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Forest land under different scenarios of future global change
<p>Forest loss is one of the most threats to biodiversity, forested lands drive a key role in the climate earth system that also affects species diversity and ecosystem services (Vale et al. 2021). Therefore, several initiatives had been driven to map land-use time series, mainly projections into the future (Chen et al. 2020 and reference therein). The availability of those products is valuable for earth science, ecology, conservation, and other research fields once land-use land-cover data are important predictors of species occurrence and biodiversity threat (Ruiz-Benito et al. 2020., Valet et al. 2021) </p> <p>A recent land-use product called <a href="https://www.nature.com/articles/s41597-020-00669-x">GCAM-Demeter</a> presents the highest global spatial resolution (0.05 º) until now (Chen et al. 2020). It provided current and future projections (2015-2100) under different scenarios of climate change (Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCP) ) and according to the most recent framework of Coupled Model Intercomparison Project phase 6 (CMIP6). The data in each year include grid-explicit fraction (in percent) of each of the 32 plant functional types (PFTs) that are widely used in current Earth system models. The complete dataset is available in five General Circulation Models (GCMs): gfdl, hadgem, ipsl, miroc, and noresm. Also includes the mean and standard deviation of those GCMs (Chen et al. 2020).</p> <p>Although the valuable contribution of the GCAM-Demeter to provide those data, it is compressed in NetCDF format, a complex file format that needs management to become usable in several analyses, especially in ecology and biodiversity analyses (Vale et al., 2021 and reference therein). Here I managed the outputs of the mean of five GCMs (harmonized projection) based on the sum analysis of plant functional types considering:</p> <p>1- Global Extent at 0.05-degree resolution </p> <p>2- Years 2020, 2030 and 2050</p> <p>3- SSPs and RCP as follow: SSP1_RCP2, SSP2_RCP45, SSP4_RCP6, SSP5_RCP85</p> <p>The goals are to assess quantitatively the forest lands under different scenarios of global change and make these data available in the Tag Image File Format (TIFF) which is a more friendly and useable format to incorporate in several spatial analyses, mainly in ecology and biodiversity studies for conservation purposes.</p> <p><strong>Methods</strong></p> <p>I downloaded the GCAM-Demeter NetCDF files (the outputs of the mean of five GCMs and the first version, i.e the harmonized projection) freely available at <a href="https://release.datahub.pnnl.gov/released_data/1190">DataHub</a> (Chean et al. 2020). I selected, extracted, and performed the sum analysis of plant functional types (codes PTF1 to PTF11- described in README attached) considering:</p> <p>1- Global Extent at 0.05-degree resolution </p> <p>2- Years 2020, 2030 and 2050</p> <p>3- SSPs and RCP as follow: SSP1_RCP2, SSP2_RCP45, SSP4_RCP6, SSP5_RCP85</p> <p>The data manipulation and analysis were done using ncdf4 and raster packages in the R environment (R Core Team 2020, Pierce 2019; Hijmans et al. 2020). The outputs range from 0 to 100 and can be identified by their file name, for example: 2020_SSP5_RCP85_Forest_GCAM-Demeter_GCMsMean_Harmonized.tif . Also, outputs are provided in the Tag Image File Format (TIFF) which is a more friendly and useable format (Vale et al. 2021 and references therein). The methods and the quantitative results for forested areas are detailed better in the <a href="https://github.com/Tai-Rocha/Forest_Scenarios.github.io">GitHub repository</a> .</p> <p><strong>Acknowledgments</strong>.</p> <p>This initiative was possible due to the high-quality data maintained and made publicly available by GCAM-Demeter authors. Also, the study was developed within the scope of the Earth System Modeling Program funded by CAPES (Coordination for the Improvement of Higher Education Personnel - Grant No. 88887.373031/2019-00) </p> <p> </p> <p><strong>References</strong></p> <p>Chen, M., Vernon, C. R., Graham, N. T., Hejazi, M., Huang, M., Cheng, Y., & Calvin, K. (2020). Global land use for 2015–2100 at 0.05 resolution under diverse socioeconomic and climate scenarios. <em>Scientific Data</em>, <em>7</em>(1), 1-11. </p> <p>Hijmans, R. J. (2020). raster: Geographic Data Analysis and Modeling (R package version 3.3-13)[Computer software]. <em>Retrieved form https://CRAN. R-project. org/package= raster</em>.