest conservation or biodiversity friendly forest management. We focused the prioritization on the most threatened forest types and areas that display some or many elements of natural forests: more than one and preferably more than two tree species, forest structure that present else than even age structure or a history of clear-cut harvesting, and the amount of dead wood that exceed the volume of dead tree material in managed forests. From the perspective of connectivity, these areas should be situated close (varying from metres to a few kilometres) to other valuable forest areas. These kinds of forest areas represent the most threatened forest types and forest species in Finland (Hyvärinen et al. 2019; Kontula and Raunio 2019).</p> <p>This data package includes 3 folders (see details of the data in the article):</p> <p>1) Data folder<br> a) DWP features: the 20 input data layers of the modelled biodiversity surrogate: dead wood potential. These are combinations of 4 tree species and 5 forest site type classes. Not that these are not normalized.<br> (1) bir = birch, obl = other broad leaved tree, st = forest site type<br> b) Other data layers:<br> i) condition_layer.img where the magnitude of the penalty is defined<br> ii) ProtectedOrNot.img layer is used in hierarchical analysis to define whether the area is permanently protected or not<br> iii) WRSCR04_PA.img layer consists of permanently protected areas cut form weighted range size corrected richness output layer from analysis version 4 to execute the positive interaction between the forests and permanently protected areas.<br> iv) similarity matrix</p> <p>2) Input folder<br> a) example setup files for analysis version 7 (hierarchical analysis where permanently protected areas are forced to highest priorities, including information on dead wood potential of the forest stands, penalties followed by the forest management, connectivity within the forests, observations of red-listed forest species, and connectivity to forest key habitats and permanently protected areas)<br> i) .spp file for list of input features for the analysis<br> ii) .dat file for the analysis settings<br> iii) .bat file to run the analysis in command line<br> iv) conditionlayer.txt to define the used condition file in the analysis<br> v) groups file to define the use of the condition layer<br> vi) interact file to define the interactions between feature layers in connectivity calculations</p> <p>3) Output folder<br> a) includes folder for each analysis version. Each folder includes<br> i) rank file in .img format which is the actual spatial priority ranking result<br> ii) wrscr file which describes the weighted range size corrected richness of all input features<br> iii) curves file: the performance of each input feature within the cell removal<br> iv) jpg picture of the result</p> <p>The package DOES NOT include sensitive data. For species observations, ask for Finnish Biodiversity Info Facility https://laji.fi/en. For forest key habitats (small forest patches protected by the Forest Act, that are classified as “habitats of special importance to safeguard the biodiversity of forests”) on state owned land and land owned by companies, ask the data providers and owners.</p> <p>See Moilanen et al. (2014) for more technical information on the input and output files.</p> <p><br> Overview of the data</p> <p>The resolution of the spatial data is 96 m x 96 m. The study area covered the forested land area in Finland, excluding the autonomous Åland Islands.</p> <p>The data on forest stands are from year 2015, the forest management year 2017, and protected area network early winter 2018. See details of the data extraction in the article, Appendix A.</p> <p>The main source of biodiversity information were the modelled dead wood potential (DWP) indices. The DWP is an estimation of the potential of a stand for hosting dead wood dependent species. The potential is increased when the stand can be expected to produce more dead wood and more varied dead wood in terms of size and tree species composition. The modelling is based on forest growth and increase of dead wood calculated with Motti forest simulator 3.3 (Salminen et al., 2005; Hynynen et al., 2014; Hynynen et al., 2015) for 168 combinations of seven tree species, six forest site types, and four vegetation zones. See detailed information on the dead wood potential modelling in doi:10.3390/f11090913 (Mikkonen et al. 2020, Modeling of Dead Wood Potential Based on Tree Stand Data)</p> <p>The DWP was calculated for each stand or pixel based on the forest data (Finnish Forest Centre 2015; Metsähallitus 2015; Metsähallitus Parks & Wildlife Finland and Centres for Economic Development Transport and the Environment 2015; Natural Resources Institute Finland 2015b; 2015a): tree species and tree stock quantities (mean diameter at breast height and volume), soil fertility (Cajander, 1926), and location. In the DWP modelling the size information was combined with stand volume and forest site type. Eventually, the data were compiled to 20 input layers. See detailed information on the pre-processing of the input-data in the Appendix B.