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134 results for “Ecosystem structure”
Indicative distribution map for Ecosystem Functional Group M4.1 Submerged artificial structures
<p>This archive contains indicative distribution maps and profiles for <strong>M4.1 Submerged artificial structures</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Biodiversity and metacommunity structure of rocky intertidal invertebrates in some coastal ecosystems in Puerto Rico
The goals of this study were to determine the relative importance of environmental (wave power density, wave height) and habitat (e.g., algal cover, slope, complexity of rock surfaces) factors associated with the structure of local assemblages at multiple shore heights and the regional metacommunity of mobile invertebrates on oceanic rocky intertidal habitats. These characteristics and abundances of 41 species of invertebrate were estimated at 10 plots at each of three tidal heights at each of ten sites on the shoreline of Puerto Rico. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
SBC LTER: BEACH: Invertebrate community structure and ecosystem functions of 24 sandy beach sites
These data result from a survey of 24 sandy beach sites in Santa Barbara and Ventura Counties in 2017 and 2018. We quantified marine macrophyte wrack subsidies, macroinvertebrates, and five ecosystem functions on three replicate transects at each site in order to elucidate the role of marine wrack subsidies on recipient ecosystem community structure and functioning. We also measured shorebirds at each site on three replicate survey days. Data are contained in four tables: 1) wrack cover and invertebrate community data by transect for each site used in our PiecewiseSEM model, 2) wrack cover and ecosystem function data by transect for each site used in our ecosystem multifunctionality estimate, 3) invertebrate species abundance and biomass by transect for each site, and 4) shorebird species abundance by survey date for each site.
Dataset: Effects of high altitude reservoirs on the structure and function of lotic ecosystems: a case study in Italy
<p>Supporting data for "Effects of high altitude reservoirs on the structure and function of lotic ecosystems: a case study in Italy"</p> <p>Macroinvertebrate community composition</p> <p>Water chemical characteristics</p> <p>Daily mean water temperature and daily temperature variations</p> <p>Results of the leaf bags experiments</p>
Data repository of multi-temporal high-resolution data products of ecosystem structure derived from country-wide airborne laser scanning surveys of the Netherlands
<p><span lang="EN-GB">This data repository contains a set of multi-temporal data products of ecosystem structure derived from four national ALS surveys of the Netherlands (AHN1–AHN4) (folders:<strong> 1_AHN1, 2_AHN2, 3_AHN3, and 4_AHN4</strong>). Four sets of 25 LiDAR-derived vegetation metrics representing ecosystem height, cover, and structural variability are provided at 10 m spatial resolution, providing valuable data sources for a wide range of ecological research and field beyond. A preview of all generated LiDAR metrics are also provided (folder: <strong>5_Maps</strong>). All 25 LiDAR metrics were calculated using Laserfarm workflow (<a href="https://laserfarm.readthedocs.io/en/latest/">https://laserfarm.readthedocs.io/en/latest/</a>) (building on the user-extendable features from the “Laserchicken” software: <a href="https://laserchicken.readthedocs.io/en/latest/#features">https://laserchicken.readthedocs.io/en/latest/#features</a>). All metrics are calculated with the normalized point cloud. More details on metric calculation are provided on GitHub (Laserchicken: <a href="https://github.com/eEcoLiDAR/laserchicken">https://github.com/eEcoLiDAR/laserchicken</a> and Laserfarm: <a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a>), as well as on the “Laserchicken” documentation page (<a href="https://laserchicken.readthedocs.io/en/latest/">https://laserchicken.readthedocs.io/en/latest/</a>). We also provided masks to minimize the influence of water surfaces, buildings and roads, powerlines and NA values in the data products (folder: <strong>6_Masks</strong>). To supplement the generated data products, we also provided a set of raster layers that contains point/pulse density of each AHN survey and the DTM and DSM raster layers for each AHN dataset (folder: <strong>7_Auxiliary_data</strong>). To test the robustness of the LiDAR metrics, we also compared the metrics generated from different pulse densities across different habitat types (folder: <strong>8_Sensitivity_analysis</strong>). Two use cases demonstrated the utility of the presented data products: (use case 1) monitoring forest structural change across time using multi-temporal ALS data and (use case 2) comparison of vegetation structural difference within Natura 2000 sites. The used data are also provided (folder: <strong>9_Use_case</strong>). Note that all the raster layers are provided at 10 m resolution under the local Dutch coordinate system “RD_new” (EPSG: 28992, NAP:5709). To gain more insights of the pre-classification accuracy of the AHN datasets, we also conducted a preliminary assessment of the effect of terrain filtering on vegetation change detection across AHN datasets (i.e. AHN2–AHN4). The data used in this analysis are made available (folder: <strong>10_Ground_classification</strong>). </span></p> <p><span lang="EN-GB">An overview of all the folders in the repository:</span></p> <p><strong><span lang="EN-GB">1. </span></strong><strong><span lang="EN-GB">AHN1</span></strong></p> <p><strong><span lang="EN-GB">2. </span></strong><strong><span lang="EN-GB">AHN2</span></strong></p> <p><strong><span lang="EN-GB">3. </span></strong><strong><span lang="EN-GB">AHN3</span></strong></p> <p><strong><span lang="EN-GB">4. </span></strong><strong><span lang="EN-GB">AHN4</span></strong></p> <p><strong><span lang="EN-GB">5. </span></strong><strong><span lang="EN-GB">Maps</span></strong></p> <p><span lang="EN-GB">Those folders contain four sets of 25 LiDAR metrics at 10 m resolution generated from each AHN dataset. The file names and their corresponding LiDAR metrics can be found in Table 1. An additional folder (5_Maps) contains the maps (.pdf format) of all 25 metrics for each AHN dataset.</span></p> <p><strong><span lang="EN-GB">6. Masks</span></strong></p> <ul> <li><span lang="EN-GB">ahn3_10m_mask_building_road_water.tif</span></li> <li><span lang="EN-GB">ahn4_10m_mask_building_road_water.tif</span></li> <li><span lang="EN-GB">ahn4_10m_mask_powerline.tif</span></li> <li><span lang="NL">ahn1_10m_NA_mask.tif</span></li> <li><span lang="NL">ahn2_10m_NA_mask.tif</span></li> <li><span lang="NL">ahn3_10m_NA_mask.tif</span></li> <li><span lang="NL">a</span><span lang="NL">hn4_10m_NA_mask.tif</span></li> </ul> <p><span lang="NL"> </span></p> <p><span lang="EN-GB">It contains two mask layers of water surfaces, buildings and roads for both AHN3 and AHN4 data products based on the Dutch cadaster data (TOP10NL) from 2018 (corresponding to AHN3) and 2021 (corresponding to AHN4) (<a href="https://www.kadaster.nl/zakelijk/producten/geo-informatie/topnl">https://www.kadaster.nl/zakelijk/producten/geo-informatie/topnl</a>). In the masks, water surfaces, buildings and roads were merged into one class with pixel value assigned to 1 and the rest has the pixel value of 0. There is also a powerline mask generated from the AHN4 dataset at 10 m resolution, where pixels containing powerlines were assigned a value of 1 and the rest as NoData. We provide those masks to minimize the inaccuracies of the data products caused by human infrastructures and water surfaces. We also provided a mask for each AHN dataset where NA value occurs — areas with no vegetation points (“unclassified” class in the AHN datasets). Pixels with NA value were assigned with a value of 1 and the rest as 0.