</p> <p>Pierce, D. (2019). ncdf4: Interface to Unidata netCDF (Version 4 or earlier) Format Data Files. R package version 1.16.</p> <p>Ruiz-Benito P, Vacchiano G, Lines ER, Reyer CP, Ratcliffe S, Morin X, Hartig F, Mäkelä A, Yousefpour R, Chaves JE, Palacios-Orueta A. Available and missing data to model impact of climate change on European forests. Ecological Modelling. 2020 Jan 15;416:108870.</p> <p>Team, R. C. (2020). R: A language and environment for statistical computing.</p> <p>Vale, M. M., Lima-Ribeiro, M. S., & Rocha, T. C. (2021). GLOBAL LAND-SE AND LAND-COVER DATA: HISTORICAL, CURRENT AND FUTURE SCENARIOS. Biodiversity Informatics, 16, 2021, pp. 28-38.</p> <p> </p> <p> </p>
Figure 1. Enteromyces callianassae Lichtwardt, 1961 in First reports of protists in land crabs of the family Gecarcinidae (Crustacea: Decapoda: Brachyura) suggest a relatively widespread phenomenon
Figure 1. Enteromyces callianassae Lichtwardt, 1961: (a) distribution along the (cleared) stomach wall of a female Gecarcinus lateralis (Guérin, 1832) (carapace width 39 mm); (b) thalli attached to multiple holdfast systems; (c) holdfast on stomach wall; (d) multinucleate thalli.
Figure 2 in First reports of protists in land crabs of the family Gecarcinidae (Crustacea: Decapoda: Brachyura) suggest a relatively widespread phenomenon
Figure 2. Relative frequency of stomachs of Gecarcinus lateralis (Guérin, 1832) according to their fullness, with and without eccrinids (0 = empty stomach; 1 = 1–50%; 2 = 51–100%).
A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China
<p>** VIC_forcings_4vars: 1/4 Degree Gridded Daily Meteorological VIC Forcing Data Set over China domain </p> <p>1. Data sources</p> <p>This dataset is from 1/1/1952 to 12/31/2012, which were derived by interpolating gauged daily precipitation, maximum temperature, minimum temperature and wind speed of 756 ground mornitoring stations from Chinese Meteorological Administration (CMA). </p> <p>** This section provides only a very brief description of the data source. For a full explanation, the user need refer to the published papers in the references ** </p> <p>2. References to Cite</p> <p>We request that users of this data set cite Zhang et al.(2014) in any reports or publications using it. </p> <p>Zhang, X., Tang, Q., Pan, M., Tang, Y., 2014. A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China. Journal of Hydrometeorology. (Accepted)</p> <p>3. Dataset format</p> <p>This data set is available in netCDF (which cannot be read directly by VIC). </p> <p>Please click the year number in the table below to download the corresponding daily forcings (i.e., precipitation, maximum temperature, minimum temperature and wind speed). </p> <p><br> ** VICoutput_fluxes: VIC Retrospective Land Surface Dadaset over China: 1952-2012</p> <p>1. Background</p> <p>The Variable Infiltration Capacity (VIC) model was driven using the gridded daily observed forcings (including precipitation, maximum temperature, minimum temperature, and wind speed; if necessary, please download the VIC forcings from http://hydro.igsnrr.ac.cn/public/vic_forcings_4vars.html) to simulate the land surface hydrological cycle from 1952-2012 over China. The modeling study was done at a 3-hourly time step and at a spatial resolution of 0.25 degree. Details can be found in the journal article:</p> <p>Zhang, X., Q. Tang, M. Pan, and Y. Tang, 2014: A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China. Journal of Hydrometeorology. doi: 10.1175/JHM-D-13-0170.1</p> <p>2. Archived data information and format</p> <p>This website provides access to parts of the model derived variables(including the water balance variables and states and energy balance varibales) at daily scale. This dataset is available in netCDF format. Please click the varible names below to download the corresponding variable.</p> <p>3. Download the dataset</p> <p>Model Derived Variables, 1952-2012 (Water Balance Variables and States)</p> <p>Evaporation<br> Runoff<br> Baseflow<br> Soil Moisture Layer 1<br> Soil Moisture Layer 2<br> Soil Moisture Layer 3<br> Snow Water Equivalent</p> <p>Contact: tangqh@igsnrr.ac.cn</p>
