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7,883 results for “Tropical”
Tropical Pacific SST and wind anomalies generated by a Nonlinear Inverse Model
<p>Tropical Pacific (40S-40N; 120E-50W) sea surface temperature (SST), zonal wind (U) and meridional wind (V) anomalies generated by the Nonlinear Inverse Model described in Martinez-Villalobos et al., 2024 (https://doi.org/10.1038/s41612-024-00675-5). The data consists in 99 realizations (<a href="../api/records/10411023/draft/files/NLIM_output_085.nc/content" target="_blank" rel="noopener noreferrer">NLIM_output_XXX.nc</a>) of 1,000yrs each emulating SST, U, and V monthly anomalies conditions during 1980-2020 (<a href="../api/records/10411023/draft/files/Monthly_obs_1980_2020.nc/content" target="_blank" rel="noopener noreferrer">Monthly_obs_1980_2020.nc</a>) given in a 2.5deg-2.5deg grid. For observations, we used the NOAA Extended Reconstruction SST v5 reanalysis (SST; Huang et al., 2017) and NCEP-NCAR reanalysis (winds; Kalnay et al., 1996) The observed anomalies are calculated as described in Martinez-Villalobos et al., 2024 (https://doi.org/10.1038/s41612-024-00675-5).</p> <p>Given that the stochastic forcing considered is white in time and space (https://doi.org/10.1038/s41612-024-00675-5; Methods, section "Offline simulation of SSH_{12}, PC2, and spatial patterns fron nonlinear inverse model output"), the spatial patterns and lead-lag relationships are better identified using composites. A modification of the methodology that allows for spatially coherent stochastic forcing will be implemented in a future article.</p> <p>When using the data please cite https://doi.org/10.5281/zenodo.10411023 (the data) and Martinez-Villalobos et al., 2024 (https://doi.org/10.1038/s41612-024-00675-5; for the methodology). </p> <p>Any question, please contact Cristian Martinez-Villalobos at his email cristian.martinez.v@uai.cl</p> <p>References</p> <p>Martinez-Villalobos, C., Dewitte, B., Garreaud, R.D. <em>et al.</em> Extreme coastal El Niño events are tightly linked to the development of the Pacific Meridional Modes. <em>npj Clim Atmos Sci</em> <strong>7</strong>, 123 (2024). https://doi.org/10.1038/s41612-024-00675-5</p> <p>Huang, B. et al. Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate 30, 8179–8205 (2017).</p> <p>Kalnay, E. et al. The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society 77, 437–471 (1996).</p> <p> </p>
Long-term dynamics of tropical rain forests in permanent inventory plots, La Selva, Costa Rica (1969-1995)
Three permanent plots comprising a total of 12.4 ha were established in 1969 in tropical rain forest at La Selva Biological Station, near Puerto Viejo de Sarapiquí, in the Caribbean lowlands of Costa Rica. The plots were established in old-growth forest on three contrasting landforms: Plot 1 (4.4 ha) on old alluvial terrace; Plot 2 (4.0 ha) in swamp forest and rolling hills; and Plot 3 (4.0 ha) on steeply dissected terrain with residual soils. The data archived here include plot inventories carried out at five census dates over a period of 27 years. The inventory starting dates were 1969; 1982; 1985; 1989; and 1995. All stems 10 cm dbh or greater were tagged with a permanent numbered tag; measured in diameter at breast height and above buttresses to the nearest mm; mapped on the ground to the nearest m; and identified to species. At each census, live trees were re-measured, dead trees were recorded along with information on the manner of death, other details on the condition of the tree were noted, and new recruits were tagged, mapped, measured, and identified. The archived data include these five components: (1) The master data file, including comprehensive data on all tagged individuals in the three plots for the five censuses from 1969-1995. Each line in the data set represents an individual tagged tree or liana. The data array comprises 8689 lines (the number of tagged individuals) x 48 columns of data. The lines in the data set are ordered first by Plot number (1, 2, 3); next by subplot within each plot; and then by tag number within each subplot. (2) A list of column identifiers, describing in detail the information represented in each of the 48 columns within the master data file. The list gives a description of the data in each column, the units of measurement, and a guide to the interpretation of zeroes in the data. (3) A key to codes used in the field to describe the condition of individual trees. (4) A taxonomic reference list, including all species found
Seedling composition, growth, and dynamics in tropical rain forest, La Selva, Costa Rica (1983-1996)
