Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
79
datasets available to search
ShareScore release 0.7.1
Dataset results
79 results for “edge effect”
Classification of New Caledonian Forests According to Edge and Elevation Effects
<h1>Description</h1> <p>This map represents a classification of forest types based on the influence of the edge effect (distance to the forest edge) and elevation effect (temperature and area) on tree community richness.</p> <ul> <li>The edge effect influences tree diversity through an environmental aridity filter. In New Caledonia, the maximum temperature recorded at the forest edge is 41°C in February, while it never exceeds 24°C beyond 100 meters from the edge. This temperature difference induces a selection for species that tolerate the most arid conditions, leading to a reduction in the biological richness of tree communities (<a href="https://doi.org/10.1007/s10980-017-0534-7" target="_blank" rel="noopener">Ibanez et al., 2017</a>; <a href="https://cnrt.nc/wp-content/uploads/2022/12/CNRT-rappsc-RELIQUES_Tome-ENV-Edition-2022-cp.pdf" target="_blank" rel="noopener">Birnbaum et al., 2022</a>; <a href="https://doi.org/10.1111/1365-2745.14105" target="_blank" rel="noopener">Blanchard et al., 2023</a>).</li> <li>Altitude also affects tree diversity due to temperature variation and available area (<a href="https://doi.org/10.1111/avsc.12070" target="_blank" rel="noopener">Ibanez et al., 2014</a>; <a href="https://doi.org/10.1093/aobpla/plv075" target="_blank" rel="noopener">Birnbaum et al., 2015</a>; <a href="https://doi.org/10.1111/ddi.12374" target="_blank" rel="noopener">Pouteau et al., 2015</a>; <a href="https://doi.org/10.1111/jvs.12396" target="_blank" rel="noopener">Ibanez et al., 2016</a>; <a href="https://doi.org/10.1093/aob/mcx107" target="_blank" rel="noopener">Ibanez et al., 2018</a>). In New Caledonia, observed tree community richness ranges from 35 to 121 species per hectare within the NC-PIPPN network, peaking at mid-altitude ranges (refer to figure '<a title="1ha Plot Tree Richness Distribution Along Elevation" href="../records/12739730/files/amap_elevation_richness.png?download=1&preview=1" target="_blank" rel="noopener">amap_elevation_richness.png</a>'). Potential richness was assessed using the S-SDM model, with the 80th percentile used as a threshold to distinguish low and high potential richness across three elevation classes: [0 - 400m[, [400 - 900m[, and [900 - 1628m[.</li> </ul> <p>The classification of forest types combines distance from the forest edge and potential richness by elevation into three major categories, as illustrated in the figure '<a title="Illustration of the three forest types" href="../records/12739730/files/amap_forest_types_nc.png?download=1&preview=1" target="_blank" rel="noopener">amap_forest_types_nc.png</a>':</p> <ol> <li><strong>Edge Forest:</strong> Parts of the forest located less than 100 meters from the forest edge.</li> <li><strong>Mature Forest:</strong> Parts of the forest located beyond 100 meters from the edge with a lower potential richness of tree communities.</li> <li><strong>Core Forest:</strong> Parts of the forest located more than 300 meters from the edge with a higher potential richness of tree communities.</li> </ol> <h1>Content</h1> <p>The map is computed from the Forest Map of New Caledonia (v2024) and the Potential Tree Species Richness in the Forests of New Caledonia (v2024). This dataset was produced, analyzed, and verified using a combination of open-source software, including QGIS, PostgreSQL, PostGIS, Python, R, and the GDAL library, all running on Linux. </p> <ul> <li>amap_forest_types_nc.png is a picture illustrating the forest type classification </li> <li>amap_forest_types_nc.zip is a compressed file contains the six essential files for an ESRI-format GIS system, using the WGS84 international coordinate system, and can be uploaded to a spatial database such as PostgreSQL/PostGIS. Each row of the attribute table represents a forest type (a multi-polygon) with associated fields :</li> </ul> <table> <tbody> <tr> <td><strong>Field</strong></td> <td><strong>Type</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><strong>type</strong></td> <td>TEXT</td> <td>One of the three forest types ("Edge Forest", "Mature