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274 results for “Understory”
Understory Vegetation in Hemlock Removal Experiment at Harvard Forest since 2003
Hemlock decline in New England is caused by direct and indirect effects of invasion of the hemlock woolly adelgid. Direct damage from the insect is causing gradual mortality of hemlock, and widespread harvesting of hemlock in advance of mortality, in contrast, causes immediate mortality and removal of biomass from the site. Although both processes affect thousands of acres of forest annually we have only a limited understanding of their effects on forest ecosystem function and productivity and the nature of the subsequent forest community. We anticipate that harvesting will yield different consequences than gradual mortality from the insect. Therefore we designed an experiment to simulate these contrasting impacts, by logging or girdling hemlock stands. Results from the experimental treatments are compared to the changes observed in forests that are being infested by the adelgid, and can also be included in integrated analyses of a suite of large experiments that form a core component of the Harvard Forest LTER program.
Understory Removal Experiment at Harvard Forest 1956-2018
In 1956, Ernie Gould and colleagues established an experiment to test whether removing understory trees will accelerate the growth of the residual overstory. The motivation for the experiment was the suggestion that the understory uses growth materials that would otherwise be available for the main canopy crop trees, and that if the understory were removed the development of the crop trees would be accelerated. In Tom Swamp Compartment I, a 300’ by 400’ rectangular plot was established in a closed-canopy hardwood forest, and every tree >2 inches dbh was mapped, measured and assigned to the overstory or understory. In half of the plot, all understory trees and woody shrubs were cut. Wood and brush were removed from the study area. Sprouts that emerged were killed by spraying with 2-4-5-T and kerosene in 1956 and 1958. Diameter growth measurements of the remaining trees (overstory in the treated area; overstory and understory in the control) were repeated in 1960, 1966, 1969, and 1975 as part of the original experiment. The control portion of the plot was measured in 2003 and 2018. This data set contains tree measurements from the control portion of the site for 1956, 1969, 1975, 2003, and 2018. Additional measurements are available on paper data sheets in the Harvard Forest Document Archives (Research File HF 1956-03). Results of the understory removal experiment are reported in Kelty and Gould (1987).
Hemlock Understory Vegetation Plots at Harvard Forest since 2002
Hemlock (Tsuga canadensis) forests in New England are changing rapidly with the invasion of the hemlock woolly adelgid (HWA, Adelges tsugae), a non-native insect pest that kills hemlock trees. As the adelgid invades hemlock stands at Harvard Forest, we are monitoring how forest structure and function changes. We surveyed community vegetation structure and composition prior to adelgid infestation, and are repeating this survey as infestation progresses. These measurements will allow us to link the long-term history of these hemlock stands to other current studies of hemlock response to adelgid infestation.
Mangrove Coast Collaborative Project, Post-hurricane Irma mangrove forest understory data, Rookery Bay NERR, February 2022 - March 2023
This dataset describes the structure and composition of the mangrove forest understory in Rookery Bay National Estuarine Research Reserve (NERR) assessed Feb 2022 - Mar 2023, approximately 5 years post Hurricane Irma (2017) and concurrent with Hurricane Ian (2022). The dataset includes information on the number of seedlings and saplings of each species as sampled in four 1 m2 quadrats in each 100 m2 structural sampling plot. The dataset also includes counts of the five tallest pneumatophores occurring in each quadrat. The height of pneumatophores was used as a proxy for the average maximum high water level in a plot. This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).
Mangrove Coast Collaborative Project, Post-hurricane Maria mangrove forest understory data, Jobos Bay NERR, March 2022 - August 2022
This dataset describes the structure and composition of the mangrove forest understory in Jobos Bay National Estuarine Research Reserve (NERR) assessed approximately 5 years after disturbance from Hurricane Maria (2017). The dataset includes information on the number of seedlings and saplings of each species as sampled in four 1 m2 quadrats in each 100 m2 structural sampling plot. The dataset also includes counts of the five tallest pneumatophores occurring in each quadrat. The height of pneumatophores was used as a proxy for the average maximum high water level in a plot. This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).
Fern Understory as an Ecological Filter at Harvard Forest 1993-1995
We investigated the role of the fern understory as an ecological filter that influences the organization of the tree seedling bank in New England deciduous forests. The data files below summarize a series of field experiments conducted from 1993 to 1995 evaluating the response of seed germination and seedling growth and survival to experimental understory manipulation. These field studies involved three experimental manipulations of the fern understory: 1) removal of ferns 2) ferns tied back (to remove above-ground shading) 3) ferns left intact (control). These manipulations were established in 180 - 1m2 plots spanning six field sites and 2 different fern species (3 understory manipulations x 2 understory fern species x 6 sites x 5 replicates/site = 180 experimental plots).
