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638 results for “biomes”
WorldSeasons: a seasonal classification system interpolating biomes within the year for improved temporal aggregation
<p>We present a seasonal classification system to improve the temporal framing of comparative scientific analysis. Research often uses yearly aggregates to understand inherently seasonal phenomena like harvests, monsoons, and droughts. This obscures important trends across time and differences through space by including redundant data. Our classification system allows for a more targeted approach. We split global land into four principal climate zones: desert, arctic and high montane, tropical, and temperate. A cluster analysis with zone-specific variables and weighting splits each month of the year into discrete seasons based on the monthly climate. We expect the data will be able to answer global comparative analysis questions like: are global winters less icy than before? Are wildfires more frequent now in the dry season? How severe are monsoon season flooding events? This is a natural extension of the historical concept of biomes, made possible by recent advances in climate data availability and artificial intelligence.</p>
Nitrogen Cycling and Environmental Data in Riparian Soils across Biomes
This dataset compiles soil nitrogen cycle data from riparian soils, sourced from peer-reviewed studies published between 1980 and 2023. The selection process was based on three inclusion criteria: (1) studies measuring in-situ net nitrification rates in the top soil layer using the incubating bag technique, (2) studies reporting net nitrification rates from laboratory incubations without altering the initial nitrogen pool, and (3) studies providing field data on soil nitrogen concentrations, moisture, and temperature. The final dataset (D1) includes data from 174 riparian sites across four continents, with the majority of sites (86%) located in North America and Europe, while only 13 were located in the Southern hemisphere. For each site, we gathered data on net nitrification rates and key soil physicochemical properties, including bulk density, depth, moisture (expressed as water-filled pore space, WFPS), temperature, and ammonium and nitrate concentrations. The dataset includes 734 observations from 99 field sites and 120 observations from 45 laboratory-incubated sites. All publications from which data were used are list in dataset 2 (D2). This comprehensive dataset offers valuable insights into nitrogen dynamics in riparian soils, supporting further research into soil nitrogen cycling across diverse biomes and environmental conditions.
Biome Transition Along Elevational Gradients in New Mexico (SEON) Study: Flux Tower Net Primary Productivity (NPP) Quadrat Study at the Sevilleta National Wildlife Refuge, New Mexico
The varied topography and large elevation gradients that characterize the arid and semi-arid Southwest create a wide range of climatic conditions - and associated biomes - within relatively short distances. This creates an ideal experimental system in which to study the effects of climate on ecosystems. Such studies are critical given that the Southwestern U.S. has already experienced changes in climate that have altered precipitation patterns (Mote et al. 2005), and stands to experience dramatic climate change in the coming decades (Seager et al. 2007; Ting et al. 2007). Climate models currently predict an imminent transition to a warmer, more arid climate in the Southwest (Seager et al. 2007; Ting et al. 2007). Thus, high elevation ecosystems, which currently experience relatively cool and mesic climates, will likely resemble their lower elevation counterparts, which experience a hotter and drier climate. In order to predict regional changes in carbon storage, hydrologic partitioning and water resources in response to these potential shifts, it is critical to understand how both temperature and soil moisture affect processes such as evaportranspiration (ET), total carbon uptake through gross primary production (GPP), ecosystem respiration (Reco), and net ecosystem exchange of carbon, water and energy across elevational gradients. We are using a sequence of six widespread biomes along an elevational gradient in New Mexico -- ranging from hot, arid ecosystems at low elevations to cool, mesic ecosystems at high elevation to test specific hypotheses related to how climatic controls over ecosystem processes change across this gradient. We have an eddy covariance tower and associated meteorological instruments in each biome which we are using to directly measure the exchange of carbon, water and energy between the ecosystem and the atmosphere. This gradient offers us a unique opportunity to test the interactive effects of temperature and soil moisture on ecosystem proce
