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3,105 results for “vegetation”
Tower-based remote sensing data for understory vegetation at Delta Junction, Alaska 2019-2020
<p> Data includes remote sensing products from PhotoSpec (a scanning spectrometer) from August 2019-December 2020. We provide daily averaged vegetation indices for a mix of understory lichen and moss species in a black spruce dominated forest. We compute near-infrared vegetation index (NIRv), normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and chlorophyll-carotenoid index (CCI) averaged for three understory targets at NEON Delta Junction. We also provide daily averaged photosynthetically active radiation (PAR) and solar zenith angle (SZA). Finally, we provide the average diurnal profiles of all the aforementioned metrics for 4 20-day windows in winter, spring, summer, and fall. </p>
EU Alerts/notifications on pesticides content in tea, vegetable and fruits
<p>The objective of this dataset is to establish a group of active substances/food products of interest and to know the importance of this risk (occurrence of pesticides) in the food trade between in EU and China. Data include active substances, concentrations, food product, notifying country, date of notification. All food products collected in this dataset come from China.</p> <p>The origin of the data is the RASFF portal.(since 2010 until January 2022). 294 entries have been collected.</p>
Application of multi-analyte / multi-matrix screening method for pesticide residues in fruits and vegetables to Interaboratory Comparison Study on Pesticide Residues in Food (ILC)
<p>The suitability of multi-analyte / multi-matrix screening method for pesticide residues in fruits and vegetables and related products, developed within activities of task (Multi-analyte / multi-matrix screening method for pesticide residues in fruits and vegetables (including tea) and fruit juices), was evaluated by the Interaboratory Comparison Study on Pesticide Residues in Food (ILC). Test material (“Pesticide Residues in green tea”), prepared from the batch used for another proficiency test (PT), was provided by Fapas (Fera Science Ltd, York, UK).</p> <p>Data set obtains (i) information about performance characteristics of the analytical method and (ii) compilation of results of interlaboratory study.</p>
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>
The location and vegetation physiognomy of ecological infrastructures determine bat activity in Mediterranean floodplain landscapes
<p>Ecological infrastructures (EI), defined as natural or semi-natural structural elements, are important to support biodiversity and could play a crucial role in counteracting the well-known impacts of intensive agriculture. Yet, the importance of EI remains largely unexplored in Mediterranean agricultural landscapes and for species providing essential ecosystem services such as bats. Here, we evaluated the role of different EI types – in terms of location (riparian vs terrestrial) and vegetation physiognomy (woody vs non-woody) – in shaping bat guild activity in crop fields located in the floodplains of the Iberian Peninsula. We recorded 60,732 bat sequences in 96 crop fields and characterized 106 EI patches via an adaptation of the Biodiversity Potential Index (BPI). We found that the activity of mid-range echolocators (MRE) and long-range echolocators (LRE) was twofold higher when the nearest EI patch was riparian (i.e., contiguous to a watercourse) than when it was terrestrial. When assessing changes in bat activity in crop fields in relation to a gradient distance from EI types, our results revealed both distinct and similar effects of the location and vegetation physiognomy of the EI on bat guilds. For instance, while only the LRE guild positively responded to the proximity of woody EI, both MRE and LRE showed a marked increase of activity when increasing distances to non-woody EI, thus suggesting low bat activity levels near these features. Our habitat quality assessment also revealed that woody EI and riparian EI had higher biodiversity potential and related habitat quality, thus contributing to our understanding of bat responses to EI type in crop fields. As riparian areas are rarely targeted in biodiversity-friendly measures in farmland, we strongly recommend including riparian EI (especially the woody type) in conservation planning as they are crucial for both biodiversity conservation and ecosystem functioning.</p>
Vegetation changes over the last centuries in the Lower Lake Constance region reconstructed from sediment-core environmental DNA
