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3,105 results for “vegetation”
Vegetation Maps of the Early Pleistocene Guadix-Baza Basin
<p>Following a methodology based on fossil material, paleogeographic data and paleoclimate calculations allows generating maps of the Early Pleistocene vegetation units of Guadix-Baza Basin for both glacial and interglacial scenarios.</p> <p>The resulting vegetation maps represent a great diversity of vegetation types in the Guadix-Baza Basin, with seven different units which change their distribution according to climatic changes, i.e., dry (glacial) and humid (interglacial) periods. During dry periods the dominant vegetation type is the steppe, with Mediterranean woodlands and deciduous and conifer forests largely reduced and restricted to valleys or mountainous areas. During humid periods, the steppes are replaced by open Mediterranean woodlands, while deciduous and conifer forests occupy larger areas in the mountain ranges.</p> <p>For additional information check the publication: Altolaguirre, Y., Schulz, M., Gibert, L., Bruch, A.A., 2021. Mapping Early Pleistocene environments and the availability of plant food as a potential driver of early <em>Homo</em> presence in the Guadix-Baza Basin (Spain). Journal of Human Evolution, ----.</p>
Early Eocene Global Vegetation Modern Plant Distribution Dataset
<p>Early Eocene Global Vegetation Modern Plant Distribution Dataset </p> <p>Global occurances for early Eocene fossil plant Nearest Living Relatives (NLRs) from the Global Biodiversity Information Facility (GBIF: https://www.gbif.org/), used for palaeocliate reconstruction.</p>
FIGURE 1 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 1. Geographic sketch showing the location of the plant-bearing sites. For locality numbers see Table 1.
FIGURE 6 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 6. Representation of modern European vegetation formations for the test set of fossil assemblages as delivered by Drudges 1 and 2. Formation H - Hygrophilous thermophytic mixed deciduous broadleaved forests; Formation G - Thermophilous mixed deciduous broadleaved forests; Formation F - Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation D - Mesophytic and hygromesophytic coniferous and mixed broadleaved-coniferous forests; Formation C - Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation. More detailed information on subdivisions and units is available in Appendix 9.
FIGURE 4 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 4. Representation of East Asian and European vegetation types and formations as delivered by Drudges 1 and Drudge 2 for the IPR Similarity, Taxonomic Similarity (TS), and Results Mix. See also Appendix 8.
FIGURE 8 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 8. Mean annual temperature (MAT), warm-month mean temperature (WMMT), and cold-month mean temperature (CMMT) based on CLAMP and the Coexistence Approach (CA) for the fossil plant record (sources are Kvaček et al., 2011; Teodoridis and Kvaček, 2015; Teodoridis et al., 2009, 2012, 2015, 2017). black columns: minimum CA. light grey columns: maximum CA, narrow, dark grey columns: CLAMP result. For more comprehensive climate data see Appendix 10.
FIGURE 9 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 9. Climate parameters of the modern European vegetation Formations F, G, and H based on Bohn et al. (2004) and Traiser and Mosbrugger (2004) represented as columns spanning the minimum and maximum of the respective data. Vegetation of Formation F tends to lower temperatures (note, however, that climate data for formations F.3 – F.1 are more complex). Vegetation of Formation G tends to lower MAP. Asterisks indicate single data points (no climate interval was available). The data are listed in Appendix 11. Abbreviations: MAT = mean annual temperature; WMMT = warm-month mean temperature; CMMT = cold-month mean temperature; MAP = mean annual precipitation.
FIGURE 3 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 3. Modern vegetation types/formations delivered as proxies by Drudges 1 and 2 for the test set of fossil assemblages. Shown are the five best fitted results for the Taxonomic Similarity (TS) and the overall scores (synthesis of all similarity approaches), i.e., 25 proxies for every plant assemblage. Pastel colours represent East Asian vegetation types, bright colours European vegetation formations. For more detailed information see Appendix 4 which provides interactive colour signature (moving the cursor over the columns provides the designation of the proxies and their relevance for every fossil assemblage).
