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Island area and remoteness shape plant and soil bacterial diversity through land use and biological invasion
<p>Biodiversity is declining dramatically due to human-driven land use change and biological invasion, but our knowledge of how such drivers influence plant and heterotroph diversity on island ecosystems remains limited. Historically island biogeography theory has focused solely on the direct effects of island size and remoteness on biodiversity, but these factors can also indirectly affect species gain and/or loss by impacting land use change and biological invasion. We built the structural equation model to explore the direct effects of island size and remoteness, and indirect effects of these factors via land use intensity and pinewood nematode invasion, on the diversity of plants and soil bacteria across 37 continental shelf islands in the largest land-bridge archipelago in eastern China.</p> <p>As expected we found that increasing island area directly promoted plant diversity. However, land use intensity increased with island area which also promoted plant diversity, and loss of pine forest by the pinewood nematode invasion increased with island remoteness which reduced plant diversity. Island remoteness only indirectly reduced plant diversity through increasing pine forest loss. Soil bacterial diversity was directly negatively impacted by island remoteness, and indirectly negatively impacted by island remoteness through increased soil electrical conductivity likely caused by greater salinity from sea spray. Furthermore, soil bacterial diversity was indirectly promoted by island area through increased plant diversity and decreased soil electrical conductivity, and indirectly reduced by pine forest loss through decreased plant diversity. Our findings highlight that island biogeography theory has relevance to understanding human impacts in the Anthropocene, and that there is a need to more explicitly recognize how island size and remoteness affect biodiversity not only directly, but also indirectly via their effects on human-induced drivers of biodiversity, such as land use change and biological invasion.</p>
A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data
<p> </p> <p>A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data.</p> <p>Introduction</p> <p>This dataset is the Planet Labs PBC (VanderSat B.V.) contribution to the ESA 4DMED hydrology project (<a href="https://www.4dmed-hydrology.org/">https://www.4dmed-hydrology.org/</a>). It includes Soil Moisture, Land Surface Temperature and Vegetation Optical Depth for the 4DMED spatial domain and time period (2015-2021) at 1km pixel size. If you use the data please include the following reference:</p> <blockquote> <p>Jaap Schellekens, Tessa Kramer, Michel van Klink, Robin van der Schalie, Yoann Malbeteau, Arjan Geers, Richard de Jeu. (2022) <em>A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data</em>. DOI: 10.5281/zenodo.7684993. Planet Labs PBC/VanderSat B.V., ESA Contract No. 4000136272/21/I-EF</p> </blockquote> <p> </p> <p><em>Figure 1: Average L-Band Soil moisture for 2020 over the 4dmed spatial domain</em></p> <p>Variables and files</p> <p>The dataset consists of the following files and products for the 4DMED domain. Detailed information about the products can also be found at <a href="https://docs.vandersat.com/data_products/soil_water_content/specification.html">docs.vandersat.com</a>:</p> <ul> <li><strong><code>planet-teff-4dmed-V4.0.zip</code></strong> - LST (TEFF) ascending (daytime) and descending (nighttime) <ul> <li><code>TEFF-AMSR2-ASC_V4.0_1000</code> <ul> <li>Land surface temperature daytime (13:30 solar time) at 1 km</li> </ul> </li> <li><code>TEFF-AMSR2-DESC_V4.0_1000</code> <ul> <li>Land surface temperature nighttime (01:30 solar time) at 1 km</li> </ul> </li> </ul> </li> <li><strong><code>planet-teff-qf-4dmed-V4.0.zip</code></strong> - LST (TEFF) quality flags <ul> <li><code>QF-TEFF-AMSR2-ASC_V4.0_1000</code> <ul> <li>Land surface temperature daytime quality flag. <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">docs.vandersat.com flags</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">docs.vandersat.com python example</a></li> </ul> </li> <li><code>QF-TEFF-AMSR2-DESC_V4.0_1000</code> <ul> <li>Land surface temperature daytime quality flag. <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">docs.vandersat.com flags</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">docs.vandersat.com python example</a></li> </ul> </li> </ul> </li> <li><strong><code>planet-vod-4dmed-V4.1.zip</code></strong> - vegetation optical depth C and X band (interpolated from C3S passive soil moisture) <ul> <li><code>VOD_AMSR2_C1_DESC_V41_1000</code> <ul> <li>C1 band Vegetation Optical Depth (nighttime, 01:30 solar time) at 1km (interpolated from 25 km)</li> </ul> </li> <li><code>VOD_AMSR2_X_DESC_V41_1000</code> <ul> <li>X band Vegetation Optical Depth (nighttime, 01:30 solar time) at 1km (interpolated from 25 km)</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-4dmed-V4.0.zip</code></strong> - All soil moisture products (C1, X and L-band) <ul> <li><code>SM-AMSR2-C1-DESC_V4.0_1000</code> <ul> <li>C1 band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>SM-AMSR2-X-DESC_V4.0_1000</code> <ul> <li>X band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>SM-SMAP-L-DESC_V4.0_1000</code> <ul> <li>L band soil moisture (06:00 solar time) at 1km</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-qf-4dmed-V4.0.zip</code></strong> - Soil moisture quality maps see <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">https://docs.vandersat.com/data_products/soil_water_content/data_flags.html</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python</a> <ul> <li><code>QF-SM-AMSR2-C1-DESC_V4.0_1000</code> <ul> <li>C1 band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>QF-SM-AMSR2-X-DESC_V4.0_1000</code> <ul> <li>X band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>QF-SM-SMAP-L-DESC_V4.0_1000</code> <ul> <li>L band soil moisture quality flags (06:00 solar time) at 1km</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-cor-4dmed-V4.0.zip</code></strong> - Yearly correlation maps of soil moisture derived from the difference microwave bands. To be used as an extra quality indicator (for example undetected RFI) or for uncertainty estimation <ul> <li><code>SM-CORR-C1-X-DESC_V4.0_1000</code> - yearly C1 vs X band pearson's correlation maps</li> <li><code>SM-CORR-L-C1-DESC_V4.0_1000</code> - yearly L vs C1 band pearson's correlation maps</li> <li><code>SM-CORR-L-X-DESC_V4.0_1000</code> - yearly L vs X band pearson's correlation maps</li> </ul> </li> <li><strong><code>planet-aux-flags-4dmed-V4.0</code></strong> - Extra flags for frozen soil and bare soil. Determined at 0.25 degree and interpolated to the 4dmed grid <ul> <li><code>QF-SNOWFROZEN-AMSR2-ASC_1000::RD</code> - Frozen soil determined from dayttime data</li> <li><code>QF-SNOWFROZEN-AMSR2-DESC_1000::RD</code> - Frozen soil determined from nighttime data</li> <li><code>QF-BARESOIL-AMSR2-DESC_1000::RD</code> - Bare soil determined from nighttime data</li> <li><code>QF-BARESOIL-AMSR2-ASC_1000::RD</code> - Bare soil determined from daytime data</li> </ul> </li> </ul> <p>All files are archived into one zip file per product group. Each individual netcdf file in the zip file consists of one observation for the whole domain. If you need you can combine the files into one file using the cdo software <a href="https://code.mpimet.mpg.de/projects/cdo">https://code.mpimet.mpg.de/projects/cdo</a> (e.g. <code>cdo -f nc4c mergetime *.nc outfile.nc</code>).