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95 results for “vegetation type”
Current and future European potential vegetation types
<p>This dataset contains Potential Natural Vegetation (PNV) estimates for the European continent at 1km grain size. Estimates are made for six different vegetation types following the MAES Ecosystem classification at level 1. The predictions have been made through an ensemble of Bayesian Habitat distribution models available through the <em>ibis.iSDM</em> package <a href="https://doi.org/10.1016/j.ecoinf.2023.102127" target="_blank" rel="noopener">(Jung 2023)</a>. For more information on the methodology, original data and used covariates, please see the accompanying preprint (<a href="https://doi.org/10.31223/X59H71">Jung 2024</a>).<br><br><strong>Uploaded are:</strong></p> <ul> <li>The most likely current PNV transition (see screenshot) as categorical raster (and screenshot, see png)<br>(Classes: 1=Woodland.and.forest | 2=Heathland.and.shrub | 3=Grassland | 4=Sparsely.vegetated.areas | 5=Wetlands | 6=Marine.inlets.and.transitional.waters)</li> <li>Current PNV estimates as cloud-optimized geoTIFF ("COG") files (.tif)</li> <li>Future PNV estimates (zipped) for each considered SSP - GCM combination as geoTIFF (.tif).</li> </ul> <p><strong>Variable naming scheme:</strong><br>Current: "pnv_XX_laea_1km.tif"<br>where XX represents the vegetation type<br>Future: Here the hierachical organization scheme of Essential Biodiversity Variables (EBV) is followed where files are separated in folders by<br>Scenario | metric | entity | time, so for example "SSP126-GFDL-ESM4/suitability_mean/grassland/"<br>Filenames are labelled by the date (e.g. "2040.tif").<br><br><strong>Metrics and layers names and their interpretation:</strong><br>For current:<br>"mean" = Average Ensemble posterior prediction<br>"sd" = Standard deviation of posterior prediction<br>"q05" = Lower percentile (5%) of posterior prediction<br>"q50" = Median or 50% percentile of posterior prediction<br>"q95" = Upper percentile (95%) of posterior prediction<br>"mode" = Most commonly encountered value of posterior prediction<br>"cv" = Coefficient of variation of posterior prediction<br><br>For future:<br>"mean" = Average Ensemble posterior prediction<br>"q05" = Lower percentile (5%) of posterior prediction<br>"q50" = Median or 50% percentile of posterior prediction<br>"q95" = Upper percentile (95%) of posterior prediction</p> <p>---<br><strong>Data properties:</strong></p> <table> <tbody> <tr> <td>Shared Socioeconomic Pathways (SSP)</td> <td>SSP1-2.6, SSP2-4.5, SSP5-8.5</td> </tr> <tr> <td>General circulation models (GCMs)</td> <td>GFDL-ESM4, <p>IPSL-CM6A-LR, </p> <p>MPI-ESM1-2-HR,</p> <p>MRI-ESM2-0,</p> <p>UKESM1-0-LL</p> </td> </tr> <tr> <td>Spatial grain</td> <td>1 km²</td> </tr> <tr> <td>Geographic projection</td> <td>LAEA</td> </tr> <tr> <td>Temporal grain</td> <td>30 year climatologies</td> </tr> <tr> <td>Spatial extent</td> <td>Continental Europe including Turkey (see screenshot)</td> </tr> <tr> <td>Temporal extent</td> <td>1990 to 2020 (Current), 2020 - 2100 (Future)</td> </tr> <tr> <td>Number of variables/entities</td> <td>7</td> </tr> </tbody> </table> <p>All files are provided as is and the author takes no responsibility for errors or misuse and misinterpretation. </p>
NICHE Flanders: reference values for the (a)biotic requirements of vegetation types in Flanders, Belgium
<p>This dataset contains site requirements/tolerance limits (or "reference values") for 28 vegetation types found in Flanders. It gives the lower and upper limits or the classes within which these vegetation types can occur, for 7 site factors that determine potential vegetation development. These reference values can be used to determine the potential distribution of the different vegetation types with the ecohydrological model NICHE Flanders (<a href="https://purews.inbo.be/ws/portalfiles/portal/5370206/Callebaut_etal_2007_NicheVlaanderen.pdf">Callebaut et al. 2007</a>, in Dutch).</p> <p>See the Technical info (available in English and Dutch) for more information.</p>
Data from: Will Current Protected Areas Harbour Refugia for Threatened Arctic Vegetation Types until 2050? A First Assessment
