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92 results for “Forest floor”
Hubbard Brook Experimental Forest: Diversity of Forest Floor Vegetation under Ash, Beech, Sugar Maple, and Yellow Birch, 2021
As the interface between plants and soil, the organic horizon is the foundation of forest ecosystems. Two potential predictors of O-layer properties, vegetation and mineral soil type, are difficult to separate because they typically covary. We conducted a factorial study involving four canopy tree species and two soil types with distinctly different hydrology and topographic position to parse patterns in chemistry and microbiota of the O-layer in a north-temperate deciduous forest. There were frequent strong effects of tree species. White ash frequently differed from the other trees: e.g., lower cation exchange capacity and exchangeable acidity, thinner Oi layer, lower %C and C:N, and, from phospholipid fatty acids, more AM fungi and less gram+ bacteria. These patterns, presumably due to species-specific attributes of leaf litter quality, root exudates, and microbial associations, must arise over decades, given that the stands in the study age between 85 and 100 years. We also found patterns in the O-layer related to underlying soil type, independent of tree species: e.g., Bh podzols, compared to Typical podzols, had higher trace metals, thicker Oa layer, and more AM fungi. Relations between mineral soil type and the organic layer, which were larger than expected, could arise because landscape features that influence hydrology and therefore soil formation over millennia also influence biogeochemistry of the organic layer over decades. It could also involve bioturbation by organisms across horizons. There is basic and applied value in models that can predict properties of the O-layer based on vegetation and soil types.
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
Mass of forest floor litter from cores in reference stands and inventory plots in the Pacific Northwest, 1992 to 2003
These data provide an inventory of the mass of forest floor organic matter stored within various forest types. This data is used to determine total organic matter, carbon, and nutrient stores in forests.
Forest floor mass and nutrient content by layer in Coweeta white pine watershed 1 from 2001 to 2003
The forest floor was sampled in 4 beetle-infested and 4 non-beetle-infested white pine plots in watershed 1. Fresh litter (Oi) was separated from decaying litter (Oe). Total litter mass, fresh versus decayed litter, and litter nitrogen content was compared between treatments for years 2001, 2002, 2003.
Forest floor weights and Oe-Oa soil depths from 9 Hillslope Project sites in Macon County, North Carolina, within the Upper Little Tennessee River Basin
Forest floor samples were collected at 9 hillslope sites representing a gradient of development, including forested, valley agriculture, and mountain housing developments in Macon County, NC. Forest floor was divided into two categories: forest floor (i.e., Oi, Oe, & Oa layers) and wood <10 cm diameter. Afterwards, the combined depth of the Oe and Oa was measured in the middle of 3 sides of the subplot. Forest floor collections were later dried and weighed before being processed for C and N analyses.
Forest floor carbon and nitrogen content from 9 Hillslope Project sites in Macon County, North Carolina, within the Upper Little Tennessee River Basin
Forest floor samples were collected at 9 hillslope sites representing a gradient of development, including forested, valley agriculture, and mountain housing developments in Macon County, NC. Forest floor was divided into two categories: forest floor (i.e., Oi, Oe, & Oa layers) and wood <10 cm diameter. Afterwards, the combined depth of the Oe and Oa was measured in the middle of 3 sides of the subplot. Forest floor collections were later dried and weighed before being processed for C and N analyses.
Forest floor cation content from 9 Hilllslope Project sites in Macon County, North Carolina, within the Upper Little Tennessee River Basin
Forest floor samples were collected at 9 hillslope sites representing a gradient of development, including forested, valley agriculture, and mountain housing developments in Macon County, NC. Forest floor was divided into two categories: forest floor (i.e., Oi, Oe, & Oa layers) and wood <10 cm diameter. Afterwards, the combined depth of the Oe and Oa was measured in the middle of 3 sides of the subplot. Forest floor collections were later dried and weighed before being processed for C and N and cation analyses.
Riparian zone seedling establishment, growth, dynamics, and the influence of Rhododendron maximum soil moisture: forest floor data at the Coweeta Hydrologic Laboratory from 1997 to 2000
The effect of Rhododendron maximum, a dominate species in the riparian zones of the Southern Appalachians, on carbon, water, and nutrients en route to the streams is an ongoing study in the LTER research program at Coweeta Hydrologic Laboratory. To study seedling establishment, growth, and dynamics in riparian zones one m2 quadrats have been established. There are four sites which include one treatment site, where the rhododendron has been removed from the riparian zone, one hurricane site, where there is extensive disturbance from Hurricane Opal, and two control sites, one upslope from the treatment site and one upstream from the hurricane site. Each of these fours sites have ten randomly located natural regeneration one m2 quadrats as well as four randomly located replicates of three adjacent one m2 quadrats. In each of the three adjacent quadrats, the litter was removed from the lower half to determine the effect of litter on the germination and growth of seedlings. Two of the adjacent quadrats have been broadcast seeded with Acer rubrum, Liriodendron tulipifera, and Quercus rubra. In one of the two quadrats that have been broadcast seeded, a predator exclusion mesh screen, 1m x 1/2m with " openings, has been installed in the quadrat to determine the effect of small mammal predation on regeneration. Quadrats were installed on 24 April 1997 and an initial vegetation survey was conducted in May 1997. All seedlings were permanently tagged at this time and quadrat physical characteristics such as slope, aspect, and distance from stream were recorded. Broadcast seeding was done on 21 May 1997. Each year censuses will be conducted in spring and fall on each quadrat and seedling species, density, age, and annual height growth will be recorded. This project will help to document the effect of Rhododendron maximum on regeneration in the riparian areas as well as the effect of hurricane disturbance on regeneration.