Forest", "Core forest")</td> </tr> <tr> <td><strong>area_ha</strong></td> <td>NUMERIC (2 DECIMALS)</td> <td>Area of the multi-polygon in hectares</td> </tr> <tr> <td><strong>description<br></strong></td> <td>TEXT</td> <td>Description of the three forest types</td> </tr> <tr> <td><strong>geom</strong></td> <td>GEOMETRY (MULTIPOLYGON, 4326))</td> <td>Geometry with datum EPSG: 4326 (WGS 84 – World Geodetic System 1984)</td> </tr> </tbody> </table> <h1>Limitations</h1> <p>We caution users that the distinction between the three classes is based on an ecological interpretation and does not reflect directly perceptible breaks in the forest. The ecological transition from the edge to the core of the forest follows multiple gradient modulated by environmental conditions.</p> <p>Moreover, this classification is based on local observations and measurements, which are complex to generalize and extrapolate across a territory as environmentally diverse as New Caledonia. Nevertheless, it allows us to address the impact of fragmentation at the scale of New Caledonia.</p>
Detecting edge effects of geese grazing at the boundary of woodland and grassland
<p>The presence of geese on different areas of lawn was estimated by the length of droppings on the lawn. Geese defecate frequently and seemingly indiscriminately. Counting dropping is a well-known method for estimating their density on areas of land (Owen, 1971). However, we found it difficult to distinguish individual defecation events as the dropping tend to break apart as they are released. Therefore, we measured the total length of dropping in an area. Geese dropping are more or less cylindrical and we consider a measure related to the volume of droppings is more reliable than a count of their number.</p> <p>Observations were conducted in July 2014 and March and April 2015 at Meise Botanic Garden, Meise, Belgium. Rectangular plots were laid out perpendicular to a woodland-lawn boundary on sections of a Botanic Garden frequently used by geese. These plots are detailed in file DroppingsPlots.csv. The sites for these plots were chosen because they were well separated from each other; were away from other trees and faced different directions. The plots were marked out using bamboo canes and a tape measure. Then either 20 or 30 randomly chosen 1 m<sup>2</sup> square quadrats were surveyed within the rectangular plot. The cumulative length of dropping in a quadrat was measured to the nearest centimeter with a ruler.</p> <p>The results are found in file DroppingsMeasurements.csv.</p> <p>The columns of this file are as follows</p> <p>Plot - The identifying number given to the plot</p> <p>X - The distance parallel to the woodland-lawn boundary</p> <p>Y - The distance from the woodland-lawn boundary</p> <p>Length - The total length in centimeters of the dropping found in a 1m<sup>2</sup> quadrat</p> <p>Prunella - coverage of <em>Prunella vulgaris</em> L. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Renoncule - coverage of <em>Ranunculus</em> sp. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Bellis - coverage of <em>Bellis perennis</em> L. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Lotus - coverage of <em>Lotus</em> sp. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Glechoma - coverage of <em>Glechoma hederacea</em> L. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Four species of geese are present in the Botanic Garden and may have contributed droppings to the observations. These species are <em>Alopochen aegyptiaca</em> (L. 1766) (Egyptian geese), <em>Branta canadensis</em> (L. 1758) (Canada geese), <em>Anser anser</em> (L. 1758) (greylag geese) and <em>Branta leucopsis</em> (Bechstein, 1803) (barnacle geese).</p>
Seed dispersal data for Warneke et al "Habitat fragmentation alters the distance of abiotic seed dispersal through edge effects and direction of dispersal"
This csv file contains seed dispersal data for five species (Carphephorus bellidifolius, Aristida beyrichiana, Liatris squarrulosa, Sorghastrum secundum, and Anthenantia villosa). Data were collected at the Savannah River Site, near Aiken, South Carolina, United States. Data were collected between November 17, 2009, to January 22, 2010 and were collected using the methods outlined in this document.