Biome Transition Along Elevational Gradients in New Mexico (SEON) Study: Flux Tower Seasonal Biomass and Seasonal and Annual NPP Data at the Sevilleta National Wildlife Refuge, New Mexico
The varied topography and large elevation gradients that characterize the arid and semi-arid Southwest create a wide range of climatic conditions - and associated biomes - within relatively short distances. This creates an ideal experimental system in which to study the effects of climate on ecosystems. Such studies are critical given that the Southwestern U.S. has already experienced changes in climate that have altered precipitation patterns (Mote et al. 2005), and stands to experience dramatic climate change in the coming decades (Seager et al. 2007; Ting et al. 2007). Climate models currently predict an imminent transition to a warmer, more arid climate in the Southwest (Seager et al. 2007; Ting et al. 2007). Thus, high elevation ecosystems, which currently experience relatively cool and mesic climates, will likely resemble their lower elevation counterparts, which experience a hotter and drier climate. In order to predict regional changes in carbon storage, hydrologic partitioning and water resources in response to these potential shifts, it is critical to understand how both temperature and soil moisture affect processes such as evapotranspiration (ET), total carbon uptake through gross primary production (GPP), ecosystem respiration (Reco), and net ecosystem exchange of carbon, water and energy across elevational gradients. We are using a sequence of six widespread biomes along an elevational gradient in New Mexico -- ranging from hot, arid ecosystems at low elevations to cool, mesic ecosystems at high elevation to test specific hypotheses related to how climatic controls over ecosystem processes change across this gradient. We have an eddy covariance tower and associated meteorological instruments in each biome which we are using to directly measure the exchange of carbon, water and energy between the ecosystem and the atmosphere. This gradient offers us a unique opportunity to test the interactive effects of temperature and soil moisture on ecosystem proces
SIA-BRA: The carbon and nitrogen stable isotope ratios of animals of Brazilian biomes and coastal marine areas
<p>SIA-BRA is a compilation of C and N stable isotope ratios of terrestrial and aquatic animals sampled in Brazilian biomes and coastal-marine areas.</p> <p>Version 1.0 contains isotopic data of c. 21,804 non-captive wildlife specimens, excluding livestock production or laboratory<br> experiments. They were 13,881 vertebrates and 7,923 invertebrates. There are 11 phyla, with a clear dominance of Chordata (64%) and Arthropoda (29%), 36 classes, 154 orders, 473 families, 894 genera and 1,157 species.</p> <p>They were divided into the following habitats: terrestrial (30% of the total), freshwater (27%), oceanic (40%)<br> and estuarine (4%) (see <a href="https://doi.org/10.1111/geb.13449">https://doi.org/10.1111/geb.13449</a>)</p> <p>Software format: Data are supplied as delimited text files (.csv).</p>
Soil visible–near infrared (vis–NIR) spectra for the Biomes of Australian Soil Environments (BASE) soil microbial diversity database
<p>Visible–near infrared spectra of 695 soil samples collected in the Biomes of Australian Soil Environments (BASE) soil microbial diversity project (Bissett et al., 2016). The spectra represent reflectance values from 2151 wavelengths that range from 350 nm to 2500 nm with a 1 nm interval. The dataset has unique sample identification numbers and the date of sampling, which can be related to the BASE (Australian Microbiome) database (https://data.bioplatforms.com/organization/australian-microbiome)</p>
Current and future global distribution of potential biomes under climate change scenarios