Recruitment, growth, and survivorship of the regeneration stages of trees and lianas were studied in old-growth tropical rain forest at La Selva Biological Station of the Organization for Tropical Studies (OTS), near Puerto Viejo de Sarapiquí, Heredia Province, in the Caribbean lowlands of Costa Rica. A total of 48 permanent seedling transects each measuring 10 m x 0.5 m were established at random locations within three La Selva permanent forest inventory plots. The forest plots occupy contrasting landforms: Plot 1 (4.4 ha), old alluvial terrace; Plot 2 (4.0 ha), swamp forest and low hills; and Plot 3 (4.0 ha), steeply dissected terrain with residual volcanic soils. Seedling locations are georeferenced within the grid system of the permanent forest inventory plots, facilitating spatial analysis of seedling populations with respect to adult cohorts. Beginning in June 1983, all seedlings ≤ 0.5 m in height belonging to tree and liana species capable of reaching 10 cm diameter at breast height (dbh) at maturity were tagged, identified to species or morphospecies, mapped to the nearest cm, and measured in height to the nearest cm. Over a period of 18 months, a total of 6403 seedlings belonging to 167 species were tagged. Monitoring and re-measurement of all tagged individuals continued through November 1996. Data include 17 census dates over a period of 13.5 years. At the time of the final census, only 97 individuals (1.52% of the tagged seedlings) were still alive, representing 43 species (25.7% of the initial number). The largest surviving seedling had grown in height from 4 cm to 13 meters during the study period. This dataset on the regeneration stages in old-growth tropical rain forest in the La Selva permanent inventory plots forms a complement to the studies of long-term growth and demography of these species and assemblages at adult stages within the plots. Forest inventory data for trees and lianas ≥ 10 cm dbh in the permanent plots in which the seedling transec
Assessing Plant Phenological Patterns in Tropical Brazil 1901–2020
Phenology is a key biological trait of an organism’s success and is one of the best indicators of its response to recent climate change. Plants are among the most well-studied organisms in this regard, but observational data bearing on this topic are largely restricted to species of the northern hemisphere, mostly from ca. the last three decades. Phenological data from tropical latitudes are especially lacking. Recent research has demonstrated that mobilized online herbarium specimens provide important, albeit mostly neglected, information on plant phenology. Here, we use the web tool CrowdCurio to crowdsource phenological data from nearly 35,000 herbarium specimens representing 260 flowering plant species broadly distributed across tropical Brazil. Our results, spanning 120 years and generated from over 1000 crowdsourcers, clarify numerous aspects of tropical plant phenology. First, they reveal that plant reproductive timing is exceptionally diverse across tropical biomes and taxa. Second, they identify that phenological responses to climate are variable across taxa and biomes. Third, among those species with broad latitudinal ranges, populations from more southern latitudes are significantly more phenologically sensitive to precipitation than those from northern populations. Our results are robust to a variety of confounding factors and span large phylogenetic distances and various life histories. These may represent more global trends in the latitudinal gradient of tropical phenological response with myriad potential ecological and evolutionary consequences. This dataset may be used for non-commercial purposes. Please provide the following attribution: Davis, C., Lyra, G., Park, D., Zhang, H., Asprino, R., Maruyama, R., Torquato, D., Cook, B., Xie, J., Ellison, A. 2022. Assessing plant phenological patterns in tropical Brazil 1901–2020. Harvard Forest Data Archive: HF427. Please note that the license we provide does not apply to images linked from the data set. P
Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from various reanalysis datasets
<h1>Dataset Description</h1> <p>Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from several reanalysis datasets. This dataset is the result of an extension of the Jenkinson-Collison circulation type classification to the entire globe, including a modification of its original formulation for the southern hemisphere.</p> <p>A modified version of the IPCC-AR6 Reference Regions that excludes the intertropical range where the method is not applicable is also included, as used in the reference paper for global assessment.</p> <p>Further details in <a href="https://doi.org/10.1007/s00382-022-06658-7" target="_blank" rel="noopener">https://doi.org/10.1007/s00382-022-06658-7 </a></p> <h2>Note for version 1.1.0</h2> <p>This version corrects an issue in the previous release, which was incorrectly labeled as <em>version 0.1</em>. That version was incomplete due to the omission of previously existing files, and should be considered <strong>incomplete</strong>. Version 1.1.0 restores all original files alongside the newly added one, ensuring the dataset is now complete and consistent. We apologize for any inconvenience this may have caused and appreciate your understanding.</p>
