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691 results for “Vegetation Data”
Data and scripts for: Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping
<p>Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high-throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle-based high-throughput phenotyping platforms (UAV-HTPPs) provide novel opportunities for large-scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV-based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at 5 time points across the growing season for 1,752 diverse maize accessions with a UAV-HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into 2 groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized-splines (P-splines) model was used to obtain genotype-specific curve parameters. Genome-wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P-splines models revealed the dynamic change of genetic effects, indicating the role of gene-environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra-high spatial resolution multispectral imagery, as that acquired using a UAV-based remote sensing, for genetic dissection of NDVI.</p>
Supplementary data to The vegetation of Chile and the EcoVeg approach in the context of the International Vegetation Classification project
<p>The rar file contains a map of Macrogroups of Chile in ESRI shapefile format. Macrogroups are hierarchically included in the categories of division and formation of IVC classification. These categories can also be displayed using the table associated with the shapefile. Likewise, Chilean zonal vegetation units of Luebert & Pliscoff (2017) are included, so the crosswalk for generating the map of Macrogroups based on the Chilean zonal vegetation units is fully documented.</p>
Data of complementary experiments of 'Vegetation-induced hyporheic exchange experiment in Ecoflume of St. Anthony Falls Laboratory on 2021'
<p>Complementary dye release experiments were conducted to support the results of pervious vegetation-induced hyporheic exchange experiments. This data set includes the raw data of dye release experiments with a side-looking camera and dye calibration.</p> <p><br> The data of pervious expirements have been deposited in Data Repository for University of Minnesota (https://doi.org/10.13020/W282-JJ11).</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>
Data for "Vegetation complexity and pool size predict species richness of forest birds"
<p>This xlsx file contains data needed to replicate analyses in our article on species richness of Australian passerine birds.</p> <p>AUTHOR of data files: Vladimir Remes<br> CONTACT: vlad.remes/at/gmail,com<br> AUTHORS of the MS: V. Remes, L. Harmacková, B. Matysiokova, L. Rubacova, E. Remesova<br> DATE CREATED: 8 June 2022</p> <p>The file was saved in MS Excel for Mac 16.42.</p> <p>The file is named "data_SR_AU_passerines.xlsx" and has two sheets:<br> "data": contains the data<br> "legend": contains explanations of data columns</p> <p>The related article is:</p> <p>Remeš V, Harmáčková L, Matysioková B, Rubáčová L and Remešová E (2022) Vegetation complexity and pool size predict species richness of forest birds. Front. Ecol. Evol. 10:964180. doi: 10.3389/fevo.2022.964180</p>
Presence data for vascular plant, bryophyte and lichen species in 100 vegetation plots (each 1 m2) from 32 shell-beds at Akerøya, Hvaler, SE Norway
<p><strong>We present a data set consisting of abundance data for 106 vascular plant species, 36 bryophyte species and 13 lichen species from 100 vegetation plots, each 1 m2, distributed on 32 shell-beds at Akerøya, Hvaler municipality, former Østfold (in 2022 Viken) county. The plots were analysed with respect to species composition in June 1979. These data formed the basis for the publication: Halvorsen, R. 1980. Numerical analysis and successional relationships of shell-bed vegetation at Akerøya, Hvaler, SE Norway. Norw. J. Bot. Vol. 27 pp. 71-95. Oslo. ISSN 0300-1156.</strong></p>
Data from: Historic deforestation and non-native plant invasions determine vegetation trajectories across an oceanic archipelago