</p> <p>License</p> <p>The data for 4DMED is released under the Creative Commons license: CC BY-NC-SA 4.0 (<a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>)</p> <ul> <li>Contains modified Copernicus Sentinel data 2015-2021</li> <li>Contains modified JAXA GCOM-W1/AMSR2 data 2015-2021</li> <li>Contains modified SMAP L1B Radiometer data: Piepmeier, J. R., P. Mohammed, J. Peng, E. J. Kim, G. De Amici, J. Chaubell, and C. Ruf. 2020. SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures, Version 4,5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: <a href="https://doi.org/10.5067/ZHHBN1KQLI20">https://doi.org/10.5067/ZHHBN1KQLI20</a></li> </ul> <p>Contact</p> <p>Jaap Schellekens: <a href="mailto:jaap@planet.com">jaap@planet.com</a></p> <p>Versions</p> <ul> <li>1.0 Initial creation</li> <li>1.1 Adjusted 4DMED Mask. Data itself unchanged but more LST (TEFF) measurements added</li> <li>1.2 Removed VOD and replaced by 25km C3S VOD interpolated to 1km (V4.1)</li> </ul> <p>Further information</p> <p>More information on the data and the flags can be found at <a href="https://docs.vandersat.com/">https://docs.vandersat.com</a> and <a href="https://www.4dmed-hydrology.org/">https://www.4dmed-hydrology.org</a></p> <p>Background publications</p> <p>R.A.M. De Jeu, A.H.A. De Nijs, M.H.W. Van Klink (2016) <em>Method and system for improving the resolution of sensor data</em>, US10643098B2,EP3469516B1, WO2017216186A1</p> <p>De Jeu, R. A., Holmes, T. R., Parinussa, R. M., & Owe, M. (2014). <em>A spatially coherent global soil moisture product with improved temporal resolution</em>. Journal of hydrology, 516, 284-296.</p> <p>Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I. and Forkel, M., 2020. <em>The global long-term microwave vegetation optical depth climate archive (VODCA)</em>. Earth System Science Data, 12(1), pp.177-196.</p> <p>Schmidt, L., Forkel, M., Zotta, R.-M., Scherrer, S., Dorigo, W. A., Kuhn-Régnier, A., van der Schalie, R., and Yebra, M.: <em>Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties, Biogeosciences Discuss.</em> [preprint], <a href="https://doi.org/10.5194/bg-2022-85">https://doi.org/10.5194/bg-2022-85</a>, in review, 2022</p> <p>Van der Schalie, R., de Jeu, R.A.M., Kerr, Y.H., Wigneron, J.P., Rodríguez-Fernández, N.J., Al- Yaari, A., Parinussa, R.M., Mecklenburg, S. and Drusch, M. (2017), <em>The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E</em>, Remote Sensing of Environment, 189, pp.180-193.</p> <p>van der Vliet, M., van der Schalie, R., Rodriguez-Fernandez, N., Colliander, A., de Jeu, R., Preimesberger, W., Scanlon, T., Dorigo, W., 2020. Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records. Remote Sensing 12, 3439. <a href="https://doi.org/10.3390/rs12203439">https://doi.org/10.3390/rs12203439</a></p>
Evolutionary history of inshore oceanic island land snails diversified in shell colour
<p><strong><span>Aim: </span></strong><span>Oceanic islands provide an excellent opportunity to study the mode and tempo of phenotypic evolution of terrestrial organisms. Many studies have focused on oceanic islands far from the mainland. Oceanic islands near the mainland may provide distinct perspectives on phenotypic evolution, but a comprehensive understanding is still lacking. To address this gap, this study aimed to reveal when a land snail species inhabiting a volcanic archipelago within 40 km of the mainland diverged and how their</span> <span>shell colours evolved.</span></p> <p><strong><span>Location:</span></strong><span> Southern Izu Peninsula and five Izu Islands (Oshima, Toshima, Niijima, Shikine, and Kozu), Japan.</span></p> <p><strong><span>Taxon:</span></strong><span> <em>Euhadra peliomphala simodae</em></span></p> <p><strong><span>Methods:</span></strong><span> Double-digest restricted-site associated DNA sequencing (ddRAD-seq) was conducted using 117 individual snails. Molecular phylogenetic and population genomic analyses were performed, including Approximation Bayesian computation (ABC). We then examined whether the island area, elevation</span>, <span>distance from the surrounding landmasses and historical event (bottleneck) explain the shell colour diversity on each island using a phylogenetic generalised linear </span>mixed<span> model (PGLMM).