</span></p> <p><strong><span lang="EN-GB">7. Auxiliary data</span></strong></p> <p><span lang="EN-GB">(1) Point_density</span></p> <ul> <li><span lang="EN-GB">ahn1_10m_point_density.tif</span></li> <li><span lang="EN-GB">ahn2_10m_point_density.tif</span></li> <li><span lang="EN-GB">ahn3_10m_point_density.tif</span></li> <li><span lang="EN-GB">ahn4_10m_point_density.tif</span></li> </ul> <p><span lang="EN-GB">(2) Pulse_density</span></p> <ul> <li><span lang="EN-GB">ahn3_10m_pulse_density.tif</span></li> <li><span lang="EN-GB">ahn4_10m_pulse_density.tif</span></li> </ul> <p><span lang="EN-GB">(3) Flighttime</span></p> <ul> <li><span lang="EN-GB">ahn3_10m_flighttime.tif</span></li> <li><span lang="EN-GB">ahn4_10m_flighttime.tif</span></li> </ul> <p><span lang="EN-GB">(4) DTM_DSM</span></p> <ul> <li><span lang="EN-GB">ahn2_10m_dtm.tif</span></li> <li><span lang="EN-GB">ahn2_10m_dsm.tif</span></li> <li><span lang="EN-GB">ahn3_10m_dtm.tif</span></li> <li><span lang="EN-GB">ahn3_10m_dsm.tif</span></li> <li><span lang="EN-GB">ahn4_10m_dtm.tif</span></li> <li><span lang="EN-GB">ahn4_10m_dsm.tif</span></li> </ul> <p><span lang="EN-GB">It contains four raster layers representing the point density of each AHN dataset, two raster layers for pulse density of the AHN3 and AHN4, two raster layers for flight timestamp of the AHN3 and AHN4, and six DTM and DSM layers for AHN2</span><span lang="EN-GB">–</span><span lang="EN-GB">AHN4. All raster layers are provide at 10 m resolution.</span></p> <p><strong><span lang="EN-GB">8. Sensitivity analysis</span></strong></p> <ul> <li><span lang="EN-GB">Dunes</span></li> <li><span lang="EN-GB">Marsh</span></li> <li><span lang="EN-GB">Grassland</span></li> <li><span lang="EN-GB">Shrubland</span></li> <li><span lang="EN-GB">Woodland</span></li> <li><span lang="EN-GB">Code</span></li> <li><span lang="EN-GB">Figure</span></li> </ul> <p><span lang="EN-GB">It contains the 25 metrics generated from point clouds with the original and down-sampled pulse densities (original pulse density of the AHN4, pulse density of the AHN3, ½ of the pulse density of the AHN3, and ¼ of the pulse density of AHN3) for each habitat type (i.e. dunes, marsh, grassland, shrubland, and woodland). We also provided the code and the figures generated from this analysis.</span></p> <p><strong><span lang="EN-GB">9. Use_case</span></strong></p> <p><span lang="EN-GB">(1) Multi-temporal_AHN</span></p> <ul> <li><span lang="EN-GB">Data</span></li> <li><span lang="EN-GB">Usecase_multi-temporal_AHN.R</span></li> </ul> <p><span lang="EN-GB">It contains the input data for the use case data processing (i.e. Data folder), including the shapefile of the area (i.e. shp folder), and extracted pixel value from six selected LiDAR metrics from AHN1–AHN5 (i.e. Metrics folder), and the selected LiDAR metrics of the area (e.g. Hp95 folder), and the R code for data processing (i.e. Usecase_multi-temporal_AHN.R). </span></p> <p><span lang="EN-GB">(2) Natura2000</span></p> <ul> <li><span lang="EN-GB">Data</span></li> <li><span lang="EN-GB">Natura2000_end2021_HABITATCLASS.csv</span></li> <li><span lang="EN-GB">Natura2000_NL_habitat_grouped.csv</span></li> <li><span lang="EN-GB">Usecase_Natura2000.R</span></li> </ul> <p><span lang="EN-GB">It contains a folder of the input data used for the use case (i.e. Data folder), including the shapefile (i.e. shp folder) of the Natura 2000 sites in the Netherlands (i.e. Nature2000_NL_RDnew.shp) and the 100 random sample plots from each habitat type (e.g. woodland_points.shp), and the LiDAR metrics from AHN4 used for demonstrating the vegetation structure within each habitat type (i.e. AHN4_metrics folder). The table “Natura2000_end2021_HABITATCLASS.csv” is the original attribute table of Natura 2000 sites, including information related to the description of habitat classes (column “DESCRIPTION”), the code corresponding to the habitat class (column “HABITATCODE”), the code for the specific site (column “SITECODE”), and the percentage of the cover of a specific habitat class in one site (column “PERCENTAGECOVER”). The table “Natura2000_NL_habitat_grouped.csv” contains two subtabs, one (i.e. “Habitatclass”) is the copy of the original attribute table of Natura 2000 sites in the Netherlands, and the other one (i.e. “Habitat_class_summary”) is the grouped habitat type based on the dominant habitat class (i.e. class with the highest percentage cover) in each site. Different colors indicate different habitat types, corresponding to the colors in the first tab (“Habitatclass”) where the dominant habitat class was highlighted