<p>This archive contains data produced in a study of the vegetation trajectories of Ogasawara Islands in 77 years related to following article:</p> <p>Ohashi, H., Kato, H., Murao, M., Kato, H., Kawakami, K., Kurokawa, H., Oguro, M., Kimura, F., Niiyama, K., Matsui, T., and Shibata, M. (2024) Historic deforestation and non-native plant invasions determine vegetation trajectories across an oceanic archipelago. <em>Applied Vegetation Science</em>, 27 (1), e12767. <a href="https://doi.org/10.1111/avsc.12767">https://doi.org/10.1111/avsc.12767</a></p> <p> </p> <p><strong>Archive contents</strong><br>The archive contents are organized into five parts, each stored as a .zip compressed file.</p> <p><strong>X1_tif_original_vegmap_scan_georeference</strong></p> <p>Scanned and georeferenced original vegetation maps in GeoTiff format, which was drawn in 1935, scanned at 300 dpi. Coordinate reference system was set at WGS84 (ESPG: 4326).</p> <p>This directory includes:</p> <p><em>kitanoshima_isl_WGS84.tif<br>mukojima_isl_WGS84.tif<br>yomejima_isl_WGS84.tif<br>ototojima_isl_WGS84.tif<br>anijima_isl_WGS84.tif<br>nishijima_isl_WGS84.tif<br>chichijima_isl_WGS84.tif<br>hahajima_isl_WGS84.tif<br>mukohjima_isl_WGS84.tif<br>kitaiwoto_isl_WGS84.tif<br>iwoto_isl_WGS84.tif</em></p> <p> </p> <p><strong>X2_shp_vegmap</strong></p> <p>Shapefile of the geospatial polygon data of vegetation map of Ogasawara Islands surveyed in 1935, and stored as a .zip compressed file. Coordinate reference system was set at WGS84 (ESPG: 4326).</p> <p>This directory includes:</p> <p><em>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.dbf<br>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.prj<br>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.shp<br>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.shx<br>attribute_ForSect_code_en.csv<br>attribute_Veg_name_en.csv<br>metadata_vegmap_shp_ogasawara1935_en.csv</em></p> <p>Following files includes Japanese character (which may corrupt in non-Japanese environment):</p> <p><em>attribute_ForSect_jp.csv<br>attribute_Veg_name_jp.csv<br>metadata_vegmap_shp_ogasawara1935_jp.csv</em></p> <p> </p> <p><strong>X3_tif_vegmap_converted_from_shp</strong></p> <p>Rasterized data of polygon data of vegetation map for analysis. Coordinate reference system was set at JGD2000 / Japan Plane Rectangular CS XIV (EPSG: 2456)</p> <p>This directory includes:</p> <p><em>vegmap_1935.zip (compressed “vegmap_1935.tif (0.7GB)”)<br>vegnap_1979.zip (compressed “vegmap_1979.tif (1.5GB)”)<br>vegmap_2011.zip (compressed “vegmap_2011.tif (1.5GB)”)<br>islcode_raster.zip (compressed “vegmap_2011.tif (1.5GB)”)<br>attribute_integratedveg_ecoltype.csv<br>attribute_vegid_1935.csv<br>attribute_vegid_1979.csv<br>attribute_vegid_2011.csv</em></p> <p> </p> <p><strong>X4_scanned_image_vegdata</strong></p> <p>Scanned images of original vegetation data in 1935.</p> <p>The directory includes:<br><em>vegetation_survey_sheet_1.pdf<br>vegetation_survey_sheet_2.pdf</em><br><em>vegetation_survey_sheet_3.pdf</em></p> <p> </p> <p><strong>X5_digitized_vegdata</strong></p> <p>Digitized vegetation data.</p> <p>The directory includes:<br><em>plot_species_abundance_matrix_v0.csv<br>plotinfo_v0.csv<br>attribute_Species_en_v0.csv</em></p> <p>Following file includes Japanese character (which may corrupt in non-Japanese environment)<br><em>attribute_Species_jp_v0.csv</em><br> </p> <p><strong>X6_code_for_analysis</strong></p> <p>Tentative.</p> <p> </p> <p>このアーカイブには、小笠原諸島の77年間の植生の変遷(1935年、1979年、2012年)に関するデータが含まれています。</p> <p> </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>
Data from: Grazing by non-native ungulates negatively impacts vegetation important to a native species of concern
<p>Non-native grazers compete with native species across the globe. In the northwestern Great Basin of the western United States competition among livestock, feral horses, and Greater Sage-grouse has been the subject of numerous legal actions and management policies, yet spatially explicit temporal data documenting the details of this competition are lacking. We present a novel approach to studying the composition of the herbaceous understory across three study areas within the Great Basin with different historic and contemporary grazing regimes. We surveyed the landscape using distance sampling for livestock and horse feces as an index of use. In addition, we surveyed the herbaceous understory of random sites as well as sites chosen by female Greater Sage-grouse to nest and brood their chicks. We used a novel Bayesian hierarchical modeling framework to link vegetation metrics with the spatial-temporal distribution of horses and livestock while accounting for observation