FIGURE 2 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 2. Modern vegetation types/formations delivered as proxies by Drudges 1 and 2 for the test set of fossil assemblages. Shown are the five best fitted results for the IPR Similarities based on Drudge 1 and Drudge 2 and for the Results Mix based on Drudge 1 and Drudge 2. Pastel colours represent East Asian vegetation types, bright colours European vegetation formations. For more detailed information see Appendix 4 which provides interactive colour signature (moving the cursor over the columns provides the designation of the proxies and their relevance for every fossil assemblage).
FIGURE 7 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 7 (previous page). Representation of modern European vegetation formations for the test set of fossil assemblages as delivered by Drudges 1 and 2 in more detail (see also Appendix 9). Formation H: H001, Colchic lowland to submontane mixed oak forests, in black; H002, Hyrcanian lowland-colline mixed broadleaved forests, in dark grey; H003, Hyrcanian colline to montane oak forests, in light grey. Formation G: G.1 - Subcontinental thermophilous (mixed) pedunculate oak and sessile oak forests, in black; G.2 - Sub-Mediterranean-subcontinental thermophilous bitter oak and Balkan oak and mixed forests, in dark grey; G.3 - Sub-Mediterranean and meso-supra-Mediterranean downy oak and mixed forests, in light grey; G.4 - Iberian supra- and meso-Mediterranean oak forests, in white. Formation F: F.1 - Species-poor acidophilous oak and mixed oak forests, in black; F.2 - Mixed oak-ash forests, in dark grey; F.3 - Mixed oak-hornbeam forests, in light grey; F.4 Lime-pedunculate oak forests, in white; F.5 - Beech and mixed beech forests, hatched lower left to upper right; F.6 - Oriental beech forests and hornbeam-oriental beech forests, hatched upper left to lower right; F.7 - Caucasian mixed hornbeam-oak forests, hatched vertically. Formation F, F.5 - Beech and mixed beech forests: F.5.1.1 - Species-poor oligotrophic to mesotrophic beech and mixed beech forests, lowland(-colline) types, in black; F.5.1.2 - Species-poor oligotrophic to mesotrophic beech and mixed beech forests, colline-submontane types, in dark grey; F.5.1.3 - Species-poor oligotrophic to mesotrophic beech and mixed beech forests, montane-altimontane types, in light grey; F.5.2.1 - Species-rich eutrophic and eu-mesotrophic beech and mixed beech forests, colline-submontane types, in white; F.5.2.2 - Species-rich eutrophic and eu-mesotrophic beech and mixed beech forests, colline-submontane types, hatched lower left to upper right; F.5.2.3 and 4 - Species-rich eutrophic and eu-mesotrophic beech and mixed beech forests, montane-altimontane types, hatched upper left to lower right. Formation D: D.1 - Western boreal spruce forests, in black; D.2 - Eastern boreal pine-spruce and fir-spruce forests, in dark grey; D.3 - Hemiboreal spruce and fir-spruce forests with broad-leaved trees, in light grey; D.4 - Montane to altimontane, partly submontane fir and spruce forests in the nemoral zone, in white; D.5 - Boreal and hemiboreal pine forests, hatched lower left to upper right; D.6 - Montane to altimontane (subalpine) pine forests in the nemoral zone; hatched upper left to lower right.
