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729 results for “Grazing”
PBG07 Grasshopper species abundances in the Patch-Burn Grazing experiment at Konza Prairie
‘PBG’ datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1(1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis.
PBG08 Grasshopper density survey in the Patch-Burn Grazing experiment at Konza Prairie
PBG datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. This data set focuses on measuring grasshopper density using ring count method (Onsager 1977*) at watersheds C03A, C03B, C03C, C01A, C3SA, C3SB, C3SC, and C01B. Grazing intensity is estimated (Joern 2005) at each site at time of density measurements to model how grasshopper populations respond to grazing.
PBG10 Soil physical and chemical characteristics in the Patch-Burn Grazing experiment at Konza Prairie
PBG datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. This data set focuses on measuring bulk density, soil organic matter, pH, cation exchange capacity, soil cations (Ca++, Mg++, Na+), phosphorous and total Kjeldahl nitrogen of soils at the vegetation transects in C3SA, C3SB, C3SC. C1SB, C3A, C3B, C3C, and C1A.
PBG11 Stream water chemistry for the Shane Creek drainage basin in the Patch-Burn Grazing experiment at Konza Prairie
PBG datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. This data set focuses on measuring Nitrate, ammonium, total N, soluble reactive P, total P, and dissolved organic C in four streams draining watersheds with 1 (N01B), 2 (N02B), 4 (N04D), and 20 (N20B) year target burn frequencies.
KKE01 The Konza-Kruger Experiment: A cross-continental fire and grazing experiment at Konza Prairie
For more than a decade, we have compared responses of mesic (subhumid) savanna grasslands (>500 mm MAP in the tropics and >600 mm MAP outside the tropics) in North America and South Africa to alterations in both fire and grazing regimes. The long-term, comparative experiment that forms the centerpiece of this cross-continental research program is located in tallgrass prairie at the Konza Prairie Biological Station (Kansas, USA) and in knob-thorn marula savanna at the Kruger National Park (Limpopo and Mpumalanga provinces, South Africa). We refer to this study as the Konza-Kruger (K-K) Experiment. At both sites, we have been manipulating grazing by removing all large herbivores (>5 kg) from research plots with permanent exclosures (each with a paired plot that grazers can freely access). These exclosures were established in replicated fire frequency experiments ongoing at each site (treatments range from >25-50 yrs of annual burning, burning every 3-4 yrs, or complete fire exclusion).
ESM01 Fire and grazing modulate the structure and resistance of plant-floral visitor networks in a tallgrass prairie
Data from the study: Welti, E.A.R. and Joern, A. 2017. Fire and Grazing modulate the structure and resistance of plant-floral visitor networks in a tallgrass prairie. Oecologia 186: 447-458. EMS011 dataset contains counts of blooming inflorescences of plant species on 12 Konza watersheds in June-July of 2014; ESM012 dataset contains associations between flower-visiting insects and insect-pollinated flowering plants on 12 Konza watersheds collected in May-July of 2014; ESM013 dataset describes insects belonging to the orders of Coleoptera, Diptera, Lepidoptera and Hymenoptera collected in pantrap transects on 12 Konza watersheds collected in June - July of 2014.
ASS01 Suspended sediments in streams impacted by prescribed buring, grazing and woody vegetation removal at Konza Prairie
To determine effects of rotational burning and riparian vegetation removal on suspended solid concentrations in streams. Two sites are burned with a frequency of 2 (N02B) and 4 (N04D) years and grazed by bison. In 2011, N02B will have woody riparian vegetation removed along the entire stream length. The Shane Creek site (SHAN) is currently ungrazed and burned most years. In 2011 the treatment will be switched to grazing and burning of 1/3 of the watershed every year. The data include before and during-treatment sampling for both experiments.