ERA5-Land weekly: Total precipitation, weekly time series for Europe at 1 km resolution (2016 - 2020)
<p>Overview:<br> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Total precipitation:<br> Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.</p> <p>Processing steps:<br> The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate proportion of ERA5-Land / aggregated CHELSA<br> 3. interpolate proportion with a Gaussian filter to 30 arc seconds<br> 4. multiply the interpolated proportions with CHELSA<br> Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA.</p> <p>The spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis starting from Saturday for the time period 2016 - 2020.<br> Data available is the weekly average of daily sums and the weekly sum of daily sums of total precipitation.</p> <p>File naming:<br> Average of daily sum: <code>era5_land_prectot_avg_weekly_YYYY_MM_DD.tif</code><br> Sum of daily sum: <code>era5_land_prectot_sum_weekly_YYYY_MM_DD.tif</code></p> <p>The date in the file name determines the start day of the week (Saturday).</p> <p>Pixel values:<br> mm * 10<br> Example: Value 218 = 21.8 mm</p> <p>Coordinate reference system:<br> ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 1km</p> <p>Temporal resolution:<br> weekly</p> <p>Period:<br> 01/01/2016 - 12/31/2020</p> <p>Lineage:<br> Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)</p> <p>Original ERA5-Land dataset license:<br> <a href="https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</a></p> <p>CHELSA climatologies (V1.2):<br> Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Other resources:<br> <a href="https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b">https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b</a></p> <p>Format: GeoTIFF</p> <p>Representation type: Grid</p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact:<br> mundialis GmbH & Co. KG, info@mundialis.de</p>
The datasets for the paper "Spatial and temporal distribution of lobate scarps in the lunar south polar region: Evidence for latitudinal variation of scarp geometry, kinematics and formation ages, continuous tectonic activity in the last 100 million years and seismically safe south pole Artemis human landing site" Geophysical Research Letters.
<p>This dataset provides the original data that were used for preparing the illustrations, figures and tables.</p>
FIGURE 4 in The invasive land flatworm Obama nungara in La Réunion, a French island in the Indian Ocean, the first report of the species for Africa
FIGURE 4. Climatic suitability of Obama nungara in La Réunion. Names of communes and their limits (white lines) are indicated. The three localities where the specimens were found (Petite France, Plaine des Grègues and Gîte de la Bergerie) are indicated.
FIGURE 1 in The invasive land flatworm Obama nungara in La Réunion, a French island in the Indian Ocean, the first report of the species for Africa
FIGURE 1. Photo of a live specimen of Obama nungara, taken in Petite France in the commune of Saint Paul, La Réunion. Photo by Régis Bretzner, taken 10 June 2021. The specimen is brown on the back, with darker brown striae, a reddish tip, and with a cream stripe running a short distance from the head towards the tail, but which soon disappears into the general colour of the back.
FIGURE 2 in The invasive land flatworm Obama nungara in La Réunion, a French island in the Indian Ocean, the first report of the species for Africa
FIGURE 2. Photo of a live specimen of Obama nungara, taken in la Plaine des Grègues in the commune of Saint Joseph, La Réunion. Specimen collected 22 April 2021 by Renaud Hoarau, photograph taken by Nicolas Huet. The photo clearly shows the dark brown striae on the brownish coloured back, and the whitish under surface.