</span></p> <p><strong><span>Results:</span></strong><span> Snails could be genetically categorised according to the island they inhabited, and on Niijima, their genetic structure was further divided within the island. The divergence times among the extant populations of the snail dated back to 2.1 million years ago (Ma), which is older than that of other animals occurring in this region. Additionally, island elevation positively affected shell colour diversity, and populations with similar shell colour profiles were phylogenetically different.</span></p> <p><strong><span>Main conclusions: </span></strong><span>Island land snails diversified early in the ecosystem of the Izu Islands. This suggests that proximity to the mainland, immobility and the physiological tolerance of passive dispersal in land snails were the main causes of early diversification. Moreover, our study proposes that environments covarying with elevation would determine shell colour diversity on each island through natural selection.</span></p>
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
<p>EuroSAT is a land use and land cover classification dataset. The dataset is based on Sentinel-2 satellite imagery covering 13 spectral bands and consists of 10 LULC classes with a total of 27,000 labeled and geo-referenced images. The dataset is associated with the publications "<a href="https://ieeexplore.ieee.org/abstract/document/8519248">Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification</a>" and "<a href="https://ieeexplore.ieee.org/abstract/document/8736785">EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification</a>".</p> <p>EuroSAT_RGB.zip contains the RGB version of the dataset, which includes the optical R, G and B frequency bands encoded as JPEG images.</p> <p>EuroSAT_MS.zip contains the multi-spectral version of the EuroSAT dataset, which includes all 13 Sentinel-2 bands in the original value range.</p>
Integration of a deep-learning-based fire model into a global land surface model
<p>JSBACH4+DL-fire simulation results & their comparison (DL-fire, JSB4-DL-fire, JSB4-simple)</p>
Data for: "Land-use intensity influences European tetrapod food-webs"
<p>These .Rdata files enable to reproduce results from our article " Signatures of land use intensity on european tetrapod food-web architectures" along with the R code provided at: https://github.com/ChrisBotella/foodwebs_vs_land_use</p> <p>- raw_data : Raw data including GBIF and iNaturalist occurrences and IUCN enveloppes used to select sites and generate species presence/absence. We provide this file for transparency and reproducibility of our methodology.</p> <p>- preprocessed_data: preprocessed data (obtained from raw_data) used to generate our article Figures along with the next file.</p> <p>- TrophicNetworksList : .Rdata containing a list of igraph objects, each igraph is a foodweb associated to a site identify by the list element name. </p> <p>- MultiRegMatrices: .Rdata containing especially the pre-computed matrix Y of cells (rows) by food web metrics (columns) and the covariate design matrix X (for the linear regressions) in order to facilitate and accelerate the reproduction of the analyses.</p>
R-script: Deterioration of respiratory health following changes to land cover and climate in Indonesia
<p>This file contains the R-script presented in "Santika, T., Muhidin, S., Haryanto, B. et al. (2023) Deterioration of respiratory health following changes to land cover and climate in Indonesia".</p> <p>** R-script.txt</p> <p>This is the main script used to produce the results of the paper, which contains three parts:</p> <ol> <li>Analysis of the change in rainfall patterns across regencies in Sumatra, Indonesia</li> <li>Analysis of the change in fire patterns across Sumatra by soil type and land cover/degradation</li> <li>Analysis of the link between respiratory illness prevalence and environmental and socio-economic variables</li> </ol>
