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Data from: Comparative productivity of six bioenergy cropping systems on marginal lands in the Great Lakes Region, United States
<p>Growing lignocellulosic crops on marginal lands is a promising solution for sustainable biofuel production. We evaluated the productivity of bioenergy cropping systems (switchgrass [<em>Panicum</em> <em>virgatum</em> L., var. Cave‐In‐Rock], miscanthus [<em>Miscanthus</em> × <em>giganteus</em>, 'Illinois clone'], hybrid poplar [<em>Populus</em> <em>nigra</em> × <em>P. maximowiczii</em> A. Henry 'NM6'], native grasses [five species], early successional vegetation, and restored prairie vs. historical vegetation [as reference control]) with and without nitrogen fertilization on low‐fertility former cropland at five sites in the Great Lakes Region, United States. We reported biomass yields for the first 7 years after establishment. Switchgrass was most consistently productive across all sites, but miscanthus was more productive at three of the five sites. When averaged across sites, years, and nitrogen (N) treatments, biomass yields followed the order miscanthus > switchgrass > hybrid poplar ≈ native grasses > restored prairie > early successional vegetation ≈ historical vegetation, but varied substantially by crop and site, with a significant crop by site interaction. Yields of miscanthus and switchgrass peaked after four to five growing seasons and declined thereafter, while yields of both native grasses and restored prairie increased throughout 6 years with no sign of follow‐on decline, suggesting that polycultures may outperform monocultures over the long term. Yields of early successional vegetation—similar in composition to historical vegetation at each site—did not improve with time. Nitrogen fertilization increased the yields of all cropping systems at all sites. Our results demonstrate the viability of low‐productivity former cropland for long‐term bioenergy production and suggest there is no single crop best suited for all low-fertility soils.</p>
Data for impacts of topography-based subgrid scheme and downscaling of atmospheric forcing on modeling land surface processes in the conterminous US
<p>The effects of small-scale topography-induced land surface heterogeneity are not well represented in current Earth System Models (ESMs). A topography-based subgrid structure and methods of downscaling of atmospheric forcing from the atmospheric grid to the subgrids of the land model grid (TGUs) have been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to improve representation of the effects of small-scale topography-induced land surface heterogeneity on land surface processes. This study evaluates the impacts of the topography-based subgrid structure and downscaling of atmospheric forcing on modeling land surface processes in E3SM over the conterminous United States (CONUS). For this purpose, ELM simulations are performed using two configurations without (NoD ELM) and with (D ELM) downscaling, both using TGUs derived for the 0.5-degree grids and the same land surface parameters. Simulations using the two ELM configurations are compared over the CONUS domain, regional levels, and at observational sites (e.g., SNOTEL). The CONUS-level results suggest that D ELM simulates more snowfall and snow water equivalent (SWE), higher runoff, and less ET during spring and summer. Regional-level results suggest more pronounced impacts of downscaling over regions dominated by higher elevation TGUs and regions with maximum precipitation occurring during cool seasons. Results at the SNOTEL sites suggest that D ELM has superior capability of reproducing the observed SWE at 83% of the sites, with more pronounced performance over topographically heterogeneous TGUs with their maximum precipitation occurring during cool seasons. The results highlight the importance of improving representation of small-scale surface heterogeneity in ESMs and motivate future research to understand their effects on land-atmosphere interactions, streamflow, and water resources management over mountainous regions.</p> <p>The data utilized to evaluate effects of the topography-based subgrid structure and downscaling of atmospheric forcing in land surface modeling include a TGU level land surface data file, atmospheric forcing to drive the land model, ELM user name list configuration parameters, regionalization variables (topographic regions, snow fraction regions, water versus energy limited regions, and regions of season of maximum precipitation), model restart files for both ELM configurations, and model outputs (grid and subgrid levels), model outputs aggregated to TGUs and grid levels.</p> <p>The data files include:</p> <ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/daily_prism_precip.zip?versionId=be97ca8d-182a-4f1e-9ae3-9da3f2b87e24">DELM.zip</a>: Directory containing the following files relevant to the D ELM configuration and model output files.