<p>We present predictions of Arctic vegetation for 2050 based on a combination of climate models (namely, EC-Earth3-Veg, IPSL-CM6A-LR, and MRI-ESM2-0), emission scenarios (names, SSP126 and SSP585) and tree dispersal rate scenarios (unrestricted, 20km and 5km) based on the methods of Pearson et al. (2013) and the new raster version of the Circumpolar Arctic Vegetation Map (CAVM) (Raynolds et al. 2019). We additionally present a dataset summarising total areas for each vegetation type in the CAVM and the forecasted models based on the computation of zonal histograms in ArcGIS (zonal_histogram_results.csv), for the total Arctic as well as only within protected areas, defined by the Map of Arctic Protected Areas (CAFF and PAME 2017). We also present a potential map of refugia for what we deem the realistic model (IPSL, SSP585, 20 km tree dispersal) as a raster file. Refugia were identified as regions where the vegetation remained the same between the CAVM and the predictions. Additionally, we present a map of model agreement, showing the degree to which other models agree with the vegetation classification for our refugia.</p> <p>All predictions named according to the tree dispersal rate, climate model, and emissions scenario, preceded by the term "pred". For example: "pred_unres_mri_585" represents the unrestricted tree dispersal, MRI-ESM-0 climate model, and SSP585 scenario-based prediction. The MRI-ESM-0 x SSP585 combination had gaps in data which results in a lack of predictions in some areas; this affects 3 models.</p> <p>Further details and all code associated with these datasets are found <a href="https://github.com/PlekhanovaElena/Arctic_vegetation_prediction">here</a>.</p>
A comparison among three ways to assemble wall-to-wall land-cover maps from distribution models of vegetation types
<p>Dataset accompanying manuscript <em>"A comparison among three ways to assemble wall-to-wall land-cover maps from distribution models of vegetation types". </em>Datasets contain a wall-to-wall map of vegetation types covering the study area of terrestrial Norway, produced using three methods for assembling individual predictions from Distribution models (<em>probability-based method</em>, <em>performance-based method</em> and <em>prevalence-based method</em>). </p>
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>
Camera trap data suggest uneven predation risk across vegetation types in a mixed farmland landscape
<p>Ground-nesting farmland birds such as the grey partridge (<em>Perdix perdix</em>) have been rapidly declining due to a combination of habitat loss, food shortage and predation. Predator activity is the least understood factor, especially its modulation by landscape composition and complexity. An important question is whether agri-environment schemes such as flower strips are potentially useful for reducing predation risk, e.g., from red fox (<em>Vulpes vulpes</em>). We employed 120 camera traps for two summers in an agricultural landscape in Central Germany to record predator activity (i.e., the number of predator captures) as a proxy for predation risk and used generalized linear mixed models (GLMMs) to investigate how the surrounding landscape affects predator activity in different vegetation types (flower strips, hedges, field margins, winter cereal and rapeseed fields). Additionally, we used 48 cameras to study the distribution of predator captures within flower strips. Vegetation type was the most important factor determining the number of predator captures and captures rates in flower strips were lower than in hedges or field margins. Red fox capture rates were the highest of all predators in every vegetation type, confirming their importance as a predator for ground-nesting birds. The number of fox captures increased with woodland area and decreased with structural richness and distance to settlements. In flower strips, capture rates in the centre were approximately 9 times lower than at the edge. We conclude that the optimal landscape for ground-nesting farmland birds seems to be open farmland with broad extensive vegetation elements and a high structural richness. Broad flower blocks provide valuable, comparatively safe nesting habitats and the predation risk can further be minimized by placing them away from woods and settlements. Our results suggest that adequate landscape management may reduce predation pressure. </p>
Dataset used in "Deep learning with multisite data reveals the lasting effects of soil type, tillage and vegetation history on biopore genesis"
<p>Please see the paper "Deep learning with multisite data reveals the lasting effects of soil type, tillage and vegetation history on biopore genesis" how the images were captured, manual counting was performed, training datasets were prepared and models were trained.</p>
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021). in Floristic, Vegetation And Climate Assessment Of The Early/Middle Miocene Parschlug Flora Indicates A Distinctly Seasonal Climate
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021).
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest.
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.