<p>Probability and uncertainty maps showing the potential current and future natural vegetation on a global scale under three different climate change scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) predicted using ensemble machine learning. Current (2022 - 2023) conditions are calculated on historical long term averages (1979 - 2013), while future projections cover two different epochs: 2040 - 2060 and 2061 - 2080.</p> <p>Files are named according to the following naming convention, e.g.:</p> <ul> <li>biomes_graminoid.and.forb.tundra.rcp85_p_1km_a_20610101_20801231_go_epsg.4326_v20230410</li> </ul> <p>with the following fields:</p> <ul> <li>generic theme: <strong>biomes</strong>,</li> <li>variable name: <strong>graminoid.and.forb.tundra.rcp85</strong>,</li> <li>variable type, e.g. probability ("<strong>p</strong>"), hard class ("<strong>c</strong>"), model deviation ("<strong>md</strong>")</li> <li>spatial resolution: <strong>1km</strong>,</li> <li>depth reference, e.g. below ("<strong>b</strong>"), above ("<strong>a</strong>") ground or at surface ("<strong>s</strong>"),</li> <li>begin time (YYYYMMDD): <strong>20610101</strong>,</li> <li>end time: <strong>20801231</strong>,</li> <li>bounding box, e.g. global land without Antarctica ("<strong>go</strong>"),</li> <li>EPSG code: <strong>epsg.4326</strong>,</li> <li>version code, e.g. creation date: <strong>v20230410</strong>.</li> </ul> <p>We provide probability and hard class layers using a revised classification system of the <a href="https://www.jstor.org/stable/2846196">BIOME 6000 project</a> explained in the work of <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a>. The 20 classes from this classification system have then been aggregated in 6 biome classes following the <a href="https://global-ecosystems.org/page/typology">IUCN Global Ecosystem Typology</a> classification system.</p> <p>For probability layers, the uncertainty (model deviation: <strong>md</strong>) is calculated as the standard deviation of the predicted values of the base learners of the ensemble model. The higher the standard deviation the more uncertain the model is regarding the right value to assign to the pixel.</p> <p>For hard class layers the uncertainty is calculated using the margin of victory (<a href="https://doi.org/10.1016/j.rse.2020.112148">Calderón-Loor et al., 2021</a>) defined as the difference between the first and the second highest class probability value in a given pixel. High values would be measures of low uncertainty, while low values would indicate a high uncertainty. It is highly recommended to use the <strong>md </strong>layers to properly interpret the results of the map.</p> <p>Styling files are provided in both <em><strong>.SLD</strong></em> and <em><strong>.QML</strong></em> format; two different styling files are provided for the uncertainty of the probability layers and the hard classes due to the different interpretation of the chosen uncertainty metrics.</p> <p>The R scripts and a tutorial will be uploaded to the <a href="https://github.com/Envirometrix/PNVmaps">PNVmaps Github repository</a>, where previous versions of the biomes maps from <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a> is currently hosted. To cite the maps and the methodology, it is possible to refer to the scientific publication:</p> <p>Bonannella C, Hengl T, Parente L, de Bruin S. 2023. Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation. PeerJ 11:e15593 <a href="https://doi.org/10.7717/peerj.15593">https://doi.org/10.7717/peerj.15593</a></p>
Data from: The legacy of the extinct Neotropical megafauna on plants and biomes
<p>The main dataset consists of ecoregion-level data on five plant functional traits (wood density, leaf size, stem spines, leaf spines and latex production), as well as ecoregion-level data on extinct megafauna historical patterns, fire, climate, soil, hurricanes and geografical variables (first spreadsheet) for the Neotropical biogeographic realm (Table 1). It also includes species-level plant functional trait data, and the abundance (presence-absence for leaf size) of these species, and the occurrences extinct megafauna and extant mammal herbivore species per Neotropical ecoregion, as well as diet data compiled for megafauna species. The species-level functional trait data was compiled from the literature and the names of the species in these data was used to search for occurrence data for these species in the Global Biodiversity Information Facility (Data available from GBIF using the following doi: WD: 10.15468/dl.3vua3x; Stem spines: 10.15468/dl.ar5ddj; Latex: 10.15468/dl.m8dzjd; Leaf spines: 10.15468/dl.vv8gw4; Leaf size: 10.15468/dl.k98nxc). During the process, species level were corrected and updated using tools from the "rgbif" package for R. We then croped only the Neotropical region, and calculate ecoregion level trait means for continuous traits (Wood Density and Leaf Size) and maximum por binary traits (Stem and Leaf Spines, Latex), using the ecoregion shapefile provided in https://storage.googleapis.com/teow2016/Ecoregions2017.zip. We obtained data on historical distribution of megafauna species and extant mammal species from the MegaPast2Future/PHYLACINE_1.2 dataset, and obtained diet information from literature sources. Climate