</li> <ol> <li>Restart file: 202201289.tgu_all_disag_yr1850surfdata.ielm.r05_r05.compy.elm.r.2005-01-01-00000.nc</li> <li>Configuration file: user_nl_elm</li> <li>Aggregated grid-level monthly output file: grd_level_output_disag_mnly_run_11_new_20221109.nc</li> <li>Aggregated grid-level daily output file: grd_level_output_disag_daily_20220512.nc</li> <li>TGU-level monthly output file: tgu_level_output_all_disag_20221109.nc</li> </ol> <li> <a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/dem_4km4.nc">NoDELM.zip</a>: Directory containing the following files relevant to the NoD ELM configuration and model output files.</li> <ol> <li>Restart file: 202201289.tgu_no_disag_yr1850surfdata.ielm.r05_r05.compy.elm.r.2005-01-01-00000.nc</li> <li>Configuration file: user_nl_elm</li> <li>Aggregated grid-level monthly output file: grd_level_output_nodisag_mnly_run_11_new_20221109.nc</li> <li>Aggregated grid-level daily output file: grd_level_output_nodisag_daily_20220512.nc </li> <li>TGU-level monthly output file: tgu_level_output_no_disag_20221109.nc</li> </ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">shared.zip</a>: Directory containing the following files relevant to both the D ELM and NoD ELM configurations.</li> <ol> <li>Subgrid-based surface data file: MASKED.half_degree_merge.surfdata_0.5x0.5_simyr1850_c200924.pft17.10262022v2.nc</li> <li>Regionalization file used to generate regions based on snow fraction, water versus energy limited state, and seasons of maximum precipitation: half_deg_budyko_curve_analysis_20230104_disag.nc</li> <li>Topographic ratio file used to generate topography-based regions: grd_level_output_nodisag_run_11_new_20221109.nc</li> <li>TGU-level surface elevation data file where surface elevation data are derived from high resolution surface elevation data (90 m) obtained from HydroSHEDS [Lehner et al. 2008, Lehner and Grill 2013]: half_deg_subgrids_with_PFTs_and_stat_20210403.nc</li> </ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">SNOTEL_files.zip</a>: Directory containing the following SNOTEL data related files used to evaluate model performance.</li> <ol> <li> SNOTEL list of stations file: SNOTEL_halfdegree_intersect4.csv</li> <li>SNOTEL data files/folders: csv</li> </ol> </ol> <p> </p> <p><strong>References</strong></p> <p>Lehner, B., et al. (2008). "New Global Hydrography Derived From Spaceborne Elevation Data." Eos, Transactions American Geophysical Union <strong>89</strong>(10): 93-94.</p> <p>Lehner, B. and G. Grill (2013). "Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems." Hydrological Processes <strong>27</strong>(15): 2171-2186.</p> <p> </p>
A flux tower site attribute dataset intended for land surface modeling
<p><span>Land surface models (LSMs) should have reliable forcing, validation, and surface attribute data as the foundation for effective model development and improvement. Eddy covariance flux tower data are considered the benchmarking data for LSMs. We conducted a comprehensive quality screening of the existing reprocessed flux tower dataset, including the proportion of gap-filled data, external disturbances, and energy balance closure (EBC), leading to 90 high-quality sites. For these sites, we collected vegetation, soil, topography information, and wind speed measurement height from literature, regional networks, and Biological, Ancillary, Disturbance, and Metadata (BADM) files. Then we obtained the final flux tower attribute dataset by global data product complement and plant functional types (PFTs) classification.</span></p> <p><span>The dataset comprises a total of 90 NetCDF files. Each site's data is formatted within a NetCDF file named according to the site name, database, and attributes (vegetation, soil, topography, and reference height), such as ‘AT-Neu_FLUXNET2015_Veg_Soil_ Topography_ReferenceHeight.nc’. This dataset addresses the lack of site attribute data to some extent, reduces uncertainty in LSMs data source, and aids in diagnosing parameter as well as process deficiencies.</span></p>
Figure 8 in The relationship between Sardinella aurita landings and the environmental factors in Moroccan waters (21°-26°N)
Figure 8. – Interannual trends of Sardinella aurita landings, SST, UI and Chl-a at latitude 22°N.
Figure 5 in The relationship between Sardinella aurita landings and the environmental factors in Moroccan waters (21°-26°N)
Figure 5. – Seasonal variability of Sardinella aurita landings in Dakhla port (2009-2016).
Figure 2 in The relationship between Sardinella aurita landings and the environmental factors in Moroccan waters (21°-26°N)
Figure 2. – Location of sampling areas of SST, Chl-a and UI data.
Figure 6 in The relationship between Sardinella aurita landings and the environmental factors in Moroccan waters (21°-26°N)
Figure 6. – Interannual trends of the SST (a) and Chl-a (b).