Pawpaws prevent predictability: A locally-dominant tree alters understory beta-diversity and community assembly
<p>Data used in "Pawpaws Prevent Predictability: A locally-dominant tree alters understory beta-diversity and community assembly" (Wassel and Myers) accepted for publication in Ecosphere.<br><br><strong>Metadata for Zenodo.pdf </strong>contains more information on the following data files including descriptions of the columns. </p> <p>The file <strong>understory_abundance_data2021.csv</strong> contains all species abundances in 1x1m plots. This data was used for analyses in publication. Each row is a plot, each column is a speceis or plot descriptor, values for columns 5 and higher are species abundances. Data was collected July-August 2021 by Anna Wassel in Missouri, USA. </p> <p>The file <strong>understory_species_list2021.csv </strong>contains a list of the species codes used in the first file with their scientific names and their status as herbs or woody. This was used to filter out herbaceous species from the data set for herbaceous-only analyses. </p> <p> </p>
Baltimore forest patch point-sample overstory and understory vegetation, 2018-2020
Urban forest patches, including woodland interiors and bounding edge habitat, result from secondary succession and fragmentation of more extensive forested landscapes in the eastern USA. Management regimes, surrounding land use, and successional processes lead to distinct environments and contribute to local and regional heterogeneity. However, many woodlands are degraded due to frequent disturbance, aggressive exotic species, and heavy browsing, which stress canopies, reduce regeneration, and may reduce ecosystem services. Effective management requires rapid, repeatable assessment of forest composition, structure, and condition at the scale of local decision-making. We present and apply a protocol for characterizing urban woodlands that generates new insight into the status of urban woodlands and baseline data for change detection over time. Samples of overstory composition, ground cover, surface soil measurements, and the Schumacher Vine Encroachment Index were collected at 845 points across each of 47 patches across Baltimore, Maryland. Simple citywide summaries allowed characterization of Baltimore’s urban overstories as overwhelmingly native, though dominated by a range of successional conditions. By contrast, we found that ground layers were predominantly exotic, with abundant invasives or ruderal native species benefitting from disturbed conditions. Seven overstory types were distinguished, the majority under threat from aggressive vines. Most soils showed little evidence of compaction, but variable organic content. Distributed data allowed crosspatch comparison as well as within-patch analyses along edge-to-interior gradients. Species diversity, nativity, and overstory basal area all increased towards woodland interiors, whereas soil compaction and vine encroachment decreased. Structural and compositional shifts in both overstory and ground layer species revealed indicators of edge (15.2-18.7 m) and interior (> 41.5 m) conditions, as well as evidence of tran
Phenology of flowers and leaves following experimental warming in the initiation and maturation years for 7 understory boreal plants at the Bonanza Creek Long Term Ecological Research (BNZ LTER) site in Interior Alaska: 2017-2019
This dataset contains the results of experimental warming of flower and leaf buds for 7 understory boreal plants: Rhododendron groenlandicum, Rosa acicularis, Rubus chamaemorus, Shepherdia canadensis, Viburnum edule, Vaccinium uliginosum, and Vaccinium vitis-idaea. Plants in two cohorts were subjected to one of four treatments: warming in the initiation year (the year prior to flowering or leaf-out) only, warming in the maturation year (the year of flowering or leaf-out) only, warming in both years, or no warming (controls). The timing of flowering (both cohorts) and leaf-out (usually one cohort) was monitored. We also tracked developmental stages of the flower bud primordia using repeated desctructive sampling followed scanning electron microscopy throughout the initiation years. Environmental data associated with the plots, including air temperature throughout the summer, soil temperature and depth of ground thaw in late May, and canopy cover, are also reported.
SBC LTER: Reef: Long-term experiment: Kelp removal: Understory kelp allometrics
These data consist of morphometric measurements of the understory kelps Pterygophora californica and Laminaria farlowii used to estimate their mass within permanent plots of a long-term experiment designed to examine trajectories of change in the structure and productivity of kelp forest communities in response to changes in the frequency and severity of disturbance to giant kelp. The mass of individual P. californica is estimated from the number of blades having a length > 30 cm, whereas the mass of L. farlowii is estimated from the length of its single blade. Measurements are recorded for up to 30 individuals of each species within the 40 m x 2 m sampling area of each plot. These data are combined with allometric relationships established in the laboratory to provide non-destructive, in situ estimates of the dry mass per unit area of bottom for each species.
Species and groundcover of understory herbaceous plants in a chronosequence of reforested urban sites, Lexington, KY USA
This dataset contains information on understory plant communities in across twenty urban reforestation sites planted as part of the Reforest the Bluegrass program in Lexington, KY. Urban reforested areas located in Lexington, Kentucky were evaluated over the course of summer 2020. At least three plots (and up to nine plots) were established in each site, with additional plots added if forested patches were sufficiently large. At each plot, we established a 0.008-ha (0.02-ac) circular sampling plot to survey understory plant species. Groundcover of all species, excepting tree- and shrub-forming species, was visually estimated in 10 grids, 0.6 m x 0.6 m. These data will contribute to understanding of understory plant community dynamics in developing urban forests.