error. When livestock and feral horses were not present, we found that Greater Sage-grouse chose sites with higher percentages of perennial grasses and forbs to build their nests and brood their chicks compared to what was available to them. As livestock increased, we found evidence for decreases in the percentage of perennial grasses, forbs, cheatgrass (<em>Bromus tectorum</em>), and increases in the amount of bare ground. These effects were consistent at available sites and brood sites, however, we found less evidence for an impact of livestock at nest sites. As feral horses increased, we observed similar results at available sites, but at sites chosen by females to nest and brood their chicks, we observed increases in the amount of invasive cheatgrass as feral horses increased, which could reflect attempts by Greater Sage-grouse to compensate for reductions in protective cover. We present a noninvasive approach to assess space use that can be applied to other species. More importantly, we document that grazing by non-native ungulates impacts components of the plant community important to Greater Sage-grouse reproduction. We provide spatial-temporal maps of livestock and feral horse use to aid managers attempting to balance the needs of livestock producers, feral horses, Greater Sage-grouse, and ecosystem function.</p>
Text-fig. 1. Location of Wiesa fossil site in eastern Germany and other fossil sites for comparison. Explanation for map b: all fossil sites – black circles; grey circles – cities; topographic names in italics – German states (Länder). For bio- and lithostratigraphic data of fossil sites, see chapter Methodologies and material and Text-fig. 3. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 1. Location of Wiesa fossil site in eastern Germany and other fossil sites for comparison. Explanation for map b: all fossil sites – black circles; grey circles – cities; topographic names in italics – German states (Länder). For bio- and lithostratigraphic data of fossil sites, see chapter Methodologies and material and Text-fig. 3.
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part III: Extra runs)
<p>This is Part III of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files for the extra simulations used in the paper (Figures S3, S8-S10).</p> <p>Each zip file corresponds to a model run. </p> <p>TIGER_XX.zip: Scenario XX, hydro-morphodynamics and vegetation dynamics, years 0-100.<br>TIGER_XX_100.zip: Scenario XX, hydro-morphodynamics and vegetation dynamics, years 100-200.<br>TIGER_XX_HYYY.zip: Scenario XX, hydro-morphodynamics only, year YYY.</p> <p>Main scenarios:<br>- 01: Spartina (Figures 1-5, S3-S10)<br>- 02: Salicornia (Figures 1-5, S3-S10)<br>- 83: No vegetation (Figures 1-5, S3, S8-S10)</p> <p>Additional scenarios:<br>- 146: Spartina, low bulk drag coefficient (Figure S3)<br>- 147: Spartina, very low bulk drag coefficient (Figure S3)<br>- 148: Salicornia, low bulk drag coefficient (Figure S3)<br>- 149: Salicornia, very low bulk drag coefficient (Figure S3)<br>- 122: Spartina, low settling velocity (Figure S8)<br>- 123: Spartina, high settling velocity (Figure S8)<br>- 124: Salicornia, low settling velocity (Figure S8)<br>- 125: Salicornia, high settling velocity (Figure S8)<br>- 126: No vegetation, low settling velocity (Figure S8)<br>- 127: No vegetation, high settling velocity (Figure S8)<br>- 128: Spartina, low critical bed erosion shear stress (Figure S8)<br>- 129: Spartina, high critical bed erosion shear stress (Figure S8)<br>- 130: Salicornia, low critical bed erosion shear stress (Figure S8)<br>- 131: Salicornia, high critical bed erosion shear stress (Figure S8)<br>- 132: No vegetation, low critical bed erosion shear stress (Figure S8)<br>- 133: No vegetation, high critical bed erosion shear stress (Figure S8)<br>- 134: Spartina, low Partheniades constant (Figure S8)<br>- 143: Spartina, high Partheniades constant (Figure S8)<br>- 136: Salicornia, low Partheniades constant (Figure S8)<br>- 144: Salicornia, high Partheniades constant (Figure S8)<br>- 138: No vegetation, low Partheniades constant (Figure S8)<br>- 145: No vegetation, high Partheniades constant (Figure S8)<br>- 150: Spartina, low sediment dry bulk density (Figure S8)<br>- 151: Spartina, high sediment dry bulk density (Figure S8)<br>- 152: Salicornia, low sediment dry bulk density (Figure S8)<br>- 153: Salicornia, high