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97 results for “vegetation structure”
Woody vegetation composition and structure at long-term monitoring plots on the Stevenson-Hamilton Research Supersite, Kruger National Park, South Africa (2012)
This dataset contains measurements of woody vegetation composition and structural attributes collected in 2012 from long-term ecological monitoring plots located on the Stevenson-Hamilton Research Supersite in the Kruger National Park, South Africa. The study region is characterized by granitic soils, broad-leaved savanna vegetation, and a long history of fire, herbivory, and climate-driven ecological dynamics. Vegetation surveys were conducted in sixteen 0.25-ha sampling plots to quantify woody species composition, stem density, and size structure. Additional measurements of vegetation structure were collected, including grass biomass, canopy cover, canopy height, and canopy diversity, providing a broader assessment of both woody and herbaceous layers. These data establish an important baseline for monitoring ecological change, evaluating woody vegetation dynamics under variable fire and herbivore regimes, and supporting ongoing research on savanna ecosystem functioning within the Kruger National Park.
Riparian Woody Vegetation Composition and Structure in Long-Term Monitoring Plots Along the Sabie River, Kruger National Park (2011-2012)
This dataset contains measurements of woody vegetation composition and structural attributes collected in 2009 and 2010 from 15 long-term riparian monitoring plots located along the Sabie River in the southern region of the Kruger National Park, South Africa. The Sabie River is the park’s most perennial river system and supports diverse riparian plant communities influenced by hydrological variability, flooding dynamics, sediment deposition, herbivory, and climate-driven disturbance. Within each monitoring plot, field teams recorded woody species identity, stem density, plant height, and stem diameter. Additional structural and condition indicators were collected, including canopy breakage, evidence of bark stripping, resprouting status, and whether individuals were toppled or alive at the time of sampling. These structural attributes provide detailed assessments of disturbance impacts and vegetation condition within riparian zones. Data collection followed the same standardized protocols as the Southern Granites long-term vegetation monitoring program, allowing for cross-site comparisons between upland savanna and riparian systems. This dataset provides a baseline for evaluating long-term ecological change in riparian woody plant communities and supports ongoing research on the ecological functioning and resilience of river corridors in Kruger National Park.
CBS05 Estimates of vegetation structure and composition collected on Konza Prairie watersheds and on the nearby Rannell’s Preserve
Data set includes estimates of vegetation structure and composition collected during ~monthly sampling events on Konza Prairie watersheds and on the nearby Rannell’s Preserve. Vegetation data were collected from three (prior to 2017) or 10 randomly-selected locations on each watershed; two from outside the 10-ha plot (see project abstract) and one inside the plot. We sampled vegetation on each watershed once a month, during May, June, and July. Additional vegetation data were collected from bird nest sites within ~3 days of nests failing. We used 5 sets of Daubenmire frame measures to determine percent cover of major plant functional groups (at the center of the plot and 5 m from center at the 4 cardinal directions). We estimated visual obstruction by placing a Robel Pole in the middle, and 5 m from the middle of the plot in each of the 4 cardinal directions. For each pole placement, we stood 4 m away with eye 1 m above the ground in each of 4 directions, and counting the highest 5-cm segment not completely obscured by vegetation. At nests, we also estimated the slope and aspect in the center of each plot.