data was obtained from WorldClim 2.1 (10 minute spatial resolution) and was based on climate data from 1970 and 2000. Soil data were obtained from SoilGrids (5 km of spatial resolution), and consisted of mean values for two depths, 0.05 and 2 m. We obtained the number (a proxy for frequency) and intensity of wildfires per ecoregion area using the MODIS active fire location product (MCD14ML). We only considered fires with detection confidence of 95% or higher occurring from November 2000 to December 2019 (both included). To ensure that only wildfires were considered, we associated each fire pixel with a land cover type (300 m of spatial resolution) from for a buffer area of 1000 m surrounding the fire pixel centroid. We excluded all of the fires occurring in areas in which more than 10% of the surrounding land cover pixels corresponded to agricultural, urban and water classes. We calculated the number of wildfires per ecoregion area by dividing the fire count of each Ecoregion by the ecoregion area, and multiplying the resulting value by the proportion of vegetated land cover pixels (same classes used to exclude fires in anthropogenic areas and water bodies above). Fire intensity was estimated as the average fire radiative power across all detected wildfires in the ecoregion. We also classified ecoregions into insular (1), when most of the ecoregion area was located in islands, vs. continental (0), otherwise. We also compiled data on hurricane activity, as woody density was suggested to confer resistance against this disturbance. We used data from 1990 to 2019 from the HURDAT2 dataset, containing six-hourly information about the location of all of the known tropical and subtropical cyclones (0.1° latitude/longitude). We used the sum of hurricane occurrences per ecoregions divided by ecoregion area as an indicator of hurricane activity.</p> <p>Three .txt files containing the custom codes developed for building the Ecoregion-level dataset (predictors and traits) and for data analyses used in the article are also included.</p>
High resolution microsection images for: Common juniper, the oldest living non-clonal woody species across the tundra biome and the European continent
<p>Two high resolution images of the stem section are available as .czi files. These images are from a living <em>Juniperus communis</em> L. branch from Abisko (Sweden) sampled in August 2021. These high-resolution photographs (2.89 pixel/μm) were created using Axio Scan 7, Zeiss, Germany. </p> <p>One high resolution image of the same stem section is archived as a .tif file (49835x25587 pixels). This image is a composition of the two .czi images created using Axio Scan 7, Zeiss, with a reduced resolution and edited adding the ring-count reference points and the reference scale.</p>
Mean Annual Herbaceous Cover for the Sagebrush Biome, USA (2020 - 2022)
<p><strong>Abstract: </strong>Cheatgrass (Bromus tectorum) and other invasive annual grasses are the single largest threat to sagebrush rangeland health and resilience (Doherty et al. 2022). To address this challenge, NRCS’ Working Lands for Wildlife, the Western Governors Association (WGA), and diverse partners are helping implement a new proactive spatial plan to tackle invasive annuals known as “Defend the Core” (Maestas et al. 2022). Foundational to implementing this new approach is the creation of a common spatial map of invasion severity to guide strategic actions. In 2020, a WGA-led cheatgrass working group an annual herbaceous cover map that summarized the extent of annuals using three remotely-sensed data products for the years 2016 - 2018 (Maestas et al. 2020). This updated product reports annual herbaceous cover for the years 2020 - 2022 using only cover data from the Rangeland Analysis Platform. Data coverage includes all rangelands within the U.S. sagebrush biome. </p> <p><strong>Purpose: </strong>The goal of the annual herbaceous cover map is to support a common spatial strategy for tackling invasive annual grasses across the western U.S. As with all remote sensing-based products, the map presented here is best used alongside local knowledge and data. The map is intended to facilitate cross-boundary regional planning, and it is anticipated that state and local partners will further refine priority areas for management using additional information.