Factors influencing the habitat choice of pangolins (Manis spp.) in low land of Nepal
<p><span>Pangolins in the genus <i>Manis </i>are nocturnal, burrowing, insectivorous mammals listed as Critically Endangered or Endangered by the International Union for Conservation of Nature. Of the eight extant pangolin species worldwide, two species are found in Nepal: the Chinese pangolin (<i>Manis pentadactyla</i>) and the Indian pangolin (<i>Manis crassicaudata</i>). Despite having a great ecological role by controlling the ants or termite population, little attention has been given to the conservation interventions of both species of pangolins found in the Terai region (low land) of Nepal. The present study assesses habitat use and factors affecting the habitat choice of pangolins in low land (Terai), Nepal. The research was focused on Amritdharapani community forest of Chitwan district. Pangolin burrows were used as the indirect signs of pangolin presence. A total of thirty-nine burrows were observed at elevations ranging from 301 to 413 m asl. Burrows were frequently associated with north-west aspects, gentle slope (15º to 20º), moderate canopy cover (51 to 75%), red-colored soil, and acidic soils with pH 6.5 to 7. The burrows were most common in areas with weak human disturbance (i.e. 1500 to 1700 m from settlements), 800 to 1200 m from roads, and with-in 300 m from a water source and with-in 20 m from the nearest termitarium. This study revealed distance to settlement, distance to road, soil pH, and canopy cover as major factors affecting the habitat choice of pangolins in the study area. </span></p>
Code and data for An Upper Bound for Extreme Temperatures over Midlatitude Land
<p>This .zip file contains data and Jupyter Notebooks to reproduce the results in "An Upper Bound for Extreme Temperatures over Midlatitude Land" by Zhang and Boos.</p>
Warming in the land of the midnight sun: breeding birds may suffer greater heat stress at high- vs low-Arctic sites
<p>Rising global temperatures are expected to increase reproductive costs for wildlife as greater thermoregulatory demands interfere with reproductive activities. However, predicting the temperatures at which reproductive performance is negatively impacted remains a significant hurdle. Using a thermoregulatory polygon approach, we derived a reproductive threshold temperature for an Arctic songbird–the snow bunting (Plectrophenax nivalis). We defined this threshold as the temperature at which individuals must reduce activity to suboptimal levels (i.e., < 4-times basal metabolic rate) to sustain nestling provisioning and avoid overheating. We then compared this threshold to operative temperatures recorded at high (82°N) and low (64°N) Arctic sites to estimate how heat constraints translate into site-specific impacts on sustained activity level. We predict buntings would become behaviourally constrained at operative temperatures above 11.7°C, whereupon they must reduce provisioning rates to avoid overheating. Low Arctic sites had larger fluctuations in solar radiation, consistently producing daily periods when operative temperatures exceeded 11.7°C. However, high-latitude birds faced entire, consecutive days when parents would be unable to sustain required provisioning rates. These data indicate that Arctic warming is likely already disrupting the breeding performance of cold-specialist birds and suggests counterintuitive and severe negative impacts of warming at higher-latitude breeding locations.</p>
Chesapeake Bay Vertical Land Motions 2019
<p>Global Positioning System (GPS) data from 2019 campaign in the Chesapeake Bay region </p>
Nesokia is sister to Bandicota and are nested in Rattus phylogenetically, making Rat- tus paraphyletic. Tarsomys, Limnomys, and Diplothrix are also phylogenetically in Rat- tus, and the clade is in need of focused re- vision at the generic level. Nesokia bunnui was originally described as a separate ge-nus, Erythronesokia, because it is morphologically very distinctive from N. indica. Type specimen was destroyed during the Iraq War, and a neotype was recently designated to replace it. Monotypic. Distribution. Tigris and Euphrates river valleys, SE Iraq. Descriptive notes. Head—body 230-260 mm, tail 205-270 mm, ear 18-21 mm, hindfoot 49-58 mm; weight 519 g. The Long-tailed Bandicoot Rat is larger than the Short-tailed Bandicoot Rat (N. indica). Pelage is soft and