<p>Many European lake ecosystems, including their respective catchment areas, underwent anthropogenic environmental changes over the last centuries. This has resulted in changes in the aquatic and terrestrial vegetation, but historical records on the composition of the past vegetation on centennial scale are scarce. In this study, we examined changes in the terrestrial and aquatic plant communities in and around Lower Lake Constance using metabarcoding of sedimentary DNA (sedDNA) of three cores from different sub- basins covering the past, up to 300 years. We successfully identified an average of c. 3000 sequence variants (molecular operational taxonomic units - MOTUs) and obtained a taxonomically annotated dataset of 127 species, 104 genera and 72 families. We could detect major changes in the terrestrial and aquatic vegetation of the Lower Lake Constance region by examining the cores. For example, alpha diversity decreased in the last c. 100 years, and this decrease was more pronounced in the terrestrial than in the aquatic plant community. Unlike the terrestrial plant-community, the current aquatic plant- community composition partially resembles the community from before the 20th-century eutrophication phase of the lake. In addition to changes that can be attributed to anthropogenic impacts, we also captured the effect of DNA sedimentation on the terrestrial DNA diversity representation in sediments during periods of extensive flooding and potentially as a consequence of extremely cold winters. With 1sedDNA from Lower Lake Constance, we provide a new local dataset to investigate and extend the historical changes of different shoreline habitats and to identify characteristic and invasive plant species. Such highly-resolved datasets spanning the past centuries can provide detailed information on human environmental history in densely populated regions that have undergone severe changes in the recent past.</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>
Empirical evidence for recent global shifts in vegetation resilience
<p>Data and codes for the publication:</p> <p>Smith, T., Traxl, D. & Boers, N. Empirical evidence for recent global shifts in vegetation resilience. <em>Nat. Clim. Chang.</em> (2022). https://doi.org/10.1038/s41558-022-01352-2</p> <p>Data is described in 'README.txt'.</p>
Non-crop vegetation characteristics and vocalizing bird richness across 44 sites in Iowa, USA in June 2019
<p>This data was derived from field work conducted in June 2019 where sixty AudioMoth passive acoustic monitors were placed along agricultural field margins in Iowa, USA. Twenty-five of the monitoring location were established by farmer and landowner collaborators, and the remaining (35) sites were established by the author (A.P.D.). Unique vocalizing bird species were counted in ninety-five recordings from 6 to 8 days during dawn hours per site. High resolution mapping identified non-crop vegetation and texture at spatial extents ranging from 100 to 1000 meters. Pesticide and fertilizer application were collected via a survey with collaborators. Site location names are included when the research site was an Iowa State Research and Demonstration Farm (ISRF). When the site was a collaborator, the site name was anonymized to "Collaborator" to respect the privacy of participants.</p>
Large contribution of woody plant expansion to recent vegetative greening of the Northern Great Plains
<p><strong>Aim:</strong> Extensive portions of high-latitude grasslands worldwide have recently experienced increased vegetative productivity (i.e., greening) and have undergone a rapid transition towards woody plant dominance via the process of woody plant expansion (WPE). This raises the underlying question: To what degree are WPE and greening spatiotemporally linked? Given that these vegetative changes are predicted to continue, we seek to understand how recent changes in vegetation extent and productivity have interacted under recent climate change and anthropogenic disturbance to provide insights surrounding the future trajectory of temperate grasslands broadly.</p> <p><strong>Location: </strong>Northern Great Plains (NGP), North America</p> <p><strong>Taxon:</strong> Woody plants</p> <p><strong>Methods:</strong> Greening was measured as the significant increase in three metrics between 2000 and 2019: leaf area index (LAI), annual maximum normalized difference vegetative index (NDVI), and annual mean NDVI. WPE was measured as the significant proportional increase in percent tree cover change between 2000 and 2019 in grasslands. We then examine these variables across a host of 26 potential driving variables.</p> <p><strong>Results:</strong> We show that average proportional greening increased by 0.2-1.3% yr <sup>-1</sup> (depending on metric), and proportional WPE increased by 6.9% yr <sup>-1</sup> since 2000 across the NGP. Both changes are largely driven by the absence of wildfire and changing climate. Furthermore, WPE was spatially coherent and positively associated with a large component of recent greening, as revealed by their coupling across 34.1-40.6% of grassland area and as evidenced by the 9.7-19.7% of the variability in greening explained by WPE.</p> <p><strong>Main conclusions: </strong>WPE and greening are spatiotemporally coupled across large portions of the NGP. Under continued climate change and wildfire suppression, WPE and greening are likely to continue across large swathes of grasslands globally. Furthermore, our results show that using a single greening metric may be insufficient to capture the large-scale vegetative changes such as the expansion of woody vegetation.</p>