</p> <p><strong>Methodology: </strong>This product used the Rangeland Analysis Platform V3 cover product from years 2020, 2021, and 2022. A mean composite was generated from the yearly raster data using the ‘annual herbaceous functional type’ (AFG) layer, representing percent cover of annual forb and grasses. The methodology for producing the cover product is described in Allred et al. 2021. Cover error for AFG in RAP Cover V3 was 7.0% (MAE) and 11.0% (RMSE). More information can be found at <a href="https://rangelands.app/products/">https://rangelands.app/products/</a>. The data is clipped to the extent of the sagebrush biome using the data from Jeffries and Finn (2019). </p> <p>Some important considerations must be kept in mind when using this product. First, the data layer depicts cover for all annual herbaceous species, not just invasive annual grasses. However, annual herbaceous cover is a useful surrogate for invasive annuals on arid rangelands in the sagebrush biome where native annuals typically represent a small proportion of vegetation cover most years. Second, the product reflects modeled predictions, so error must also be considered. This data product is best suited to highlight patterns of invasive annuals where they are known to be widely distributed and cannot be used in isolation to confirm the absence of invasive species. </p> <p><strong>Time Period of Data:</strong></p> <ul> <li>Start Date: 2020-01-01</li> <li>End Date: 2022-12-31</li> </ul> <p><strong>Coordinate Reference System</strong>: Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution is approximately 30m.</p> <p><strong>Data format: </strong>Cloud Optimized GeoTiff</p> <p><strong>Data Value: </strong>Percent (%) annual herbaceous cover</p> <p><strong>Data type: </strong>Byte </p> <p><strong>Nodata value: </strong>255</p> <p><strong>Bounding Coordinates:</strong></p> <ul> <li>West: -122.116081408</li> <li>East: -102.260259357</li> <li>North: 49.0016614443</li> <li>South: 34.2918384983</li> </ul> <p><strong>Keywords:</strong></p> <ul> <li>Terrestrial ecosystems</li> <li>Vegetation</li> <li>Invasive species</li> <li>Grassland ecosystems</li> <li>Remote sensing</li> <li>Grasslands</li> <li>Cheatgrass</li> <li>Great Basin</li> <li>Biota</li> <li>Geoscientific information</li> </ul> <p><strong>Access Constraints: </strong>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit <a href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</a> Data are provided "as is" without warranty of any kind, express or implied.</p> <p><strong>Use Constraints:</strong> None. </p> <p><strong>Previous Version(s): </strong>Maestas et al. 2020</p> <p><strong>Data Credit: </strong>University of Montana, USDA-NRCS</p> <p><strong>Data Attribution: </strong>Allred et al. 2021</p>
Lotic Intersite Nitrogen eXperiment II (LINX II): a cross-site study of the effects of anthropogenic land use change on nitrate uptake and retention in 72 streams across 8 different biomes (2003 – 2006).
The LINX II (Lotic Intersite Nitrogen eXperiment) project was designed to quantify the rates and mechanisms of nitrate retention in streams using stable isotope tracer additions. The study encompassed 72 stream reaches spread across 8 North American biomes. Within each biome, 9 streams were selected in three watershed land-use categories: 3 reference, 3 agricultural, and 3 urbanized. The core of the study was a 24-hour release of 15N- labeled nitrate. Prior to the isotope addition, physical, chemical and biological characteristics of the stream were measured. The measurements included, but were not limited to, dissolved nutrient concentrations, dissolved conservative tracer additions (to quantify hydraulic and hyporheic retention, velocity and discharge), standing stocks of primary uptake biota (including suspended and benthic particulate materials) as well as channel dimensions, photosynthetically active radiation, and water temperature. During the isotope release, whole stream rates of ecosystem metabolism were quantified (including quantification of re-aeration coefficients using tracer gas additions), and concentrations of 15N-labeled NO3, NH4, N2 and N2O were measured. Immediately following the isotope addition, 15N uptake by aquatic organisms was quantified by sampling biomass components on the stream bed. The data generated from these 72 stream reaches were used to develop a stream nitrogen retention model for each biome, which was expanded to entire drainage networks to predict nitrogen fluxes. The LINX II study demonstrated how biotic uptake of nitrate and denitrification increased with increasing nitrate concentrations. However, the efficiency of total uptake and denitrification actually declined with increasing nitrate concentrations (such as those seen on agricultural or urbanized streams), yielding higher rates of dissolved nitrogen exports downstream. The datasets presented here consist of the primary data collected by the LINX II study participants.