NSC01 Chemistry and physical characteristics of soils from Konza LTER watersheds with different fire and grazing treatments
Soil chemical and physical characteristics are quantified on selected LTER watersheds adjacent to LTER vegetation sampling plots. Sampling was initiated in 1982, and is repeated every five years. A subset of variables (e.g., pH, Bray extractable P, total C, exchangeable cations) is measured on all sample dates, while additional specific variables (e.g., bulk density, soil texture, CaCO3 content, trace metals, extractable inorganic N) are measured less frequently. Methods for C and N analysis have changed over time. C content of samples from 1982, 1987 and 1997 was derived from Walkley-Black measurements of % soil organic matter (OM) content, using a conversion factor of 1.72 (%C = %OM / 1.72). Soil C content of samples from 1992, 2002 and later were determined by dry combustion and gas chromatography (i.e., Carlo-Erba C/N analyzer). N content of samples prior to 1992 was based on Kjeldahl digeston. N content of samples from 1992 on were determined by dry combustion and gas chromatography (i.e., Carlo-Erba C/N analyzer). Additional details regarding sampling protocols and analytical methods are available in the Konza LTER Methods Manual.
PEB01 Aboveground net primary productivity of tallgrass prairie based on accumulated plant biomass in grazing exclsoures on bison-grazed watersheds
Data set contains estimates of end-of-season standing crop biomass (grams per square meter) of live graminoids, forbs, woody plants, and previous year's dead vegetation in grazing exclosures. Date from exclosures is used to determine long-term effects of bison grazing on aboveground net primary productivity.
PBG01 Plant species composition in the Patch Burning-grazing Experiment at Konza Prairie
‘PBG’ datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1(1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis.
MCR LTER: Coral Reef Resilience: Algae, Coral, and Sediment data from Grazing Intensity Experiment, 2010 - 2012
These data describe the percent cover of benthic space holders (primarily algae), the biomass of algae and sediment, and the number of corals recruiting on 15 cm X 15 cm terra cota tiles experimentally manipulated on the forereef on the north shore of Moorea. The experiment was established to test whether and how different levels of grazing influence benthic community development. Five treatments were initially established to create a gradient in grazing pressure with a sixth treatment established shortly thereafter. Each treatment was replicated ten times using a randomized block design. Each cage initially contained four tiles, and one tile from each cage has been photographed and destructively sampled for biomass at regular intervals. In addition to the original tiles deployed in July 2010, tiles were subsequently deployed in March 2011, August 2011, and March 2012 to test whether community development varies among seasons. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Earth grazing meteor over Northern Europe, September 22, 2020, 03:53UTC
<p>An earth grazing meteor was observed over Northern Europe on September 22, 2020, around 03:53UTC.</p> <p>From Dwingeloo in the Netherlands, two images were obtained.</p> <ul> <li>2020-09-22T03:53:33.445.fits (FITS format, BGGR bayer matrix)</li> <li>2020-09-22T03:53:33.445.png (Debayered image)</li> <li>2020-09-22T03:53:50.135.fits (FITS format, BGGR bayer matrix)</li> <li>2020-09-22T03:53:50.135.png (Debayered image)</li> </ul> <p>The image times refer to the start of the exposure.</p>
CLRD-GLPS: A Long-term Seasonal Dataset of Ruminant Livestock Distribution in China's Grazing Production Systems (2000-2021) Using Stacking-based Interpretable Machine Learning