Input data for 'forest_carbon_edge_effects'
<p>1. af.tif: Land-cover from MODIS for the continent of Africa clipped to the tropical regions to match the biomass dataset; 16 classes defined by the UMD classification. From Friedl, M. A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang. 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment 114:168–182.<br /> 2. af_biov2ct1.tif: Above-ground biomass for the tropical regions of Africa; biomass measured as tons/ha. From Baccini, A., S. J. Goetz, W. S. Walker, N. T. Laporte, M. Sun, D. Sulla-Menashe, J. Hackler, P. S. A. Beck, R. Dubayah, M. A. Friedl, S. Samanta, and R. A. Houghton. 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change 2:182–185.<br /> 3. am.tif: Land-cover from MODIS for the Americas; 16 classes defined by the UMD classification. From Friedl, M. A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang. 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment 114:168–182.<br /> 4: am_biov2ct1.tif: Above-ground biomass for the tropical regions of the Americas; biomass measured as tons/ha. From Baccini, A., S. J. Goetz, W. S. Walker, N. T. Laporte, M. Sun, D. Sulla-Menashe, J. Hackler, P. S. A. Beck, R. Dubayah, M. A. Friedl, S. Samanta, and R. A. Houghton. 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change 2:182–185.5: anthrome_0.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(0): No data. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 5: anthrome_11.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(11):Urban. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 6: anthrome_12.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(12):Mixed settlements. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 7: anthrome_21.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(21):Rice villages. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 8: anthrome_22.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(22):Irrigated villages. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 9: anthrome_23.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(23):Rainfed villages. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 10: anthrome_24.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(24):Pastoral villages. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 11: anthrome_31.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(31):Residential irrigated croplands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 12: anthrome_32.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(32):Residential rainfed croplands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 13: anthrome_33.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(33):Populated croplands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 14: anthrome_34.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(34):Remote croplands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 15: anthrome_41.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(41):Residential rangelands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 16: anthrome_42.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(42):Populated rangelands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 17: anthrome_43.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(43):Remote rangelands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 18: anthrome_51.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(51):Residential woodlands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 19: anthrome_52.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(52):Populated woodlands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 20: anthrome_53.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(53):Remote woodlands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 21: anthrome_54.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(54):Inhabited treeless and barren lands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 22: anthrome_61.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(61):Wild woodlands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 23: anthrome_62.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(62):Wild treeless and barren lands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 24: as.tif: Land-cover from MODIS for the continent of Asia; 16 classes defined by the UMD classification. From Friedl, M. A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang. 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment 114:168–182.<br /> 25: as_biov2ct1.tif: Above-ground biomass for the tropical regions of Asia; biomass measured as tons/ha. From Baccini, A., S. J. Goetz, W. S. Walker, N. T. Laporte, M. Sun, D. Sulla-Menashe, J. Hackler, P. S. A. Beck, R. Dubayah, M. A. Friedl, S. Samanta, and R. A. Houghton. 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change 2:182–185.<br /> 26-30: ecoregions_projected.(.dbf/.prj/.qpj/.shp/.shx): Terrestrial Ecoregions of the World is a biogeographic regionalization of the Earth’s terrestrial biodiversity. Units are ecoregions, defined as relatively large units of land or water containing a distinct assemblage of natural communities sharing a large majority of species, dynamics, and environmental conditions. From Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D'Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938.<br /> 31: fi_average.tif: Average fire density 1997-2011. Based on the modified algorithm 1 product of World Fire atlas (WFA, ESA-ESRIN) dataset. UNEP/GRID-Europe compiled the monthly data and processed the global fire density. Unit is expected average number of event per 0.1 decimal degree pixel per year multiplied by 100 (e.g. 64 value means 0.64 events per year) and slightly smoothed. From UNEP, DEWA, GRID -Europe, Collection: Global Estimated Risk Index for Multiple Hazards. Web. 30 Sep 2014,http://preview.grid.unep.ch/index.php?preview=data&events=fires.