Forest Floor Mass, Organic Matter and Chemistry in Watershed 5 at the Hubbard Brook Experimental Forest, 1982 Pre-Harvest Collection
Watershed 5 was surveyed in 1982 and clearcut in 1983. A pre-cut forest floor survey was done in 1982. This data set includes organic matter mass, thickness and trace metals for samples collected in the summer of 1982 as two separate collections, one in July and one in August. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Dataset for "Topography-based statistical modelling reveals high spatial variability and seasonal emission patches in forest floor methane flux"
<p>This dataset provides measured and upscaled forest floor methane (CH4) fluxes and soil moisture.</p> <p>This dataset is related to the following manuscript:</p> <p>Vainio et al., Topography-based statistical modelling reveals high spatial variability and seasonal emission patches in forest floor methane flux, Biogeosciences, in review. (The discussion preprint is available at https://doi.org/10.5194/bg-2020-263.)</p>
Changes in the factors influencing forest floor BVOC emissions during forest succession
<p>The files have been uploaded to comply with AGU and journal requirements, particularly the "Open Research" section, which provides links to the data and analytical code necessary for the peer review process. This initiative aims to support transparent and reproducible science. </p> <ul> <li>Data analysis and the ploting of Figure2 in manuscript, along with Figure S1-S3 and Table S1-S4 in supporting information, were conducted using R Studio. The file "Forest floor BVOC emissions_analyses and plots.R" and datasets "ForestFloor.csv", "boxplot_BVOC_ca.csv", "boxplot_BVOC_fi.csv", "boxplot_BVOC_ru.csv", "SamplingSite_1.csv" were utilized for this purpose.</li> <li>To generate Figure 3 in the manuscript, the file "SIMCA 18 for Fig 3.dox" and dataset "ForestFloor.xlsx" were used. The word file provide the the trial software link. </li> <li>For the analysis and ploting of Figure 4 in the manuscript, the file "PLS_PM.R" and dataset "BVOC_PLSR_PM.csv" were employed. </li> <li>The file "For Fig S4.xlsx" was used to create Figure S4 in the supporting information. </li> </ul> <p>Abstract in article</p> <p><span>The boreal forest floor is a crucial source of diverse biogenic volatile organic compounds (BVOCs) emitted into the atmosphere. Climate change is increasing in the frequency of wildfires in the boreal forest, major disturbances with lasting impacts on the ecosystem, particularly the forest floor. Wildfires changed BVOC sources and emissions, influencing aerosol formation during forest succession across various age classes. This study quantified BVOC emissions from the forest floor and characterized microenvironmental conditions, including abiotic factors (air temperature, soil temperature, soil moisture, light intensity) and biotic factors (ground vegetation composition, species coverage, soil respiration). Our objective was to understand how abiotic and biotic factors influence the forest floor BVOC emissions during forest succession. Path models revealed direct influences of ground vegetation composition on isoprene and monoterpene emissions. Sesquiterpene emissions were mainly regulated by abiotic factors, while isoprene and monoterpene emissions were influenced both directly and indirectly by abiotic factors. The indirect impact of abiotic factors was manifested through biotic factors, including vegetation and soil processes. Effect sizes of influencing factors varied across different forest age areas, with temperature exerting a larger impact in earlier burned areas compared to recently burned areas. The influence of soil moisture on BVOC emissions diminished with forest age. Our findings indicated the importance of identifying influencing factors and their relationship with forest floor BVOC emissions during different stages of forest succession for predicting the effect of post-wildfire forest succession on the BVOC emission patterns and, consequently, their impact on climate.<span> </span></span></p>
Figure 31 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 31. Monthly changes in mean biomass in each row of C. (C.) udumalpetense per month per m2 (Vertical line indicates SE).
Figure 15 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 15. Showing the monthly fluctuations of total population and temperature in Rows I, II and III.
Figure 14 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 14. Showing the monthly fluctuations of male, female and nymph population, temperature and humidity in Row III.
Figure 11 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 11. Showing the month wise vertical distribution of total Ctenolepisma (C.) udumalpetense population.
Figure 10 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 10. Showing the month wise vertical distribution of Ctenolepisma (C.) udumalpetense nymphal population.
Figure 8 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 8. Showing the vertical distribution of total population of Ctenolepisma (C.) udumalpetense in each row.
Figure 6 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 6. Showing the mean of total male, female and nymph of Ctenolepisma (C.) udumalpetense in three rows with standard error.
Figure 5 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 5. Showing the relative density of Ctenolepisma (C.) udumalpetense (Male, Female, Nymph) in each month in the forest floor of Trimurti Dam roadside, Tamil Nadu.
Figure 4 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 4. Showing the relative density of Ctenolepisma (C.) udumalpetense (Male, Female, Nymph) in each row in the forest floor of Trimurti Dam roadside, Tamil Nadu.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.