for each site. </span></p> <p><strong><span lang="EN-GB">10. Ground classification</span></strong></p> <ul> <li><span lang="EN-GB">Raw_point_cloud</span></li> <li><span lang="EN-GB">Computed_metrics </span></li> <li><span lang="EN-GB">Plottings_and_code</span></li> <li><span lang="EN-GB">ArcGIS_project</span></li> </ul> <p><span lang="EN-GB">It contains four subfolders: (1) The original point cloud for each sample area (AHN2–AHN4) (subfolder: Raw_point_cloud); (2) The 25 LiDAR metrics computed from the original point clouds with pre-classification of AHN and from the new terrain filtering method across AHN2–AHN4 (subfolder: Computed_metrics); (3) Generated violin plots for the comparison of vegetation change detection and the python code employed (subfolder: Plottings_and_code); (4) an ArcGIS project which the shapefiles of the study area and sample plots are provided (subfolder: ArcGIS_project).</span></p> <p><strong><span lang="EN-GB">Code availability</span></strong></p> <p><span lang="EN-GB">Jupyter Notebooks for processing AHN datasets: </span></p> <p><span lang="EN-GB"><a href="https://github.com/ShiYifang/AHN">https://github.com/ShiYifang/AHN</a></span></p> <p><span lang="EN-GB">Laserfarm workflow repository: </span></p> <p><span lang="EN-GB"><a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a></span></p> <p><span lang="EN-GB">Laserchicken software repository: </span></p> <p><span lang="EN-GB"><a href="https://github.com/eEcoLiDAR/laserchicken">https://github.com/eEcoLiDAR/laserchicken</a></span></p> <p><span lang="EN-GB">Code for downloading AHN dataset: <a href="https://github.com/ShiYifang/AHN/tree/main/AHN_downloading">https://github.com/ShiYifang/AHN/tree/main/AHN_downloading</a></span></p> <p><span lang="EN-GB">Code for generating masks for AHN datasets: <a href="https://github.com/ShiYifang/AHN/tree/main/AHN_masks">https://github.com/ShiYifang/AHN/tree/main/AHN_masks</a></span></p> <p><span lang="EN-GB">Code for demonstration of ecological use cases: <a href="https://github.com/ShiYifang/AHN/tree/main/Use_case">https://github.com/ShiYifang/AHN/tree/main/Use_case</a></span></p> <p> </p>
Data used in the article: "Climate change impacts the vertical structure of marine ecosystem thermal ranges"
<p>This dataset is used in the manuscript "Climate change impacts the vertical structure of marine ecosystem thermal ranges" accepted in Nature Climate Change 2022.</p>
dataset: Responses of the structure and function of the understory plant communities to precipitation reduction across forest ecosystems in Germany
<p><strong>Context</strong>: Understory plant communities play a central role in forest biogeochemistry and the recruitment of trees making up the future forest. It is so far poorly understood how climate change will affect understory structure and functions in forest of different management intensity.</p> <p> </p><p><strong>Aims</strong>: We monitored understory functional traits including transpiration and carbon isotope discrimination, community structure and diversity during two growing seasons as affected by drought in forests subjected to different management intensities. We hypothesized that drought would affect ecophysiological traits such as transpiration but not species richness and diversity. Moreover, we assumed that stand-specific characteristics and forest management intensity modify the drought-resistance of the understory community.</p> <p></p> <p><strong>Methods</strong>: We set up roofs in beech and conifer stands with different management intensity in three different regions across Germany and a drought event close to the 2003 drought was imposed in two consecutive years.</p> <p><strong>Results</strong>: Precipitation reduction decreased soil water content by 2 to 8%, depending on stand and region, in comparison to the control subplots. In the first year, leaf level transpiration was reduced for different functional groups, which scaled to community transpiration modified by additional effects of drought on functional group specific leaf area. Acclimation effects in most functional groups were observed in the second year. We did not observe a significant reduction of plant diversity or a consistent management effect upon drought.