Fruit, seed dispersal, and life history traits of tropical rainforest trees of the Anamalai Hills, Western Ghats, India
<p>This dataset contains compiled Fruit, seed dispersal, and life history traits of tropical rainforest trees of the Anamalai Hills, Western Ghats, India. The list of species included are mainly from the following two related publications:<br>- Muthuramkumar, S., Ayyappan, N., Parthasarathy, N., Mudappa, D., Raman, T.R.S., Selwyn, M.A. and Pragasan, L.A. (2006), <a href="https://doi.org/10.1111/j.1744-7429.2006.00118.x">Plant Community Structure in Tropical Rain Forest Fragments of the Western Ghats, India</a>. <em>Biotropica</em>, 38: 143-160. https://doi.org/10.1111/j.1744-7429.2006.00118.x<br>- Osuri, A., Chakravarthy, D., Mudappa, D., Raman, T., Ayyappan, N., Muthuramkumar, S., & Parthasarathy, N. (2017). <a href="http://httpd//doi.org/10.1017/S0266467417000219">Successional status, seed dispersal mode and overstorey species influence tree regeneration in tropical rain-forest fragments in Western Ghats, India</a>. <em>Journal of Tropical Ecology</em>, 33(4), 270-284. doi:10.1017/S0266467417000219<br>The present dataset is an expanded and updated version of the related dataset available at <a href="https://doi.org/10.5061/dryad.vd0nn">https://doi.org/10.5061/dryad.vd0nn</a><br> <br>Species traits information was collated from <a href="http://www.biotik.org/">BIOTIK (http://www.biotik.org/</a>), <a href="http://www.flowersofindia.net/">Flowers of India (http://www.flowersofindia.net/)</a>, India Biodiversity Portal (http://indiabiodiversity.org/), <a href="https://doi.org/10.5061/dryad.234/1">Global wood density database (https://doi.org/10.5061/dryad.234/1)</a> and <a href="https://doi.org/10.1017/S0266467417000219">Osuri et al. (2014): https://doi.org/10.1017/S0266467417000219</a>. We also referred to the following previous studies that provided information on the successional status of rain-forest species in the Western Ghats (Chetana 2013, Pascal 1988, Raman et al. 2009, Sreejith 2005).</p> <p><strong>References:</strong><br>CHETANA, H. C. 2013. Assessing the ecological processes in abandoned tea plantations and its implication for ecological restoration in the Western Ghats, India. PhD thesis, Manipal University.<br>OSURI, A. M., KUMAR, V. S. & SANKARAN, M. 2014. Altered stand structure and tree allometry reduce carbon storage in evergreen forest fragments in India’s Western Ghats. <em>Forest Ecology and Management </em>329: 375–383.<br>PASCAL, J. P. 1988. <em>Wet evergreen forests of the Western Ghats of India: Ecology, structure, floristic composition and succession</em>. Institut Français de Pondichéry, Pondicherry.<br>RAMAN, T. R. S., MUDAPPA, D. & KAPOOR, V. 2009. Restoring rainforest fragments: survival of mixed-native species seedlings under contrasting site conditions in the Western Ghats, India. <em>Restoration Ecology</em> 17:137–147.<br>SREEJITH, K. A. 2005. Ecological and ecophysiological studies on the successional status of tree seedlings in tropical wet evergreen and semi-evergreen forests of Kerala. PhD thesis, Forest Research Institute, Dehradun.</p> <p><strong>Geographic Coverage:</strong><br>1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India<br>2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p> <p><strong>Temporal Coverage:</strong><br>1. Begins: 2003-03-01 (Year, Month, Day)<br>2. Ends: 2024-02-10 (Year, Month, Day)</p> <p>Besides the <strong>README.txt</strong> file, the dataset includes the following comma-delimited text (csv) file with the data in columns as explained below:</p> <p><strong>Anamalai_tree_traits_2024.csv</strong></p> <p><strong>spec_name_ORIG:</strong> Scientific name of the species used during the data collection<br><strong>genus:</strong> Genus of the taxon<br><strong>specificEpithet:</strong> Specific epithet of the taxon in the Latin binomial name<br><strong>Accept_name_WFO:</strong> Updated scientific name of the species as in Plants of the World Online (POWO, https://powo.science.kew.org/)<br><strong>Habit:</strong> life form of the species(tree/shrub/cane/palm)<br><strong>Distribution:</strong> Distribution