POI-based land use map for Africa
<p>The combination of spatial distribution, semantic characteristics, and sometimes temporal dynamics of POIs inside a geographic region can capture its unique land use characteristics. We developed a scalable POI-based land use modeling framework. By combining POIs with a neural network language model, we developed a spatially explicit approach to learn the embedding representation of POIs and AOIs. We trained supervised classifiers using AOI embeddings as input features to predict AOI land use at different semantic granularities. </p>
The origin of blinking in both mudskippers and tetrapods is linked to life on land
<p>Blinking, the transient occlusion of the eye by one or more membranes, serves several functions including wetting, protecting, and cleaning the eye. This behavior is seen in nearly all living tetrapods and absent in other extant sarcopterygian lineages, suggesting that it might have arisen during the water-to-land transition. Unfortunately, our understanding of the origin of blinking has been limited by a lack of known anatomical correlates of the behavior in the fossil record and a paucity of comparative functional studies. To understand how and why blinking originates, we leverage mudskippers (Oxudercinae), a clade of amphibious fishes that have convergently evolved blinking. Using micro-computed tomography and histology, we analyzed two mudskipper species, <em>Periophthalmus</em> <em>barbarus</em> and <em>Periophthalmodon</em> <em>septemradiatus</em>, and compared them to the fully aquatic round goby, <em>Neogobius</em> <em>melanostomus</em>. Study of gross anatomy and epithelial microstructure shows that mudskippers have not evolved novel musculature or glands to blink. Behavioral analyses show the blinks of mudskippers are functionally convergent with those of tetrapods: <em>P. barbarus</em> blinks more often under high evaporation conditions to wet the eye, a blink reflex protects the eye from physical insult, and a single blink can fully clean the cornea of particulates. Thus, eye retraction in concert with a passive occlusal membrane can achieve functions associated with life on land. Osteological correlates of eye retraction are present in the earliest limbed vertebrates, suggesting blinking capability. In both mudskippers and tetrapods, therefore, the origin of this multifunctional innovation is likely explained by selection for increasingly terrestrial lifestyles.</p>
Output data: China's energy-water-land system co-evolution under carbon neutrality goal and climate impacts
<p>Outputs for Wang, J., Duan, Y., Wang, C., 2023. China’s energy-water-land system co-evolution under carbon neutrality goal and climate impacts. (In progress)</p> <p>Folder demeter contains the spatially downscaled land use/land cover datasets.<br> Folder tethys contains the spatially downscaled water withdrawal datasets.</p> <p>The sub-folder names correspond to the scenarios described in the paper.</p> <p>For landcover datasets, land type ratios in each grid are presented. Land types include water, forest, shrub, grass, urban, snow, sparse and crops.</p> <p>For water withdrawal datasets, “wd” = “water withdrawal spatially downscaled”, “twd” = “water withdrawal spatially and temporally downscaled”, “dom”=”domestic/municipal sector”, “elec”=”electricity sector”, ”irr”=”irrigation sector”, “liv”=”livestock sector”, “mfg”=”manufacturing/industry sector”, “min”=”mining/primary energy sector”, “nonag”=”non-agricultural sector”, “total”=”all sectors”.<br> </p>
Burnt Area and Land Cover in South-West Pará, Brazil, 2014 to 2020.