<br /> 32: gl_anthrome.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. All values(see items 5-24). From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 33: glbctd1t0503m.tif: Gridded Livestock of the World: Cattle. Number per square kilometer. These maps are created through the spatial disaggregation of sub-national statistical data based on empirical relationships with environmental variables in similar agro-ecological zones. From Robinson, T. P. et al. Mapping the Global Distribution of Livestock. PLoS One 9, e96084 (2014).<br /> 34: glbgtd1t0503m.tif: Gridded Livestock of the World: Goats. Number per square kilometer. These maps are created through the spatial disaggregation of sub-national statistical data based on empirical relationships with environmental variables in similar agro-ecological zones. From Robinson, T. P. et al. Mapping the Global Distribution of Livestock. PLoS One 9, e96084 (2014).<br /> 35: glbpgd1t0503m.tif: Gridded Livestock of the World: Pigs. Number per square kilometer. These maps are created through the spatial disaggregation of sub-national statistical data based on empirical relationships with environmental variables in similar agro-ecological zones. From Robinson, T. P. et al. Mapping the Global Distribution of Livestock. PLoS One 9, e96084 (2014).<br /> 36: glbshd1t0503m.tif: Gridded Livestock of the World: Sheep. Number per square kilometer. These maps are created through the spatial disaggregation of sub-national statistical data based on empirical relationships with environmental variables in similar agro-ecological zones. From Robinson, T. P. et al. Mapping the Global Distribution of Livestock. PLoS One 9, e96084 (2014).<br /> 37: glds00ag.tif: Gridded Population Density of the World, Version 3: (GPWv3): Population Density Grid. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative units, is used to assign population values to grid cells. The population density grids are derived by dividing the population count grids by the land area grid and represent persons per square kilometer. From CIESIN, IFPRI, Bank, T. W. & CIAT, Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Population Density Grid. (2011). Web. 26 Sep 2014. http://dx.doi.org/10.7927/H4R20Z93<br /> 38: glds00g.tif: Gridded Population Density of the World, Version 3: (GPWv3): Population Density Grid. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative units, is used to assign population values to grid cells. The population density grids are derived by dividing the population count grids by the land area grid and represent persons per square kilometer. From CIESIN, IFPRI, Bank, T. W. & CIAT, Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Population Density Grid. (2011). Web. 26 Sep 2014. http://dx.doi.org/10.7927/H4R20Z93<br /> 39: global_elevation.tiff: GTOPO30 is a global digital elevation model (DEM) with a horizontal grid spacing of 30-arc seconds (0.008333333333333 degrees or approximately 1 kilometer), resulting in a DEM having dimensions of 21,600 rows and 43,200 columns. The horizontal coordinate system is decimal degrees of latitude and longitude referenced to World Geodetic System 84 (WGS84). The vertical units represent elevation in meters above mean sea level. The elevation values range from -407 to 8,752 meters. In the DEM, ocean areas have been masked as no data and have been assigned a value of -9999. Lowland coastal areas have an elevation of at least 1 meter (so in the event that a user reassigns the ocean value from -9999 to 0 the land boundary portrayal will be maintained). Small islands in the ocean less than approximately 1 square kilometer are not represented. GTOPO30 was derived from several raster and vector sources of topographic information. These sources include: Digital Terrain Elevation Data, Digital Chart of the World, USGS 1-degree Digital Elevation Models, Army Map Service 1:1,000,000-scale Maps, International 1:1,000,000-scale Map of the World, Peru 1:1,000,000-scale Map, New Zealand DEM, and Antarctic digital Database. GTOPO30 was developed to meet the needs of the geospatial data user community for regional and continental scale topographic data. The data are suitable for many regional and continental applications, such as climate modeling, continental-scale land cover mapping, extraction ofdrainage features for hydrologic modeling and geometric and atmospheric correction of medium and coarse resolution satellite image data. An example of a recent application derived from GTOPO30 is HYDRO1k, a geographic database (at a resolution of 1 km) developed to provide comprehensive and consistent global coverage of topographically derived data sets, including streams, drainage basins, and ancillary layers . HYDRO1k provides a suite of geo-referenced data sets, both raster and vector, which will be of value for all users who need to organize, evaluate, or process hydrologic information on a continental scale. The raster data sets are the hydrologically correct DEM, derived flow directions, flow accumulations, slope, aspect, and a compound topographic (wetness) index. The derived streamlines and basins are distributed as vector data sets. GTOPO30 was developed through a collaborative effort led by staff at the U.S. Geological Survey's EROS EDC. The following organizations participated by contributing funding or source data: the National Aeronautics and Space Administration (NASA), the United Nations Environment Programme/Global Resource Information Database (UNEP/GRID), the U.S. Agency for International Development (USAID), the Instituto Nacional de Estadistica Geografica e Informatica (INEGI) of Mexico, the Geographical Survey Institute (GSI) of Japan, Manaaki Whenua Landcare Research of New Zealand, and the Scientific Committee on Antarctic Research (SCAR). From Grenlee S., Gesch, D, available online [http://webmap.ornl.gov/wcsdown/dataset.jsp?ds_id=10003] from ORNL DAAC, Oak Ridge, Tennessee, U.S.A..