Understory plant community data from repeated plot sampling (1978-2019) in old-growth northern hardwood forest, northern Michigan (Dukes RNA, Hiawatha National Forest)
This data-set includes long-term, permanent-plot-based data for understory plant communities in old-growth mixed northern hardwood-hemlock forest and forested peatland in the Upper Great Lakes region. Data for over 900 understory quadrats (all associated with long-term canopy data from larger permanent plots) included multiple (2-5) remeasurements over 23-40 years, with longest periods and most remeasurements for upland forest types. The Dukes Research Natural Area (RNA) (https://www.fs.usda.gov/research/nrs/rnas/locations/dukes) in the Hiawatha National Forest (Marquette Co., MI) includes ca. 100 ha of largely unlogged, original forest. Publications cited below include more detailed information about the site. About half of the RNA supports upland forests intergrading from hemlock (Tsuga candensis) dominance to mixtures of hemlock and northern hardwoods species. Sugar maple (Acer saccharum) is dominant over much of the upland area, with, locally, significant admixtures of beech (Fagus grandifolia), yellow birch (Betula alleghaniensis), and red maple (Acer rubrum). Topographic relief is very slight with total elevational change within the RNA only about 10 m. The stand is within a few km of the western limit of the continuous range of beech. In 1935, 248 continuing forest inventory (CFI) plots (circular, 0.2 acre) were established on a regular grid throughout the RNA, and these have been the subject of repeated sampling through 2018-2019 and support continuing long-term study addressing canopy tree communities (canopy data to be deposited in a separate project). Examples of resulting publications are cited elsewhere in metadata, and can provide more detailed information about the RNA. In 1978-80, U.S. Forest Service researchers, directed by Jan Schultz and Frederick Metzger, initiated studies of understory communities, including herbaceous species and woody seedlings. Data were derived from four sub-quadrats within each of the CFI plots. These quadrats were re-estab
Understory percent cover, plant traits, canopy LAI, PAR, temperature, and soil moisture data at multiple time points for sites in the burn chronosequence and Indian Point forest at the University of Michigan Biological Station, Pellston, MI (2022-2023)
Community ecology has sought to understand the mechanisms by which plant communities are assembled through time and space. One prominent way to address how communities are assembled is by quantifying functional traits. While there is a tremendous body of literature on functional traits, debate persists about how to account for variation in measured traits. For example, intraspecific trait variation (ITV) can be equal to or greater than interspecific trait variation and ITV has also been found to vary greatly across years. Therefore, there is a need to account for variability in functional trait measures among and within species and through time to improve our understanding of community assembly. Chronosequences are a powerful tool to address temporal changes in community dynamics, however, the inclusion of understory plants in forest chronosequence studies is still relatively uncommon. Previous chronosequence studies have been primarily performed in grasslands or in a limited subset of forest types, so further work is needed in understory plant traits across other ecosystems and climates to improve trait-based understanding of understory plant communities through time. Additionally, because plant traits change as ecosystems age, community interactions are likely to change with ecosystem age. Interactions of particular interest are herbivory, arthropod predation, and the influence of plant traits on arthropod diversity.
Effects of fallen Spanish moss (Tillandsia usneoides) on understory plant, invertebrate, and fungi communities
For nearly two years, natural deposition of Spanish moss was excluded from 2m x 2m plots positioned in the understory of a single live oak at each of two field sites. After 29 months, we measured the effects of fallen Spanish moss, relative to unmanipulated control plots that intercepted natural levels of fallen Spanish moss, on understory grass, invertebrate, and fungi communities as well as on litter layer depth, and litter decomposition rates using litter bags. This research was conducted in two fields on Sapelo Island, GA: Long Tabby (LT) and King's Field (KF). Experimental exclusions were maintained by manually removing Spanish moss from experimental plots monthly over the duration of the experiment.
Experimental Understory Food Web data in the El Verde area of the Luquillo Experimental Forest
These data include date, treatment, block number, number of coquies, number of anoles, number of insects collected on two sticky traps, number of insects counted on 4 Piper glabrescens and 4 Manilkara bidentata seedlings, percent herbivory on the aforementioned plants and number of spiders. All these measurements were taken within exclosures for closed controls, anole exclusions, coqui exclusions and total exclusions. Open controls were sampled from an area of similar dimension not enclosed in an exclosure. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Luquillo Understory invertebrates Sticky Trap Samples
Sticky traps, designed to resemble resin globules that eventually could become amber, were coated with adhesive and placed on lower tree boles or on logs at one non-gap site at the LEF for 5 days during June 2004, January and July 2005, July 2006 and July 2007 (total 35 samples). Samples were dominated by Diptera (89% of specimens), with phorids representing 60%, mycetophilids 16%, dolichopodids 4%, sarcophagids 2%, cecidomyiids and chironomids 1% each, and other Diptera 5%. Other collected taxa included Hymenoptera (6% of specimens), with chalcidoid wasps 4%, little fire ant (Wasmania auropunctata) 1% and other Hymenoptera 1%; Coleoptera (2% of specimens), with curculionids 1% and other Coleoptera 1%; and a variety of other insects, spiders and mites representing 3%. Two small Anolis lizards and a small Eleutherodactylus frog also were captured. Arthropod composition in sticky traps from the LEF and in Dominican amber were similar. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Riparian and upland understory vegetation lifeforms and leaf-litterfall ordination analyses in the Luquillo Forest Dynamics Plot