</p> <p>Spatial conservation prioritizations were made with the Zonation software 4.0 (Moilanen et al. 2005; Moilanen et al. 2009; Moilanen et al. 2011). With multiple analysis versions, the greatest interest is on those areas that repeatedly receive high ranks – these areas are important from all perspectives included in analysis.</p> <p>The ecological model of conservation value included seven analysis versions that start from a local perspective and then evolve towards regional and national levels (following Lehtomäki et al. 2009). Each new analysis version included everything that had been included in the previous simpler versions. The versions are 1) local estimation of the conservation potential of the forests based on tree stock alone, 2) local estimation with additional information about forest management and drainage, 3) landscape level (not local but not regional either) estimation with internal forest connectivity, 4) landscape level estimation with additional information about observations of red-listed forest species, 5) landscape-level estimation with added short distance connectivity to key forest habitats, 6) regional estimation with added long distance connectivity to permanently protected areas, and 7) regional estimation of the most appropriate addition to the present conservation network.</p> <p>These results do not replace in-depth ecological inventory assessment. They can be used as one source of information in land use planning.</p> <p><br> Literature</p> <p>Finnish Forest Centre. 2015. [dataset] Field and forest stand database AARNI.</p> <p>Hyvärinen, E., Juslén, A., Kemppainen, E., Uddström, A. & Liukko, U.-M. (Eds.). 2019. The 2019 Red List of Finnish Species. Helsinki, Ministry of the Environment & Finnish Environment Institute. 704 p.</p> <p>Kontula, T. & Raunio, A. (Eds.). 2019. Threatened Habitat Types in Finland 2018. Red List of Habitats – Results and Basis for Assessment. Helsinki, Finnish Environment Institute and Ministry of the Environment. The Finnish Environment 2/2019. 254 p. http://urn.fi/URN:ISBN:978-952-11-5110-1<br> http://hdl.handle.net/10138/308426.</p> <p>Lehtomäki, J., Tomppo, E., Kuokkanen, P., Hanski, I. & Moilanen, A. 2009. Applying spatial conservation prioritization software and high-resolution GIS data to a national-scale study in forest conservation. Forest Ecology and Management 258(11): 2439-2449.</p> <p>Metsähallitus. 2015. [dataset] SutiGIS 2015. Forestry resource and planning system for Metsähallitus Forestry Ltd and Protected Area Biotope Information System; biotope, and tree stock data on state-owned conservation areas, for Metsähallitus Parks & Wildlife Finland.</p> <p>Metsähallitus Parks & Wildlife Finland & Centres for Economic Development Transport and the Environment. 2015. [dataset] SutiGIS 2015: Protected area biotope information system, biotope and tree stock data on private conservation areas.</p> <p>Mikkonen, N., Leikola, N., Lehtomäki, J., Halme, P. & Moilanen, A. 2023. National high-resolution conservation prioritisation of boreal forests. Forest Ecology and Management, Volume 541. <a href="https://doi.org/10.1016/j.foreco.2023.121079">https://doi.org/10.1016/j.foreco.2023.121079</a></p> <p>Mikkonen, N., Leikola, N., Halme, P., Heinaro, E., Lahtinen, A. & Tanhuanpää, T. 2020. Modeling of Dead Wood Potential Based on Tree Stand Data. Forests 11(913): 21.</p> <p>Moilanen, A., Franco, A. M. A., Early, R. I., Fox, R., Wintle, B. & Thomas, C. D. 2005. Prioritizing multiple-use landscapes for conservation: methods for large multi-species planning problems. Proceedings of the Royal Society B-Biological Sciences 272(1575): 1885-1891.</p> <p>Moilanen, A., Kujala, H. & Leathwick, J. 2009. The Zonation framework and software for conservation prioritization. In: Moilanen, A., Wilson, K. A. & Possingham, H. P. (Eds.). Spatial conservation prioritization - Quantitative Methods & Computational tools. New York, Oxford University Press Inc. p. 196-210.