Structural, ecological and biogeographical attributes of European vegetation alliances
<p>This is a database of structural, ecological and biogeographical attributes of 1115 European phytosociological alliances. The original version was published by Preislerová et al. (2024). This article also contained definitions and descriptions of individual attributes.</p> <p>Version 2 of this dataset has been updated to match Version 3 of EuroVegChecklist (Mucina et al. 2016) published on https://floraveg.eu/download/. This version contains syntaxonomic changes in the vegetation of coastal dunes (classes <em>Ammophiletea arundinaceae</em>, <em>Helichryso-Crucianelletea maritimae</em> and <em>Honckenyo peploidis-Leymetea arenarii</em>), Mediterranean pine forests (order <em>Pinetalia halepensis</em>) and bogs (class <em>Oxycocco-Sphagnetea</em>) approved by the European Vegetation Classification Committee in January 2024 following the proposals published by Marcenò et al. (2018, 2024), Bonari et al. (2021) and Jiroušek et al. (2022), respectively.</p> <p>The data are provided in two files with identical contents, one in the XLSX format, and the other in the TXT format with columns separated by tabs.</p> <p><strong>References</strong></p> <div> <div> <div> <ul> <li>Bonari G., Fernández‐González F., Çoban S., Monteiro‐Henriques T., Bergmeier E., Didukh Ya. P. … Chytrý, M. (2021). Classification of the Mediterranean lowland to submontane pine forest vegetation. <em>Applied Vegetation Science</em>, 24, e12544. <a href="https://doi.org/10.1111/avsc.12544">https://doi.org/10.1111/avsc.12544</a></li> <li>Jiroušek, M., Peterka, T., Chytrý, M., Jiménez-Alfaro, B., Kuznetsov, O.L., Pérez-Haase, A. … Hájek, M. (2022). Classification of European bog vegetation of the Oxycocco-Sphagnetea class. <em>Applied Vegetation Science</em>, 25, e12646. <a href="https://doi.org/10.1111/avsc.12646">https://doi.org/10.1111/avsc.12646</a></li> <li>Marcenò, C., Guarino, R., Loidi, J., Herrera, M., Isermann, M., Knollová, I. … Chytrý, M. (2018). Classification of European and Mediterranean coastal dune vegetation. <em>Applied Vegetation Science</em>, 21, 533–559. <a href="https://doi.org/10.1111/avsc.12379">https://doi.org/10.1111/avsc.12379</a></li> <li>Marcenò, C., Danihelka, J., Dziuba, T., Willner, W. & Chytrý, M. (2024). Nomenclatural revision of the syntaxa of European coastal dune vegetation. <em>Vegetation Classification and Survey</em>, 5, 27–37. <a href="https://doi.org/10.3897/VCS.108560">https://doi.org/10.3897/VCS.108560</a></li> <li>Mucina, L., Bültmann, H., Dierßen, K., Theurillat, J.-P., Raus, T., Čarni, A. … Tichý, L. (2016). Vegetation of Europe: Hierarchical floristic classification system of vascular plant, bryophyte, lichen, and algal communities. <em>Applied Vegetation Science</em>, 19(Suppl. 1.), 3–264. <a href="https://doi.org/10.1111/avsc.12257">https://doi.org/10.1111/avsc.12257</a></li> <li>Preislerová Z., Marcenò C., Loidi J., Bonari G., Borovyk D., Gavilán R.G., Golub V., Terzi M., Theurillat J.-P., Argagnon O., Bioret F., Biurrun I., Campos J.A., Capelo J., Čarni A., Çoban S., Csiky J., Ćuk M., Ćušterevska R., Dengler J., Didukh Ya., Dítě D., Fanelli G., Fernández-González F., Guarino R., Hájek O., Iakushenko D., Iemelianova S., Jansen F., Jašková A., Jiroušek M., Kalníková V., Kavgacı A., Kuzemko A., Landucci F., Lososová Z., Milanović Đ., Molina J.A., Monteiro-Henriques T., Mucina L., Novák P., Nowak A., Pätsch R., Perrin G., Peterka T., Rašomavičius V., Reczyńska K., Rūsiņa S., Sánchez Mata D., Santos Guerra A., Šibík J., Škvorc Ž., Stešević D., Stupar V., Świerkosz K., Tzonev R., Vassilev K., Vynokurov D., Willner W. & Chytrý M. (2024) Structural, ecological and biogeographical attributes of European vegetation alliances. <em>Applied Vegetation Science</em>, 27, e12766. <a href="https://doi.org/10.1111/avsc.12766">https://doi.org/10.1111/avsc.12766</a></li> </ul> </div> </div> </div>