Biome Transition Along Elevational Gradients in New Mexico (SEON) AmeriFlux Data (2007- )
The varied topography and large elevation gradients that characterize the arid and semi-arid Southwest create a wide range of climatic conditions - and associated biomes - within relatively short distances. This creates an ideal experimental system in which to study the effects of climate on ecosystems. Such studies are critical givien that the Southwestern U.S. has already experienced changes in climate that have altered precipitation patterns (Mote et al. 2005), and stands to experience dramatic climate change in the coming decades (Seager et al. 2007; Ting et al. 2007). Climate models currently predict an imminent transition to a warmer, more arid climate in the Southwest (Seager et al. 2007; Ting et al. 2007). Thus, high elevation ecosystems, which currently experience relatively cool and mesic climates, will likely resemble their lower elevation counterparts, which experience a hotter and drier climate. In order to predict regional changes in carbon storage, hydrologic partitioning and water resources in response to these potential shifts, it is critical to understand how both temperature and soil moisture affect processes such as evaportranspiration (ET), total carbon uptake through gross primary production (GPP), ecosystem respiration (Reco), and net ecosystem exchange of carbon, water and energy across elevational gradients.We are using a sequence of six widespread biomes along an elevational gradient in New Mexico -- ranging from hot, arid ecosystems at low elevations to cool, mesic ecosystems at high elevation to test specific hypotheses related to how climatic controls over ecosystem processes change across this gradient. We have an eddy covariance tower and associated meteorological instruments in each biome which we are using to directly measure the exchange of carbon, water and energy between the ecosystem and the atmosphere. This gradient offers us a unique opportunity to test the interactive effects of temperature and soil moisture on ecosystem proce
Data from: A new approach to map landscape variation in forest restoration success in tropical and temperate forest biomes
1. A high level of variation of biodiversity recovery within a landscape during forest restoration presents obstacles to ensure large scale, cost-effective, and long-lasting ecological restoration. There is an urgent need to predict landscape variation in forest restoration success at a global scale. 2. We conducted a meta-analysis comprising 135 study landscapes to predict and map landscape variation in forest restoration success in tropical and temperate forest biomes. Our analysis was based on the amount of forest cover within a landscape – a key driver of forest restoration success. We contrasted 17 generalized linear models measuring forest cover at different landscape sizes (with buffers varying from 5 to 200 km radii). We identified the most plausible model to predict and map landscape variation in forest restoration success. We then weighted landscape variation by the amount of potentially restorable areas (agriculture and pasture land areas) within the same landscape. Finally, we estimated restoration costs of implementing Bonn Challenge commitments in three specific temperate and tropical forest biome types in USA, Brazil and Uganda. 3. Landscape variation decreased exponentially as the amount of forest cover increased in the landscape, with stronger effects within a 5 km radius. Thirty-eight percent of forest biomes have landscapes with more than 27% of forest cover and showed levels of landscape variation below 10%. Landscapes with less than 6% of forest cover showed levels of variation in forest restoration success above 50%. 4. At the biome level, Tropical and Subtropical Moist Broadleaf Forests had the lowest (12.6%), while Tropical and Subtropical Dry Broadleaf Forests had the highest (22.9%) average of weighted landscape variation in forest restoration success. Our approach can lead to a reduction in implementation costs for each Bonn Challenge commitment between US$ 973 Mi and 9.9 Bi. 5. Policy implications. Our approach identifies landscape characteristics that increase the likelihood of biodiversity recovery during forest restoration – and potentially the chances of natural regeneration and long-term ecological sustainability and functionality. Identifying areas with low levels of landscape variation can help to reduce the risks and financial costs associated with implementing ambitious restoration commitments.
FIGURES 1 – 4 in A new species of Pelecium Kirby (Coleoptera: Carabidae: Peleciini) from the Atlantic Forest biome, Brazil
FIGURES 1 – 4. Pelecium igneus Orsetti & Lopes-Andrade sp. nov., male holotype. 1, 3 dorsal view 2, 4 ventral view. 3. Head, arrowhead points to excavations of frontal fovea 4. Detail of labial and maxillary palpomeres. Scale bars: 5 mm (1 − 2); 1 mm (3 − 4).
FIG. 7 in The Miocene La Venta Biome (Colombia): A century of research and future perspectives
FIG. 7. — Reconstitution of the La Venta biome (Colombia). Illustration by Guillermo Torres. Banco de Imágenes Ambientales (BIA). Instituto de Investigaciones de Recursos Biológicos Alexander von Humboldt.