of the species in the study area (Native/Endemic/Introduced)<br><strong>IUCN_status:</strong> IUCN status of the species (CR-Critically Endangered,DD-Data deficient,EN-Endangered,LC-Least Concern,NT-Near Threatened,VU-Vulnerable,NA-Unknown)<br><strong>Wden_final:</strong> Wood density value assigned for the species (g cm^-3); NA - not available; sourced from Global wood density database (https://doi.org/10.5061/dryad.234/1)<br><strong>wd_level:</strong> Level in which the wood density value belongs (Species - wood density value is from species level; genus - wood density value assigned is the genus level average value)<br><strong>fruit_type:</strong> Morphological type of fruit<br><strong>fleshy_dry:</strong> Whether fruit is a dry fruit or fleshy, with aril or other parts <br><strong>seed_size:</strong> Species seed size: L = Large (>3 cm); M = Medium (1-3 cm); S = Small (<1 cm)<br><strong>disperser:</strong> Categories indicating seed dispersal mode: Bird, mammal, bird and mammal (Mammal_bird), gravity, wind, or unknown<br><strong>habitat:</strong> Habitat affinity category: EG_edg - evergreen forest edge; EG_for - evergreen forest; Dec_for - deciduous forest; Int – Introduced species; Unknown – Unknown<br><strong>habt_new:</strong> Habitat affinity new category: Mature – mature forest; Secondary – secondary forest, NA - unknown/Introduced species<br><strong>ad_ht:</strong> Species maximum adult height (m)</p>
Tropical multi-datasource and multi-frequency temperature anomalies (TROPTEMP)
<p>Multi-frequency temperature anomalies in the tropical region computed from the datasets: CPC, ECCO2_JPL cube92, J-OFURO, MODIS-Aqua, University of Delaware and Windsat.</p>
Dataset - Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks
<p>This data is complementary to the paper by Leijnse et al. 2022 "Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks" <br> https://doi.org/10.5194/nhess-2021-181</p> <p>This data is made available in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE</p> <p>For questions about the data ask: tim.leijnse@deltares.nl</p> <p>For more information about the tool to generate the used synthetic tracks TCWiSE see: <a href="https://www.deltares.nl/en/software/tcwise/">https://www.deltares.nl/en/software/tcwise/</a></p> <p> </p>
Data from: Basin-scale biogeochemical and ecological impacts of islands in the tropical Pacific Ocean
<p><strong>Abstract</strong></p> <p>In the relatively unproductive waters of the tropical ocean, islands can enhance phytoplankton biomass and create hotspots of productivity and biodiversity that sustain upper trophic levels, including fish that are crucial to the survival of islands’ inhabit- ants. This phenomenon, termed the island mass effect 65 years ago, has been widely described. However, most studies focused on individual islands, and very few documented phytoplankton community composition. Consequently, basin-scale impacts on phytoplankton biomass, primary production and biodiversity remain largely unknown. Here we systematically identify enriched waters near islands from satellite chlorophyll concentrations (a proxy for phytoplankton biomass) to analyse the island mass effect for all tropical Pacific islands on a climatological basis. We find enrichments near 99% of islands, impacting 3% of the tropical Pacific Ocean. We quantify local and basin-scale increases in chlorophyll and primary production by contrasting island-enriched waters with nearby waters. We also reveal a significant impact on phytoplankton community structure and biodiversity that is identifiable in anomalies in the ocean colour signal. Our results suggest that, in addition to strong local bio- geochemical impacts, islands may have even stronger and farther-reaching ecological impacts.</p> <p> </p> <p><strong>Data set and method</strong></p> <p>For each island, an algorithm detected the Island Mass Effect (IME) from climatological satellite chlorophyll maps as a contour enclosing the island and surrounding high-chlorophyll waters, termed IME region. A reference (REF) region of the same size was detected alongside each IME region, enclosing nearby non-IME waters. The IME and REF regions were used to build the IME database described in Messié et al. (2022), that includes variables related to satellite chlorophyll, primary production, and PHYSAT phenoclass diversity metrics in IME and REF regions on a climatological basis.</p> <p>This data set includes 4 files:</p> <ul> <li>island_database.csv: information regarding the 664 islands and shallow reefs where the IME detection was applied</li> <li>IME_masks.nc: monthly climatological masks for the IME and REF regions for all islands,</li> <li>IME_database.nc: IME database as a function of island and climatological month (chlorophyll, primary production, and phenoclass-derived variables calculated within the IME and REF masks).</li> <li>PHYSAT_climatology.nc: climatological maps for each PHYSAT phenoclass, used to calculate phenoclass-derived variables in the IME database.</li> </ul> <p>See details regarding data sources and calculations in <a href="https://rdcu.be/cO4qr">Messié et al. (2022)</a>.</p>