Riparian areas are proportionally a small component of the forested landscape, they are significant contributors to ecosystem process, terrestrial and aquatic linkages, plant community composition, as well to basal energy resources for aquatic fauna. We describe vegetation and leaf-litterfall composition in relation to past land use in riparian and upland locations in tropical wet forest, Luquillo Forest Dynamics Plot (LFDP), Luquillo Experimental Forest, Puerto Rico. Data collected from 2003 to 2005. Stratified sampling was conducted in riparian and upland areas of LFDP with high and low past land use. Understory vegetation life-form composition were sampled in plots. \<para\> Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.\</para\>
Soil profile, climatic, physiographic, overstory and understory data in mixed and monospecific plots of Pinus sylvestris and Pinus pinaster in Spain
<p>This dataset provides valuable environmental information about a triplets’ essay of Scots pine and Maritime pine in Spain. The data characterizes the soil profile (physicochemical parameters of organic and mineral horizons), climate, physiography, understory and overstory.</p> <p>The essay, located in North-Central Spain, consists of eighteen forest plots divided in six triplets. Each triplet includes three circular plots of 15 m-radius located less than 1 km from each other: two monospecific plots dominated by <em>P. sylvestris</em> or <em>P. pinaster</em>, and one mixed plot of both species. In each plot, one pit up to 50 cm depth, one 15 m-radius overstory features inventory and ten understory 1x1 m inventories were carried out. Additionally, physiographic and climatic variables were collected per plot.</p> <p>The file contains information about the 218 environmental variables studied in the eighteen forest plots.</p> <p>Triplet: Triplet to which the plot belongs(1: Triplet 1; 2: Triplet 2; 3: Triplet 3; 4: Triplet 4; 5: Triplet 5; 6: Triplet 6).</p> <p>Stand_type: Type of stand (PS: monospecific stand of <em>Pinus sylvestris</em> L.; PP: monospecific stand of <em>Pinus pinaster</em> Ait.; MM: mixed stand of <em>Pinus sylvestris</em> L.and <em>Pinus pinaster</em> Ait.).</p> <p>Plot: Plot identification (PS01: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 1; PS02: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 2; PS03: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 3; PS04: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 4; PS05: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 5; PS06: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 6; MM01: mixed stand of <em>Pinus sylvestris</em> L.and <em>Pinus pinaster</em> Ait. of triplet 1; MM02: mixed stand of <em>Pinus sylvestris </em>L.and <em>Pinus pinaster</em> Ait. of triplet 2; MM03: mixed stand of <em>Pinus sylvestris</em> L.and <em>Pinus pinaster</em> Ait. of triplet 3; MM04: mixed stand of <em>Pinus sylvestris</em> L.and <em>Pinus pinaster</em> Ait. of triplet 4; MM05: mixed stand of <em>Pinus sylvestris </em>L.and <em>Pinus pinaster</em> Ait. of triplet 5; MM06: mixed stand of <em>Pinus sylvestris </em>L.and <em>Pinus pinaster Ait</em>. of triplet 6; PP01: monospecific stand of <em>Pinus pinaster</em> Ait. of triplet 1; PP02: monospecific stand of <em>Pinus pinaster</em> Ait. of triplet 2; PP03: monospecific stand of <em>Pinus pinaster </em>Ait. of triplet 3; PP04: monospecific stand of <em>Pinus pinaster</em> Ait. of triplet 4; PP05: monospecific stand of <em>Pinus pinaster</em> Ait. of triplet 5; PP06: monospecific stand of<em> Pinus pinaster </em>Ait. of triplet 6).</p> <p>Lat: Plot latitude in degrees.</p> <p>Long: Plot longitude in degrees.</p> <p>Province: Province to which the plot belongs (B: Province of Burgos; Sp: Province of Soria).</p> <p>Municipality: Municipality to which the plot belongs (M: Town of Mamolar; HP: Town of Hontoria del Pinar; N: Town of Navaleno; St: Town of Soria; CP: Town of Cabrejas del Pinar).</p> <p>Forest: Name of the forest where is located the plot (MB: Mata Blanca; MR: Mata Robledo; FP: Fuente del Pardo; PM: Pajar de la molinera; MP: Mojon Pardo; CM: Cueva de Matarubias).</p> <p>Alti: Plot elevation above sea level in m a.s.l.</p> <p>Slope: Slope (gradient) of the plot in percentage.</p> <p>Ori: Plot orientation in degrees.</p> <p>Clim: Climate classification according to Köppen classification (1936) (Cfb: Temperate without a dry season and temperate summer climate; Csb: Temperate with dry summer climate).</p> <p>XR: Accumulated rainfall in one year according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>JR: January rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ mm</p> <p>FR: February rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>MR: March rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>AR: April rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>MyR: May rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>JnR: June rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>JlR: July rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>AgR: August rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>SR: September rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>OR: October rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>NR: November rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>DR: December rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>XT: Anual mean temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>JT: January temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>FT: February temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>MT: March temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>AT: April temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>MyT: May temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>JnT: June temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>JlT: July temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>AgT: August temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>ST: September temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>OT: October temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>NT: November temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>DT: December temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>Par_mat: Soil parental material according to Spanish Geological Map on a 1M scale. (IGME , 2015) (SM: Sandstones and Marls).</p> <p>Geo_age: Geological age of plot according to Spanish Geological Map on a 1M scale. (IGME, 2015) (Mz: Mesozoic age).</p> <p>Soil: Soil type according to Soil-Survey-Staff (2014) (TpDx: Typic Dystroxerept; TpHx:: Typic Humixerept; AqHx:: Aquic humixerept)</p> <p>Litter_B: Total Leaf Litter Biomass in Mg/ha.</p> <p>FF_Th: Forest floor Thickness in cm.</p> <p>Fs: Percentage of Fresh to Total Leaf Litter in %.</p> <p>Fr: Percentage of Fragmented to Total Leaf Litter in %.</p> <p>Hm: Percentage of Humified to Total Leaf Litter in %.</p> <p>GH1: Fist genetic soil horizon according to Soil Survey-Staff (2014) (Ah: Mineral horizon with accumulation of organic matter. This horizon is formed at the soil surface or below an O horizon).</p> <p>GH2: Second genetic soil horizon according to Soil Survey-Staff (2014) (AB: Transition horizon between A and B. A is a mineral horizon formed at the surface or below an O horizon, B is a subsurface horizon in which the structure of the rock is obliterated; AC: Transition horizon between A and C. A is a mineral horizon formed at the surface or below an O horizon; C is a mineral horizon, excluding hard bedrock, that is little affected by pedogenetic processes; Bw: Mineral B horizon where the development of color or structure are its more important diagnostic characteristics).</p> <p>GH3: Third genetic soil horizon according to Soil Survey-Staff (2014) (Bw: Mineral B horizon where the development of color or structure are its more important diagnostic characteristics; C: Mineral horizon, excluding hard bedrock, that is little affected by pedogenetic processes; Cg: Mineral horizon in which a distinct pattern of mottling occurs that reflects alternating conditions of oxidation and reduction of sesquioxides, caused by seasonal surface waterlogging).</p> <p>Th_H1: Thickness of the first soil horizon in cm.</p> <p>Th_H2: Thickness of the second soil horizon in cm.</p> <p>Th_H3: Thickness of the third soil horizon in cm.</p> <p>moistCol_H1: Wet matrix color (Hue Value/Chroma) of the first soil horizon according to Munsell soil color chards (10YR2/1: black; 10YR2/2: very dark brown; 10YR3/1: very dark grey; 10YR3/2: very dark greyish brown; 10YR4/1: dark grey; 10YR6/3: pale brown).</p> <p>moistCol_H2: Wet matrix colour (Hue Value/Chroma) of the second soil horizon according to Munsell soil color chards (5YR5/8: yellowish red; 7.5YR4/6: strong brown; 10YR3/2: very dark greyish brown; 10YR4/1: dark grey; 10YR4/2: dark greyish brown; 10YR4/4: dark yellowish brown with chroma 4; 10YR4/6: dark yellowish brown with chroma 6; 10YR5/3: brown; 10YR5/4: yellowish brown with chroma 4; 10YR5/6: yellowish brown with chroma 6; 10YR5/8: yellowish brown with chroma 8; 10YR6/4: light yellowish brown; 10YR6/6: brownish yellow).</p> <p>moistCol_H3: Wet matrix colour (Hue Value/Chroma) of the third soil horizon according to Munsell soil color chards (5YR4/6: yellowish red; 10YR4/4: dark yellowish brown with chroma 4; 10YR4/6: dark yellowish brown with chroma 6; 10YR5/8: yellowish brown; 10YR6/1: grey).</p> <p>dryCol_H1:Dry matrix color (Hue Value/Chroma) of the first soil horizon according to Munsell soil color chards (10YR4/1: dark grey; 10YR4/2: dark greyish brown; 10YR5/1: grey with value 5; 10YR5/2: greyish brown; 10YR5/3: brown; 10YR6/1: grey with value 6; 10YR6/2: light yellowish brown; 10YR7/2: light grey).</p> <p>dryCol_H2: Dry matrix color (Hue Value/Chroma) of the second soil horizon according to Munsell soil color chards (7.5YR6/6: redish brown; 10YR4/1: dark grey; 10YR6/1: grey with value 6; 10YR6/2: light yellowish brown with chroma 2; 10YR6/3: pale brown; 10YR6/4: light yellowish brown with chroma 4; 10YR6/6: brownish yellow; 10YR7/3: very pale brown with value 7 and choma 3; 10YR7/4: very pale brown withvalue 7 and choma 4; 10YR8/4: very pale brown with value 8 and choma 4).</p> <p>dryCol_H3: Dry matrix color (Hue Value/Chroma) of the third soil horizon according to Munsell soil color chards (5YR5/6: yellowish red; 7.5YR5/6: strong brown; 10YR6/4: light yellowish brown; 10YR6/6: brownish yellow; 10YR7/4: very pale brown; 10YR8/1: white).</p> <p>Sand_H1: Percentage of sand of the first soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Sand_H2: Percentage of sand of the second soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Sand_H3: Percentage of sand of the third soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Silt_H1: Percentage of silt of the first soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Silt_H2: Percentage of silt of the second soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Silt_H3: Percentage of silt of the third soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Clay_H1: Percentage of clay of the first soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Clay_H2: Percentage of clay of the second soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Clay_H3: Percentage of clay of the third soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Tex_H1: Textural class of the first soil horizon according to Soil Survey Staff (2014) (SL: Sandy Loam; L: Loam).