Data from: Davison et al. (2023) Vegetation structure from LiDAR explains the local richness of birds across Denmark
<p>Environmental and biodiversity data associated with the article: Davison et al. (2023) <strong>Vegetation structure from LiDAR explains the local richness of birds across Denmark</strong>, <em>Journal of Animal Ecology</em>.</p> <p>Bird richness and abundance at points across Denmark, with matched land cover and LiDAR structural data. Bird observations are a subset of the Common Bird Monitoring programme (DOF – Birdlife Denmark) and pooled from summer counts of 2014, 15, and 16. Bird functional group assignments and environmental data are from open access data sets (see below).</p> <table> <tbody> <tr> <td>Data source</td> <td>Reference</td> </tr> <tr> <td>Danish Common Bird Monitoring programme</td> <td>Eskildsen, D. P., Vikstrøm, T., & Jørgensen, M. F. (2021). Overvågning af de almindelige fuglearter i Danmark 1975-2020. Dansk Ornitologisk Forening.</td> </tr> <tr> <td>EcoDes-DK15 LiDAR data set of Denmark</td> <td>Assmann, J. J., Moeslund, J. E., Treier, U. A., & Normand, S. (2022). EcoDes-DK15: high-resolution ecological descriptors of vegetation and terrain derived from Denmark’s national airborne laser scanning data set. Earth System Science Data, 14(2), 823–844. https://doi.org/10.5194/essd-14-823-2022</td> </tr> <tr> <td>Pan-European land cover map of the year 2015 </td> <td>Pflugmacher, D., Rabe, A., Peters, M., & Hostert, P. (2019). Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey. Remote Sensing of Environment, 221, 583–595. https://doi.org/10.1016/j.rse.2018.12.001</td> </tr> <tr> <td>AVONET bird traits data</td> <td>Tobias, J. A., Sheard, C., Pigot, A. L., Devenish, A. J. M., Yang, J., Neate-Clegg, M. H. C., Alioravainen, N., Weeks, T. L., Barber, R. A., Walkden, P. A., MacGregor, H. E. A., Jones, S. E. I., Vincent, C., Phillips, A. G., Marples, N. M., Montaño-Centellas, F., Leandro-Silva, V., Claramunt, S., Darski, B., … Schleunning, M. (2022). AVONET: morphological, ecological and geographical data for all birds. Ecology Letters, 25(3), 581–597. https://doi.org/10.1111/ele.13898</td> </tr> <tr> <td>Birds of the Palearctic - original source of trait data </td> <td>Cramp, S. (2006). The birds of the western Palearctic interactive. Oxford University Press and BirdGuides.</td> </tr> <tr> <td>Life-history characteristics of European birds - trait database</td> <td>Storchová, L., & Hořák, D. (2018). Life-history characteristics of European birds. Global Ecology and Biogeography, 27(4), 400–406. https://doi.org/10.1111/geb.12709</td> </tr> </tbody> </table>
Carbon Dynamics Along a Permafrost Gradient at Caribou-Poker Creeks Research Watershed (CPCRW) in Interior Alaska: Forest stand structure in a 75x75m spatial domain along a permafrost and vegetation gradient.
This dataset includes forest stand structure. Project summary: Specific leaf area (SLA, leaf area per unit dry mass) is a key canopy structural characteristic, a measure of photosynthetic capacity, and an important input into many terrestrial process models. Although many studies have examined SLA variation, relatively few data exist from high latitude, climate-sensitive permafrost regions. We measured SLA and soil and topographic properties across a boreal forest permafrost transition, in which forest composition changed as permafrost deepened from 54 to >150 cm over 75 m hillslope transects in Caribou-Poker Creeks Research Watershed, Alaska. This is an exploratory study to begin understanding SLA variation and controls thereof in a non-contiguous permafrost system.