sediment dry bulk density (Figure S8)<br>- 154: No vegetation, low sediment dry bulk density (Figure S8)<br>- 155: No vegetation, high sediment dry bulk density (Figure S8)<br>- 76: Spartina, replicate #1 (Figures S9-S10)<br>- 77: Spartina, replicate #2 (Figures S9-S10)<br>- 78: Spartina, replicate #3 (Figures S9-S10)<br>- 88: Spartina, replicate #4 (Figures S9-S10)<br>- 80: Salicornia, replicate #1 (Figures S9-S10)<br>- 81: Salicornia, replicate #2 (Figures S9-S10)<br>- 82: Salicornia, replicate #3 (Figures S9-S10)<br>- 89: Salicornia, replicate #4 (Figures S9-S10)<br>- 85: No vegetation, replicate #1 (Figures S9-S10)<br>- 86: No vegetation, replicate #2 (Figures S9-S10)<br>- 87: No vegetation, replicate #3 (Figures S9-S10)<br>- 90: No vegetation, replicate #4 (Figures S9-S10)</p> <p> </p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part V: Figures)
<p>This is Part V of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files to generate the figures of the paper.</p> <p>To be able to run the scripts as is, the path (at the beginning of each script) to the following folders must be updated:</p> <p>Runs (includes all model run folders from Part II and Part III)<br>Post (includes all post-processing folders from Part IV)</p>
Data from: Tree growth-forms reveal dominant browsers shaping the vegetation
<p>This data repository belongs to the publication "Tree growth-forms reveal dominant browsers shaping the vegetation" by Churski et al. in Functional Ecology</p> <p>Authors Marcin Churski<sup>1</sup>, Dries P.J. Kuijper<sup>1</sup>, Katharina Semmelmayer<sup>1</sup>, William J. Bond<sup>2</sup>, Joris P.G.M. Cromsigt<sup>3</sup><sup>,</sup><sup>4</sup>, Yan Wang<sup>5</sup> & Tristan Charles-Dominique<sup>6</sup><sup>,</sup><sup>7</sup></p> <p>Author for correspondence: Marcin Churski email: <a href="mailto:mchurski@ibs.bialowieza.pl">mchurski@ibs.bialowieza.pl</a></p> <p><sup>1</sup>Mammal Research Institute Polish Academy of Sciences, ul. Stoczek 1, 17-230 Białowieża; <sup>2</sup>University of Cape Town, HW Pearson Building, University Ave N, Rondebosch, Cape Town, 7701; <sup>3</sup>SLU, Department of Wildlife, Fish and Environmental Studies, 901 83 Umeå, Sweden; <sup>4</sup>Centre for African Conservation Ecology, Department of Zoology, Nelson Mandela University, PO Box 77000, Gqeberha, 6031, South Africa; <sup>5</sup>Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland; <sup>6</sup>AMAP, University of Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France; <sup>7</sup>CNRS UMR7618; Sorbonne University; Institute of Ecology and Environmental Sciences Paris; 4, place Jussieu 75005 PARIS</p> <div> <h4>Summary</h4> <a href="https://github.com/mripasteam/transformers#summary"></a></div> <ul> <li>Plants adopt particular growth-forms when they are exposed to extreme environmental conditions. In this study, we describe a unique woody plant growth-form induced by large mammalian herbivores and discuss that this growth-form could have evolved as a strategy for escaping the browser zone in herbivore driven ecosystems.</li> <li>We analysed responses of key architectural and morphological attributes (branching and thorn density, tree dimensions, presence of flowers and fruits) of three Eurasian spiny tree species (Malus sylvestris, Prunus cerasifera, Pyrus pyraster) to different levels of browsing by large herbivores in the temperate Białowieża Forest, Poland.</li> <li>Under high browsing pressure, studied trees displayed two distinct forms of the crown: a bottom sterile part developing into a densely branched structure with high density of thorns (‘cage-form’), and an upper reproductive part that escaped from herbivore control (‘escaped-form’). The size of cage-form influenced the feeding behaviour of red deer (Cervus elaphus) by increasing the time deer spend foraging and increasing the bite rate. The height at which cages started to escape and their diameter matched with foraging reach of red deer.</li> <li>Synthesis. We argue that the frequency and cage dimensions of this woody growth-form in the landscape could inform on the type and intensity of recent herbivory. Moreover, its distinctive inducibility suggests that this growth-form did not emerge recently under anthropogenic pressure but could be the legacy of ancient herbivory effects. Observational evidence suggests that this growth-form emerged in several herbivore-driven systems around the globe and may be used to identify the dominant herbivores that control vegetation structure in these ecosystems.