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59 results for “biomass use”
Plasma reforming of biomass gasification tars using mixed naphthalene and toluene as model compounds
<p>Dataset for the paper; DOI: 10.1016/j.enconman.2019.05.002</p>
Environmental stoichiometry mediates phytoplankton diversity effects on communities' resource use efficiency and biomass
<p>Positive biodiversity-ecosystem functioning (BEF) relationships are predicted to increase in strength when high environmental variability allows for complementarity between resource use strategies in diverse communities. This environmental variability can be represented by spatial or temporal variation in nutrient ratios, but resource use efficiency (RUE) and therefore biomass build up of primary producers might be restricted when nutrient ratios are highly imbalanced (i.e., limitation by one nutrient and beyond optimal ratios for growth). Whereas the linkages between ecosystem functioning, diversity and nutrient availability are theoretically well understood, we lack experimental evidence on how phytoplankton diversity affects resource use and biomass under variable nutrient ratios (N:P ratios).</p> <p>Combining a mesocosm and a microcosm experiment we tested diversity effects on ecosystem functioning by exposing a species diversity gradient generated by the loss of rare species in a natural community to different N:P ratios (uniform vs a gradient). The N:P supply ratio gradient also allowed us to evaluate responses across balanced and imbalanced ratios.</p> <p>We found that increased species diversity led to increased community RUE when supplied a gradient of N:P ratios; but restricted to the highest diversity level. However, diversity did not affect RUE under uniform nutrient ratios. The overall phytoplankton biomass and carbon:nutrient ratios responses to diversity reflected the patterns detected for RUE. Contrary to theoretical predictions, RUE was maintained under highest N:P supply ratios (extreme phosphorous limitation) suggesting that imbalanced N:P ratios do not necessarily decrease function. Thus, we showed that the nutrient context influences diversity effects on RUE and biomass.</p> <p><i>Synthesis</i>. Overall, our results suggest that the effect of rare phytoplankton species losses on community RUE and biomass can be compensated by the persistent species when nutrient ratios are uniform, but leads to decreases in ecosystem functioning under variable nutrient ratios. This work provides a first attempt for testing interactions between the nutrient context (including concentrations and ratios) and the diversity of (natural) communities experimentally, which is conceptually understood but poorly tested for phytoplankton.</p>
Measuring plant biomass remotely using drones in arid landscapes
<p>Measurement of variation in plant biomass is essential for answering many ecological and evolutionary questions. Quantitative estimates require plant destruction for laboratory analyses, while field studies use allometric approaches based on simple measurement of plant dimensions. We estimated the biomass of individual shrub-sized plants, using a low cost Unmanned Aerial System (drone), enabling rapid data collection and non-destructive sampling. We compared volume measurement (a surrogate for biomass) and sampling time, from the simple dimension measurements and drone, to accurate laboratory-derived biomass weights. We focused on three Australian plant species which are ecologically important to their floodplain and terrestrial ecosystems: porcupine grass <em>Triodia scariosa, </em>Queensland bluebush<em> Chenopodium auricomum</em> and lignum <em>Duma florulenta</em>.</p> <p>Estimated volume from the drone was more accurate than simple dimension measurements for porcupine grass and Queensland bluebush, compared to estimates from laboratory analyses but, not for lignum. The latter had a sparse canopy, with thin branches, few vestigial leaves and a similar colour to the ground. Data collection and analysis consistently required more time for the drone method than the simple dimension measurements, but this would improve with automation. </p>
Data Shared: Human land-uses homogenize stream assemblages and reduce animal biomass production
<p>This is a dataset compiled from 117 sampling stream sites distributed across two neotropical biomes (rainforest and grassland). Dataset included single scaled vales of human land-use (agriculture, pasture, urbanization, and afforestation), scaled values of multifaceted biodiversity of fish, arthropods, and macrophytes (taxonomic richness, functional diversity[FDis], CWV and CWM of trait categories [recruitment and life-history, resource and habitat-use, and body size]), scaled values of local environmental predictors (stream site depth, water quality deterioration index, and sediment heterogeneity index), scaled values of climate predictors (temperature and precipitation), and log of animal biomass (fish and arthropod). </p>