woolly, interspersed with harsher coarse hair and long black hairs near mid-back. Dorsum is fawn to ocherous red, washed with purple or chestnuton darker individuals. Hairs are basally slate-gray and distally rufous, occasionally with whitish or black tips. Muzzle is drab. Sides arefawn, with gray edge toward venter. Venteris whitish, extending onto cheeks where the same pattern from gray to fawn to dorsal pelage occurs. Feet are large and robust, being light brown and well-furred dorsally. Claws are amber on forefeet and dull brown on hindfeet; pollux is extremely small. Ears are moderately long and brownish, with no hair internally. Tail is ¢.82-104% of head-body length and deep brownish drab, interspersed with visible white hair. Skull is large and robust, similarly to the Short-tailed Bandicoot Rat. Habitat. Marsh and swamp land. Food and Feeding. No information. Breeding. No information. Activity patterns. The Long-tailed Bandicoot Rat is terrestrial, although it isfound in swampy and marshy areas and is probably amphibious. Movements, Home range and Social organization. No information. Status and Conservation. Classified as Endangered on The IUCN Red List. The Longtailed Bandicoot Rat is apparently rare and is known from very few specimens. Marsh and swamp habitats in which it is found were completely destroyed during the Iraq War by draining, war damage, and agricultural expansion. In recent years, flooding from Tigris and Euphrates rivers and high snow fall and melt haveresulted in partial restoration ofits native habitat, although restoration is not a complete. Populations are now probably highly fragmented. Bibliography. Al-Ansari et al. (2012), Al-Robaae & Felten (1990), Khajuria (1981), Krystufek et al. (2017), Musser & Carleton (2005), Richardson & Hussain (2006), Stuart (2008). in Muridae
Nesokia is sister to Bandicota and are nested in Rattus phylogenetically, making Rat- tus paraphyletic. Tarsomys, Limnomys, and Diplothrix are also phylogenetically in Rat- tus, and the clade is in need of focused re- vision at the generic level. Nesokia bunnui was originally described as a separate ge-nus, Erythronesokia, because it is morphologically very distinctive from N. indica. Type specimen was destroyed during the Iraq War, and a neotype was recently designated to replace it. Monotypic. Distribution. Tigris and Euphrates river valleys, SE Iraq. Descriptive notes. Head—body 230-260 mm, tail 205-270 mm, ear 18-21 mm, hindfoot 49-58 mm; weight 519 g. The Long-tailed Bandicoot Rat is larger than the Short-tailed Bandicoot Rat (N. indica). Pelage is soft and woolly, interspersed with harsher coarse hair and long black hairs near mid-back. Dorsum is fawn to ocherous red, washed with purple or chestnuton darker individuals. Hairs are basally slate-gray and distally rufous, occasionally with whitish or black tips. Muzzle is drab. Sides arefawn, with gray edge toward venter. Venteris whitish, extending onto cheeks where the same pattern from gray to fawn to dorsal pelage occurs. Feet are large and robust, being light brown and well-furred dorsally. Claws are amber on forefeet and dull brown on hindfeet; pollux is extremely small. Ears are moderately long and brownish, with no hair internally. Tail is ¢.82-104% of head-body length and deep brownish drab, interspersed with visible white hair. Skull is large and robust, similarly to the Short-tailed Bandicoot Rat. Habitat. Marsh and swamp land. Food and Feeding. No information. Breeding. No information. Activity patterns. The Long-tailed Bandicoot Rat is terrestrial, although it isfound in swampy and marshy areas and is probably amphibious. Movements, Home range and Social organization. No information. Status and Conservation. Classified as Endangered on The IUCN Red List. The Longtailed Bandicoot Rat is apparently rare and is known from very few specimens. Marsh and swamp habitats in which it is found were completely destroyed during the Iraq War by draining, war damage, and agricultural expansion. In recent years, flooding from Tigris and Euphrates rivers and high snow fall and melt haveresulted in partial restoration ofits native habitat, although restoration is not a complete. Populations are now probably highly fragmented. Bibliography. Al-Ansari et al. (2012), Al-Robaae & Felten (1990), Khajuria (1981), Krystufek et al. (2017), Musser & Carleton (2005), Richardson & Hussain (2006), Stuart (2008).