<br /> 40: global_precip.tiff: The Global Precipitation Climatology Centre (GPCC), which is operated by the Deutscher Wetterdienst (National Meteorological Service of Germany), is a component of the Global Precipitation Climatology Project (GPCP) with the main emphasis on the treatment of the global in-situ observations. The GPCC simultaneously contributes to the Global Climate Observing System (GCOS) and other international research and climate monitoring projects. This rain gauge-only data set was acquired from GPCC and resampled to 0.5 degree grid boxes for use in the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II. The GPCC collects precipitation data which are locally observed at rain gauge stations and distributed as CLIMAT and SYNOP reports via the Global Telecommunication System of the World Weather Watch (GTS) of the World Meteorological Organization (WMO). The Centre acquires additional monthly precipitation data from meteorological and hydrological networks which are operated by national services. Meeson B., Los, S, Landis, D., Hall F., Collatz, G., Brown de Colstoun, E. available online [http://webmap.ornl.gov/wcsdown/wcsdown.jsp?dg_id=995_20] from ORNL DAAC, Oak Ridge, Tennessee, U.S.A..<br /> 41: global_soil_types.tiff: A global data set of soil types is available at 1-degree latitude by 1-degree longitude resolution. There are 26 soil units based on Zobler’s assessment of FAO Soil Units (Zobler, 1986). The data set was compiled as part of an effort to improve modeling of the hydrologic cycle portion of global climate models. A more extensive version of these data, including 106 soil units as well as soil texture and slope, is available from NCAR, Scientific Computing Division, Data Support Section; the more extensive data set is entitled "Staub and Rosenweig's GISS Soil & Sfc Slope, 1-Deg" [http://www.dss.ucar.edu/datasets/ds770.0/]. A help file prepared by Matthews and Fung (1987) (soil1x1.help) is provided as a companion file. Image of 26 soil types available at 1-degree by 1-degree resolution. Additional documentation from Zobler’s assessment of FAO soil units is available from the NASA Center for Scientific Information. <br /> 42: global_water_capacity: Plant-extractable water capacity of soil is the amount of water that can be extracted from the soil to fulfill evapotranspiration demands. It is often assumed to be spatially invariant in large-scalecomputations of the soil-water balance. Empirical evidence, however, suggests that this assumption is incorrect. This data set provides an estimate of the global distribution of plant-extractable water capacity of soil. A representative soil profile, characterized by horizon (layer) particle size data and thickness, was created for each soil unit mapped by FAO (Food and Agriculture Organization of the United Nations)/Unesco. Soil organic matter was estimated empirically from climate data. Plant rooting depths and ground coverages were obtained from a vegetation characteristic data set. At each 0.5 x 0.5 degree grid cell where vegetation is present, unit available water capacity (cm water per cm soil) was estimated from the sand, clay, and organic content of each profile horizon, and integrated over horizon thickness. Summation of the integrated values over the lesser of profile depth and root depth produced an estimate of the plant-extractable water capacity of soil. The global average of the estimated plant-extractable water capacities of soil is 8.6 cm (Greenland, Antarctica and bare soil areas excluded). Estimates are less than 5, 10 and 15 cm - over approximately 30, 60, and 89 per cent of the area, respectively. Estimates reflect the combined effects of soil texture, soil organic content, and plant root depth or profile depth. The most influential and uncertain parameter is the depth over which the plant-extractable water capacity of soil is computed, which is usually limited by root depth. Soil texture exerts a lesser, but still substantial, influence. Organic content, except where concentrations are very high, has relatively little effect. The file is available in an ascii array format. The format is such that j=1 corresponds to the grid cell bounded by 90.0 and 89.5 degrees south latitude (centered on 89.75) and i=1 corresponds to the grid cell bounded by 0.0 and 0.5 degrees east longitude (centered on 0.25). No data are given for land ice grid cells, most of which occur in Antarctica and Greenland, or for other unvegetated areas. A value of -99.0 indicates either a water grid cell or a land ice grid cell. A value of -1.0 indicates that vegetation is absent (and the plant-extractable water capacity of soil is undefined). Units are cm. The data file may be read as follows: dimension whcdat(720,360) do j=1,360 read(iunit,'(36f5.1)') (whcdat(i,j),i=1,720) enddo Data Citation The data set should be cited as follows: Dunne, K. A., and Cort J. Willmott. 2000. Global Distribution of Plant-extractable Water Capacity of Soil (Dunne). Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.43-49: ilf2000_last_proj(.cpg/.dbf/.prj/.qpj/.shp/.shx/.tif): Intact Forest Landscape, 2000 (IFL2000). The world's IFL map is a spatial database (scale 1:1,000,000) that shows the extent of the intact forest landscapes (IFL) for year 2000. IFL is an unbroken expanse of natural ecosystems within the zone of current forest extent, showing no signs of significant human activity, and large enough that all native biodiversity, including viable populations of wide-ranging species, could be maintained. From Potapov P., Yaroshenko A., Turubanova S., Dubinin M., Laestadius L., Thies C., Aksenov D., Egorov A., Yesipova Y., Glushkov I., Karpachevskiy M., Kostikova A., Manisha A., Tsybikova E., Zhuravleva I. 2008. Mapping the World's Intact Forest Landscapes by Remote Sensing. Ecology and Society, 13 (2) http://www.ecologyandsociety.org/vol13/iss2/art51/<br /> 50: lighted_area_luminosity.tif: NASA Earth Observation Satellite.</p>