Labels for Emergency Response Imagery from Hurricane Barry, Delta, Dorian, Florence, Ida, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon
<p>The csv files contain human-generated labels for Emergency Response Imagery collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. All authors contributed to labeling the imagery. All labeling was done with an open-source labeling tool (Rafique et al., 2020).</p> <p>All csv files provide the userID (the ID of the anonymous labeler), the NOAA flight, the NOAA image, and 6 labels — allWater (if the image was all water), devType (if the image had buildings/development), washoverType (if the image had washover deposits), dmgType (if the image showed damage to built environment), impactType (if the labeler could identify the coastal impact, using the Storm Impact Scale from Sallenger, 2000), and terrainType (the type of physical environment).</p> <p>Images labeled here correspond to multiple NOAA flights — all listed in the csv file for each jpeg image. These jpeg images can be downloaded directly from NOAA (https://storms.ngs.noaa.gov/) or using Moretz et al. (2020a, 2020b).</p> <p>There are three csv files:</p> <p>ReleaseData_10172022.csv has 10,237 labels for 4250 images. These labels were generated by coastal scientists. The csv also contains the Latitude and Longitude of the image center (from NOAA).</p> <p>ReleaseDataQuads.csv has 400 labels for 100 images. These labels were generated by coastal scientists. The images labeled in this set correspond to original NOAA images that have been split into quadrants. Splitting images was done with ImageMagick. The command used to split the images was:</p> <p>`magick mogrify -crop 2x2@ +repage -path ../quadrants *.jpg`</p> <p>The naming convention corresponds to the image quarter — the *-0.jpg is upper left, *-1.jpg is upper right, *-2.jpg is lower left, and *-3.jpg is the lower right.</p> <p>ReleaseDataNCE.csv has 400 labels for 100 images. These images were labeled by non-coastal scientists. Note that the 100 images were also labeled by coastal scientists — those labels can be found in ReleaseData_v3.csv.</p> <p>There is another companion dataset to this, with slightly different labels (Goldstein et al., 2020).</p> <p>A zip file of images is also provided for demonstration purposes (images.zip). These are resized copies made with imagemagick, with the longest dimension set at 2000 pixels ( `mogrify -resize 2000x2000`). For full size images, please download the jpegs directly from NOAA.</p>
Dataset for "Holocene hydroclimatic variability in the tropical Pacific explained by changing ENSO diversity."
<p>This repository contains the tropical Pacific sea surface temperature and global precipitation data from the CESM1 time slice experiments, which were used for the analysis presented in Karamperidou & DiNezio (2022), Nature Communications (https://www.nature.com/articles/s41467-022-34880-8)</p> <p> </p> <p>From Karamperidou & DiNezio (2022):</p> <p>“To assess the response of ENSO flavors to orbital forcing over the past 12,000 years (12ka), we use a suite of time-slice experiments in 3ka intervals with version 1 of the Community Earth System Model (CESM1). Each experiment is 400-600 years long and was run until the surface climate and oceanic processes controlling tropical climate, such as the depth of the thermocline in the equatorial Pacific or the Atlantic Meridional Overturning Circulation (AMOC), have reached equilibrium. All simulations exhibit minimal drift in global mean surface temperature (less than 0.05<sup>o</sup>C per century), tropical mean surface temperature (less than 0.04<sup>o</sup>C per century), the depth of the equatorial thermocline in the Pacific (less than 0.3m per century), and the strength of the AMOC (less than 0.25 Sv per century) during the periods used in the analyses. With the exception of the 12 ka BP interval which includes ice sheet changes and lower greenhouse gases, the primary forcing in the 0, 3, 6, and 9 ka BP intervals is changes in Earth's precession, and each simulation branched off its preceding one, starting from 0ka sequentially through the Holocene. The maximum TOA energetic imbalance does not exceed 0.45 Wm<sup>-2</sup>, which is much smaller than the imposed radiative forcing.”</p> <p> </p> <p> </p> <p>Karamperidou, C., DiNezio, P.N. Holocene hydroclimatic variability in the tropical Pacific explained by changing ENSO diversity. <em>Nat Commun</em> <strong>13</strong>, 7244 (2022). https://doi.org/10.1038/s41467-022-34880-8</p>