</p> <p><strong>Conclusion</strong>: Our results indicate high plasticity and acclimation responses of the forest understory vegetation to changing climate conditions and recurrent drought events.</p> <p><strong>Abbreviations:</strong></p> <p>sp12 - campaign spring 2012; ls12 - campaign late summer 2012; es13 - campaign early summer 2013; ls13-campaign late summer 2013</p> <p>SEW16 - Schorfheide plot 16; SEW49 - Schorfheide plot 49; SEW48 - Schorfheide plot 48;HEW03 - Hainich plot 03; HEW12 - Hainich plot 12; HEW47- Hainich plot 47; AEW13 - Alb plot 13; AEW29 - Alb plot 29; AEW08 - Alb plot 08<br> explo - exploratory<br> SEW - Schorfheide; HEW - Hainich; AEW - Schwäbische Alb<br> in - conifer intensive managed; ma - beech managed; un - beech unmanaged<br> c- control; r - roof<br> LAIs - community leaf area index m<sup>2</sup>/m<sup>2</sup>; H - Shannon´s diversity index; Ts - community transpiration rate (weighted by LAI) mmol H<sub>2</sub>O m-<sup>2</sup> leaf area s-<sup>1</sup>; Ets - Evapotranspiration (mmol/m2/sec); E - Evaporation (mmol/m2/sec); C - leaf photosynthetic carbon isotope discrimination (∆<sup>13</sup>C) according to Farquhar et al. (1982); Cs - community photosynthetic carbon isotope discrimination (∆<sup>13</sup>C) according to Farquhar et al. (1982) (weighted by LAI)</p> <p> </p>
Country-wide data products for the ecosystem structure metrics derived from ALS data across the Netherlands (AHN3)
<p>This data repository contains country-wide data products for the ecosystem structure metrics generated from Airborne Laser Scanning (ALS) data across the Netherlands (AHN3). Twenty-five ecosystem structure metrics (at 10-meter resolution, GeoTIFF format) were derived from AHN3 dataset (<a href="https://downloads.pdok.nl/ahn3-downloadpage/">https://downloads.pdok.nl/ahn3-downloadpage/</a>) using <a href="https://laserfarm.readthedocs.io/en/latest/">Laserfarm</a> workflow (<a href="../record/5636773">https://zenodo.org/record/5636773</a>). Laserfarm is a free and open-source workflow that enables efficient, scalable, and distributed processing of multi-terabyte LiDAR point clouds from national and regional ALS surveys into LiDAR metrics of ecosystem structure. All code of Laserfarm is hosted and freely available on GitHub (<a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a>). The Jupyter Notebooks for the processing of the AHN3 dataset are available on GitHub (<a href="https://github.com/eEcoLiDAR/AHN/tree/main/AHN3">https://github.com/eEcoLiDAR/AHN/tree/main/AHN3</a>).</p> <p>The twenty-five LiDAR metrics are related to three key dimensions of ecosystem structure (ecosystem height, ecosystem cover, and ecosystem structural complexity), and a layer of point density and a layer of building/road/water mask are also provided. Each GeoTIFF layer represents one LiDAR metric at 10 m resolution covering the whole Netherlands (file name as "ahn3_10m_feature_name.tiff").</p> <p>An overview of all the listed metrics (maps) is also provided in the PDF version (AHN3.pdf).</p> <p>A detailed description of the dataset is available from the following data publication:<br>Kissling, W. D., Y. Shi, Z. Koma, C. Meijer, O. Ku, F. Nattino, A. C. Seijmonsbergen, and M. W. Grootes. 2022. Country-wide data of ecosystem structure from the third Dutch airborne laser scanning survey. Data in Brief: 108798.<br><a href="https://eur04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1016%2Fj.dib.2022.108798&data=05%7C01%7Cy.shi%40uva.nl%7C177a19a4359a422b0ef808dad9d30ef8%7Ca0f1cacd618c4403b94576fb3d6874e5%7C0%7C0%7C638061797757145956%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=2R7NSGli4Mw6Pp5FAIyOzBu4USPZXigng46EFVT4X68%3D&reserved=0">https://doi.org/10.1016/j.dib.2022.108798</a></p> <p>A detailed description of all the metrics can be found in the README file (README.docx). </p> <p>A .zip file is also provided containing all the data for the validation of the AHN3 data products (AHN3_validation.zip). </p>
LAGOS-NE – Lake nutrient chemistry and geospatial data to measure spatial structure of ecosystem properties in a 17-state region of the U.S.