FIG. 6 in The Miocene La Venta Biome (Colombia): A century of research and future perspectives
FIG. 6. — Miocene Freshwater invertebrates from La Venta, Huila, Colombia: A-D, trichodactylid freshwater crab remains, assigned to Sylviocarcinus sp., from La Victoria and Villavieja formations (Honda Group); A, fixed finger (pollex) of right cheliped (claw), specimen VPPLT-0954; B, mobile finger (dactylus) of right cheliped, specimen VPPLT-1306; C, D, articulated right cheliped, outer (C) and inner (D) views, showing details of the dactylus, propodus and pollex, specimen IGM 89-499, Colombian Geological Survey; E, F, freshwater bivalves, tentatively assigned to Anodontites Bruguière, 1792; G, H, gastropods. The bivalves and gastropods are possibly from the Barzalosa Formation, surveyed at locality Río Cabrera (La Venta region), from the Hoffstetter's collection housed by Muséum national d'Histoire naturelle, Paris. Scale bars: A, 5 mm; B-H, 10 mm.
FIG. 3 in The Miocene La Venta Biome (Colombia): A century of research and future perspectives
FIG. 3. — Paleontological research and outreach in La Victoria, Colombia: A, B, Images of the exhibit Fossil Territory, Living Stories at the Museo de Historia Natural La Tatacoa (MHNT) (Oviedo et al. 2023); C, researchers working at the MHNT; D, the Vanegas brothers, Rubén (left) and Andrés (right), who manage the MHNT and founders of Vigías; E, celebration event in La Victoria organized by the MHNT on the occasion of the 100 years of paleontological research in the region; F, César Perdomo, fossil collector and founder of the Museo La Tormenta prospecting for fossils; G, researchers, students, journalists and members of Vigías that participated in the fieldwork in 2023. All the photos were taken during the fieldwork in May 2023 by C. Ziegler.
FIG. 4 in The Miocene La Venta Biome (Colombia): A century of research and future perspectives
FIG. 4. — Selection of fossils from La Tatacoa Desert housed at the Museo de Historia Natural La Tatacoa, including some specimens featured in this thematic issue: A, Lepidosiren paradoxa (VPPLT 1483); B, parieto-supraoccipital frontal fragment of Phractocephalus sp. (VPPLT 1272; Carrillo-Briceño et al. 2023); C, skull of Purussaurus neivensis; D, skeleton of Caimaninae; E, skull of Caninemys tridentata Meylan, Gaffney & de Almeida Campos, 2009 (VPPLT-1720; Cadena et al. 2021); F, shell of Podocnemis tatacoensis Cadena & Vanegas, 2023 (VPPLT 1727; Cadena & Vanegas 2023); G, skull of Miocochilius anomopodus Stirton, 1953 (VPPLT 1512); H, skull of "Prodolichotis" pridiana Fields, 1957 (VPPLT 1614); I, skull of Anachlysictis gracilis Goin, 1997 (VPPLT 1612; Suarez et al. 2023); J, partial mandible of Megadolodus molariformis McKenna, 1956 (VPPLT 974; Carrillo et al. 2023); K, skull of Cebupithecia sarmientoi Stirton & Savage, 1950. Abbreviation: VPPLT, Vigías del Patrimonio Paleontológico La Tatacoa. Specimens are not shown to scale. Photos by C. Ziegler.
FIG. 5 in The Miocene La Venta Biome (Colombia): A century of research and future perspectives
FIG. 5. — Fabaceae fossil wood from La Venta (VPPLT 008): A, diffuse porous wood, solitary vessels and in radial multiples of two vessels, aliform parenchyma (arrow); B, banded parenchyma in tangential lines; C, intervascular pits (arrow), uniseriate rays and non-septate fibers; D, vessel-ray parenchyma pits similar to intervessel pits (arrow), simple perforation plates (PP) and weakly heterocellular rays (RLS); E, fossil wood of Fabaceae (VPPLT 008) in the Cerro Gordo Beds. Scale bars: A, 500 µm; B-D, 200 µm.
FIG. 2 in The Miocene La Venta Biome (Colombia): A century of research and future perspectives
FIG. 2. — Cumulative number of publications from the La Venta fossil site during a century of paleontological research. The results are derived from a compilation of all the publications produced in La Venta that were compiled in a database (see Appendix 2).
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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.