Experimental disturbance treatments of wetland vegetation
<p><em>Study sites</em></p> <p>Our study sites were located in Kastbjerg Ådal (river valley) in Eastern Jutland, Denmark. It is within the Natura 2000 and habitat area no. 223 appointed because of the wide stretch of fens and mires among other qualities. The water course is in good ecological status according to the Water Framework Directive. Nitrogen deposition in this area is low to moderate, 12.5-14.5 kgN/ha/yr (Ellermann et al. 2021). Meadows and fens dominate the study area, known for ‘the longest stretch of rich fen’ in Denmark. Large parts of the river valley are heavily degraded by drainage, fertilization and scrub encroachment, but there have also been recent efforts to restore the watercourse and the valuable rich fens in the valley. Most fens and wet meadows have been abandoned and are now increasingly dominated by tall grasses, tall forbs and willow scrub, but summer grazing occurs in some areas and efforts are made to ensure grazing in the most valuable fens. The drier meadows are typically mown by heavy machinery. The sites were selected to represent gradients in soil moisture from moist to wet and gradients in nutrient status or productivity from poor to rich and included rich fens with characteristic species, fens dominated by <em>Juncus subnodulosus</em> and by <em>Equisetum fluviatile</em>, drained fens encroached by <em>Phragmites australis</em> and natural meadows with characteristic species and encroached by <em>Epilobium hirsutum</em> and meadows characterized by clovers and cultural grasses.</p> <p>The nine sites were of 10 m<sup>2</sup>, each with ten 1 m<sup>2</sup> plots. The 10 plots within each site had treatments assigned randomly. Despite the location in the same river valley, the sites were considered independent because of their different management history and starting conditions and a typical inter-site distance of c. 225 meters. The experiment was established in June 2017 and treatments were repeated monthly during summer and bimonthly during winter, depending on treatment. Responses were recorded in July 2019.</p> <p> </p> <p><em>Experimental set-up and treatments</em></p> <p>Each of the 9 sites were divided into ten 1 m × 1 m plots each with a 0.5 m × 0.5 m inner square and a surrounding plot buffer zone with a control and the following treatments: burning, mowing, trampling, intensive summer grazing (SI), intensive summer grazing with trampling (SIT), extensive summer grazing (SE), extensive summer grazing with trampling (SET, year-round grazing (YR), and year-round grazing with trampling (YRT). Treatments were allocated randomly to each plot with the restriction that the control plot was always in one corner. The experiment was multifactorial with respect to grazing and trampling, whereas burning and mowing were stand-alone treatments. Initial biomass in each plot was estimated at the beginning of the experiment in June 2017 as follows: all standing biomass and litter was removed from the plots by manual cutting at the soil surface and following the micro-topography. Bryophytes were harvested by hand plucking. Biomass, litter and bryophytes from the plot buffer zone were cut separately from the inner square. To estimate the species abundances, a representative sample of the inner square was sorted into litter and live biomass (including bryophytes) by species as sorting the complete biomass was not feasible. All species, litter and biomass from the buffer zone were dried at 55° C and weighed. Using the relative abundance of species in the representative sample and with respect to the weight of the total biomass in the inner square, we estimated the abundance of the species in the inner square.</p> <p>Burning was simulated in March 2018 and 2019. We used wooden boards to shield and adjacent areas were watered before burning the focal plot with a gas weed burner. We burned on a calm day following a dry period with frost to ensure minimum risk of igniting underlying peat and fire spreading over ground, but ensuring that the standing biomass and litter would be dry enough to ignite. This is not a simulation of a naturally occurring wildfire, but corresponds to the conditions that managers would prefer for prescribed conservation burning at larger scales. We simulated mowing as a biomass removal in June 2018. Biomass was removed uniformly across the whole plot in a height of c. 5 cm depending on microtopography. This corresponds to conservation mowing in management but without the added disturbance and pressure from machines. Trampling disturbance was applied using short stilts that could be attached to the field biologist’s boot. The surface of the stilt was 49 cm<sup>2</sup> which corresponds to a pressure of 1.3-1.5 kg/cm<sup>2</sup> with the added weight of the field biologist. This again corresponds to the pressure