This dataset includes data for the lake water quality and geospatial variables that describe climate, hydrology, land use land cover, and lake characteristics that were used to study spatial structure in lake properties at the sub-continental scales (Lapierre et al. Quantifying spatial structure to improve understanding of the relationships between climate, landscape, and lake ecosystem properties, to be submitted to Ecology). All observations came from LAGOS-NELIMNO v. 1.054.1 and LAGOS-NEGEO v. 1.03 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS-NE contains a complete census of lakes great than or equal to 4 ha with corresponding geospatial information for a 17-state region of the U.S., and a subset of the lakes has observational data on morphometry and chemistry. Approximately 54 different sources of data were compiled for the LAGOS-NELIMNO v. 1.054.1 dataset and were mostly generated by government agencies (state, federal, tribal) and universities. In this analysis, we compiled lake water quality data from the summer stratified season (June 15-September 15) in the most recent 10 years of data included in LAGOS-NELIMNO v. 1.054.1 (2002-2011). We report the median total nitrogen, total phosphorus, secchi depth, and chlorophyll values for each lake, which was calculated as the grand median of each yearly median value. We also include data for lake and landscape characteristics including variables related to lake morphometry, climate, hydrology, atmospheric deposition, land use and land cover.
Plant silicon content as a proxy for understanding plant community properties and ecosystem structure
<p>Main dataset from the paper entitled "Plant silicon content as a proxy for understanding plant community properties and ecosystem structure".</p>
High elevation forest age structure across an elevational gradient in the Greater Yellowstone Ecosystem
<p>Dataset for Blomdahl et al. 2022. Drivers of forest change in the Greater Yellowstone Ecosystem. Journal of Vegetation Science. </p> <p>See publication for site description and methods. </p> <p>Descriptions for variables in “trees_seedlings.csv”:</p> <p><strong>Plot_ID: </strong>Plot identifier. Nomeclature follows transect name and plot number. ECO="Ecotone" transect, SBM="South Bird Mountain" transect.</p> <p><strong>Year_Sampled: </strong>Samples collected 2017-2019.</p> <p><strong>Tree_ID: </strong>Identifier for unique trees and seedlings. </p> <p><strong>Core: </strong>Tree core sample identifier. Applies only to trees (cores not taken from seedlings). Generally, 2 cores were taken per Tree >5 cm DCH, though sometimes up to 4 were collected if a sample was rotten.</p> <p><strong>Sample_ID: </strong>Identifier for unique samples, some of which come from the same tree (for unique individuals: "Tree_ID"). Applies to trees and seedlings.</p> <p><strong>Form: </strong>Stems >5 cm diameter at coring height (DCH), coring height=30 cm; Seedlings >30: Stems <5 cm DCH and >30 cm in height (sometimes referred to as "saplings"); Seedlings <30: Stems <30 cm in height</p> <p><strong>Species: </strong>ABLA=<em>Abies</em> <em>lasiocarpa</em>, PIAL=Pinus <em>albicaulis</em>, PICO=<em>Pinus</em> <em>contorta</em>, PIEN=<em>Picea</em> <em>engelmannii</em>, PSME=<em>Pseudotsuga</em> <em>menziesii</em></p> <p><strong>Diam_30_cm: </strong>Diameter (cm) at 30 cm sample height.</p> <p><strong>Diam_0_cm: </strong>Diameter (cm) at 0 cm sample height (i.e., the base). Only seedlings were measured at base, not trees.</p> <p><strong>Seedling_Ht_cm: </strong>Length of seedling stem (cm).</p> <p><strong>Bark_Thick_cm: </strong> Bark thickness (cm). Not recorded in 2018. Bark thickness assumed to be <0.1 cm for seedlings.</p> <p><strong>Live_Dead: </strong>Live/Dead status when sampled. L=Live, D=Dead.</p> <p><strong>Canopy: </strong>Canopy position. D=Dominant, C=Codominant. S=Suppressed. Not recorded in 2017. All seedlings assumed suppressed.</p> <p><strong>Outer_Ring: </strong>Last complete year of growth, generally one year prior to Year_Sampled for live trees. Mortality year for dead trees.