Online Resources Chapter 3 - Decomposition of standing litter biomass in newly constructed wetlands associated with direct effects of sediment and water characteristics and the composition and activity of the decomposer community using Phragmites australis as a single standard substrate
<p>Online Resources to Chapter 3 "Decomposition of standing litter biomass in newly constructed wetlands associated with direct effects of sediment and water characteristics and the composition and activity of the decomposer community using Phragmites australis as a single standard substrate" of PhD thesis from Ciska Overbeek, "Peat formation on a former landfill - Production and decomposition of aquatic pioneer vegetation". </p> <p>Published by Overbeek et al in 2019 in Wetlands 39(1): 113-125. https://doi.org/10.1007/s13157-018-1081-y. </p>
Data from dissertation: 'Landscape and Aboveground Biomass Dynamics of Brazilian Savanna using airborne LIDAR and MapBiomas datasets : case study of Rio Vermelho Watershed, Brazil'
<p>This dissertation was submitted to University of Manchester as part of MSc GIS program</p> <p>This repository contains:</p> <p>1) Contains the R language code used in the dissertation (CHM_&_LiDAR_metric.R; Landscape_metric.R; Generalized_Linear_Model.R; Random_Forest_Model.R).</p> <p><br> 2) Canopy Height Model (CHM) and 56 LiDAR metric raster files with a resolution of 1m (CHM_&_LiDAR_metric_2014.zip; CHM_&_LiDAR_metric_2018.zip), the original LiDAR data come from Brazil project supported by the Brazilian Agricultural Research Corporation (EMBRAPA), the US Forest Service, USAID, and the US Department of State.</p> <p><br> 3) AGB raster files with a resolution of 10m (AGB_2014.tif; AGB_2018.tif; AGB_dynamic.tif), field plots used for AGB estimation come from Sabrinado Couto de Miranda from University of Goiás State (UEG), Brazil, and her team.</p> <p><br> 4) Landscape metric interpolation raster file (SHDI.tif; SHEI.tif; AREA_CV.tif; CIRCLE_MN.tif; SHAPE_MN.tif) with a resolution of 10m, land cover map Map come from Biomas team for landscape metric calculation.</p>
Relationship of insect biomass and richness with land use along a climate gradient
<p>This dataset contains data from a field study conducted in 2019 and described in the paper "Relationship of insect biomass and richness with land use along a climate gradient" by Uhler et al.</p> <p>In this study, arthropod communities were collected by Malaisetraps along a land use and climate gradient and identified by Metabarcoding. Malaise traps are versatile passive net-traps, capable of catching a large spectrum of insect taxa and are very effective when the aim is to get a representative snapshot of the local insect community. Overall, 179 Malaise traps were set up in 2019 along a gradient of increasing land use intensity, ranging from forests, to meadows, to arable fields, and settlements. The analyzed sites were spatially distributed over 400km and covered an elevational gradient of 1000m.</p> <p>Generalized additive models were fitted to test for the effects of local and landscape-level land-use categories on insect biomass and species richness.</p> <p>We found the largest difference in biomass between semi-natural and urban environments (−42%), whereas differences in total richness (−29%) and the richness of threatened species (−56%) were largest from semi-natural to agricultural environments. These results point to urbanization and agriculture as major drivers of decline. We also found that richness and biomass increase monotonously with increasing temperature, independent of habitat. The contrasting patterns of insect biomass and richness question the use of these indicators as mutual surrogates. Our study provides support for the implementation of more comprehensive measures aimed at habitat restoration in order to halt insect declines.</p>
Data for: Using a demographic model to project the long-term effects of fire management on tree biomass in Australian savannas
<p>Tropical savannas are characterised by high primary productivity and high fire frequency, such that much of the carbon captured by vegetation is rapidly returned to the atmosphere. Hence, there have been suggestions that management-driven reductions in savanna fire frequency and/or severity could significantly reduce greenhouse gas emissions and sequester carbon in tree biomass. However, a key knowledge gap is the extent to which savanna tree biomass will respond to modest shifts in fire regimes due to plausible, large-scale management interventions. Here, we: (1) characterise relationships between the frequency and severity of fires and key demographic rates of savanna trees, based on long-term observations in vegetation monitoring plots