Input dataset for carbon forest edge effects analysis
<p>Dataset that is used for the calculation of forest edge biomass effect from the following github project: [DOI forest_carbon_edge_effect] (http://dx.doi.org/10.5281/zenodo.15697)</p> <p>Contains global biomass, landcover data, anthrome, soil, elevation, water capacity, fire, luminosityr, cattle, goat, sheep, human population, and anthrome data.</p>
Skyrmions at the edge: Confinement effects in Fe/Ir(111)
<p>We have employed spin-polarized scanning tunneling microscopy and Monte-Carlo simulations to investigate the effect of lateral confinement onto the nanoskyrmion lattice in Fe/Ir(111). We find a strong coupling of one diagonal of the square magnetic unit cell to the close-packed edges of Fe nanostructures. In triangular islands this coupling in combination with the mismatching symmetries of the islands and of the square nanoskyrmion lattice leads to frustration and triple-domain states. In direct vicinity to ferromagnetic NiFe islands, the surrounding skyrmion lattice forms additional domains. In this case a side of the square magnetic unit cell prefers a parallel orientation to the ferromagnetic edge. These experimental findings can be reproduced and explained by Monte-Carlo simulations. Here, the single-domain state of a triangular island is lower in energy, but nevertheless multi-domain states occur due to the combined effect of entropy and an intrinsic domain wall pinning arising from the skyrmionic character of the spin texture.</p>
Edge effects and vertical stratification of aerial insectivorous bats across the interface of primary-secondary Amazonian rainforest
<p><span>Edge effects - abiotic and biotic changes associated with habitat boundaries - are key drivers of community change in fragmented landscapes. Their influence is heavily modulated by matrix composition. With over half of the world's tropical forests predicted to become forest edge by the end of the </span><span>century, it is paramount that conservationists gain a better understanding of how tropical biota is impacted by edge gradients. Bats comprise a large fraction of tropical mammalian fauna and are demonstrably sensitive to habitat modification. Yet, </span><span>knowledge about how bat assemblages are affected by edge effects remains scarce</span><span>. Capitalizing on a whole-ecosystem manipulation in the Central Amazon, the aims of this study were to i) assess the consequences of edge effects for twelve aerial insectivorous bat species across the interface of primary and secondary forest and ii) investigate if the activity levels of these species differed between the understory and canopy and if they were modulated by distance from the edge</span><span>. Acoustic surveys were conducted along four 2-km transects each traversing equal parts of primary and ca. 30-year-old secondary forest. Five models were used to assess the changes in the relative activity of forest specialists (three species), flexible forest foragers (three species), and edge foragers (six species). Modelling results revealed no evidence of edge effects, except for forest specialists in the understory. No significant differences in activity were found between the secondary or primary forest but most species exhibited pronounced vertical stratification. Our study highlights that forest specialist bats are more edge-sensitive than both flexible forest and edge foraging bats and suggests that the influence of edge effects on aerial insectivorous bats may exceed 2 km. The absence of pronounced edge effects and the comparable activity levels between primary and old secondary forests indicates that old secondary forest can help ameliorate the consequences of fragmentation on tropical aerial insectivorous bats. </span></p>
Figs. 1 A, B. A. Richness and B in Influence of the Edge Effect on A Soil Seed BAnk of A NAturAl FrAgment in the AtlAntic Forest
Figs. 1 A, B. A. Richness and B. abundance of the soil seed bank in relation to the edge from Mata Grande of the PEI.
Fig. 3 in Influence of the Edge Effect on A Soil Seed BAnk of A NAturAl FrAgment in the AtlAntic Forest
Fig. 3 NMDS of the composition of the soil seed bank differences in distances from the edge from Mata Grande of the PEI.
Figs. 2 A, B. A in Influence of the Edge Effect on A Soil Seed BAnk of A NAturAl FrAgment in the AtlAntic Forest
Figs. 2 A, B. A Linear regression of the richness and B. abundance of the soil seed bank in relation to the edge from Mata Grande of the PEI (y=Ax+B).
Edge effects and vertical stratification of aerial insectivorous bats across the interface of primary-secondary Amazonian rainforest
Open the record for dataset details and reuse information.
Relative effects of seed mix design, consumer pressure, and edge proximity on community structure in restored prairies
Open the record for dataset details and reuse information.
Habitat fragmentation affects plant-arthropod interactions through connectivity loss and edge effects
Open the record for dataset details and reuse information.