</p> <p><strong>Inner_Ring:</strong> Year of innermost ring measured in tree core sample measured at 30 cm sample height. Does not apply to seedlings, which were sampled as cross sections, and therefore the pith was always measureable.</p> <p><strong>Pith_30: </strong>Year of the first ring of the tree or sapling, measured at 30 cm sampling height. </p> <p><strong>Pith_0: </strong>Year of the first ring of the seedling, measuring at 0 cm sampling height (i.e., the base). Applies only to seedlings, which were destructively sampled at the base.</p> <p><strong>Estab_Year: </strong>Estimated year of establishment for trees and saplings, same as Pith_0 for seedlings. See methods of Blomdahl et al., 2022, for how establishment year was estimated.</p> <p><strong>Age:</strong> Estimated age of the tree.</p>
The interaction of physical structure and nutrient loading drives ecosystem change in a large tropical lake over 40 years (DATA)
<p><strong><span>Datasets for manuscript entitled "The interaction of physical structure and nutrient loading drives ecosystem change in a large tropical lake over 40 years"</span></strong></p> <p><em><span>Includes data collected by Fadum and Hall as well as unplublished data from Vaux and Goldman 1984.</span></em></p> <p><span><strong>Abstract: </strong>Many lakes across the world are entering novel states and experiencing altered biogeochemical cycling due to local anthropogenic stressors. In the tropics, understanding the drivers of these changes can be difficult due to a lack of documented historic conditions or an absence of continuous monitoring that can distinguish between intra- and inter-annual variation. Over the last forty years (1980–2020), Lake Yojoa (Honduras) has experienced increased watershed development as well as the introduction of a large net-pen Tilapia farm, resulting in a dramatic reduction in seasonal water clarity, increased trophic state and altered nutrient dynamics, shifting Lake Yojoa from an oligotrophic (low productivity) to mesotrophic (moderate productivity) ecosystem. To assess the changes that have occurred in Lake Yojoa as well as putative drivers for those changes, we compared Secchi depth (water clarity), dissolved inorganic nitrogen (DIN), and total phosphorus (TP) concentrations at continuous semi-monthly intervals for the three years between 1979 and 1983 and again at continuous 16-day intervals for 2018–2020. Between those two periods we observed the loss of a clear water phase that previously occurred in the months when the water column was fully mixed. Seasonal peaks in DIN coincident with mixing suggest that an enhanced accumulation of ammonium in the hypolimnion (the bottom layer of a stratified lake) during stratification, and release to the epilimnion (the top layer of a stratified lake) with mixing maintains high algal abundance and subsequently low Secchi depth during what was previously the clear water phase. This interaction of nutrient loading and Lake Yojoa's monomictic stratification regime illustrates a key phenomenon in how physical water column structure and nutrients interact in tropical monomictic lakes. This work highlights the need to consider nutrient dynamics of warm anoxic hypolimnions, not just surface water nutrient concentrations, to understand environmental change in these societally important but understudied ecosystems.</span></p> <p> </p> <p><em><strong><span>(for more recent years of data collection see additional zenodo repositories by Fadum and/or Hall) </span></strong></em></p>
Fig. 3 in Diet and trophic structure of the fish fauna in a subtropical ecosystem: impoundment effects
Fig. 3. Proportion in number and biomass (CPUE) of the trophic guilds along the longitudinal gradient of the Salto Caxias Reservoir, Iguaçu River, before and after the impoundment. (1 = upstream; 2 = middle region; 3 = dam; 4 = downstream) (Alg = algivores; Det = detritivores; Her = herbivores; Ain = aquatic insectivores; Tin = terrestrial insectivores; Inv = invertivores; Omn = omnivores; Pis = piscivores; Pla = planktivores; Car = carcinophages).