On following pages: 347. Plains Mouse (Pseudomys australis); 348. Bolam''s Mouse (Pseudomys bolami); 349. Kakadu Pebble Mouse (Pseudomys calabyi); 350. Western Pebble Mouse (Pseudomys chapmani); 351. Desert Mouse (Pseudomys desertor); 352. Shark Bay Mouse (Pseudomys field); 353. Australian Smoky Mouse (Pseudomys fumeus); 354. Eastern Chestnut Mouse (Pseudomys gracilicaudatus); 355. Sandy Inland Mouse (Pseudomys hermannsburgensis); 356. Long-tailed Mouse (Pseudomys higginsi); 357. Central Pebble Mouse (Pseudomys johnson); 358. Western Chestnut Mouse (Pseudomys nanus); 359. New Holland Mouse (Pseudomys novaehollandiae); 360. Western Mouse (Pseudomys occidentalis); 361. Hastings River Mouse (Pseudomys oralis); 362. Eastern Pebble Mouse (Pseudomys patrius); 363. Heath Mouse (Pseudomys shortridgel); 364. Common Australian Rock Rat (Zyzomys argurus); 365. Arnhem Land Rock Rat (Zyzomys maini); 366. Carpentarian Rock Rat (Zyzomys palatalis); 367. Central Australian Rock Rat (Zyzomys pedunculatus); 368. Kimberley Rock Rat (Zyzomys woodward); 369. Malayan Tree Rat (Pithecheir parvus); 370. Red Tree Rat (Pithecheir melanurus); 371. Bornean Tree Rat (Pithecheirops otion); 372. Cutch Rat (Cremnomys cutchicus); 373. Elvira Rat (Cremnomys elvira); 374. Crump's Rat (Diomys crumpi); 375. White-tailed Wood Rat (Madromys blanfordi); 376. Sand-colored Soft-furred Rat (Millardia gleadowi); 377. Kondana Soft-furred Rat (Millardia kondana); 378. Common Soft-furred Rat (Millardia in Muridae
On following pages: 347. Plains Mouse (Pseudomys australis); 348. Bolam''s Mouse (Pseudomys bolami); 349. Kakadu Pebble Mouse (Pseudomys calabyi); 350. Western Pebble Mouse (Pseudomys chapmani); 351. Desert Mouse (Pseudomys desertor); 352. Shark Bay Mouse (Pseudomys field); 353. Australian Smoky Mouse (Pseudomys fumeus); 354. Eastern Chestnut Mouse (Pseudomys gracilicaudatus); 355. Sandy Inland Mouse (Pseudomys hermannsburgensis); 356. Long-tailed Mouse (Pseudomys higginsi); 357. Central Pebble Mouse (Pseudomys johnson); 358. Western Chestnut Mouse (Pseudomys nanus); 359. New Holland Mouse (Pseudomys novaehollandiae); 360. Western Mouse (Pseudomys occidentalis); 361. Hastings River Mouse (Pseudomys oralis); 362. Eastern Pebble Mouse (Pseudomys patrius); 363. Heath Mouse (Pseudomys shortridgel); 364. Common Australian Rock Rat (Zyzomys argurus); 365. Arnhem Land Rock Rat (Zyzomys maini); 366. Carpentarian Rock Rat (Zyzomys palatalis); 367. Central Australian Rock Rat (Zyzomys pedunculatus); 368. Kimberley Rock Rat (Zyzomys woodward); 369. Malayan Tree Rat (Pithecheir parvus); 370. Red Tree Rat (Pithecheir melanurus); 371. Bornean Tree Rat (Pithecheirops otion); 372. Cutch Rat (Cremnomys cutchicus); 373. Elvira Rat (Cremnomys elvira); 374. Crump's Rat (Diomys crumpi); 375. White-tailed Wood Rat (Madromys blanfordi); 376. Sand-colored Soft-furred Rat (Millardia gleadowi); 377. Kondana Soft-furred Rat (Millardia kondana); 378. Common Soft-furred Rat (Millardia
Distribution. Restricted to the sandstone plateau and escarpment of W Arnhem Land, in monsoonal N Australia. in Muridae
Distribution. Restricted to the sandstone plateau and escarpment of W Arnhem Land, in monsoonal N Australia.