</p> <p>Moilanen, A., Leathwick, J. R. & Quinn, J. M. 2011. Spatial prioritization of conservation management. Conservation Letters 4(5): 383-393.</p> <p>Moilanen, A., Pouzols, F. M., Meller, L., Veach, V., Arponen, A., Leppänen, J. & Kujala, H. 2014. Zonation - Spatial conservation planning methods and software. Version 4. User Manual. 4. Helsinki, C-BIG Conservation Biology, Informatics Group, Department of Biosciences, University of Helsinki, Finland. 290 p.</p> <p>Natural Resources Institute Finland. 2015a. [dataset] Segmented multi-source national forest inventory data of Finland: estimates of mean diameter at breast height for tree species based on National Forest Inventory 2013. Unpublished. Date of datacut 19.8.2015.</p> <p>Natural Resources Institute Finland. 2015b. [dataset] The Multi-Source National Forest Inventory of Finland (MS-NFI) 2013, CC BY 4.0.</p> <p>The Finnish Government. 2008. Decision-in-Principle of The Finnish Government on the Forest Biodiversity Programme for Southern Finland for years 2008-2016 (in Finnish). 13.</p> <p>The Finnish Government. 2014. Decision-in-Principle of the Finnish Government on extension of the Forest Biodiversity Programme for Southern Finland (METSO) for years 2014-2025. 18.</p> <p> </p>
Argentina National Map of Crops 2021/2022
<p>Crop type map covering the main agricultural areas of Argentina for growing season 2021/2022. This version includes two different maps for winter and summer crops. Maps were generated using supervised classification methods with samples obtained from on-road surveys and Landsat satellite images along the growing season. Files provided include a Geotiff version of each map with a resolution of 30 m. The report includes the methodological details, map legend and accuracy assessments (in Spanish). A web visualizer can be accessed through the following link: <a href="https://intalulc.users.earthengine.app/view/mnc21-22">https://intalulc.users.earthengine.app/view/mnc21-22</a></p>
Argentina National Map of Crops 2018/2019
<p>Crop type map covering the main agricultural areas of Argentina for growing season 2018/2019. This version includes a unique map for the complete growing season considering single and double crops. Map was generated using supervised classification methods with samples obtained from on-road surveys and Landsat satellite images along the growing season. Files provided include a Geotiff version with a resolution of 30 m. The report includes the methodological details, map legend and accuracy assessments (in Spanish). A web visualizer can be accessed through the following link: <a href="https://deabelle.users.earthengine.app/view/mncv1r1">https://deabelle.users.earthengine.app/view/mncv1r1</a></p>
Argentina National Map of Crops 2019/2020
<p>Crop type map covering the main agricultural areas of Argentina for growing season 2019/2020. This version includes two different maps for winter and summer crops. Maps were generated using supervised classification methods with samples obtained from on-road surveys and Landsat satellite images along the growing season. Files provided include a Geotiff version of each map with a resolution of 30 m. The report includes the methodological details, map legend and accuracy assessments (in Spanish). A web visualizer can be accessed through the following link: <a href="https://intalulc.users.earthengine.app/view/mnc19-20">https://intalulc.users.earthengine.app/view/mnc19-20 </a></p>
Argentina National Map of Crops 2020/2021
<p>Crop type map covering the main agricultural areas of Argentina for growing season 2020/2021. This version includes two different maps for winter and summer crops. Maps were generated using supervised classification methods with samples obtained from on-road surveys and Landsat satellite images along the growing season. Files provided include a Geotiff version of each map with a resolution of 30 m. The report includes the methodological details, map legend and accuracy assessments (in Spanish). A web visualizer can be accessed through the following link: <a href="https://intalulc.users.earthengine.app/view/mnc20-21">https://intalulc.users.earthengine.app/view/mnc20-21</a></p>
Young forests and fire: Using lidar-imagery fusion to explore fuels and burn severity in a subalpine forest reburn, Grand Teton National Park, Wyoming.