Dataset for "Phylogenetic structure of European forest vegetation" - Journal of Biogeography (DOI: 10.1111/jbi.14046)
<p>This dataset contains the list of plant occurrences and geographical and environmental attributes of the vegetation-plots analyzed in the paper titled “Phylogenetic structure of European forest vegetation” by Padullés Cubino et al. (2021; Journal of Biogeography; DOI: 10.1111/jbi.14046). </p> <p>The dataset contains 3 tables:</p> <ol> <li>“Table_taxa.csv”: It includes the list of angiosperm plant taxa in selected vegetation plots.</li> <li>“Table_sites.csv”: It includes data on the environmental variables of plots, their classification into different forest types, their location in 1<sup>o</sup> × 1<sup>o</sup> grid cells, and the reference to the original datasets archived in the European Vegetation Archive (EVA; http://euroveg.org/eva-database-participating-databases).</li> <li>“Metadata.csv”: It includes a description of the fields found in the two previous tables.</li> </ol>
Vegetation structure and fuel dynamics in fire-prone, Mediterranean-type Banksia woodlands
<p>Increasing extreme wildfire occurrence globally is boosting demand to understand the fuel dynamics and fire risk of fire-prone areas. This is particularly pressing in fire-prone, Mediterranean climate-type vegetation, such as the Banksia woodlands surrounding metropolitan Perth, southwestern Australia. Despite an extensive wildland-urban interface and frequent fire occurrence, fuel accumulation and the spatial variation in fuel risk is not well quantified across the broad extent of this ecosystem. Using a space for time sampling approach to generate a chronosequence of time since fire, we selected sites that spanned across two distinct sandy soil types (Spearwood and Bassendean sands) and a rainfall gradient (550 to 750 mm north–south). We examined 82 sites in Banksia woodlands, southwestern Australia. Of the 82 sites, 44 burnt during the measurement period (2016 to 2021), which provided the opportunity for fuel measurements following fire (resulting in total N = 126). We wanted to answer two key questions: 1) How do measures of fuel load (mass) and arrangement (structure and continuity) vary across space and time, particularly with respect to time since the last fire? 2) How do biophysical drivers, such as soil type and rainfall, influence fuel accumulation and arrangement, and do these covariates improve litter fuel modelling beyond traditional asymptotic models? We found that fine surface fuel loads (litter and small twigs) differed between sand types, accumulating faster and reaching a higher peak on Spearwood sands (7–9 Mg ha−1) compared to Bassendean sands (6–7 Mg ha−1). Shrub layer fuel loads also accumulated faster on Spearwood sands than on Bassendean sands. While shrub layer fuels on Spearwood sands peaked at 14 years and declined thereafter, those on Bassendean sand did not decline over time but have lower overall connectivity. Total fine fuels (fine surface plus fine shrub layer fuels) had no significant decline over the same time period, on either sand type. Total fine fuel loads reached a peak of 9–10 Mg ha−1 between 13- and 20-years following fire, depending on the underlying sand type. Our quantitative fuel accumulation models confirmed the strength of time since fire as a predictor of hazard, but nonetheless included up to 40% unexplained variance. Importantly, while components fluctuated over time, the combined total of fine fuels did not decline with the long absence of fire, suggesting fire risk does not necessarily decrease in long unburned vegetation.</p>
Multi-temporal Structure from Motion ponit clouds of riparian vegetation
<p>the dataset consists of three pointclouds and two NIR orthomosaics generated through a Structure from Motion standard workflow of the same forested area. The study area is typical riparian habitat vegetation. The data were acquired in different phenological stages:</p> <p>the first acquisition was realised in leaves-off conditions (march 2020)</p> <p>The second acquisition was realised in June 2020</p> <p>the third acquisition was realised in July 2020.</p> <p>Reference system: WGS84/32N [EPGS: 32632]</p> <p>For further information regarding the data processing please refer to https://doi.org/10.3390/rs13091756<br> </p>
Fig. 1 in Relationship Between Grazing Intensity, Vegetation Structure And Survival Of Nests In Semi-Natural Grasslands
Fig. 1. Daily nest survival rate (±SE) for artificial ground nests according to edge vs. interior and grazing intensity in three grassland regions of Hungary
Fig. 2 in Relationship Between Grazing Intensity, Vegetation Structure And Survival Of Nests In Semi-Natural Grasslands
Fig. 2. Grass height and vegetation cover (mean±SE) around predated and not predated (intact) artificial ground nests in Hungarian grasslands. (**: P <0.01; ***: P <0.001)
Fig. 5. A in Ciliate Community Structure in Vegetation in Eastern Mexico Bromeliads of Different Types of
Fig. 5. A. Trait loadings of retained components in the PCA axes 1 and 2, (B) for each sampling carried out in the seven localities, and (C) for vegetation type and ciliate species richness according to the principal components of model A, of an ordination based on species richness, two continuous and two categorical functional traits of 24 ciliate species at Eastern Veracruz, Mexico. Model C shows prediction ellipses at 95% confidence interval to delimitate the vegetation types. Numbers in B refer to the localities. SDTF = Semideciduous tropical forest, C = Coffee plantation, MCF = Montane cloud forest, PF = Pinus forest.