<p>Advanced computational methods integrating ensemble learning with interpretable machine learning are essential for precision livestock management under increasing environmental constraints and food security pressures. This study develops a novel stacking-based interpretable machine learning (IML) framework that combines multiple algorithms with SHAP analysis techniques to generate the China's Long-term Ruminant Livestock Distribution in Grazing Livestock Production Systems (CLRD-GLPS) dataset. Our computational approach addresses critical challenges in livestock distribution modelling: livestock segmentation and spatial prediction accuracy. The framework integrates Random Forest, XGBoost, CatBoost, LightGBM, and Extra Trees through a two-layer stacking architecture, enhanced with SHAP (Shapley Additive Explanations) analysis for model interpretability. We also implemented interpretable machine learning for livestock production system segmentation to distinguish grazing from total livestock populations. The stacking ensemble demonstrated superior performance over individual algorithms, achieving R² values of 0.954-0.961 for cattle and 0.896-0.901 for sheep and goats, with improvements of up to 8.3% compared to best performance single-model approaches. Multi-scale validation confirmed computational robustness: livestock segmentation achieved R² = 0.80 at county level, while independent city-level validation of CLRD-GLPS datasets yielded R² = 0.76-0.80. SHAP interpretability analysis revealed distinct environmental drivers, with vegetation indices and topography primarily influencing cattle distribution, while snow conditions and elevation dominated sheep and goat patterns. This computational framework advances livestock distribution modelling through enhanced prediction accuracy, model stability, and interpretability, while the CLRD-GLPS dataset provides essential spatial-temporal information for rangeland sustainability assessments and evidence-based livestock management policies. This dataset is supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, grant no. 2019QZKK0906).</p>
Detecting edge effects of geese grazing at the boundary of woodland and grassland
<p>The presence of geese on different areas of lawn was estimated by the length of droppings on the lawn. Geese defecate frequently and seemingly indiscriminately. Counting dropping is a well-known method for estimating their density on areas of land (Owen, 1971). However, we found it difficult to distinguish individual defecation events as the dropping tend to break apart as they are released. Therefore, we measured the total length of dropping in an area. Geese dropping are more or less cylindrical and we consider a measure related to the volume of droppings is more reliable than a count of their number.</p> <p>Observations were conducted in July 2014 and March and April 2015 at Meise Botanic Garden, Meise, Belgium. Rectangular plots were laid out perpendicular to a woodland-lawn boundary on sections of a Botanic Garden frequently used by geese. These plots are detailed in file DroppingsPlots.csv. The sites for these plots were chosen because they were well separated from each other; were away from other trees and faced different directions. The plots were marked out using bamboo canes and a tape measure. Then either 20 or 30 randomly chosen 1 m<sup>2</sup> square quadrats were surveyed within the rectangular plot. The cumulative length of dropping in a quadrat was measured to the nearest centimeter with a ruler.</p> <p>The results are found in file DroppingsMeasurements.csv.</p> <p>The columns of this file are as follows</p> <p>Plot - The identifying number given to the plot</p> <p>X - The distance parallel to the woodland-lawn boundary</p> <p>Y - The distance from the woodland-lawn boundary</p> <p>Length - The total length in centimeters of the dropping found in a 1m<sup>2</sup> quadrat</p> <p>Prunella - coverage of <em>Prunella vulgaris</em> L. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Renoncule - coverage of <em>Ranunculus</em> sp. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Bellis - coverage of <em>Bellis perennis</em> L. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Lotus - coverage of <em>Lotus</em> sp. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Glechoma - coverage of <em>Glechoma hederacea</em> L. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Four species of geese are present in the Botanic Garden and may have contributed droppings to the observations. These species are <em>Alopochen aegyptiaca</em> (L. 1766) (Egyptian geese), <em>Branta canadensis</em> (L. 1758) (Canada geese), <em>Anser anser</em> (L. 1758) (greylag geese) and <em>Branta leucopsis</em> (Bechstein, 1803) (barnacle geese).</p>