</li> </ul> <p>Data</p> <p>The table 'cage_traits.csv' contains data on morphological traits measured on individual trees and was used to describe the key architectural attributes defining the cage and escape forms (trapped branches vs. escaped branches), test if the cage form is induced by mammalian herbivores or not and if the dimensions of the cage form could inform on which animal induced them.</p> <div> <pre><code>Column headers description: N_Protocol: Tree ID Status: if the tree individual grow taller than animal reach (escaped or trapped) Species: tree species Branch_N: observed branch ID Length: branch length (cm) N_Thorns: number of thorns N_Twigs: number of twigs Longest_thorn: the length of longest thorn on the branch (mm) N_browsed_Twigs: Number of browsed twigs (1) vs non browsed (0) twigs on a branch Branch_esc: branch position, "escape" indicates the branch growing on the escaped part of a tree FlowerOrFruit: flower or fruit number found on the branch BDI: branch density index. It is calculated by twigs number divided by branch length Thorn_density:Thorn number divided by branch length BrowRate: observed number of browsed_Twigs divided by branch length Bite: 1 indicates the branch was browsed, 0 indicated the branch was not browsed. browsing_environment: if the tree is exposed to high browsing environment or not. </code></pre> <div> </div> </div> <p>The table 'foraging_time_barplot.csv' contains camera trap data on total foraging time of all the animal on all the tree species and was used to answer the question how the presence of cage form affect herbivore foraging behaviour. This data set was specifically used to produce the bar plot in Figure 5C.</p> <div> <pre><code>Column headers description: N_Protocol : Tree ID animal_species : observed animal species Species: tree species Foraging_time: observed total foraging time. </code></pre> <div> </div> </div> <p>The table 'foraging_time.csv' contains camera trap data on total foraging time of all the animal on all the tree species and was used to answer the question how the presence of cage form affect herbivore foraging behaviour.</p> <div> <pre><code>Column headers description: N_Protocol: Tree ID Total_foraging_time: total record foraging time per tree individual Total_bite_rate : bite rate per tree individual Bite_rate : bite rate per tree branch BDI: branch density index. Foraging_time_av: record foraging time per tree branch Surface: the surface of the crown </code></pre> <div> </div> </div> <p>The table 'escape_height.csv' contains data on individual tree heights in relation to their status (escaped vs trapped). This data set was used to test if the dimensions of the cage form could inform on which animal induced them.</p> <div> <pre><code>Column headers description: N_Protocol: Tree ID Status: if the tree individual grow taller than animal reach Species: Tree species Height: Tree height</code></pre> </div>
Data from: Vegetation dynamics 1946–2018 in an old-growth conifer forest
<p>We analysed ground vegetation in 250 plots in 2018 in the old-growth <em>Picea abies-</em>dominated<em> </em>forest Säby Västerskog, SE Sweden as a follow-up of studies in 1946 and 1998 with the same layout of plots. The vegetation changes were not clearly directional: the species composition in 2018 was intermediate between that of 1946 and 1998, whereas in 1998 species richness was higher and with the unique presence of a number of species indicating small-scale disturbances. <em>Vaccinium myrtillus</em> increased in cover since 1946 and <em>Avenella flexuosa</em> decreased. This goes against regional trends, attributed to climate warming and changes in nitrogen deposition. Regional changes are overshadowed by fine-scale disturbances and micro-successions.</p>
Data from: Cryogenic land surface processes shape vegetation biomass patterns in northern European tundra