Anticipating fire behavior as climate change and fire activity accelerate is an increasingly pressing management challenge in fire-prone landscapes. In subalpine forests adapted to infrequent, stand-replacing fire, self-limitation of burn severity in short-interval fire is incompletely understood. Spatially explicit fuels data can support assessments of landscape-scale fire risk and fuels feedbacks on burn severity. For a about 1,450 km2 largely forested landscape in the US Northern Rocky Mountains, we used airborne lidar and imagery to predict and map canopy and surface fuels. In a fire that burned mature ( greater than 125-year-old) and also reburned young (~30-year-old) subalpine forest, we then asked: (1) How do pre-fire fuels and burn severity compare between young and mature forests that burned under similar fire weather conditions? (2) How well do pre-fire fuels and forest structure predict burn severity under extreme versus moderate fire weather? Lidar-imagery fusion predicted fuel characteristics with high accuracy across forest and shrubland vegetation (R2 from 0.65-0.95). Young post-fire forests had abundant, densely packed canopy fuels, and both young and mature forests had similar canopy fuel loads and coarse wood biomass. Under similar weather conditions, young and mature forests burned at similar severity. Overall, fuels were weak predictors of burn severity and, surprisingly, better predicted severity under extreme (R2LMM(m) = 0.27) rather than moderate (R2LMM(m) = 0.15) fire weather. Our findings are relevant for subalpine landscapes increasingly dominated by young lodgepole pine (Pinus contorta var. latifolia) forests vulnerable to short-interval fire and provide a benchmark to assess how fuels influence burn severity in future fires. Fire managers should continually reassess fuels and update expectations about fire behavior as landscapes change. Although recovering post-fire forests can limit fire spread and severity for a period of time, our resu
Virgin Islands National Park: Coral Reef: Population Dynamics: Landscape-scale Variation in Scleractinian Corals
This study provides a landscape-scale context to a decadal-scale analysis of community structure on shallow reefs along 4 km of the south shore of St. John, US Virgin Islands. By focusing on 12-14 sites along ~100 km of the shores of St. John and St. Thomas, surveys conducted in 2011 were used to contrast: (1) a local-scale with a landscape-scale analysis on two islands, (2) reefs around St. John and St. Thomas, and (3) reefs on north and south shores. Reefs were censused using photoquadrats that were analyzed for percentage cover first by functional groups (coral, macraolagae and CTB), and then by coral genus. In general, among-site variation for the coarse-resolution analysis eclipsed shore and island effects, but the fine-resolution analysis revealed strong site-specific differences for multiple coral genera that could be the product of priority effects in community succession. Over the next decade these differences probably will create unique community trajectories at each site.
Quantity and composition of POM and MAOM in 156 soil samples collected from 20 National Ecological Observatory Network (NEON) sites in 2019
While it is generally assumed that particulate organic matter (POM) and mineral associated organic matter (MAOM) have distinct biogeochemical characteristics, it remains unresolved where and why POM and MAOM differ in their composition and relationships to total SOM decomposition among heterogenous soils. To address these questions, we analyzed elemental, isotopic, and chemical composition, including diffuse reflectance infrared Fourier transform (DRIFT) spectra, of POM and MAOM in 156 soil samples collected from 20 National Ecological Observatory Network (NEON) sites spanning diverse ecosystems (tundra to tropics) across North America in 2019. We used a classic size separation method for POM (53–2000 µm) and MAOM (< 53 µm) following chemical dispersion.