Soil microarthropods, ground-dwelling arthropods and soil properties in mown and grazed grasslands in the Veluwe region
<p>In order to find out which factors limit the restoration of soil life and their ecosystem services under grasslands on sandy soils, we studied 40 grasslands of which 20 had agricultural and 20 nature land use, all after an agricultural history.</p> <p> </p> <p><strong>Site selection</strong></p> <p>Within the Veluwe region (The Netherlands), we selected 40 grasslands: 20 agricultural grasslands and 20 nature grasslands which were managed as new nature reserves since last tillage. Within each of these two land-use types, two types of grassland management were selected: mowing and grazing. Within each of the four combinations of land use and management we selected ten grasslands over a broad age range since last tillage. All grasslands were located on sandy soils (Typic Haploquod and Plaggeptic Haploquod; Soil Survey Staff 1999) with a deep water table to rule out dispersal of soil fauna during waterlogging (Siepel 1996; Jabbour & Barbercheck 2008).</p> <p> </p> <p><strong>Vegetation and insect surveys</strong></p> <p>Within each grassland a 5×5 meter monitoring plot was laid-out for plant cover surveys, insect and soil-microarthropod sampling and soil analyses. The vegetation surveys were carried out in 2019 at the end of May and in early June, using the Braun-Blanquet method (Braun-Blanquet 1932). In June 2019 soil-surface dwelling insects were sampled with a pitfall trap (Wiggers et al. 2015). Three pitfall traps (8 cm diameter, ca. 20 cm deep) were placed in each plot. Traps were half filled with a solution of water and glycol (3:1) and 3 % Extran soap. A plexiglass cover 20 cm above the trap prevented rainfall diluting the liquid. Traps were removed and emptied after seven days. Insects were identified and grouped at the order level, however, predator groups (carabid and staphylinid beetles, ants and spiders) were identified to the species level in order to group those by their feeding guild.</p> <p> Before analyzing the pitfall trap catches we first removed certain groups from the counts because pitfall traps are not well-suited to catch them systematically: Acari, Collembola, Psocoptera, Thysanoptera, Trichoptera, Lepidoptera, Siphonaptera, Diptera, Symphyta, Apocrita, and Parasitica. The remaining 62.0% of the caught individuals were surface-dwelling animals, and their totals (of three pitfall traps per site) were analyzed with negative-binomial generalized linear models. We also analyzed the subset of predators (73.6% of the surface dwellers).</p> <p> </p> <p><strong>Soil chemical and pesticide sampling and analysis</strong></p> <p>On 8, 9 and 16 October 2019, a bulk soil sample of 50 soil cores (0 - 10 cm) was collected from each 5×5 meter monitoring plot. After homogenization a sub-sample was analyzed for soil chemical analysis. Prior to chemical analysis, samples were oven-dried at 40 °C. Soil acidity of the oven-dried samples was measured in 1 M KCl (pH-KCl). Soil Organic Matter (SOM) was determined by loss-on-ignition (Ball 1964). Ammonium-lactate-extractable P (PAL) was determined according to the standard method (Bronswijk et al. 2003). Total potassium (K) in solution was determined using flame photometry after extraction of soil with HCl (0.1 M) and oxalic acid (0.5 M) in a 1:10 M:V ratio and filtration (Bronswijk et al. 2003). Clay (<2 μm diameter) content was determined through density fractionation (NEN 5753, 2018). Another soil sub-sample was sent to Eurofins Zeeuws-Vlaanderen for pesticide/residue analysis. Samples were freeze-dried and homogenized prior to analysis. Homogenized samples were extracted with acetone, petroleum ether and dichloro-methane using an optimized mini-Luke method. In total 664 pesticides and pesticide residues were analyzed with gas chromatography (Agilent) and liquid chromatography (LC-chromatograph (Agilent) and MSMS (Sciex)). Glyphosate, its residue AMPA and gluphosinate were analyzed using single residue analysis. The detection limit (LOD) was 0,1 mg per kg sample.