Fig. 3 in Ciliate Community Structure in Vegetation in Eastern Mexico Bromeliads of Different Types of
Fig. 3. Dendogram of the Jaccard's similarities among localities based on the unweighted pair-group method analysis (UPGMA). For the name of the localities see table 1.
Fig. 2 in Ciliate Community Structure in Vegetation in Eastern Mexico Bromeliads of Different Types of
Fig. 2. Average of water temperature of the samples in relation to the localities were samples were collected. Bars indicate standard deviation.
Fig. 1. A in Ciliate Community Structure in Vegetation in Eastern Mexico Bromeliads of Different Types of
Fig. 1. A. Location of the seven localities of the study. B. Schematic representation of the vegetation from the mountain region to the seashore in east Veracruz, Mexico. L = locality, 1 = La Joya, Acajete; 2 = Santuario de Bosque de Niebla, Xalapa; 3 = Coffee plantation La Onza, Coatepec; 4 = Coffee plantation Arcos Vegas y Rincón de Yeguas, Tuzamapan; 5 = Tlacuitlapa, Jalcomulco; 6 = Unidad de Manejo Ambiental Nace El Río, Descabezadero, Actopan; 7 = Centro de Investigaciones Costeras La Mancha (CICOLMA), Actopan.
Fig. 3 in Effect of vegetation and abiotic factors on the abundance and population structure of Crocodylus acutus (Cuvier, 1806) in coastal lagoons of Colima, Mexico
Fig. 3. Dendrogram considering the crocodiles observed, water salinity, temperature, depth, and the four vegetation types present. Acronym definitions and characteristics of the sites are given in Table 1.
Fig. 1 in Effect of vegetation and abiotic factors on the abundance and population structure of Crocodylus acutus (Cuvier, 1806) in coastal lagoons of Colima, Mexico
Fig. 1. Selected sites in the study area. Acronym definitions and characteristics of the sites are given in Table 1.
Fig. 2 in Effect of vegetation and abiotic factors on the abundance and population structure of Crocodylus acutus (Cuvier, 1806) in coastal lagoons of Colima, Mexico
Fig. 2. Non-metric Multidimensional Scaling (NMDS) analysis showing the formation of two groups, by taking into account the crocodiles observed, water salinity, temperature, depth, and the four vegetation types present. Acronym definitions and characteristics of the sites are given in Table 1.
Vegetation structure and fuel dynamics in fire-prone, Mediterranean-type Banksia woodlands
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SGS-LTER Long-Term Monitoring Project: Vegetation Structure on Small Mammal Trapping Webs on the Central Plains Experimental Range, Nunn, Colorado, USA 1999 -2006, ARS Study Number 118
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83458. The abundance and diversity of small mammals in shortgrass steppe is strongly influenced by the structure and composition of vegetation. Vegetation structure provides cover from predators and harsh abiotic conditions. Plant species composition affects the types of seeds and herbaceous material available to granivores and herbivores, and influences arthropod populations, which are important prey for the omnivorous species that dominate in shortgrass steppe. Both vegetation structure and plant community composition are sensitive to the availability of precipitation as well as the activity of large mammalian herbivores. In 1999, we began measuring vegetation structure and plant community composition on the three grassland and three shrubland trapping webs where we live-trap small mammals. Vegetation measurements are made once each year, usually in mid-July. Percent canopy cover of each plant species was estimated visually in 30 0.10-m2 Daubenmire quadrats on each web. To estimate habitat structure, we measured the height of grass, forb and shrub plants adjacent to each quadrat, the density of half-shrubs, small mammal mounds and burrows, harvester ant mounds and the dimension
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