Climate data for Mojave National Preserve Granite Mountains 2019
Basic climate data derived from a local weather station. Mean and max temp with humidity relevant to avian abundance surveys conducted at that location during those specific time blocks.
Decomposition of Microstegium vimineum litter, plants grew through the Big Oaks National Wildlife Refuge in 2019. Litter used in this experiment naturally senesced in the fall 2019, decomposition data collected through 2020. Plants were infected or not-infected with the foliar fungal pathogen Bipolaris gigantea during the 2019 growing season.
Decomposition of plant litter, facilitated primarily by microbial decomposers, plays a critical role in biogeochemical cycling and ecosystem function. Emerging pathogens have the potential to impact litter decomposition by altering the chemical composition and associated microbial community of host tissue. Here, we compared litter decomposition of the invasive grass Microstegium vimineum collected from sites with Bipolaris leaf spot symptoms and sites with no apparent disease symptoms in a common garden experiment. Our results revealed that leaf tissue from litter from non-infected sites decomposed more rapidly through the spring than litter from infected sites. Differences in fungal composition between infected and non-infected litter at the start of the experiment largely persisted through the summer. Our work demonstrates that pathogen colonization may facilitate the persistence of infected host litter, potentially slowing the return of nutrients to the environmental pool while also promoting the survival and dispersal of primary inoculum the following season.
Limnological data for 17 mountain lakes in Banff and Yoho National Parks (Canadian Rocky Mountains) from 2015 to 2022
From 2015 to 2022, mid-summer vertical profiles of temperature, chlorophyll a fluorescence, turbidity, and fDOM were collected in a set of 17 lakes in Banff and Yoho National Parks, Canada. These lakes are located across montane, sub-alpine and alpine ecoregions and they vary widely in elevation (1300-2423 m a.s.l.), surface area (1.5-116 ha) and maximum depth (2.4-39.2 m). Eight of the lakes receive surface and/or groundwater hydrologic inputs from glaciers within the catchment, and the other nine lakes are not glacially-fed. Vertical profiles were collected in each lake within one or two days of an index sampling date between late July and early August using an Exo2 vertical profiling sonde. Measurements were taken at 1 s intervals as the sonde was lowered slowly through the water column, and then averaged over 0.5 m depth intervals. In addition, attenuation rates were estimated for 305 nm, 320 nm, and 380 nm, and PAR (400-700 nm) as the slopes of log-linear regressions of irradiance vs. depth. Downwelling irradiance measured with a Biospherical Instruments underwater radiometer. Vertical Profile Data are contained in Can_Rocky_Mtn_Lakes_Profiles.csv. Attenuation rates are contained in Can_Rocky_Mtn_Lakes_Kd.csv. Information about study lakes is contained in Can_Rocky_Mtn_Lakes_Site_Information.csv.
COI and 18S metabarcoding data from Hidden Lake (Banff National Park, Canada) over two rotenone applications between 2018 and 2020.
Water samples were taken in Hidden Lake at five different time points around two rotenone applications: (i) five weeks prior to the first rotenone application, on July 12 2018; (ii) approximately three weeks after the first application of rotenone, on 7 September 2018; (iii) approximately 10 months after the first rotenone application, on 10 July 2019; (iv) four weeks following the final treatment of rotenone on the 17 September 2019; and (v) one year after the final rotenone treatment, on 19 August 2020. For each time point there is a pelagic, a littoral and a profundal sample. COI and 18S metabarcoding methods were used to produce community data. The objective of this study was to assess the non-target effect of rotenone application (in summer 2018 and 2019) on aquatic communities (i.e. phytoplankton, fungi, zooplankton and benthic macroinvertebrates).
Brook trout (Salvelinus fontinalis) cyt b qPCR data from Hidden Lake (Banff National Park, Canada) over two rotenone applications between 2018 and 2020.