across northern Australia; (2) use these relationships to develop a process-explicit demographic model describing the effects of fire on savanna tree populations; and (3) use the demographic model to address the question: to what extent is it feasible, through the strategic application of prescribed burning, to increase tree biomass in Australian tropical savannas? Our long-term tree monitoring dataset included observations of 12,344 tagged trees in 236 plots, monitored for between 3 and 24 years. Analysis of this dataset showed that frequent high-severity fires significantly reduced savanna tree recruitment, survival and growth. Our demographic model suggested that: (1) despite the negative effects of frequent high-severity fires on demographic rates, savanna tree biomass appears to be suppressed by only a relatively small amount by contemporary fire regimes, characterised by a mix of low- to high-severity fires; and (2) plausible, management-driven reductions in the frequency of high-severity fires are likely to lead to increases in tree biomass of about 11.0 t DM ha<sup>–1</sup> (95% confidence interval: -1.2–20.8) over a century. Accounting for this increase in carbon storage could generate significant carbon credits, worth on average three times those generated annually by current greenhouse gas (methane and nitrous oxide) abatement projects, and has the potential to significantly increase the economic viability of fire/carbon projects, thereby promoting ecologically sustainable management of tropical savannas in Australia and elsewhere. This growing industry has the potential to bring much-needed economic activity to savanna landscapes, without compromising important natural and cultural values.</p>
Exploring Soil Exchangeable Cations and Auditing the Potential of Phoenix dactylifera and Mangifera indica in CO2 Sequestration into Soil Biomass in a Naturally Occurring Tree Patches Using Infrared Gas Analyzer
<p><em>Soil Exchangeable Cations (EC) were audited, and the potential of economic trees in atmospheric CO2 sequestration into soil biomass was investigated. . The experiment indicated that economic trees are perfect for CO2 sequestration. </em></p>
Plume detection and estimate emissions for biomass burning plumes from TROPOMI Carbon monoxide observations using APE v1.1
<p>This data is based on the paper: Plume detection and estimate emissions for biomass burning plumes from TROPOMI Carbon monoxide observations using APE 1.1 (unpublished).</p>
Long-term warming of a forest soil reduces microbial biomass and its carbon and nitrogen use efficiencies
<p>Global warming impacts biogeochemical cycles in terrestrial ecosystems, but it is still unclear how the simultaneous cycling of carbon (C) and nitrogen (N) in soils could be affected in the longer-term. Here, we evaluated how 14 years of soil warming (+4°C) affected the soil C and N cycle across different soil depths and seasons in a temperate mountain forest. We used H<sub>2</sub><sup>18</sup>O incorporation into DNA and <sup>15</sup>N isotope pool dilution techniques to determine gross rates of C and N transformation processes. Our data showed different warming effects on soil C and N cycling, and these were consistent across soil depths and seasons. Warming decreased microbial biomass C (−22%), but at the same time increased microbial biomass-specific growth (+25%) and respiration (+39%), the potential activity of β-glucosidase (+31%), and microbial turnover (+14%). Warming reduced gross rates of protein depolymerization (−19%), but stimulated gross N mineralization (+63%) and the potential activities of N-acetylglucosaminidase (+106%) and leucine-aminopeptidase (+46%), and had no impact on gross nitrification (+1%). Microbial C and N use efficiencies were both lower in the warming treatment (−15% and −17%, respectively). Overall, our results suggest that long-term warming drives soil microbes to incorporate less C and N into their biomass (and necromass), and to release more inorganic C and N to the environment, causing lower soil C and N storage in this forest, as indicated by lower soil C and total N contents. The decreases in microbial CUE and NUE were likely triggered by increasing microbial P constraints in warmed soils, limiting anabolic processes and microbial growth and promoting pervasive losses of C and N from the soil.</p>
Impacts of Pleistocene extinctions on the biomass and energy use of local mammal assemblages around the world
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Long-term warming of a forest soil reduces microbial biomass and its carbon and nitrogen use efficiencies
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Data for: Using a demographic model to project the long-term effects of fire management on tree biomass in Australian savannas
Open the record for dataset details and reuse information.