<p>This dataset BLCM.zip contains the Burnt Area and Land Cover Maps (BLCM) described in:</p> <p>Jakimow, B., Baumann, H., Salomão, C., Bendini, H. & Hostert, P. (2023). Deforestation and agricultural fires in South-West Pará, Brazil, under political changes from 2014 to 2020. Journal of Land Use Science. <a href="https://doi.org/10.1080/1747423X.2023.2195420">https://doi.org/10.1080/1747423X.2023.2195420</a></p> <p>The raster maps have use the BU MEaSUREs Lambert Azimuthal Equal Area - SA - V01 project. See <a href="https://github.com/measures-glance/glance-grids">https://github.com/measures-glance/glance-grids</a> for details.</p> <table> <thead> <tr> <th scope="col">File</th> <th scope="col">Content</th> </tr> </thead> <tbody> <tr> <td>MAP_BLCM_<year>.tif</td> <td>BLCM map for <year></td> </tr> <tr> <td>MAP_YoD.tif</td> <td>year of deforestation raster</td> </tr> <tr> <td>AOI.gpkg</td> <td>Polygon of Area of Interest used in Jakimow et al. (2023)</td> </tr> <tr> <td>wkt.txt</td> <td>WKT string for BU MEaSUREs Lambert Azimuthal Equal Area - SA - V01 coordinate reference system</td> </tr> <tr> <td>BLCM.qgs</td> <td>QGIS Project file to visualize these map, generated with QGIS 3.30</td> </tr> <tr> <td> </td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p>
Data from: Multi-level thresholds of residential and agricultural land use for elk avoidance across the Greater Yellowstone Ecosystem
<p>1. Conversion of land for settlements and agriculture is increasing globally and can influence wildlife space use. However, there is limited research to identify the thresholds of land use change that incur wildlife avoidance, and how these thresholds might vary across levels of selection.</p> <p>2. We evaluated multi-level avoidance thresholds of elk (<em>Cervus canadensis</em>) impacted by residential development and irrigated agriculture across the Greater Yellowstone Ecosystem in Idaho, Montana, and Wyoming. Using GPS data from 765 elk in 21 herds, we estimated habitat selection in relation to development and agriculture at 3 levels (home range selection, within home range selection, and movement path selection). Next, using individual selection covariates and associated measures of land use availability, we used functional-response models to evaluate how selection varied based on availability, and in turn, to estimate avoidance thresholds.</p> <p>3. We found individual and level-specific variation in elk responses to environmental factors. Elk exhibited stronger responses (either selection or avoidance) when selecting home range locations (i.e. second-order selection) than when selecting areas within home ranges (i.e. third-order selection) or selecting movement paths (i.e. fourth order selection). Importantly, elk avoidance of development and agriculture changed as the amount of land in these categories changed. Across all levels of selection, elk exhibited neutral selection for human development at low levels of availability (<1.1–2.2% developed) but avoided areas that were >1.1–2.2% developed. Conversely, elk selected positively for irrigated agriculture at low to moderate levels of availability (<52.0–66.2% agriculture) but exhibited neutral selection in areas that were > 52.0–66.2% agriculture.</p> <p>4. Synthesis and Applications: Elk avoidance of low levels of human development suggests conservation efforts such as restrictions on future development or conservation easements could focus on areas that are still below 2% developed. Additionally, because elk selection was strongest at the landscape scale, conservation actions that are based on information about the overall landscape structure may be most impactful. Our results highlight the importance of understanding variability in wildlife habitat selection at multiple levels, particularly in relation to land use change and highlight how functional response modelling can help inform landscape conservation.</p>
CRLC: Cross-resolution national-scale land-cover mapping based on noisy label learning: a case study of China
<p><strong>CRLC are the 10-meter resolution land cover maps for China in 2020 achieved by a deep classification network.</strong></p> <p><strong>The maps include eight land cover classes:</strong></p> <p>1: Cropland</p> <p>2: Forest</p> <p>3: Grass/shrubland</p> <p>5: Wetland</p> <p>6: Water bodies</p> <p>8: Impervious</p> <p>9: Bareland</p> <p>10: Snow/ice</p> <p> </p> <p><strong>Reference:</strong></p> <pre>@article{liu2023cross, title={Cross-resolution national-scale land-cover mapping based on noisy label learning: A case study of China}, author={Liu, Yinhe and Zhong, Yanfei and Ma, Ailong and Zhao, Ji and Zhang, Liangpei}, journal={International Journal of Applied Earth Observation and Geoinformation}, volume={118}, pages={103265}, year={2023}, publisher={Elsevier} }</pre>
Data for: Distribution of capitalized benefits from land conservation