<p>Tundra ecosystems have experienced changes in vegetation composition, distribution, and productivity over the past century due to climate warming. However, the increase in above-ground biomass (AGB) may be constrained by cryogenic land surface processes (LSP) that cause topsoil disturbance and variable microsite conditions. These effects have remained unaccounted for in tundra biomass models, although they can impact multiple opposing feedbacks between the biosphere and atmosphere, ecosystem functioning and biodiversity. Here, by using field-quantified data from northern Europe, remote sensing, and machine learning, we show that LSP substantially constrain AGB in tundra. The three surveyed LSP (cryoturbation, solifluction and nivation) collectively reduced AGB by an average of 123.0 g m<sup>-2 </sup>(-30.0%). This effect was significant over landscape positions and was especially pronounced in snowbed environments, where the mean reduction in AGB was 57.3%. Our results imply that LSP are pivotal in shaping future patterns of tundra biomass, as long as cryogenic ground activity is retained by climate warming.</p> <p>These are the key data and codes related from Aalto et al., (2021).</p> <p> </p>
Boreal forest tower-based remote sensing data (solar-induced fluorescence and reflectance-based vegetation indices)
<p>Data includes remote sensing products from PhotoSpec (a scanning spectrometer) from August 2019-December 2021 at the Southern Old Black Spruce site in Saskatchewan Canada and the National Ecological Observatory Network (NEON) Delta Junction. We provide half-hourly averaged vegetation indices (NIRv, NDVI, PRI, CCI) and Solar-Induced Fluorescence (SIF) and for stand-representative targets. Additionally, we provide half-hourly Photosynthetically Active Radiation (PAR), a fraction of direct vs. diffuse radiation (Df), Air Temperature (Tair) and Gross Primary Productivity (GPP). </p>
Normalized Difference Vegetation Index (NDVI) data
<p> </p> <p>The raster data 'viCH_Landsat_NAfill.tif' represents Normalized Difference Vegetation Index (NDVI) data of Switzerland. Raster pixels have 250x250 m resolution and contain median values of the years 2013 to 2017. Data was collected by the Landsat satellite and was downloaded via Google Earth Engine. Missing values were closed with a local neighborhood average.</p> <p>The raster data 'viNE_Modis2018_NAfill.tif' represents NDVI data of North Eurasia, at 1x1 km resolution, and averaged over the growing season 2018. Data was collected by the Modis sensor and was downloaded via Google Earth Engine. Missing values were closed with a local neighborhood average.</p>
Regional plot x species data for alpine vegetation
<p>Whether the distribution and assembly of plant species are adapted to current climates or legacy effects poses a problem for their conservation during ongoing climate change. The alpine regions of southern and central Europe (SACEU) are compared to those of the western US and Canada (WUSAC) because they differ in their geographies and histories. Individual-based simulation experiments disentangled the role of geography in species adaptations and legacy effects in four combinations: approximations of observed alpine geographies vs. regular lattices with the same number of regions (realistic and null representations), and virtual species with responses to either climatic or simple spatial gradients (adaptations or legacy effects). Additionally, dispersal distances were varied using five Gaussian kernels. Because the similarity of pairs of regional species pools indicated the processes of assembly at extensive spatiotemporal scales and is a measure of beta diversity, this output of the simulations was correlated to observed similarity for Europe and North America. In North America, correlations were highest for simulations with approximated geography and location-adapted species; those in Europe had their highest correlation with the lattice pattern and climate-adapted species. Only SACEU correlations were sensitive to dispersal limitation. The southern and central European alpine areas are more isolated and with more distinct climates to which species are adapted. In the western US and Canada, less isolation and more mixing of species from refugia has caused location to mask climate adaptation. Among continents, the balance of explanatory factors for the assembly of regional species pools will vary with their unique historical biogeographies, with isolation lessening disequilibria.</p>
HeadwaterstreamSNevada: data on riparian vegetation and water parameters of headwater streams in Sierra Nevada, Spain