Corridors promote fire via connectivity and edge effects
Landscape corridors, strips of habitat that connect otherwise isolated habitat patches, are commonly employed during management of fragmented landscapes. To date, most reported effects of corridors have been positive; however, there are long-standing concerns that corridors may have unintended consequences. Here, we address concerns over whether corridors promote propagation of disturbances such as fire. We collected data during prescribed fires in the world's largest and best replicated corridor experiment (Savannah River Site, South Carolina, USA), six ca. 50-ha landscapes of open (shrubby/herbaceous) habitat within a pine plantation matrix, to test several mechanisms for how corridors might influence fire. Corridors altered patterns of fire temperature through a direct connectivity effect and an indirect edge effect. The connectivity effect was independent of fuel levels and was consistent with a hypothesized wind-driven "bellows effect." Edges, a consequence of corridor implementation, elevated leaf litter (fuel) input from matrix pine trees, which in turn increased fire temperatures. We found no evidence for corridors or edges impacting patterns of fire spread: plots across all landscape positions burned with similar probability. Impacts of edges and connectivity on fire temperature led to changes in vegetation: hotter-burning plots supported higher bunch grass cover during the field season after burning, suggesting implications for woody/herbaceous species coexistence. To our knowledge, this represents the first experimental evidence that corridors can modify landscape-scale patterns of fire intensity. Corridor impacts on fire should be carefully considered during landscape management, both in the context of how corridors connect or break distributions of fuels and the desired role of fire as a disturbance, which may range from a management tool to an agent to be suppressed. In our focal ecosystem, longleaf pine woodland, corridors might provide a previously u
Edge effects, not connectivity, determine the incidence and development of a foliar fungal plant disease
Using a model plant-pathogen system in a large-scale habitat corridor experiment, we found that corridors do not facilitate the movement of wind-dispersed plant pathogens, that connectivity of patches does not enhance levels of foliar fungal plant disease, and that edge effects are the key drivers of plant disease dynamics. Increased spread of infectious disease is often cited as a potential negative effect of habitat corridors used in conservation, but the impacts of corridors on pathogen movement have never been tested empirically. Using sweet corn (Zea mays) and southern corn leaf blight (Cochliobolus heterostrophus) as a model plant-pathogen system, we tested the impacts of connectivity and habitat fragmentation on pathogen movement and disease development at the Savannah River Site, South Carolina, USA. Over time, less edgy patches had higher proportions of diseased plants, and distance of host plants to habitat edges was the greatest determinant of disease development. Variation in average daytime temperatures provided a possible mechanism for these disease patterns. Our results show that worries over the potentially harmful effects of conservation corridors on disease dynamics are misplaced, and that, in a conservation context, many diseases can be better managed by mitigating edge effects.
Data from: Edge effects and mating patterns in a bumblebee-pollinated plant
<p></p><p>Researchers have long assumed that plant spatial location influences plant reproductive success and pollinator foraging behavior. For example, many flowering plant populations have small, linear, or irregular shapes that increase the proportion of plants on the edge, which may reduce mating opportunities through both male and female function. Additionally, plants that rely on pollinators may be particularly vulnerable to edge effects if those pollinators exhibit restricted foraging and pollen carryover is limited. To explore the effects of spatial location (edge vs. interior) on siring success, seed production, pollinator foraging patterns, and pollen-mediated gene dispersal, we established a square experimental array of 49 Mimulus ringens (monkeyflower) plants. We observed foraging patterns of pollinating bumblebees and used paternity analysis to quantify male and female reproductive success and mate diversity for plants on the edge vs. interior. We found no significant differences between edge and interior plants in the number of seeds sired, mothered, or the number sires per fruit. However, we found strong differences in pollinator behavior based on plant location, including 15% lower per flower visitation rates and substantially longer interplant moves for edge plants. This translated into 40% greater pollen-mediated gene dispersal for edge than for interior plants. Overall, our results suggest that edge effects are not as strong as is commonly assumed, and that different plant reproduction parameters respond to spatial location independently.</p><p></p>
Data from: Cascading effects of climate variability on the breeding success of an edge population of an apex predator
<p>1. Large-scale environmental forces can influence biodiversity at different levels of biological organization. Climate, in particular, is often associated to species distributions and diversity gradients. However, its mechanistic link to population dynamics is still poorly understood.</p> <p>2. Here, we unraveled the full mechanistic path by which a climatic driver, the Atlantic trade winds, determines the viability of a bird population.</p> <p>3. We monitored the breeding population of Eleonora's falcons in the Canary Islands for over a decade (2007-2017) and integrated different methods and data to reconstruct how the availability of their prey (migratory birds) is regulated by trade winds. We tracked foraging movements of breeding adults using GPS, monitored departure of migratory birds using weather radar, and simulated their migration trajectories using an individual-based, spatially explicit model.</p> <p>4. We demonstrate that regional easterly winds regulate the flux of migratory birds that is available to hunting falcons, determining food availability for their chicks and consequent breeding success. By reconstructing how migratory birds are pushed towards the Canary Islands by trade winds, we explain most of the variation (up to 86%) in annual productivity for over a decade.</p> <p>5. This study unequivocally illustrates how a climatic driver can influence local-scale demographic processes, while providing novel evidence of wind as a major determinant of population fitness in a top predator. 06-Jul-2020</p>