Fig. 1 in Diet and trophic structure of the fish fauna in a subtropical ecosystem: impoundment effects
Fig. 1. Location of sampling sites along the longitudinal gradient of the Iguaçu River, in the area influenced by the Salto Caxias Reservoir, Paraná State. a) before the impoundment; b) after the impoundment. (site 1 = upstream; site 2 = middle region; site 3 = dam; site 4 = downstream).
Fig. 2 in Diet and trophic structure of the fish fauna in a subtropical ecosystem: impoundment effects
Fig. 2. Graphical representation of the first two axes of the Nonmetric multidimensional scaling (nNMDS), demonstrating the food resources used by the fish fauna in the different sites and phases, in the area influenced by the Salto Caxias Reservoir, Iguaçu River. FR = Food resources (AI = aquatic insects; TI = terrestrial insects; DE = decapods; MC = microcrustaceans; MA = macroinvertebrates; MI = microinvertebrates; FI = fish; FS = fish scales; AP = aquatic plants; TP = terrestrial plants; AL = algae; DS = detrit/sediment); B = before impoundment, A = after impoundment; 1 to 4 = sampling sites.
Fig. 1 in Trophic Structure Of Amphibian And Reptile Communities In Terrestrial And Aquatic Ecosystems Of Belarus
Fig. 1. Scheme of food relations of amphibians and reptiles in communities of terrestrial and aquatic ecosystems of Belarus.
Fig. 2 in Trophic Structure Of Amphibian And Reptile Communities In Terrestrial And Aquatic Ecosystems Of Belarus
Fig. 2. Degree of similarity of taxonomic composition of food ration of amphibians and reptilesentomophages in natural ecosystems of Belarus.
Figure 5 in Composition and structure of plant communities in the Moist Temperate Forest Ecosystem of the Hindukush Mountains, Pakistan
Figure 5. CCA plot Analysis of illustrating the influence of elevation on spreading pattern of plant communities in Lalkoo valley Swat.
Figure 4 in Composition and structure of plant communities in the Moist Temperate Forest Ecosystem of the Hindukush Mountains, Pakistan
Figure 4. Results of CCA joint biplot showing results for eleven plant communities' correlation with environmental variable. BAB-I: Berberis- Abies- Bergenia; PIP-II: Picea-Indigofera- Poa; APP-III: Abies- Parrotiopsis- Poa,QVP-IV:Quercus-Viburnum-Poa,PSP-V:PiceaSalix-Primula,AVP-VI:Abies-Viburnum -Poa; VTP-VII: ViburnumTaxus-Poa; PVL-VIII: Pinus-Viburnum-Lithospermum; ABC-IX: Abies-Berberis-Carex; PVP-X: Pinus-Viburnum-Poa; and PPP-XI: Parrotiopsis-Picea-Poa represents community types.
FIGURE 4 in Temporal genetic structure of a stock of Prochilodus lineatus (Characiformes: Prochilodontidae) in the Mogi-Guaçu River ecosystem, São Paulo, Brazil
FIGURE 4 | Bayesian skyline plot (BSP) showing change in effective population size of Prochilodus lineatus in Feb_15 group from Cachoeira de Emas in the Mogi-Guaçu River based on Dloop marker. The y-axis, population size × generation time*; x-axis, time (indicated in thousands of years ago). *Generation time measured in million years. Solid lines represent median estimates, and shaded areas represent the 95% HPD limits.
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Allen Brain Atlas
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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.
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OpenNeuro
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