</p> <p> </p> <p><strong>Soil microarthropods sampling and determination</strong></p> <p>Grasslands were sampled for microarthropods on 8, 9 and 16 October 2019, taking three cores per monitoring plot of 5×5 m. Cores were 5 cm Ø and 5 cm deep mineral soil plus upper litter. Cores were taken in the middle of the monitoring plots, 1 m apart from each other. Cores were extracted on a Tullgren funnel for 7 days. During that period temperature was increased from 35 to 45 <sup>0</sup>C. Ethanol 70% was used as conservation fluid and microarthropods obtained were put into lactic acid 30% for clarification and identification (Siepel & van de Bund 1988). Identification for the main groups is according to Weigmann (2006) for Oribatida, Karg (1993) for Gamasina and Karg (1989) for Uropodina. Nomenclature is according to Siepel et al. (2009) (Oribatida), Siepel et al. (2016) (Astigmatina) and Siepel et al. (2018) (Mesostigmata).</p> <p> </p> <p><strong>Litter decomposition</strong></p> <p>To determine the potential decomposition of soil organic matter on each grassland the Tea Bag Index (TBI) was used (Keuskamp et al. 2013). In each grassland four green tea and four rooibos tea bags were buried at 8 cm deep in May 2019 in the 5×5 meter monitoring plots. After 90 days tea bags were collected and stored at 4 ⁰C prior to drying at 70 ⁰C for 48 hours. After drying, remaining sand and (fine) plant roots were carefully removed and the teabags were weighted to determine weight loss. The decomposition rate (<em>k</em>) and the litter stabilization factor (<em>S</em>) of the tea was calculated using the Tea Bag Index (Keuskamp et al. 2013).</p> <p> </p> <p><strong>Data files</strong></p> <p><em><strong>siteData.csv</strong></em></p> <p>site: grassland ID</p> <p>landuse: agricultural or nature land use</p> <p>treat: mowing or grazing management</p> <p>yearsManaged: number of years since last tillage</p> <p>fertilization: kg available nitrogen applied per hectare</p> <p>nGrazingDaysPerHa: livestock days per hectare per year</p> <p>N: mg nitrogen per 100 g </p> <p>PAl: mg P<sub>2</sub>0<sub>5</sub> per 100 g</p> <p>organicMatter: soil organic matter percentage</p> <p>clay: soil clay percentage</p> <p>nPlantSpecies: number of plant species</p> <p>nForbSpecies: number of forb species</p> <p>nMitesSpringtails: total number of individuals of mites and springtails in three core samples</p> <p>nMitesSpringtailsSpecies: number of mite and springtail species in three core samples</p> <p>shannonMitesSpringtails: Shannon diversity index for microarthropods (mites and springtails)</p> <p>nHerboFungivorousGrazerMitesSpringtails: total number of individuals of mites and springtails that are (herbo-)fungivorous grazers, in three core samples</p> <p>nInsectsSpidersPitfall: number of ground-dwelling insect and spider individuals in pitfall traps</p> <p>nPredatorInsectsSpidersPitfall: number of ground-dwelling insect and spider individuals that are predators, in pitfall traps</p> <p>decompositionRate: decomposition rate based on the Tea Bag Index</p> <p>litterStabilisationFactor: litter stabilization factor based on the Tea Bag Index</p> <p>nPesticides: number of detected pesticides</p> <p>avicidesTotalConcentration: microgram antraquinon per kg dry soil</p> <p>fungicidesTotalConcentration: total microgram of fungicides per kg dry soil</p> <p>insecticidesTotalConcentration: total microgram of insecticides per kg dry soil</p> <p>herbicidesTotalConcentration: total microgram of herbicides per kg dry soil</p> <p>pesticidesTotalConcentration: total microgram of pesticides (avicides+fungicides+herbicides+insecticides) per kg dry soil</p> <p>nPredatorCarabids: number of predator carabid beetles in pitfall traps</p> <p>nPredatorStaphylinids: number of predator staphylinid beetles in pitfall traps</p> <p>distanceToNearestHighway: shortest distance (in meters) to the nearest highway (A-road)</p> <p>distanceToNearestNroad: shortest distance (in meters) to the nearest national road (N-road)</p> <p> </p> <p><em><strong>mitesSpringtails.csv</strong></em></p> <p>core: core ID, consisting of the site ID (number) and core-within-site ID (letter)</p> <p>species: soil mite or springtail taxon encountered in a soil core</p> <p>guild: feeding guild of the soil mite or springtail taxon:</p> <p> b: bacterivorous</p> <p> fb: fungivorous browser</p> <p> fg: fungivorous grazer</p> <p> gp: general predator</p> <p> hb: herbivorous browser</p> <p> hfg: (herbo-)fungivorous grazer</p> <p> hg: herbivorous grazer</p> <p> o: omnivore</p> <p> ohf: opportunistic herbo-fungivore</p> <p>droughtSens: drought strategy of soil mite and springtail taxa</p> <p> 1: drought avoiders</p> <p> 2: drought sensitive</p> <p> 3: drought mesotolerant</p> <p> 4: drought tolerant</p> <p>microart: number of individuals of a taxon found in a soil core</p> <p> </p> <p><em><strong>insecticideData.csv</strong></em></p> <p><em><strong>fungicideData.csv</strong></em></p> <p><em><strong>herbicideData.csv</strong></em></p> <p>site: grassland ID</p> <p>other variables: microgram of a certain pesticide per kg dry soil</p> <p> </p>
Data from: Vegetation change in acidic dry grasslands in Moravia (Czech Republic) over three decades: slow decrease in habitat quality after grazing cessation
<p>This dataset contains the original data used in the article:</p> <p>Harásek M., Klinkovská K. & Chytrý M. (2023) Vegetation change in acidic dry grasslands in Moravia (Czech Republic) over three decades: slow decrease in habitat quality after grazing cessation. <em>Applied Vegetation Science</em>, 26, e12726. https://doi.org/10.1111/avsc.12726</p> <p>The data contain plant species composition data from resurveyed vegetation plots in southwestern and central Moravia (Czech Republic). The plots were first surveyed by Milan Chytrý in 1986–1991 (“old plots”) and resurveyed by Martin Harásek, under the supervision of Milan Chytrý, in 2018–2019 (“new plots”).</p> <p>Of the old plots, 86 were sampled between 26 June and 16 September and 8 in May. Their size ranged from 5 to 49 m<sup>2</sup> (mean 33 m<sup>2</sup>). These plots were subjectively selected at different sites to document maximum variation in species composition and environmental conditions of the grasslands and heathlands studied. In each plot, all vascular plant species were recorded, and their covers were estimated using the nine-grade Braun-Blanquet scale (van der Maarel 1979). Plot locations were recorded in the form of text descriptions. Geographic coordinates of approximate location were added for each plot prior to the resurvey by the original surveyor using georeferenced aerial photographs and various information recorded in the field during the first survey, including slope, aspect and elevation. Location uncertainty (mean = 139 m) was indicated as the possible distance of the actual location from the given coordinates.</p> <p>The resurvey was conducted between 4 June and 16 August. Care was taken to select the most likely location of the original plot based on the coordinates of the approximate location, the original site description, and the occurrence of the species recorded during the first survey. New plots always had the same plot size as in the original sampling. Each old plot was resurveyed using 1–3 new plots depending on the uncertainty of the location of the old plot. A total of 94 old plots were resurveyed at 47 sites with 153 new plots. Of these, 71 old plots at 32 sites were in current protected areas, while 23 old plots at 15 sites were outside protected areas. All new plots were located using GPS with a location uncertainty of approximately 5 m.</p> <p>For each old plot resurveyed with more than one new plot, the most similar new plot (based on Bray-Curtis dissimilarity in species composition) was selected, resulting in a dataset of 94 old and 94 new plots (“best-fit dataset”). To test the robustness of the results, we created another dataset (“validation dataset”) that included the least similar of the corresponding new plots for each old plot. This dataset also included the 94 old and 94 new plots. If the old plot was resurveyed using a single new plot, that new plot was included in both the best-fit and validation datasets.</p> <p>The header data structure follows that of the ReSurveyEurope Database (<a href="http://euroveg.org/eva-database-re-survey-europe">http://euroveg.org/eva-database-re-survey-europe</a>). In addition, fields are added to indicate whether the new plot was used in the best-fit dataset (Best_fit) or the validation dataset (Validation). The information about location within or outside the protected area is given in the field Protection.