Output from carbon forest edge effect analysis: edge effect distance and magnitude
<p>Carbon edge effect distance and magnitude results for edge effects on forest carbon stocks across the tropics. In grid cells where the majority of pixels were from forest biomes, we consider three candidate regression models to represent the relationship between biomass density and distance to forest edge. In particular, we consider:</p> <p>method 1: Biomass= θ_1-θ_2⋅exp(-θ_3⋅Distance)</p> <p>method 2: Biomass= β_0+β_1⋅ln(Distance)</p> <p>method 3: Biomass = \eta_0+\eta_1 * Distance</p> <p>Then, for each grid cell, the candidate with the highest $R^2$ is used to best represent the relationship between density and distance to forest edge. Models (2) and (3) were deemed as suitable (and more simplistic) alternatives in cells where higher distances were generally not observed and as a result the forest core was not firmly established. We also note that in the vast majority of grid cells, model (1) was optimal. For each cell the magnitude and distance of the edge effect were again estimated. In cells using models (2) or (3) the forest core () was estimated as the average biomass density at the largest observed distance in the cell.</p> <p>regression_coefficients_as_shapefile - projected spatially as an ESRI Shapefile where the methods are defined as:</p>
Data from: Intraspecific leaf trait variation mediates edge effects on litter decomposition rate in fragmented forests
<p>There is strong trait dependence in species-level responses to environmental change and their cascading effects on ecosystem functioning. However, there is little understanding of whether intraspecific trait variation (ITV) can also be an important mechanism mediating environmental effects on ecosystem functioning. This is surprising, given that global change processes such as habitat fragmentation and the creation of forest edges drive strong trait shifts within species. On 20 islands in the Thousand Island Lake, China, we quantified intraspecific leaf trait shifts of a widely distributed shrub species, <em>Vaccinium carlesii</em>, in response to habitat fragmentation. Using a reciprocal transplant decomposition experiment between forest edge and interior on 11 islands with varying areas, we disentangled the relative effects of intraspecific leaf trait variation vs. altered environmental conditions on leaf decomposition rates in forest fragments. We found strong intraspecific variation in leaf traits in response to edge effects, with a shift towards recalcitrant leaves with low specific leaf area and high leaf dry matter content from forest interior to the edge. Using structural equation modelling, we showed that such intraspecific leaf trait response to habitat fragmentation had translated into significant plant afterlife effects on leaf decomposition, leading to decreased leaf decomposition rates from the forest interior to the edge. Importantly, the effects of intraspecific leaf trait variation were additive to and stronger than the effects from local environmental changes due to edge effects and habitat loss. Our experiment provides the first quantitative study showing that intraspecific leaf trait response to edge effects is an important driver of the decrease in leaf decomposition rate in fragmented forests. By extending the trait-based response-effect framework towards the individual level, intraspecific variation in leaf economics traits can provide the missing functional link between environmental change and ecological processes. These findings suggest an important area for future research on incorporating ITV to understand and predict changes in ecosystem functioning in the context of global change.</p>
Edge effects increase soil respiration without altering soil carbon stocks in temperate broadleaf forests
<p>Anthropogenic disturbance has left the world's forests highly fragmented, with a significant proportion of edge-affected area. Abiotic changes at forest edges are likely to affect forest soil carbon cycling, as higher temperatures and lower moisture availability in edge environments have well-documented effects on soil respiration. The present study sought to quantify persistent changes in soil carbon cycling in the fragmented broadleaf forests of southeastern Pennsylvania. At three sites with >80 year old forest-field edges, three 100 m transects perpendicular to the edge were established. Monthly measurements of soil respiration, temperature, and moisture were made at fixede distances along each transect throughout the growing season. Soil carbon storage from 0-20 cm depth, litter biomass, and decomposition rates were also assessed. Soil respiration was significantly higher at forest edges, relative to the interior, and this effect penetrated 60 m into the forest. Significantly elevated surface soil temperature and decreased soil moisture were also observed in edge environments. Despite elevated soil respiration at the edge, soil carbon storage, litter bssomass, and decomposition rates were invariant along edge to interior gradients. The temperature responsiveness of soil respiration was significantly higher in the forest interior (100 m), relative to locations ≤60 m from the edge. Edge effects altering elements of the soil carbon cycle were apparent in the forests of southeastern Pennsylvania, and principally manifest as increased soil respiration rates and decreased temperature responsiveness of soil respiration. Lack of variation in soil carbon pools and decomposition rates from the forest edge to interior suggests that increased soil respiration may be related to changes in root and rhizosphere respiration at the edge. These findings contribute to a growing body of evidence documenting increased soil respiration in the edge environments of temperate broadleaf forests. Discounting the alterations imposed by forest fragmentation on carbon cycling has the potential to produce misleading estimates of land-atmosphere CO<sub>2</sub> exchange and terrestrial carbon storage.</p>
ScienceDex guides
Understand access before you commit
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.