of a hoof of cattle weighing c. 300-400 kg. Trampling was applied by stepping into the field randomly 60 times once every month from May to September and was the same treatment in combination with intensive, extensive and year round grazing. Grazing was simulated by cutting the above-ground biomass using a 1 m<sup>2</sup> frame divided into a 10 cm coordinate system using the letters A-J on the x-axis and the numbers 1-10 on the y-axis. We cut tufts of biomass within the coordinate system using a list of random combinations of letters and numbers. This system enables “ungrazed” individuals to flower and set seeds. Based on our experience with grazing as an agri-environmental management practice in Denmark, we defined intensive summer grazing as taking place between May and September with the goal of removing all standing biomass by September. Extensive summer grazing also takes place May-September, but we carried this out at half the intensity as intensive summer grazing. Year-round grazing obviously takes place during the whole year (here administered May-September and November, January and March) with the goal of removing all standing biomass by the end of winter (March) before the beginning of a new growing season. We used the initial standing biomass (June 2017) as a measurement of plot productivity and estimated the amount of biomass to be removed during “grazing” as c. 20 % of the initial productivity each month May-September in intensive plots and with all standing biomass “grazed” in September. For extensive plots, we estimated removed biomass as c. 10 % of the initial productivity each month May-September leaving some standing biomass in September. Year-round grazing biomass removal was estimated as c. 10 % of yearly productivity removed every month May-September and November and 20 % removed in January and March resulting in no standing biomass at the end of the winter. As expected plot productivity changed as a result of the treatments, the amount of biomass removed had to be adjusted throughout the experiment. In practice, we aimed for removing twice the amount of biomass in intensive plots relative to extensive plots within the same site and always ensuring that no standing biomass was left in intensive plots in September, c. 50 % of the standing biomass was left in extensive plots in September and no standing biomass was left in year-round grazing plots in March (see actual removed biomass by treatment in Appendix A). All treatments were applied to the whole plot (1 m × 1 m), while the biomass response was only measured in the inner square (0.5 m × 0.5 m), leaving a buffer zone between plots with different treatments.</p> <p> </p> <p><em>Response variables</em></p> <p>A full plot (1 m × 1 m) species list was recorded in the field at the end of the experiment. From this total plot richness, vascular plant plot richness, bryophyte plot richness and number of indicator species per plot were calculated. Indicator species of conservation status are species considered moderately to very sensitive towards habitat degradation as defined by Fredshavn et al. (2010, see Appendix C). Indicator species are often adapted to relatively infertile habitats revealed by low Ellenberg N values and high Grime’s S values reflecting tolerance to nutrient shortage.</p> <p>Mean plot Grime’s C and S values (Grime et al. 1989) were calculated based on vascular plant species lists. We converted Grime’s life strategies to numerical values based on Ejrnæs and Bruun (2000).</p> <p>We performed a Nonmetric Multi-dimensional Scaling analysis (NMDS) on the presence-absence of vascular plant and bryophyte species at the end of the experiment using the function metaMDS in R-package ‘vegan’ (Oksanen et al. 2017) in R version 4.0.3 (R Core Team 2017), using Sørensen dissimilarity and a four-dimensional solution (k =4). The plot coordinates at the three first NMDS axes were extracted (NMS4 was discarded as noise) and these, along with the four richness variables as well as Grime’s C and S values, were used as response variables in Linear Mixed Models (LME) as described in ‘Statistical analyses’.</p> <p>Supplementary to regression models of single response variables we carried out a quadratic discriminant analysis (QDA) as described in ‘Statistical analyses’ using the change in six indicators during the course of the experiment. The difference between plot species richness at the beginning and end of the experiment was calculated based on the species lists from sorted initial biomass and end biomass (0.5 m × 0.5 m). Start-end differences were also calculated separately for vascular plant species richness, bryophyte species richness, richness of indicator species, the ratio between biomass of forbs and graminoids (grasses, sedges and rushes) and Grime’s C and S mean site values.