</p> <p>The data on species composition and environmental variables are provided in two formats:</p> <ul> <li>Turboveg 2 database (see <a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) – file <strong>TurbovegDbBackup_SW_moravia_acidgrass.zip</strong>. For using this dataset in Turboveg, the database dictionary (TurbovegDdBackup_Default dictionary.zip) and the species list (TurbovegSlBackup_Czechia_slovakia_2015.zip) must be installed.</li> <li>Three TXT files with columns separated by tabs: <ul> <li><strong>SW_moravia_acidgrass_species.txt</strong> contains the percentage covers of plant species in the plots, which are mid-values for cover-abundance categories of the Braun-Blanquet scale. Plant nomenclature was harmonised according to Danihelka et al. (2012).</li> <li><strong>SW_moravia_acidgrass _head.txt</strong> contains information on the number of species in each plot (number_species), the number, proportion and relative cover of threatened species (IUCN categories CR, EN, VU, NT, columns CR_NT_number, CR_NT_perc_number and CR_NT_perc_cover), alien species (alien_number, alien_perc_number, alien_perc_cover), species characteristic of dry grasslands (TH_number, TH_perc_number, TH_perc_cover), sand and rock-outcrop grasslands (TF_number, TF_perc_number, TF_perc_cover), mesotrophic grasslands (TD_number, TD_perc_number, TD_perc_cover) and herbaceous ruderal vegetation (XA_XC_number, XA_XC _perc_number, XA_XC _perc_cover) and unweighted means of Ellenberg-type indicator values for light (light), temperature (temperature), moisture (moisture), soil reaction (reaction) nutrients (nutrients) and salinity (salinity) used to test changes in these variables through time.</li> <li><strong>SW_moravia_life_forms.txt </strong>contains information about the assignment of individual species to the life form, which was used to analyse changes in frequency and cover of the life forms.</li> </ul> </li> </ul> <p>These data are also stored in the Czech National Phytosociological Database (Chytrý & Rafajová 2003; <a href="https://botzool.cz/vegsci/phytosociologicalDb">https://botzool.cz/vegsci/phytosociologicalDb</a>) and the ReSurveyEurope database (Knollová et al. 2023; <a href="http://euroveg.org/eva-database-re-survey-europe">http://euroveg.org/eva-database-re-survey-europe</a>).</p>
Drone raw images of cattle in french grazing areas
<p><strong>The updated data are part of the Horizon Europe project ICAERUS</strong> regarding the livestock monitoring use case in a task where the objective is to test, optimize and scale up models regarding animal counting (cattle and sheep). More information here : <a href="https://icaerus.eu/">https://icaerus.eu/</a></p> <p>The dataset encompasses around <strong>900 raw .jpeg drone images </strong><strong>of grazing areas where cattle graze</strong> collected between June and August 2023. Drone used were Mavic 3 Enterprise and Thermal. Data collection is underway, with the aim of collecting images throughout the year and on several farms, to capture variability in animal and background colors and brightness conditions. Image tags (“cattle” vs “no cattle”) are not available for the moment but will be in the next months in next versions of this dataset. There is a strong imbalance between images with “cattle” and image with “no cattle” representative of areas to monitor. </p> <p><br> The nadir images were collected during flight planned with DJI Pilot 2 at a constant altitude regarding the take-off position (30 m, 60 m, 100 m). </p> <p>The data are organized by a first directory by farm where the images were collected and then with one directory by flight planned. <br> A summary is available in the Table_summary.xls. Name of the directory of each planned flight is defined such as DJI_YYYYMMDDHHMM_XX with the date (YYYYMMDD), the hour in UTC+2 (HHMM), and XX representing a mission number. </p> <p><br> .exif data of each images provide many information regarding the drone (GPS position, absolute and relative altitude, gimble information, speed etc.). Further details will be added in the next versions of the dataset.</p> <p><strong>The authors of the dataset are opened to any collaboration regarding animal counting models.</strong></p> <p><br> For more information, please contact: adrien.lebreton@idele.fr </p>