</p> <p>Tex_H2: Textural class of the second soil horizon according to Soil Survey Staff (2014) (SL: Sandy Loam; L: Loam).</p> <p>Tex_H3: Textural class of the third soil horizon according to Soil Survey Staff (2014) (SL: Sandy Loam; L: Loam; CL: Clay loam; C: Clay).</p> <p>Stones_H1: Coarse soil material (> 2 mm) of the first soil horizon in % weight/weight.</p> <p>Stones_H2: Coarse soil material (> 2 mm) of the second soil horizon in % weight/weight.</p> <p>Stones_H3: Coarse soil material (> 2 mm) of the third soil horizon in % weight/weight.</p> <p>bD_H1: Bulk density of the first soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>bD_H2: Bulk density of the second soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>bD_H3: Bulk density of the third soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>pD_H1: Particle density of the first soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>pD_H2: Particle density of the second soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>pD_H3: Particle density of the third soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>Poro_H1: Porosity of the first soil horizon according to (Van-Reeuwijk 2002) in % vol/vol.</p> <p>Poro_H2: Porosity of the second soil horizon according to (Van-Reeuwijk 2002) in % vol/vol.</p> <p>Poro_H3: Porosity of the third soil horizon according to (Van-Reeuwijk 2002) in % vol/vol.</p> <p>pH_H1: pH (1:2.5 H2O) of the first soil horizon according to (Van-Reeuwijk 2002)</p> <p>pH_H2: pH (1:2.5 H2O) of the second soil horizon according to (Van-Reeuwijk 2002)</p> <p>pH_H3: pH (1:2.5 H2O) of the third soil horizon according to (Van-Reeuwijk 2002)</p> <p>EC_H1: Electrical conductivity of the first soil horizon according to (Van-Reeuwijk 2002) in dS/m.</p> <p>EC_H2: Electrical conductivity of the second soil horizon according to (Van-Reeuwijk 2002) in dS/m.</p> <p>EC_H3: Electrical conductivity of the third soil horizon according to (Van-Reeuwijk 2002) in dS/m.</p> <p>avP_H1: Available phosphorus of the first soil horizon according to Olsen and Sommers (1982) in mg/kg.</p> <p>avP_H2: Available phosphorus of the second soil horizon according to Olsen and Sommers (1982) in mg/kg.</p> <p>avP_H3: Available phosphorus of the third soil horizon according to Olsen and Sommers (1982) in mg/kg.</p> <p>avPstock_H1: Available phosphorus stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>avPstock_H2: Available phosphorus stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>avPstock_H3: Available phosphorus stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>avPstock_50: Available phosphorus stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TN_Fs: Total nitrogen of the fresh forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TN_Fg: Total nitrogen of the fragmented forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TN_Hm: Total nitrogen of the humified forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TN_H1: Total nitrogen of the first soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TN_H2: Total nitrogen of the second soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TN_H3: Total nitrogen of the third soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TNstock_H1: Total nitrogen stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TNstock_H2: Total nitrogen stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TNstock_H3: Total nitrogen stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TNstock_50: Total nitrogen stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TOC_Fs: Total organic carbon of the fresh forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TOC_Fg: Total organic carbon of the fragmented forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TOC_Hm: Total organic carbon of the humified forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TOC_H1: Total organic carbon of the first soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TOC_H2: Total organic carbon of the second soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TOC_H3: Total organic carbon of the third soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TOCstock_H1:Total organic carbon stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TOCstock_H2: Total organic carbon stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TOCstock_H3: Total organic carbon stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TOCstock_50: Total organic carbon stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>C/N_Fs: Ratio of total organic carbon to total nitrogen of the fresh forest floor </p> <p>C/N_Fg: Ratio of total organic carbon to total nitrogen of the fragmented forest floor </p> <p>C/N_Hm: Ratio of total organic carbon to total nitrogen of the humified forest floor </p> <p>C/N_H1: Ratio of total organic carbon to total nitrogen of the first soil horizon</p> <p>C/N_H2: Ratio of total organic carbon to total nitrogen of the second soil horizon</p> <p>C/N_H3: Ratio of total organic carbon to total nitrogen of the third soil horizon</p> <p>OxC_H1: Easily oxidizable carbon of the first soil horizon according to Walkley (1947) in mg/kg.</p> <p>OxC_H2: Easily oxidizable carbon of the second soil horizon according to Walkley (1947) in mg/kg.</p> <p>OxC_H3: Easily oxidizable carbon of the third soil horizon according to Walkley (1947) in mg/kg.</p> <p>OxCstock_H1: Easily oxidizable carbon stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>OxCstock_H2: Easily oxidizable carbon stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>OxCstock_H3: Easily oxidizable carbon stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>OxCstock_50: Easily oxidizable carbon stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>CEC_H1: Cation exchange capacity of the first soil horizon according to Mehlich (1953) in cmol<sub>+</sub>/kg.</p> <p>CEC_H2: Cation exchange capacity of the second soil horizon according to Mehlich (1953) in cmol<sub>+</sub>/kg.