</p> <p> </p> <p><em>Explanatory and co-variables</em></p> <p>Leaf nitrogen, carbon and phosphorous were determined from plot level sampling of leaf plates of grasses, i.e., the most abundant species group across sites. Fresh leaf plates were collected at the beginning and end of the project and then dried, ground and analyzed in the lab. Soil moisture (% volumetric water content) was measured as the mean of four measurements per plot at the beginning and end of the project using a FieldScout TDR 300 Soil Moisture Meter.</p> <p>The total number of species found in each site was used as a co-variable in species richness models reflecting the local species pool.</p> <p> </p> <p><em>Data processing</em></p> <p>All species names were checked for synonyms using the national database arter.dk.</p> <p> </p> <p>References:</p> <p> </p> <p>Ejrnæs, R. and H. H. Bruun (2000). "Gradient analysis of dry grassland vegetation in Denmark." Journal of Vegetation Science <strong>11</strong>(4): 573-584.</p> <p>Ellermann, T., R. Bossi, J. Nygaard, J. H. Christensen, P. Løfstrøm, C. Monies, C. Geels, I. E. Nielsen and M. B. Poulsen (2021). Atmosfærisk deposition 2019. NOVANA. Aarhus, Aarhus Universitet, DCE - Nationalt Center for Miljø og Energi.</p> <p>Fredshavn, J., R. Ejrnæs and B. Nygaard (2010). "Teknisk anvisning for kortlægning af terrestriske naturtyper. TA-N3, Version 1.04. Fagdatacenter for Biodiversitet og Terrestriske Naturdata, Danmarks Miljøundersøgelser. 18 s. ."</p> <p>Grime, J. P., J. G. Hodgson and R. Hunt (1989). Comparative plant ecology: a functional approach to common British species. London, Unwin Hyman.</p> <p>Oksanen, J., F. G. Blanchet, R. Kindt, P. Legendre, R. B. O'Hara, G. L. Simpson, P. Solymos, M. H. H. Stevens and H. Wagner (2017). "Package 'vegan': Community Ecology Package. Version 2.4-3. <a href="http://cran.r-project.org/web/packages/vegan/vegan.pdf">http://cran.r-project.org/web/packages/vegan/vegan.pdf</a>."</p> <p> </p> <p>R Core Team (2017). R: A language and environment for statistical computing. Vienna, Austria, R Foundation for Statistical Computing.</p>
Alpine and subalpine vegetation of Mt. Midzhur, Stara Planina, Bulgaria
<p>This dataset contains data used in my diploma thesis. The fieldwork was carried out in the Western Stara Planina Mountains, mainly in the area of Mt. Midzhur. I recorded 78 vegetation plots using the Braun-Blanquet approach. The dataset was classified using modified Twinspan algorithm. A DCA ordination graph was created to present the dissimilarity of the vegetation types. The vegetation was divided into 12 classes. Seven alliances not previously known from Bulgaria and one newly described alliance of subalpine tall-herb vegetation on screes were reported. Numerous associations were newly reported for Bulgaria or were newly described. The role of altitude and soil pH on the vegetation was analyzed using the general linear model; the floristic composition was analyzed on the level of floristic elements.</p>
Decomposition, topology, properties, and graphs of woody crown networks of 15 tree species of Cerrado vegetation
<p>Data of decomposition, topology, properties, and the corresponding graphs of 15 adult tree species of Cerrado vegetation, <em>sensu stricto</em> physiognomy. The woody crown networks (WCN) representations in a bidimensional space were obtained by drawing followed the methodology described by Prado et al. (2020, Prado, C.H.B.A., Trovão, D.M.B.M., Souza, J.P.<strong>,</strong> 2020. A network model for determining the woody crown's decomposition, topology, and properties. Journal of Theoretical Biology, v. 499, p. 110318. https://doi.org/<a href="https://www.x-mol.com/paperRedirect/1258515479077781504">10.1016/j.jtbi.2020.110318</a>.). The branching regions were the nodes, and the woody crown segments connecting the nodes or merely emerging from them were the connectors. Those trees grew under natural conditions in a most common (<em>sensu stricto</em>) physiognomy of Cerrado vegetation, in a reservoir of 86 ha, located at 850 m above sea level in São Carlos city, São Paulo state, Brazil, at 21°58'- 22°00'S and 47°51'-47°52'W. Following the Köppen climatic classification, this region is between Aw and Cwa, a tropical climate with dry winter and wet summer. The rainy season occurs between October-March, and the dry season between April and September. </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>
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>
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>
Puntos de recogida urbana de aceite vegetal
<p>Este conjunto de datos incluye un directorio con los datos de los <strong>Puntos de recogida urbana de Aceite Vegetal</strong> de Zaragoza.</p> <p>El Ayuntamiento de Zaragoza pone a disposición una visualización en la que se muestran datos sobre puntos de recogida urbanos de aceite vegetal. El conjunto de datos contiene información de localización (dirección, código postal, latitud y longitud), contacto (email y teléfono), horario y url a la web de cada centro.</p> <p><a href="https://www.zaragoza.es/sede/servicio/catalogo/2690">Acceso a la ficha descriptiva del dataset</a></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>
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