GeoTIFF Dataset for: Land-to-sea mapping of the glacial erosion unconformity reveals evolution of the Jasmund Glacitectonic Complex East of Rügen Island (SW Baltic Sea)
<p>This dataset comprises two GeoTIFF files, both with a WGS84 UTM 33 N (EPSG: 32633) projection. The erosional unconformity has a grid size of 100 x 100 metres, while the moraine or raft feature was gridded using a grid size of 20 x 20 metres. The two files were created using marine-multichannel seismic data from the Hübscher et al. (2024) dataset (<a href="https://deref-gmx.net/mail/client/7eMD7NnHVnY/dereferrer/?redirectUrl=https%3A%2F%2Fzenodo.org%2Fdoi%2F10.5281%2Fzenodo.11242567" target="_blank" rel="noopener">10.5281/zenodo.11242567</a>). The dataset was employed in the preparation of Paper by the same authors (<a href="https://doi.org/10.1029/2024GL111603">https://doi.org/10.1029/2024GL111603</a>).</p>
Implications of Lateral Groundwater Flow Across Varying Spatial Resolutions in Global Land Surface Modeling
<p>This folder contains the data used for plotting and analysis in the manuscript.</p>
Forest quality and land use intensity indicators
<b>Description: </b><p>Trends in Biophysical Vegetation Traits of Tropical Forests under Logging and Fragmentation</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/81"><b>Trends in Biophysical Vegetation Traits of Tropical Forests under Logging and Fragmentation</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=22">here</a></p><p><b>Data worksheets: </b>There are 4 data worksheets in this dataset:</p><ol><li><p><b>Canopy-based forest quality metrics</b> (Worksheet Canopy)</p><p>Dimensions: 213 rows by 6 columns</p><p>Description: Fractional canopy cover and leaf area index</p><p>Fields: </p><ul><li><b>Plot</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date images were collected (Field type: Date)</li><li><b>LAI</b>: Leaf area index, corrected for clumping of leaves at plot level (25 m x 25 m) (Field type: Numeric)</li><li><b>fcover</b>: Fractional canopy cover (mean) (Field type: Numeric)</li><li><b>sdfcov</b>: Fractional canopy cover (standard deviation) (Field type: Numeric)</li></ul><br></li><li><p><b>Above-ground biomass</b> (Worksheet AGB)</p><p>Dimensions: 203 rows by 11 columns</p><p>Description: Was derived from DBH and Height of trees for individuals >= 10 cm DBH using five different algorithms on the raw data. We additionally binned heights of trees to account for uncertainties in tree height measurements and used multiple published equations combined with wood density estimates drawn from a distribution of wood density values that differs for unogged, logged and severely logged forest stands. We used oil palm specific equations for biomass estimations in oil palm plots. They will be identical estimates across the five algorithms used. Oil palms have a fundamentally different physical structure to forest trees, so we estimated AGB in oil palm plantations separately using the equation 〖AGB〗_palm= (0.3747*height*100+3.6334)/1000 (Thenkabail et al. 2004). See Pfeifer M, Lefebvre V, Turner E, Cusack J, Khoo M, Chey VK, Peni M, Ewers RMet al. 2015, Deadwood biomass: an underestimated carbon stock in degraded tropical forests?, ENVIRONMENTAL RESEARCH LETTERS, Vol: 10:044019. </p><p>Fields: </p><ul><li><b>Plot</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date field plot data were collected (Field type: Date)</li><li><b>AGB_Saner</b>: AGB estimates developed for mixed-species forest stands in East Kalimantan, Indonesia (Field type: Numeric)</li><li><b>AGB_Chave_wet</b>: AGB estimates developed for wet forest (Field type: Numeric)</li><li><b>AGB_Chave_moist</b>: AGB estimates developed for moist forest (Field type: Numeric)</li><li><b>AGB_K09</b>: AGB estimates developed for logged over old growth forest in Malaysian Sabah (Field type: Numeric)</li><li><b>AGB_N10</b>: AGB estimate developed for old growth forest in Malaysia for forests 110 km south-east of Kuala Lumpur (Field type: Numeric)</li><li><b>AGB_Chave14</b>: AGB estimates developed for pantropical forest assuming a wood density of 0.64 (Field type: Numeric)</li><li><b>AGB_Chave14_simulWD</b>: AGB estimates developed for pantropical forest and reflecting disturbance-induced changes to wood density (Field type: Numeric)</li><li><b>AGB_Chave14_Bin5_simulWD</b>: AGB estimates developed for pantropical forest and reflecting disturbance-induced changes to wood density (Field type: Numeric)</li></ul><br></li><li><p><b>SAFE project forest quality scores</b> (Worksheet Quality)</p><p>Dimensions: 203 rows by 4 columns</p><p>Description: Visual assessment of forest disturbance</p><p>Fields: </p><ul><li><b>Plot</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date of assessment (Field type: Date)</li><li><b>ForestQuality</b>: SAFE Project forest quality scores (Field type: Ordered Categorical)</li></ul><br></li><li><p><b>Caneye software analyses carried out using Caneye v6.3.8 in August/September 2013</b> (Worksheet LAI_Caneye)</p><p>Dimensions: 237 rows by 16 columns</p><p>Description: LAI, fcover and fAPAR estimates derived from hemispherical images or using Sunscan Delta T device (Cambridge) if applicable</p><p>Fields: </p><ul><li><b>Plotname</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date on which photographs were taken (Field type: Date)</li><li><b>HemiUp</b>: Number of sample points = number of pictures taken - upward looking fisheye pictures (Field type: Numeric)</li><li><b>LAI_eff_v6</b>: LAI effective estimated following algorithm of Caneye version 6 (v6.3.8) (Field type: Numeric)</li><li><b>LAI_true_v6</b>: LAI true (accounting for vegetation clumping) estimated following algorithm of Caneye version 6 (v6.3.8) (Field type: Numeric)</li><li><b>LAI_eff_v5</b>: LAI effective estimated according to Caneye version 5 (Field type: Numeric)</li><li><b>LAI_true_v5</b>: LAI true (accounting for vegetation clumping) estimated according to Caneye version 5 (Field type: Numeric)</li><li><b>ALAeffv5</b>: Effective average leaf inclination angle following alogorith used in Caneye v5 (Field type: Numeric)</li><li><b>ALAtruev5</b>: True average leaf inclination angle following alogorith used in Caneye v5 (Field type: Numeric)</li><li><b>Fap_meas_Dir</b>: Black - sky direct fAPAR (fraction of absorbed photosynthetically active radiation) measured (Field type: Numeric)</li><li><b>Fap_mod_Dir</b>: Black - sky direct fAPAR (fraction of absorbed photosynthetically active radiation) modelled (Field type: Numeric)</li><li><b>Fap_meas_Dif</b>: White - sky diffuse fAPAR (fraction of absorbed photosynthetically active radiation) measured (Field type: Numeric)</li><li><b>Fap_mod_Dif</b>: White - sky diffuse fAPAR (fraction of absorbed photosynthetically active radiation) modelled (Field type: Numeric)</li><li><b>fcover</b>: Fractional canopy cover (mean). Cover fraction (fcover) is defined as the fraction of the soil covered by the vegetation viewed in the nadir direction. Using hemispherical images, the cover fraction must be integrated over a range of zenith angles (0-10 degrees) (Field type: Numeric)</li><li><b>sdfcov</b>: Fractional canopy cover (standard deviation). Cover fraction (fcover) is defined as the fraction of the soil covered by the vegetation viewed in the nadir direction. Using hemispherical images, the cover fraction must be integrated over a range of zenith angles (0-10 degrees) (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2010-07-01 to 2014-01-10</p><p><b>Latitudinal extent: </b>4.4245 to 4.7714</p><p><b>Longitudinal extent: </b>116.9477 to 117.7028</p>
A preliminary study of the allochthonous inputs into tropical streams across a land use gradient in Sabah, Malaysia
<b>Description: </b><p>Leaf and invertebrate biomass in streams</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/174"><b>A preliminary study of the allochthonous inputs into tropical streams across a land use gradient in Sabah, Malaysia</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=19">here</a></p><p><b>Data worksheets: </b>There are 2 data worksheets in this dataset:</p><ol><li><p><b>Insects</b> (Worksheet Insects)</p><p>Dimensions: 23 rows by 11 columns</p><p>Description: Insect capture rates</p><p>Fields: </p><ul><li><b>Location</b>: SAFE project riparian site (Field type: Location)</li><li><b>Stream</b>: SAFE project stream (Field type: ID)</li><li><b>Repeat</b>: sample number for that stream (Field type: ID)</li><li><b>Total Mass of Insects (g)</b>: the total dried mass of insects collected for each of the repeats (Field type: Numeric)</li><li><b>Total Insects</b>: the total number of insects collected in each repeat (Field type: Abundance)</li><li><b>Hymenoptera</b>: the total number of hymenoptera in each repeat (Field type: Abundance)</li><li><b>Diptera</b>: the total number of diptera in each repeat (Field type: Abundance)</li><li><b>Coleoptera</b>: the total number of coleoptera in each repeat (Field type: Abundance)</li><li><b>Other.Insect</b>: the grouped total of Hemiptera, Thysanoptera, Orthoptera, Blattodea, Trichoptera, Mantodea, Ephemeroptera, Dermaptera for each repeat (Field type: Abundance)</li><li><b>Other</b>: the grouped total of Arachnida, Entognatha, Diplopoda, Chilopoda for each repeat (Field type: Abundance)</li></ul><br></li><li><p><b>Hydrology</b> (Worksheet Hydrology)</p><p>Dimensions: 60 rows by 17 columns</p><p>Description: River characteristics and litter quantities</p><p>Fields: </p><ul><li><b>Location</b>: SAFE project riparian site (Field type: Location)</li><li><b>Stream Code</b>: The stream from which the sample was taken (LFE, 15m, 30m, VJR or OP) (Field type: ID)</li><li><b>Transect No.