<p>Land conservation efforts throughout the U.S. enhance ecological amenities while generating wealth in the housing market through capitalization of amenities. This paper estimates the benefits of conservation that are capitalized into proximate home values and quantifies how those benefits are distributed across demographic groups. Using detailed property and household-level data from Massachusetts, we estimate that new land conservation led to $62 million in new housing wealth equity. However, houses owned by low-income or Black or Hispanic households are less likely to be located near protected areas, and hence, these populations are less likely to benefit financially. Direct study of the distribution of this new wealth from capitalized conservation is highly unequal, with the richest quartile of households receiving 43%, White households receiving 91%, and the richest White households receiving 40%, which is nearly 140% more than would be expected under equal distribution. We extend our analysis using census data for the entire United States and observe parallel patterns. We estimate that recent land conservation generated $9.8 billion in wealth through the housing market and that wealthier and White households benefited disproportionately. These findings suggest regressive and racially disparate incidence of the wealth benefits of land conservation policy.</p>
A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by AVHRR observations from 1981 to 2021 (2006–2021)
<ul> <li>Land surface temperature (LST) is a key variable for monitoring and evaluating global long-term climate change. However, existing satellite-based twice-daily LST products only date back to 2000, which makes it difficult to obtain robust long-term temperature variations. We developed the first global historical twice-daily LST dataset (GT-LST), with a spatial resolution of 0.05°, using Advanced Very High Resolution Radiometer Level-1b Global Area Coverage data from 1981 to 2021.</li> <li>Validation with in situ measurements from Surface Radiation Budget sites showed that the overall root-mean-square errors of GT-LST varied from 2.0 K to 3.9 K. Inter-comparison with a common LST product (i.e., MYD11A1) revealed that the overall root-mean-square-difference was approximately 3.2 K.</li> <li>More details of this dataset can be seen in <em>readme.pdf.</em></li> <li>This dataset provides GT-LST product from 2006 to 2021.</li> </ul>
Landing Gear System Models
<p>The dataset contains a translated Simulink model, of the landing gear system introduced by Boniol and Wiels (2014) and Boniol et al. (2017). Additionally, corresponding SAML and NuSMV models are included, which were created from the Simulink model using tool support.</p>
Fig. 16 in Revision of the genus Prestonella (Mollusca: Gastropoda: Orthalicoidea: Bulimulidae s. l.), a distinctive component of the African land snail fauna
Fig. 16. Distribution of Prestonella nuptialis (9) and P. quadingensis ().
Fig. 26 in Revision of the genus Prestonella (Mollusca: Gastropoda: Orthalicoidea: Bulimulidae s. l.), a distinctive component of the African land snail fauna
Fig. 26. Prestonella nuptialis jaw, extended through mouth after drowning (NMSA W5507).
Fig. 9 in Revision of the genus Prestonella (Mollusca: Gastropoda: Orthalicoidea: Bulimulidae s. l.), a distinctive component of the African land snail fauna
Fig. 9. Prestonella bowkeri distribution.
Inconsistent responses of carabid beetles and spiders to land-use intensity and landscape complexity in Northwestern Europe
<p>Reconciling biodiversity conservation with agricultural production requires a better understanding of how key ecosystem service providing species respond to agricultural intensification. Carabid beetles and spiders represent two widespread guilds providing biocontrol services. Here we surveyed carabid beetles and spiders in 66 winter wheat fields in four Northwestern European countries and analyzed how the activity density and diversity of carabid beetles and spiders were related to crop yield (proxy for land-use intensity), percentage cropland (proxy for landscape complexity) and soil organic carbon content, and whether these patterns differed between dominant and non-dominant species. Less than 17% of carabid or spider species were classified as dominant, which accounted for more than 90% of individuals respectively. We found that carabids and spiders were generally related to different aspects of agricultural intensification. Carabid species richness was positively related with crop yield and evenness was negatively related to crop cover. The activity density of non-dominant carabids was positively related with soil organic carbon content. Meanwhile, spider species richness and non-dominant spider species richness and activity density were all negatively related to percentage cropland. Our results show that practices targeted to enhance one functionally important guild may not promote another key guild, which helps explain why conservation measures to enhance natural enemies generally do not ultimately enhance pest regulation. Dominant and non-dominant species of both guilds showed mostly similar responses suggesting that management practices to enhance service provisioning by a certain guild can also enhance the overall diversity of that particular guild.</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.