Water samples were taken in Hidden Lake at five different time points around two rotenone applications: (i) five weeks prior to the first rotenone application, on July 12 2018; (ii) approximately three weeks after the first application of rotenone, on 7 September 2018; (iii) approximately 10 months after the first rotenone application, on 10 July 2019; and (iv) one year after the final rotenone treatment, on 19 August 2020. For each time point, four pelagic and four littoral water samples were taken from Hidden Lake, as well as 8 to 13 water samples from Hidden Creek and Coral Creek for a total of 16 to 21 samples per time point. Quantitative PCR (qPCR) method was used to produce brook trout (Salvelinus fontinalis) cytochrome b copy number for each sample. The objective of this study was use eDNA to assess the efficacy of invasive brook trout removal using rotenone.
Projected climate and canopy change lead to thermophilization and homogenization of forest floor vegetation in a hotspot of plant species richness, Berchtesgaden National Park, Bavaria, Germany
Mountain forests are plant diversity hotspots, but changing climate and increasing forest disturbances will likely lead to far-reaching plant community change. Projecting future change, however, is challenging for forest understory plants, which respond to forest structure and composition as well as climate. Here, we jointly assessed effects of both climate and forest change, including wind and bark beetle disturbances, using the process-based simulation model iLand in a protected landscape in the northern Alps (Berchtesgaden National Park, Germany), asking: (1) How do understory plant communities respond to 21st-century change in a topographically complex mountain landscape, representing a hotspot of plant species richness? (2) How important are climatic changes (i.e., direct climate effects) versus forest structure and composition changes (i.e., indirect climate effects and recovery from past land use) in driving understory responses at landscape scales? Stacked individual species distribution models fit with climate, forest, and soil predictors (248 species currently present in the landscape, derived from 150 field plots stratified by elevation and forest development, overall AUC = 0.86) were driven with projected climate (RCP4.5 and RCP8.5) and modeled forest variables to predict plant community change. Nearly all species persisted in the landscape in 2050, but on average 8% of the species pool was lost by the end of the century. By 2100, landscape mean species richness and understory cover declined (-13% and -8%, respectively), warm-adapted species increasingly dominated plant communities (i.e., thermophilization, +12%), and plot-level turnover was high (62%). Subalpine forests experienced the greatest richness declines (-16%), most thermophilization (+17%), and highest turnover (67%), resulting in plant community homogenization across elevation zones. Climate rather than forest change was the dominant driver of understory responses. The magnitude of unabated 2
Extreme Drought in Grasslands Experiment (EDGE): High frequency measurements from the northern Chihuahuan Desert site, Sevilleta National Wildlife Refuge, NM, USA (2013-2023)
The Extreme Drought in Grasslands Experiment (EDGE) is distributed across six representative grassland ecosystems of the central United States. EDGE serves as an important research platform for understanding the resistance and resilience of these grassland ecosystems to extreme prolonged drought as well as to changes in precipitation seasonality. This data package contains high-frequency environmental sensor measurements from the northern Chihuahuan Desert site, dominated by black grama (Bouteloua eriopoda), located in the Sevilleta National Wildlife Refuge in central New Mexico.
Extreme Drought in Grasslands Experiment (EDGE): High frequency measurements from the southern Great Plains site, Sevilleta National Wildlife Refuge, NM, USA (2013-2023)
The Extreme Drought in Grasslands Experiment (EDGE) is distributed across six representative grassland ecosystems of the central United States. EDGE serves as an important research platform for understanding the resistance and resilience of these grassland ecosystems to extreme prolonged drought as well as to changes in precipitation seasonality. This data package contains high-frequency environmental sensor measurements from the southern Great Plains site, dominated by blue grama (Bouteloua gracilis), located in the Sevilleta National Wildlife Refuge in central New Mexico.
ScienceDex guides
Understand access before you commit
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.