</b>: The point of each sample within the 100m transect at each stream (Field type: ID)</li><li><b>Channel Width</b>: The bank full width of the channel at this point (Field type: Numeric)</li><li><b>Wetted Width</b>: The width of the runnin water at this point (Field type: Numeric)</li><li><b>SAFE Habitat Quality Right</b>: the SAFE Habitat quality on the right of the channel when looking upstream (Field type: Ordered Categorical)</li><li><b>SAFE Habitat Quality Centre</b>: the SAFE Habitat quality in the centre of the channel when looking upstream (Field type: Ordered Categorical)</li><li><b>SAFE Habitat Quality Left</b>: the SAFE Habitat quality on the left of the channel when looking upstream (Field type: Ordered Categorical)</li><li><b>Flow Rate Right (s)</b>: the time taken for a tennis ball to travel 10m in the water on the right of the channel when looking upstream (Field type: Numeric)</li><li><b>Flow Rate Centre (s)</b>: the time taken for a tennis ball to travel 10m in the water in the centre of the channel when looking upstream (Field type: Numeric)</li><li><b>Flow Rate Left (s)</b>: the time taken for a tennis ball to travel 10m in the water on the left of the channel when looking upstream (Field type: Numeric)</li><li><b>Average Flow Rate (s)</b>: an average of flow rate centre, flow rate left and flow rate right (Field type: Numeric)</li><li><b>Leaf Litter Retention (g)</b>: the dried mass of leaf litter retained across the wetted width of the stream at each point (Field type: Numeric)</li><li><b>Average Substrate Size</b>: the average size of the substrate across the channel width of the stream at each point (Field type: Numeric)</li><li><b>Leaf Litter Trap Position</b>: the position where the leaf litter trap was placed relative to the stream when looking upstream (left, right or centre) (Field type: Categorical)</li><li><b>Leaf Litter Mass</b>: the dried mass of leaf litter collected in the leaf litter trap at each point (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2017-02-06 to 2017-07-06</p><p><b>Latitudinal extent: </b>4.6314 to 4.7273</p><p><b>Longitudinal extent: </b>117.4556 to 117.6233</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Coleoptera<br> -  -  - Diptera<br> -  -  - Hymenoptera<br> -  - [Other.Insect]<br></div><p></p>
The effect of land-use modification in Sabah, Malaysia on the morphology of two beetle families: Carabidae and Chyrsomelidae
<b>Description: </b><p>Feeding morphology and body size of carabid and chrysomelid beetles</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/82"><b>Spatial scaling of beetle community diversity</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=14">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>Beetle morphometrics</b> (Worksheet Data)</p><p>Dimensions: 762 rows by 18 columns</p><p>Description: Feeding morphology and body size of carabid and chrysomelid beetles</p><p>Fields: </p><ul><li><b>Date</b>: Date sample was collected in field (Field type: Date)</li><li><b>Trap_ID </b>: SAFE Project sample site (Field type: Location)</li><li><b>Traptype</b>: Component of the insect collection trap (Field type: Categorical)</li><li><b>Individual_ID</b>: Specimen reference code (Field type: ID)</li><li><b>Family</b>: Family ID (Field type: Taxa)</li><li><b>Pronotum_Length</b>: Elytra length (Field type: Numeric Trait)</li><li><b>Elytra_Length</b>: Elytra length (Field type: Numeric Trait)</li><li><b>Body_length</b>: Body length (Field type: Numeric Trait)</li><li><b>Mean_Antennae_length</b>: Antennal length (Field type: Numeric Trait)</li><li><b>Labrum_Width</b>: Labrum width (Field type: Numeric Trait)</li><li><b>Mean_Maxillary_Palp_Length</b>: Length of maxillary palp (Field type: Numeric Trait)</li><li><b>Distance_Protruding_from_Labrum</b>: distance that the mandibles protruded from the labrum (Field type: Numeric Trait)</li><li><b>No_of_Hairs_on_Upper_Lips</b>: Number of hairs on clypeus (Field type: Numeric Trait)</li><li><b>Hairs_on_Palps</b>: NA (Field type: Numeric Trait)</li><li><b>Curved_Labrum</b>: Is the labrum curved? (Field type: Categorical Trait)</li><li><b>Mandible_Width</b>: Mandible width (Field type: Numeric Trait)</li><li><b>Fringe_extending_from_Labrum</b>: Is there a fringe of hairs extending from the labrum? (Field type: Categorical Trait)</li></ul><br></li></ol><p><b>Date range: </b>2011-02-21 to 2012-07-31</p><p><b>Latitudinal extent: </b>4.6350 to 4.7716</p><p><b>Longitudinal extent: </b>116.9477 to 117.7028</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Coleoptera<br> -  -  -  - Carabidae<br> -  -  -  - Cerambycidae<br> -  -  -  - Chrysomelidae<br></div><p></p>