Figure 4 in Bird communities of different woody vegetation types from the Niraj Valley, Romania
Figure 4. Species richness (a), abundance (b), diversity (c), and equitability (d) in different phytocoenoses. St- Salicetum triandrae, SP- Salici–Populetum, CF- Carpino–Fagetum, CQ- Carpino–Quercetum petraeae, GQmu- Genisto tinctoriae– Quercetum petraeae subass. melicetosum uniflorae, PC- Pruno spinosae–Crataegetum, and orchards. Different letters signify P <0.05.
Figure 3 in Bird communities of different woody vegetation types from the Niraj Valley, Romania
Figure 3. The abundance of different ecological categories in the studied plant associations. O- Orchards, PC- Pruno spinosae–Crataegetum, SP- Salici–Populetum, St- Salicetum triandrae, and forests.
Fig. 1. Location and vegetation types where small mammals were sampled between November 2012 and September 2013 in Small mammals from the lasting fragments of Araucaria Forest in southern Brazil: a study about richness and diversity
Fig. 1. Location and vegetation types where small mammals were sampled between November 2012 and September 2013, at Piraí do Sul National Forest, ParanÁ state, Brazil (A, Pine Plantation; B, Riparian Forest; C, Araucaria Plantation; D, Natural Regeneration forest; E, High Altitude forest). Original distribution of Atlantic Forest biome (light gray) and Araucaria forest (dark gray).
LT-Brazil: A database of leaf traits across biomes and vegetation types in Brazil
<p><span>Motivation: Leaf traits represent an important component of plant functional strategies, and those related to carbon fixation and nutrient acquisition form the leaf economics spectrum. However, observations of functional leaf traits are underrepresented in tropical regions in comparison with those in temperate areas. Brazil, a country with continental scale and vast biodiversity is a timely example, where many biomes are impacted by human activities and climate change. However, leaf traits relevant to understand vegetation responses to these impacts remain poorly quantified for many species found in the country. We compiled an extensive data set of four functional leaf traits for native woody species occurring in the Brazilian territory. In addition to trait observations, sampling dates and geo-references were compiled and climatic parameters and soil properties of each sampling site were extracted from several databases.</span></p> <p><span>Main types of variables contained: The LT-Brazil data set contains 3479, 1216, 775, and 775 clean observations of leaf mass per area, leaf nitrogen (N) concentration per unit mass, leaf phosphorus (P) concentration per unit mass, and leaf N : P ratio, respectively, from native woody species, encompassing information of biome, vegetation, taxonomic data, geographical coordinates, climatic parameters, as well as soil properties.</span></p> <p><span>Spatial location and grain: We compiled trait observations from 223 sites under native vegetation distributed in all main biomes (i.e., Amazônia, Caatinga, Cerrado, Mata Atlântica, Pampa, and Pantanal) across the Brazilian territory.</span></p> <p><span>Time period and grain: The data represent information published and/or sampled during the last 25 years.</span></p> <p><span>Major taxa and level of measurement: Our compilation was focused on trait data observed for native woody species, excluding monocots, palm trees, herbs, and hemiparasitic plants. Thus, 108, 478, and 1321 botanical families, genera, and species were included, covering <em>c.</em> 9% of the woody angiosperm flora of Brazil.</span></p> <p>Software format: Data are provided as comma-separated value (.csv) files.</p>
Figs 3, 4 in The megadrile fauna (Annelida: Oligochaeta) of Queen Elizabeth Park, South Africa: species composition and distribution within different vegetation types
Figs 3, 4. Tritogenia howickiana: (3) anterior part of the body, showing papillae and tubercula pubertatis ventrally; (4) dorsal dissection showing thickened septa. Abbreviations: Cl – clitellum, DB – double blood vessel, P – papillae of 15–17, S – septa 4/5–6/7, Tp – tubercula pubertatis,.
Fig. 8. Peruvian vegetation types. A. Amazonian forest, Pasco Region. B. Northwest Peruvian montane forest, Piura Region. C. Dry forest, Piura Region. D in The genus Begonia (Begoniaceae) in Peru
Fig. 8. Peruvian vegetation types. A. Amazonian forest, Pasco Region. B. Northwest Peruvian montane forest, Piura Region. C. Dry forest, Piura Region. D. Lomas, Lima Region. All photographs taken by P.W. Moonlight.
FIG. 4 in Lichen community assemblages and functional traits as indicators of vegetation types in central Mexico, based on herbarium specimens
FIG. 4. — Community weighted mean proportions of functional traits for each vegetation type: A, growth forms; B, substrate; C, reproductive structure types; D, photobionts.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.