</p> <p>CEC_H3: Cation exchange capacity of the third soil horizon according to Mehlich (1953) in cmol<sub>+</sub>/kg.</p> <p>Na<sup>+</sup>_H1: Exchangeable sodium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Na<sup>+</sup>_H2: Exchangeable sodium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Na<sup>+</sup>_H3: Exchangeable sodium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Na<sup>+</sup>stock_H1: Exchangeable sodium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Na<sup>+</sup>stock_H2: Exchangeable sodium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Na<sup>+</sup>stock_H3: Exchangeable sodium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Na<sup>+</sup>stock_50: Exchangeable sodium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>K<sup>+</sup>_H1: Exchangeable potassium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>K<sup>+</sup>_H2: Exchangeable potassium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>K<sup>+</sup>_H3: Exchangeable potassium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>K<sup>+</sup>stock_H1: Exchangeable potassium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>K<sup>+</sup>stock_H2: Exchangeable potassium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>K<sup>+</sup>stock_H3: Exchangeable potassium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>K<sup>+</sup>stock_50: Exchangeable potassium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Ca<sup>+2</sup>_H1: Exchangeable calcium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Ca<sup>+2</sup>_H2: Exchangeable calcium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Ca<sup>+2</sup>_H3: Exchangeable calcium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Ca<sup>+2</sup>stock_H1: Exchangeable calcium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Ca<sup>+2</sup>stock_H2: Exchangeable calcium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Ca<sup>+2</sup>stock_H3: Exchangeable calcium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Ca<sup>+2</sup>stock_50: Exchangeable calcium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Mg<sup>+2</sup>_H1: Exchangeable magnesium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Mg<sup>+2</sup>_H2: Exchangeable magnesium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Mg<sup>+2</sup>_H3: Exchangeable magnesium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Mg<sup>+2</sup>stock_H1: Exchangeable magnesium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Mg<sup>+2</sup>stock_H2: Exchangeable magnesium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Mg<sup>+2</sup>stock_H3: Exchangeable magnesium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Mg<sup>+2</sup>stock_50: Exchangeable magnesium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>SB_H1: Sum of bases of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>SB_H2: Sum of bases of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>SB_H3: Sum of bases of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>SBstock_H1: Sum of bases stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>SBstock_H2: Sum of bases stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>SBstock_H3: Sum of bases stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>SBstock_50: Sum of bases stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>FC_H1: Field capacity of the first soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>FC_H2: Field capacity of the second soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>FC_H3: Field capacity of the third soil horizon according to Van-Reeuwijk (2002)) in %.</p> <p>PWP_H1: Permanent wilting point of the first soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>PWP_H2: Permanent wilting point of the second soil horizon according to Van-Reeuwijk (2002) in %. </p> <p>PWP_H3: Permanent wilting point of the third soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>AW_H1: Available water of the first soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>AW_H2: Available water of the second soil horizon according to MAPA (1994) in %.</p> <p>AW_H3: Available water of the third soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>WHC_H1: Water holding capacity of the first soil horizon according to López-Marcos et al. (2019) in g/cm<sup>2</sup>.</p> <p>WHC_H2: Water holding capacity of the second soil horizon according to López-Marcos et al. (2019) in g/cm<sup>2</sup>.</p> <p>WHC_H3: Water holding capacity of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in g/cm<sup>2</sup>.</p> <p>WHC_50: Water holding capacity of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in g/cm<sup>2</sup>.</p> <p>Pot_veg: Potential vegetation according to Rivas-Martínez (1987) (LfQp: <em>Luzulo forsteri-Querceto pyrenaicae </em>S.; FhQp: <em>Festuco heterophyllae-Querceto pyrenaicae</em> S.; Jht: <em>Junipereto hemisphaerico-thuriferae</em> S.).</p> <p>Cur_veg: Current vegetation according to WMS service of MAPAMA(<a href="http://wms.mapama.es/sig/Biodiversidad">http://wms.mapama.es/sig/Biodiversidad</a>) (ps: <em>Pinus sylvestris</em> L.; pp: <em>Pinus pinaster</em> Ait.; pi: <em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.).</p> <p>NT: Stems per hectare of both <em>Pinus </em>species (<em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.) in trees/ha.</p> <p>NPs: Stems per hectare of <em>Pinus sylvestris</em> L. in trees/ha.</p> <p>NPp: Stems per hectare of <em>Pinus pinaster</em> Ait. in trees/ha.</p> <p>GT: Basal area per hectare of both <em>Pinus</em> species (<em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.) in m<sup>2</sup>/ha.</p> <p>GPs: Basal area per hectare of <em>Pinus sylvestris</em> L. in m<sup>2</sup>/ha.</p> <p>GPp: Basal area per hectare of <em>Pinus pinaster</em> Ait. in m<sup>2</sup>/ha.</p> <p>%PS: Percentage of basal area of <em>Pinus sylvestris</em> L. from total basal area</p> <p>%PP: Percentage of basal area of <em>Pinus pinaster</em> Ait. from total basal area</p> <p>dgT: Quadratic mean diameter of both <em>Pinus </em>species (<em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.) in cm.