Near real-time ultrahigh-resolution imaging from unmanned aerial vehicles for sustainable land use management and biodiversity conservation in semi-arid savanna under regional and global change (SAVMAP)
<p>To prevent aggravation of existing poverty in semi-arid savannas, a comprehensive concept for the sustainable adaptive management and use of these ecosystems under unprecedented conditions is needed. SAVMAP is an innovative, trans-, and inter-disciplinary initiative whose goal is to develop a valuable monitoring tool for both sustainable land-use management and rare species conservation (black rhinoceros) in semi-arid savanna in Namibia. SAVMAP uses near real-time ultrahigh-resolution photographic imaging (NURI) facilitated by unmanned aerial vehicles (UAVs) designed at EPFL.</p>
MPAS-Albany Land Ice v6.0 Test Cases
<p>This archive includes test case for the MPAS-Albany Land Ice v6.0 model.</p> <p>See http://mpas-dev.github.io/ for links to model source code and User's Guide.</p>
Quantifying Predation Pressure Along a Gradient of Land Use Intensity
<b>Description: </b><p>Experimentally quantified mealworm predation rates</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/2"><b>Quantifying Predation Pressure Along a Gradient of Land Use Intensity</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=76">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>Experimental predation trials</b> (Worksheet Data)</p><p>Dimensions: 1030 rows by 16 columns</p><p>Description: Full experimental and environmental data accompanying each predation measurement</p><p>Fields: </p><ul><li><b>Date</b>: Date mealworm was placed in field (Field type: Date)</li><li><b>Location</b>: SAFE Project sample site (Field type: Location)</li><li><b>Point</b>: SAFE Project sample site (Field type: ID)</li><li><b>Group</b>: Group nested within Point within Date (Field type: ID)</li><li><b>Time</b>: 12 hour period during which mealworm was left in the field (Field type: Categorical)</li><li><b>Treat</b>: Experimental treatment (Field type: Categorical)</li><li><b>Level</b>: Height strata mealworm was placed at (Field type: Categorical)</li><li><b>Status</b>: Was the mealworm predated or not? (Field type: Categorical)</li><li><b>Zfloor</b>: Percentage vegetation cover at ground level (Field type: Numeric)</li><li><b>Z1m</b>: Percentage vegetation cover 1.5m above ground (Field type: Numeric)</li><li><b>Z2m</b>: Percentage vegetation cover 2m above ground (Field type: Numeric)</li><li><b>Litdep</b>: Leaf litter depth (Field type: Numeric)</li><li><b>Canh</b>: Canopy height (Field type: Numeric)</li><li><b>Cand</b>: Canopy cover (Field type: Numeric)</li><li><b>Rain</b>: How heavily did it rain last night? 0 being no rain and 5 being torrential rain (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2012-06-21 to 2012-12-13</p><p><b>Latitudinal extent: </b>4.6350 to 4.7510</p><p><b>Longitudinal extent: </b>116.9632 to 117.5861</p>
Partitioning Seed Dispersal Rate Amongst Vertebrates Vs Invertebrates Along a Land-Use Gradient
<b>Description: </b><p>Seed perdation and dispersal experiments</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/72"><b>Partitioning Seed Dispersal Rate Amongst Vertebrates Vs Invertebrates Along a Land-Use Gradient</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=77">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>Seed removal experiments</b> (Worksheet Data)</p><p>Dimensions: 904 rows by 13 columns</p><p>Description: Experimental seed removal trials. Each trial consisted of 20 pumpkin seeds being placed on a plate, with seed fates ascertained the following day.</p><p>Fields: </p><ul><li><b>Location</b>: SAFE project sample site (Field type: Location)</li><li><b>Point </b>: SAFE project sample site (Field type: ID)</li><li><b>Date</b>: Date seeds were placed in field (Field type: Date)</li><li><b>Treatment</b>: Experimental treatment (Field type: Categorical)</li><li><b>NPlacement</b>: Unknown variable (Field type: ID)</li><li><b>RemainUneat</b>: How many seeds remained on the plate and had no evidence of having been eaten? (Field type: Numeric)</li><li><b>RemovUneat</b>: How many seeds were removed from the plate and had no evidence of having been eaten? (Field type: Numeric)</li><li><b>RemainEat</b>: How many seeds remained on the plate but had evidence of having been eaten? (Field type: Numeric)</li><li><b>RemovEat</b>: How many seeds were removed from the plate and also had evidence of being eaten? (Field type: Numeric)</li><li><b>RemovUnknown</b>: How many seeds were removed from the plate and had an unknown fate? (Field type: Numeric)</li><li><b>Rain</b>: How heavily did it rain last night? 0 being no rain and 5 being torrential rain (Field type: Numeric)</li><li><b>TreatmentSuccessFail</b>: Was the treatment successful? (Field type: Categorical)</li></ul><br></li></ol><p><b>Date range: </b>2013-05-07 to 2013-07-27</p><p><b>Latitudinal extent: </b>4.6350 to 4.7523</p><p><b>Longitudinal extent: </b>116.9632 to 117.5934</p>