Figure 1 in Vegetation of the supralittoral and upper sublittoral zones of the Western German Baltic Sea coast: a phytosociological study
Figure 1: Map of sampling sites in Northern Germany. Insets (a–c) provide higher resolution. Sampling areas: 1 – Glücksburg; 2 – Glücksburg Estuary; 3 – Bockholmwik; 4 – Neukirchen; 5 – Norgaardholz; 6 – Falshoft; 7 – Maasholm, Schlei; 8 – SchÖnhagen; 9 – Fischleger; 10 – Karlsminde; 11 – EckernfÖrde, port; 12 – EckernfÖrde, Kiekut; 13 – Aschau, sea; 14 – Aschau, lagoon; 15 – Kiel-Bülk; 16 – Kiel-Schilksee, marina; 17 – Kiel-Friedrichsort; 18 – Kiel-Holtenau, Tonnenhof; 19 – Kiel-Düsternbrook; 20 – Kiel-MÖnkeberg; 21 – Kiel-Heikendorf, Hafen; 22 – Kiel-Laboe; 23 – Kiel-Marina Wendtorf; 24 – Kiel-Brasilien; 25 – Hohwacht; 26 – Weissenhäuser Strand; 27 – Heiligenhafen, sea; 28 – Heiligenhafen, Binnensee; 29 – Heiligenhafen, marina; 30 – Grossenbroderfahre; 31 – Strukkamphuk, Fehmarn; 32 – Westerberg, Fehmarn; 33 – Flügge, Orther Bucht, Fehmarn; 34 – Gruner Brink, Fehmarn; 35 – Burgtiefe, Fehmarn; 36 – Burger Binnensee, Fehmarn; 37 – Wulfen, Fehmarn; 38 – Marina Grossenbrode; 39 – Süssau; 40 – Kellenhusen; 41 – Neustadt, Binnenwasser; 42 – Brodtener Ufer; 43 – Rosenhagen; 44 – Steinbeck; 45 – Boltenhagen; 46 – Wohlenberg; 47 – Hohen-Wieschendorf; 48 – Zierow; 49 – Redentin; 50 – Bridge to Poel, S side; 51 – Bridge to Poel, N side; 52 – Kirchdorf; 53 – Timmendorf, Poel; 54 – Gollwitz, Poel.
Figure 4 in Vegetation of the supralittoral and upper sublittoral zones of the Western German Baltic Sea coast: a phytosociological study
Figure 4: Number of species from different phytogeographical elements in each macrophyte community of the SW Baltic Sea. Phytogeographical elements are indicated in accordance with Cormaci et al. (1982), supplemented by data from Zinova (1962) and Kalugina-Gutnik (1975): C – Cosmopolitan, SC – Sub-cosmopolitan, AP – Atlanto-Pacific, IP – Indo-Pacific, CB – Circumboreal, IA – Indo-Atlantic, Abt – Boreo-tropical Atlantic, CT – Circumtropical, Ab – Boreo-Atlantic, Aba – Boreo-Arctic Atlantic, Pb – Boreo-Pacific.
Figure 3 in Vegetation of the supralittoral and upper sublittoral zones of the Western German Baltic Sea coast: a phytosociological study
Figure 3: Distribution of the macrophyte communities of the SW Baltic Sea in habitats with different exposure. The y-axis shows the proportion of habitats with different exposure grades in which the different communities were found.
Figure 2 in Vegetation of the supralittoral and upper sublittoral zones of the Western German Baltic Sea coast: a phytosociological study
Figure 2: Maximum likelihood phylogram based on tufA sequence data, showing the phylogenetic relationships of 12 Ulvales samples from the Baltic Sea (bold) identified in this study. Numbers after species names indicate collection sites (see Figure 1). Numbers below branches are bootstrap values; poorly supported nodes (>0.70) are not labelled. Branch lengths are proportional to sequence divergence.