</p> <p>dgPs: Quadratic mean diameter of <em>Pinus sylvestris</em> L. in cm.</p> <p>dgPp: Quadratic mean diameter of <em>Pinus pinaster</em> Ait. in cm.</p> <p>HoT: Dominant height of both <em>Pinus</em> species (<em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.) in cm.</p> <p>HoPs: Dominant height of <em>Pinus sylvestris</em> L. in m.</p> <p>HoPp: Dominant height of <em>Pinus pinaster</em> Ait. in m.</p> <p>AgePs: Normal age of <em>Pinus sylvestris</em> L. in years.</p> <p>AgePp: Normal age of <em>Pinus pinaster</em> Ait. In years.</p> <p>SIPs: Site index of <em>Pinus sylvestris</em> L. related at age 100 for total plot according to Rojo and Montero (1999)</p> <p>SIPp: Site index of <em>Pinus pinaster</em> Ait. related at age 100 for total plot according to Bravo-Oviedo et al. (2007)</p> <p>Litter_cov: Cover of leaf litter in %.</p> <p>Vasc: Cover of understory vascular plants in %.</p> <p>Bryo: Cover of understory bryophytes in %.</p> <p>Under_sp: More abundant specie of understory vegetation (Aica: <em>Aira caryophyllea</em> L.; Aruv: <em>Arctostaphylos uva-ursi</em> (L.) Spreng.; Cavu: <em>Calluna vulgaris</em> (L.) Hull; Erar: <em>Erica arborea </em>L.; Erau: <em>Erica australis</em> L.; Pipi: <em>Pinus pinaster</em> Aiton. (seedlings/saplings); Pisy: <em>Pinus sylvestris</em> L. (seedlings/saplings; Ptaq: <em>Pteridium aquilinum</em> (L.) Kuhn)</p> <p>Aqu: Understory cover of family Aquifoliaceae in %.</p> <p>Aste: Understory cover of family Asteraceae in %.</p> <p>Cari: Understory cover of family Cariophyllaceae in %.</p> <p>Cist: Understory cover of family Cistaceae in %.</p> <p>Cupr: Understory cover of family Cupresaceae in %.</p> <p>Eric: Understory cover of family Ericaceae in %.</p> <p>Faba: Understory cover of family Fabaceae in %.</p> <p>Faga: Understory cover of family Fagaceae in %.</p> <p>Junc: Understory cover of family Juncaceae in %.</p> <p>Lili: Understory cover of family Liliaceae in %.</p> <p>Pina: Understory cover of family Pinaceae in %.</p> <p>Poac: Understory cover of family Poaceae in %.</p> <p>Poli: Understory cover of family Poligalaceae in %.</p> <p>Rosa: Understory cover of family Rosaceae in %.</p> <p>Rubi: Understory cover of family Rubiaceae in %.</p> <p>Scro: Understory cover of family Scrofulariaceae in %.</p> <p>Viol: Understory cover of family Violaceae in %.</p> <p>Xant: Understory cover of family Xanthorrhoeaceae in %.</p>
Dataset and R code: Above and belowground functional trait coordination in the Neotropical understory genus Costus
<p>Dataset and R code accompanying the paper "Above and belowground functional trait coordination in the Neotropical understory genus <em>Costus</em>" published by AoB Plants. </p>
dataset: Responses of the structure and function of the understory plant communities to precipitation reduction across forest ecosystems in Germany
<p><strong>Context</strong>: Understory plant communities play a central role in forest biogeochemistry and the recruitment of trees making up the future forest. It is so far poorly understood how climate change will affect understory structure and functions in forest of different management intensity.</p> <p> </p><p><strong>Aims</strong>: We monitored understory functional traits including transpiration and carbon isotope discrimination, community structure and diversity during two growing seasons as affected by drought in forests subjected to different management intensities. We hypothesized that drought would affect ecophysiological traits such as transpiration but not species richness and diversity. Moreover, we assumed that stand-specific characteristics and forest management intensity modify the drought-resistance of the understory community.</p> <p></p> <p><strong>Methods</strong>: We set up roofs in beech and conifer stands with different management intensity in three different regions across Germany and a drought event close to the 2003 drought was imposed in two consecutive years.</p> <p><strong>Results</strong>: Precipitation reduction decreased soil water content by 2 to 8%, depending on stand and region, in comparison to the control subplots. In the first year, leaf level transpiration was reduced for different functional groups, which scaled to community transpiration modified by additional effects of drought on functional group specific leaf area. Acclimation effects in most functional groups were observed in the second year. We did not observe a significant reduction of plant diversity or a consistent management effect upon drought.</p> <p><strong>Conclusion</strong>: Our results indicate high plasticity and acclimation responses of the forest understory vegetation to changing climate conditions and recurrent drought events.</p> <p><strong>Abbreviations:</strong></p> <p>sp12 - campaign spring 2012; ls12 - campaign late summer 2012; es13 - campaign early summer 2013; ls13-campaign late summer 2013</p> <p>SEW16 - Schorfheide plot 16; SEW49 - Schorfheide plot 49; SEW48 - Schorfheide plot 48;HEW03 - Hainich plot 03; HEW12 - Hainich plot 12; HEW47- Hainich plot 47; AEW13 - Alb plot 13; AEW29 - Alb plot 29; AEW08 - Alb plot 08<br> explo - exploratory<br> SEW - Schorfheide; HEW - Hainich; AEW - Schwäbische Alb<br> in - conifer intensive managed; ma - beech managed; un - beech unmanaged<br> c- control; r - roof<br> LAIs - community leaf area index m<sup>2</sup>/m<sup>2</sup>; H - Shannon´s diversity index; Ts - community transpiration rate (weighted by LAI) mmol H<sub>2</sub>O m-<sup>2</sup> leaf area s-<sup>1</sup>; Ets - Evapotranspiration (mmol/m2/sec); E - Evaporation (mmol/m2/sec); C - leaf photosynthetic carbon isotope discrimination (∆<sup>13</sup>C) according to Farquhar et al. (1982); Cs - community photosynthetic carbon isotope discrimination (∆<sup>13</sup>C) according to Farquhar et al. (1982) (weighted by LAI)</p> <p> </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.