<p>Providing historical data on riparian plant biodiversity and physico-chemical parameters of stream water in Mediterranean mountains helps to assess the effects of climate change and other human stressors on these sensitive and critical ecosystems. This database collects data from the main natural headwater streams of the Sierra Nevada (southeastern Spain), a high mountain (up to 3,479 meters above sea level, m.a.s.l.) recognized as a biodiversity super hotspot (Arroyo et al., 2022) in the Mediterranean Basin. On this mountain, rivers and landscapes depend on snowmelt water, representing an excellent scenario for evaluating global change's impacts. This dataset covers first- to third-order headwater streams at 41 sites from 832 to 1,997 m.a.s.l., collected from December 2006 to July 2007. Our goal is to supply information on the vegetation associated with streambanks, the essential physico-chemical parameters of stream water, and the physiographic features of the subwatersheds. Riparian vegetation data correspond to six plots sampled at each site, including total canopy, individual number, height and DBH (diameter at breast height) in woody species, and cover percentage for herbs. Physico-chemical parameters were measured in situ (electric conductivity, pH, dissolved O2 concentration, stream discharge) and determined in the laboratory [alkalinity, soluble reactive phosphate-phosphorus (SRP), total phosphorus (TP), nitrate-nitrogen (NO3-–N), ammonium-nitrogen (NH4+–N), total nitrogen (TN)]. Watershed physiographic variables comprise drainage area, minimum altitude, maximum altitude, mean slope, orientation, stream order, stream length, and land cover surface percentage. We recorded 197 plant taxa (67 species, 28 subspecies and 2 hybrids), representing 8.4% of the Sierra Nevada vascular flora. Due to the botanical nomenclature used, the database can be linked to FloraSNevada database (Lorite et al., 2020), contributing to Sierra Nevada (Spain) as a laboratory of global processes.</p> <p>For more information about the data, see the metadata document (Metadata_HeadwaterstreamSNevada.docx).</p>
Data from: Flying high: Sampling savanna vegetation with UAV-lidar
<p>The flexibility of UAV-lidar remote sensing offers a myriad of new opportunities for savanna ecology, enabling researchers to measure vegetation structure at a variety of temporal and spatial scales. However, this flexibility also increases the number of customizable variables, such as flight altitude, pattern, and sensor parameters, that, when adjusted, can impact data quality as well as the applicability of a dataset to a specific research interest. <br>To better understand the impacts that UAV flight patterns and sensor parameters have on vegetation metrics, we compared 7 lidar point clouds collected with a Riegl VUX-1LR over a 300 x 300 m area in the Kruger National Park, South Africa. We varied the altitude (60 m above ground, 100 m, 180 m, and 300 m) and sampling pattern (slowing the flight speed, increasing the overlap between flightlines, and flying a crosshatch pattern), and compared a variety of vertical vegetation metrics related to height and fractional cover. <br>Comparing vegetation metrics from acquisitions with different flight patterns and sensor parameters, we found that both flight altitude and pattern had significant impacts on derived structure metrics, with variation in altitude causing the largest impacts. Flying higher resulted in lower point cloud heights, leading to a consistent downward trend in percentile height metrics and fractional cover. The magnitude and direction of these trends also varied depending on the vegetation type sampled (trees, shrubs, or grasses), showing that the structure and composition of savanna vegetation can interact with the lidar signal and alter derived metrics. While there were statistically significant differences in metrics among acquisitions, the average differences were often on the order of a few centimeters or less, which shows great promise for future comparison studies.<br>We discuss how these results apply in practice, explaining the potential trade-offs of flying at higher altitudes and alternating flight pattern. We highlight how flight and sensor parameters can be geared toward specific ecological applications and vegetation types, and we explore future opportunities for optimizing UAV-lidar sampling designs in savannas.</p>
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
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.