Island size predicts mammal diversity in insular environments, except for land-bridge islands
<p class="MsoBodyText"><span>Insular environments are among the most endangered ecosystems as they face a myriad of anthropogenic stressors. Forest mammals perform a wide range of ecological services, with their persistence being vital for ecosystem functionality in both natural and artificial islands. Studies revealed that shrinkage in island size usually leads to the decay of mammal species richness and abundance in patchy landscapes. However, mammal species-area (SARs) and abundance-area (AARs) relationships can differ among insular environments: oceanic, fluvial, artificial, and land-bridge islands (i.e., natural islands connected to the mainland). Large dams create vast insularized landscapes after river impoundment, leading to pervasive habitat loss and potentially causing even worse biodiversity losses than other insular systems. We conducted an extensive literature search and used meta-analysis techniques to quantify the magnitude of SAR and AAR for forest mammals across different archipelago landscapes worldwide. After a screening process, we ended up with 26 studies comprising 55 different effect sizes representing the magnitude of SARs and AARs. Our global analysis unveiled a positive relationship between effect sizes and island area, with mammal species richness and abundance increasing in fluvial, oceanic, and artificial islands accordingly with island area, but not in land-bridge islands. These results demonstrate that, except for land-bridge islands, SAR and AAR are still fair models to predict mammal diversity.<span> </span>These results could improve the prediction of SAR and AAR in insular environments under habitat loss scenarios and propose sound conservation strategies since the rate at which insular communities have been lost is presently unknown.</span></p>
The global land fAOD dataset (2001–2020) using DLFE-Satellite
<p>The fAOD is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Global fAOD can be obtained from point-scale Aerosol Robotic Network (AERONET) measurements, but the spatial coverage is limited. To obtain spatially-continuous global fAODs, we applied the DLFE-Satellite method with Moderate Resolution Imaging Spectroradiometer (MODIS) data, ERA5 climate reanalyses, and AERONET data from 2001 to 2020 as inputs.</p> <p>For independent validation, we used fAOD retrievals obtained from six Surface Radiation budget network (SURFRAD) sites in the USA. Note that SURFRAD sites are in the vicinity of AERONET sites and were not used to train the model. Data from these sites could thus be used for the independent validation of fAOD estimates. The linear relation between DLFE-Satellite and AERONET retrievals had an R value of 0.93 and RMSE of 0.09 μg/m<sup>3 </sup>; for validation with SURFRAD retrievals , R = 0.77 and RMSE = 0.14 μg/m<sup>3</sup>.</p>
The minimum land area requiring conservation attention to safeguard biodiversity
<p><span>Ambitious conservation efforts are needed to stop the global biodiversity crisis. Here, we estimate the minimum land area to secure important biodiversity areas, ecologically intact areas, and optimal locations for representation of species ranges and ecoregions. We discover that at least 64 million km<sup>2</sup> (44% of terrestrial area) would require conservation attention (ranging from protected areas to land-use policies) to meet this goal. Over 1.8 billion people live on these lands, so responses that promote autonomy, self-determination, equity, and sustainable management for safeguarding biodiversity are essential. Spatially explicit land-use scenarios suggest that 1.3 million km<sup>2</sup> of this land is at risk of being converted to intensive human land-uses by 2030, which requires immediate attention. However, there is a seven-fold difference between the amount of habitat converted under optimistic and pessimistic land-use scenarios, highlighting an opportunity to avert this crisis. Appropriate targets in the post-2020 Global Biodiversity Framework, to encourage conservation of the identified land, would contribute substantially to safeguarding biodiversity.</span></p>
Data and script for "Sensitivity of Precipitation Displacement of a Simulated MCS to the Changes in Land Surface Conditions"
<p>This is a placeholder for Data and script for ""<strong>Sensitivity of Precipitation Displacement of a Simulated MCS to the Changes in Land Surface Condition" submitted to JGR-atmosphere.</strong></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.