Long-term response of wetland plant communities to management intensity, grazing abandonment, and prescribed fire
Isolated, seasonal wetlands within agricultural landscapes are important ecosystems. However, they are currently experiencing direct and indirect effects of agricultural management surrounding them. Because wetlands provide important ecosystem services, it is crucial to determine how these factors affect ecological communities. Here, we studied the long-term effects of land use intensification, cattle grazing, prescribed fires, and their interactions on wetland plant diversity, community dynamics, and functional diversity. To do this, we used vegetation and trait data from a 14-year-old experiment on 40 seasonal wetlands located within semi-natural and intensively managed pastures in Florida. These wetlands were allocated different grazing and prescribed fire treatments (grazed vs. ungrazed; burned vs. unburned). Our results showed that wetlands within intensively managed pastures have lower native plant diversity, floristic quality, evenness, higher non-native species diversity, and exhibited the most resource-acquisitive traits. Wetlands embedded in intensively managed pastures were also characterized by lower species turnover over time. We found that 14 years of cattle exclusion reduced species diversity in both pasture management intensities and had no effect on floristic quality. Fenced wetlands exhibited lower functional diversity and experienced a higher rate of community change both due to an increase in tall, clonal, and palatable grasses. The effects of prescribed fires were often dependent on grazing treatment. For instance, prescribed fires increased functional diversity in fenced wetlands but not in grazed wetlands. Our study suggests that cattle exclusion and prescribed fires are not enough to restore wetlands in intensively managed pastures and further highlights the importance of not converting semi-natural pastures to intensively managed pastures. Our study also suggests that grazing levels applied in semi-natural pastures maintained high plant dive
Phenotypic trait variation of Herminium monorchis in the Qinghai-Tibetan Plateau with grazing intensity and climatic conditions
This data set contains raw data supporting the research entitled “Livestock grazing outweighs climate in driving trait variation of a widespread alpine plant” (currently under peer review), which documents how phenotypic traits of a widespread herbaceous plant in the Qinghai-Tibetan Plateau, Herminium monorchis, vary with grazing intensity and environmental conditions.
Gut Fluorescence measurements of mesozooplankton grazing on autotrophic prey. Samples collected in the CCE-LTER region on Process Cruises from 2006 to the present. Summaries for each Lagrangian Cycle.
Mesozooplankton are collected with plankton nets (typically a 71-cm diameter, 202-um mesh Bongo net) and samples flash frozen at sea in liquid N2 for subsequent shore-based measurements of ingested phytoplankton chlorophyll-a. Measurements of mesozooplankton gut fluorescence are done by fluorometric analysis on a Turner Designs fluorometer of gut pigments extracted in 90% acetone. Analyses are done on mesozooplankton size-fractionated into 5 different categories on Nitex mesh (> 0.2 mm, 0.5 mm, 1.0 mm, 2.0 mm, 5.0 mm). The pigment content (as Chl-a and phaeopigments) is then expressed as mass of pigment ingested per m3 of water filtered, or divided by the dry weight biomass of the mesozooplankton in the same sample in order to obtain mass-specific ingestion per m3 of water. Application of published values of the temperature-dependent gut passage time are used to estimate the mesozooplankton grazing rate, as pigments ingested per m3 per unit time, or the corresponding mass-specific rate of ingestion. Samples for gut fluorescence assays have been collected on CCE-LTER Process Cruises since 2006 and these collections are ongoing.
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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.