Environmental stoichiometry mediates phytoplankton diversity effects on communities’ resource use efficiency and biomass
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Measuring plant biomass remotely using drones in arid landscapes
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Relationship of insect biomass and richness with land use along a climate gradient
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Energy consumption and greenhouse gas emissions data of activated carbon production using different biomass
<p>This dataset includes the energy consumption and Greenhouse Gas emissions data of activated carbon production using 73 different types of woody biomass.</p> <p>Understanding the environmental implications of activated carbon (AC) produced from diverse biomass feedstocks is critical for biomass screening and process optimization for sustainability. Many studies have developed Life Cycle Assessment (LCA) for biomass-derived AC. However, most of them either focused on individual biomass species with differing process conditions or compared multiple biomass feedstocks without investigating the impacts of feedstocks and process variations. Developing LCA for AC from diverse biomass is time-consuming and challenging due to the lack of process data (e.g., energy and mass balance).</p> <p>This study addresses these knowledge gaps by developing a modeling framework that integrates artificial neural network (ANN), a machine learning approach, and kinetic-based process simulation. The integrated framework is able to generate Life Cycle Inventory data of AC produced from 73 different types of woody biomass with 250 characterization data samples. The results show large variations in energy consumption and GHG emissions across different biomass species (43.4–277 MJ/kg AC and 3.96–22.0 kg CO<sub>2</sub>-eq/kg AC). The sensitivity analysis indicates that biomass composition (e.g., hydrogen and oxygen content) and process operational conditions (e.g., activation temperature) have large impacts on energy consumption and GHG emissions associated with AC production.</p>
Data, Metadata, R-codes and R data files for publication "Comparative ungulate diversity and biomass change with human use and drought: implications for community stability and protected area prioritization in African savannas" by Bartzke et al. in Ecology and Evolution
<p>These files contain data and metadata for modeling ungulate diversity and biomass in the Maasai Mara ecosystem in Kenya in the drought year of 1999 and a year with normal rainfall, 2002. The files also contain R codes and R data files.</p> <p>Metadata.pdf: Metadata for files "mc_333m.csv" and "mc_1km.csv"</p> <p>mc_333m.csv: A data file for 333-meter-by-333-meter sub-blocks.</p> <p>prepare_data.r: R code to impute missing vegetation records in 333-meter-by-333-meter subblocks and summarize the data over 1-kilometer-by-1-kilometer blocks for analysis.</p> <p>krige_vegetation.RData: An R data file containing the imputed vegetation records.</p> <p>mc_1km.csv: A data file for 1-kilometer-by-1-kilometer blocks for analysis.</p> <p>mc_1km.r: R code for modeling ungulate diversity and biomass; mc_1km_mod.RData: An R data file for loading the ungulate diversity and biomass models.</p> <p>mc_1km.RData: An R data file containing model predictions of ungulate diversity and biomass.</p> <p>mc_1km_plots.r: R code for plotting model predictions of ungulate diversity and biomass.</p> <p>MMNR_boundary.shp: A shapefile of the Maasai Mara National Reserve boundary in Kenya and associated files. These files are used for plotting the predictions of ungulate diversity and biomass.</p> <p>MMNR_border.zip: A shapefile and associated files for the Maasai Mara National Reserve border with Tanzania. These files are also used for plotting predictions of ungulate diversity and biomass.</p>
Predicted HHV values for woody biomass samples from USDA-AFRI project using the best performing models.
<p>Predicted HHV values for samples from the USDA-AFRI project using the best-performing models from the cross-validation process.</p>
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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.