Investigating Temperature Tolerance in Mosquito Disease Vectors Across a Land-Use Gradient
<b>Description: </b><p>Mosquito larval survey and thermotolerance data</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/172"><b>Investigating Temperature Tolerance in Mosquito Disease Vectors Across a Land-Use Gradient</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=82">here</a></p><p><b>Data worksheets: </b>There are 4 data worksheets in this dataset:</p><ol><li><p><b>Field microclimate data</b> (Worksheet Microclimate)</p><p>Dimensions: 6917 rows by 11 columns</p><p>Description: Data was recorded using EasyLog USB dataloggers. They were put out either hung on a tree, or suspended off the ground (in the absense of trees) and covered to avoid direct sunlight.</p><p>Fields: </p><ul><li><b>Block</b>: SAFE Project sampling block (Field type: Location)</li><li><b>Plot</b>: Sample site (Field type: Location)</li><li><b>Site</b>: SAFE Project sampling block (Field type: ID)</li><li><b>Point</b>: SAFE Project sample location (Field type: ID)</li><li><b>DATE</b>: Date of the measurement (Field type: Date)</li><li><b>TIME</b>: Time of the measurement (Field type: Time)</li><li><b>Temp</b>: Air temperature (Field type: Numeric)</li><li><b>RelHumid</b>: Relative humidity (Field type: Numeric)</li><li><b>DewPoint</b>: The temperature at which the air would condense and dew would form (Field type: Numeric)</li><li><b>SerialNumber</b>: Serial number of the microclimate datalogger used to collect the data (Field type: ID)</li></ul><br></li><li><p><b>Thermal tolerance</b> (Worksheet CTmax)</p><p>Dimensions: 317 rows by 6 columns</p><p>Description: Thermal tolerance experiments on mosquito larvae</p><p>Fields: </p><ul><li><b>Date</b>: The Date the CT max value was taken (Field type: Date)</li><li><b>VialNumber</b>: the vial that the individual came from, and how it is referred to in my field notebook (Field type: ID)</li><li><b>LandType</b>: Habitat type from which the individual was collected (Field type: Categorical)</li><li><b>CriticalMax</b>: The temperature in celcius that the individual became unresponsive to stimulus (Field type: Numeric)</li><li><b>GivenSpecies</b>: Identity of the individual being tested (Field type: Taxa)</li></ul><br></li><li><p><b>Site x species data</b> (Worksheet SpeciesData)</p><p>Dimensions: 192 rows by 9 columns</p><p>Description: Field observations of field mosquito communities</p><p>Fields: </p><ul><li><b>Block</b>: SAFE Project sampling block (Field type: Location)</li><li><b>Plot</b>: Sample site (Field type: Location)</li><li><b>Site</b>: SAFE Project sampling block (Field type: ID)</li><li><b>Point</b>: SAFE Project sample site (Field type: ID)</li><li><b>Replicate</b>: is the replicate of sampling each data collection took place. There were three weeks of sampling so the only values are 1, 2, or 3. (Field type: Replicate)</li><li><b>CollectionType</b>: Method of collection (Field type: Categorical)</li><li><b>Count</b>: Total number of individuals sampled (Field type: Abundance)</li><li><b>GivenSpecies</b>: Identity of the individual(s) (Field type: Taxa)</li></ul><br></li><li><p><b>Forest canopy measurements</b> (Worksheet Densiometer)</p><p>Dimensions: 36 rows by 12 columns</p><p>Description: Densiometer estimates of tree canopy cover</p><p>Fields: </p><ul><li><b>Block</b>: SAFE Project sampling block (Field type: Location)</li><li><b>Plot</b>: Sample site (Field type: Location)</li><li><b>Site</b>: SAFE Project sampling block (Field type: ID)</li><li><b>Point</b>: SAFE Project sample site (Field type: ID)</li><li><b>Val1</b>: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li><b>Val2</b>: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li><b>Val3</b>: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li><b>Val4</b>: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li><b>AveVal</b>: Average densiometer reading (max = 96) (Field type: Numeric)</li><li><b>AdjustedVal</b>: Average canopy openness (Field type: Numeric)</li><li><b>CanopyCover</b>: Average canopy cover (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2017-03-20 to 2017-09-04</p><p><b>Latitudinal extent: </b>4.6314 to 4.7436</p><p><b>Longitudinal extent: </b>117.4556 to 117.6249</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Diptera<br> -  -  -  - Culicidae<br> -  -  -  -  - <i>Aedes</i><br> -  -  -  -  -  - <i>Aedes albopictus</i><br> -  -  -  -  - <i>Anopheles</i><br> -  -  -  -  -  - [Anoph]<br> -  -  -  -  - <i>Armigeres</i><br> -  -  -  -  -  - [Armigeres]<br> -  -  -  -  - <i>Culex</i><br> -  -  -  -  -  - [Culex1]<br> -  -  -  -  -  - [Culex2]<br> -  -  -  -  -  - [Culex3]<br> -  -  -  -  -  - [CulexOP]<br> -  -  -  -  - [Species7]<br> -  -  -  -  - [Species8]<br> -  -  -  -  - [Species9]<br> -  -  -  -  - <i>Uranotaenia</i><br> -  -  -  -  -  - [Urano1]<br> -  -  -  -  -  - [Urano2]<br> -  -  -  -  - <i>Zeugnomyia</i><br> -  -  -  -  -  - <i>Zeugnomyia gracilis</i><br></div><p></p>