Data for fitting a statistical global burned area model for seamless integration into Dynamic Global Vegetation Models
<p>The dataset is a large R data.table object saved in RDS format. It contains global, monthly data spanning the period from 2002 to 2018, with a 0.5 degrees spatial resolution. The dataset is utilized to develop and validate statistical models for predicting global burnt areas resulting from wildfires.</p>
Experimental Data for Wave Decay by Submerged Rigid Vegetation under Orthogonal Wave-Current Conditions
<p>This dataset includes wave amplitude decay data, force prediction and measurement data (organized in spreadsheets), and phase-averaged force measurement data stored in a MATLAB <code>.mat</code> file. The accompanying paper, titled <em>"Wave Decay by Submerged Rigid Vegetation under Orthogonal Wave-Current Conditions,"</em> will be published in <em>Geophysical Research Letters.</em> A detailed description of the variables is provided at the end of each spreadsheet. The detailed measurement methods are described in the paper. The data in the <code>.mat</code> file is arranged according to the experimental case order specified in the spreadsheet named <em>"force measurement."</em></p>
Response of Vegetation Canopy Growth to Climate Change in Northeast China
<p>Our study uniquely addresses gaps in existing research by investigating how vegetation canopy changes during various growth phases—development (April-June), maturation (July-August), and senescence (September-October)—and how these changes respond to preseason climatic factors. We highlight significant findings, such as the early advancement of the canopy maturation phase and the delayed senescence, particularly in forested areas. Moreover, we demonstrate that preseason air temperature exerts a considerable influence on canopy growth, with a transition from positive to negative correlations across different phases and vegetation types.The results contribute to understanding vegetation dynamics under climate change and provide actionable insights for sustainable agricultural, forestry, and animal husbandry management.</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>
EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set
<p><strong>Eighteen high-resolution ecological descriptors of vegetation and terrain for Denmark "EcoDes-DK15"</strong></p> <p>The data are derived from the nationwide airborne laser scanning / LiDAR campaign of Denmark from 2014-2015 provided by the Danish Agency for Data Supply and Efficiency.</p> <p><strong>Update: EcoDes-DK15 v1.1.0 (4 Dec. 2021)</strong></p> <p>Following the recommendations and feedback during the first round of peer-review, we updated the EcoDes-DK processing pipeline and EcoDes-DK15 data set. The key changes are:</p> <ul> <li>New version of the source data optimised to contain only point data collected before the end of 2015. The source data for EcoDes-DK15 v1.0.0 unintentionally contained data from 2018. The new source data is documented <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/documentation/source_data/readme.md">here</a>.</li> <li>New "date_stamp_*" auxiliary variables that illustrate the survey dates for the vegetation points in each cell. See updated descriptor documentation <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/documentation/descriptors.md">here</a>.</li> <li>Re-scaling of "solar_radiation" variable to MJ per 100 m<sup>2</sup> per year.</li> </ul> <p><strong>Detailed documentation for the data set can be found in the accompanying manuscript and GitHub repository:</strong></p> <p>Assmann, J. J., Moeslund, J. E., Treier, U. A., and Normand, S.: EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2021-222">https://doi.org/10.5194/essd-2021-222</a>, in review, 2021<strong><em>.</em></strong></p> <p><a href="https://github.com/jakobjassmann/ecodes-dk-lidar">https://github.com/jakobjassmann/ecodes-dk-lidar</a></p> <p>Files are compressed using bzip2 and tar archiving. The compressed archives can be extracted using commonly available archiving tools (for example <a href="https://www.7-zip.org/">7z </a>on Windows, the archiving tool on macOS and bz2 on Linux). </p> <p>A small example "teaser" subset (5 MB) of the data set, covering the Husby Klit area from Figure 7 in the manuscript, can be found <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/manuscript/figure_7/EcoDes-DK15_teaser.zip">here</a>.</p> <p><strong>Abstract (from manuscript)</strong></p> <p>Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as Light Detection and Ranging (LiDAR). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote-sensing knowledge. We processed Denmark’s publicly available national Airborne Laser Scanning (ALS) data set acquired in 2014/15 together with the accompanying elevation model to compute 70 rasterized descriptors of interest for ecological studies. With a grain size of 10 m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than forty thousand square kilometres covering almost all of Denmark’s terrestrial surface. The resulting data set is comparatively small (~94 GB, compressed 16.8 GB) and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark’s national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterized data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond.</p> <p><strong>Acknowledgements (from manuscript)</strong></p> <p>We would like to thank Andràs Zlinszky for his contributions to earlier versions of the data set, Charles Davison for feedback regarding data use and handling, as well as Matthew Barbee and Zsófia Koma for sharing their insights on the source data merger and Zsófia’s script to generate summary statistics for the different versions of the DHM point clouds. Funding for this work was provided by the Carlsberg Foundation (Distinguished Associate Professor Fellowships) and Aarhus University Research Foundation (AUFF-E-2015-FLS-8-73) to Signe Normand (SN). This work is a contribution to SustainScapes – Center for Sustainable Landscapes under Global Change (grant NNF20OC0059595 to SN).</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>
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