Role of Troposphere-Convection-Land Coupling in the Southwestern Amazon Precipitation Bias of the Community Earth System Model version 1 (CESM1)
<p>Necessary outputs and scripts for recreating the figures for the journal article with the same title.</p>
Land facet data for North America at 100m resolution.
<p>We developed a dataset categorizing the North American continent into physical habitat types at 100m resolution. The input data used included elevation and soil type. Further details on methodology are available <a href="http://www.cacpd.org/Facets/landfacetmethods.pdf">here</a>. The data is provided via a link to a zipfile containing TIFF (.tif) format files that can be imported into ArcGIS or other GIS applications. Projection information is available <a href="http://www.cacpd.org/Facets/lazea.prj">here</a>.</p> <p>One potential strategy for protecting biodiversity in a changing climate is based on the idea of protecting the diversity of abiotic conditions that influence patterns of biodiversity. In this strategy, conservation features (the “targets” considered in the conservation planning process) are derived from data on physical features such as topography, soils, and geology. The approach has been described as “conserving the ecological stage” or protecting “land facets” or “enduring features”. Species distributions, communities, ecosystems, and broader patterns of biodiversity are clearly influenced by abiotic drivers such as soils, geology, topography, and climate. Although climates will change relatively rapidly over the coming century, soils, geology, and topography will not. Thus, local, and some regional, climate patterns and gradients influenced by topography will persist (e.g., higher elevations will still be cooler than lower elevations, although both will likely be warmer) as climates change. The hypothesis underlying use of land facets in climate adaptation planning is that by protecting a diversity of land facets, it may be possible to protect areas that will foster a diversity of biota in the future, albeit different biota than those areas would protect today. Although land facets are clearly an imperfect coarse-filter surrogate for biodiversity, physical habitat diversity may still represent a useful additional source of data that can augment biodiversity data in conservation planning processes.</p>
GISS ModelE2 Simulations with different land-surface conditions
<p>Dataset for JGR manuscript #2018JD028874 "<strong>Distinct influences of land-cover and land-management on seasonal climate"</strong></p> <p>This is a repository of climate simulations to examine the influence of agricultural expansion and intensification on climate. Climate model simulations are conducted with the ModelE tag modelE_AR5_v2_branch, which is an intermediate version of the GISS ModelE2. This model version is available in the GISS development repository (https://simplex.giss.nasa.gov/cgi-bin/gitweb.cgi?p=modelE.git;a=tag;h=refs/tags/modelE_AR5_v2_branch). All the input files and simulation output are archived here. The simulations are run for 50 years with identical forcings under 3 scenarios and are run in non-interactive, prescribed ocean mode.</p> <p>Following is a description of the data available here.</p> <p>1. The folder input_files includes the land-surface schemes used in the simulations, and the crop cover dataset.</p> <p>2. "PotVeg_VAr" - contains variables from simulations with natural vegetation alone.</p> <p>3. "CropA_Var_new" - contains variables from simulations with year 2000 crop cover.</p> <p>4. "CropsIRA_Var_new" - contains variables from simulations with year 2000 crop cover and irrigation rates.</p> <p>5. "evap_diags" and "low_clouds" - contains evapotranspiration and low-cloud cloudiness from all three simulations.</p> <p>6. The file VarList - contains a list of variables available from the model and their names.</p> <p>7. Explanation_of_Diagnostics - contains a description of the variables.</p> <p>8. referenceRegions.csv - contains the domains of the SREX regions in this study.</p> <p> </p>
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