Natural Regeneration of Puerto Rican Tropical Dry Forest Sites with Limited Burn History, Guánica Forest, 2012
This dataset documents the natural regeneration of tropical dry forest sites with limited burn history in Guánica Forest, Puerto Rico, following fire disturbance. Featuring a 29-year chronosequence, the dataset encompasses both short-term (2–5 months) and long-term (2–29 years) recovery, alongside mature forest control sites (including forest sites containing native grass). Data collection was conducted between May and August 2012 and focuses on woody tree species only. The dataset includes tree census data from seven sites (chrono_census.csv), recording species identity, stem diameter, aboveground biomass destruction, and resprouting dynamics. Additional data include species mean trait values, such as relative bark thickness and specific leaf area, to examine relationships with post-fire resprouting (chrono_traits.csv), and a species abundance matrix for evaluating community composition shifts over time (chrono_ndmsmatrix.csv). Field data were collected from circular 100 m² plots randomly placed at each site, with all woody plants tagged and identified. Stem diameters (≥1 cm) were measured at breast height (DBH) or ground height (DGH) in new-burn sites. Tree mortality was assessed, and aboveground biomass loss (0–100%) was estimated in new-burn sites. Resprouting was also quantified in short-term regeneration sites in mid-August 2012. In long-term sites, tree height was measured for the five tallest individuals. The dataset is relevant for researchers investigating tropical dry forest dynamics, fire ecology, and regeneration processes in Caribbean forest ecosystems. The dataset is complete and not ongoing.
Life History Traits of Resprouting Puerto Rican Tropical Dry Forest Trees, Guánica Forest, 1981-2018
This dataset provides trait and demographic data for 44 tropical dry forest tree species from the Guánica State Forest in southwest Puerto Rico. The study area spans 4,500 ha of semi-deciduous TDF, where the sampled species represent over 90% of all individuals with a diameter at breast height (dbh) ≥2.5 cm. The dataset integrates ten functional traits, combining newly collected measurements (2017–2018) with previously published data (Vargas et al. 2021b). Previously published data includes xylem-specific hydraulic conductivity (ks), Huber value (hv), and hydraulic safety margin (HSM), with species-level data availability ranging from 19 to 44 species, except for HSM, which was measured for six species. Trait measurements were primarily collected during the wet season (August–November), except stomatal behaviour traits (psimax, psidv, and gsmax), which were assessed during the winter dry season before leaf fall. Demographic data encompass species-specific growth rates and annual survival rates for adult trees, derived from four permanent census plots (625 m² to 10,000 m²) distributed across the forest. These plots, established in mature upland TDF on limestone substrates with mollisol soils, were monitored between 1992 and 2019. Growth rate estimates are based on diameter increments recorded at regular censuses over 20.4–26.4 years. Survival rates were calculated over a 21-year period (1998–2019), mitigating the influence of extreme drought events. Standardised measurement protocols ensured data consistency, including repeated diameter assessments at multiple stem locations and the exclusion of wet-season measurements to prevent water-related swelling artifacts. Growth rates were derived from the regression slope of dbh against time, incorporating a minimum of two dbh measurements per individual (following Poorter et al. 2010). Annual survival rate was calculated over a 21-year timespan (1998–2019) to avoid bias introduced by an intense drought in 1997. The followin
Hydrochemical Data from a Tropical Andean Glacierized Catchment: δ18O, electrical conductivity, maximum fluorescence intensity, and dissolved organic carbon concentrations from short-term sampling campaigns, Ecuador (2022 and 2024)
Fluorescent dissolved organic matter (FDOM) quality, dissolved organic carbon (DOC) concentration, electrical conductivity (EC), and stable water isotopes (δ¹⁸O and δ2H) were determined in water, snow, and ice samples from a tropical glacierized catchment in the Ecuadorian Andes. The sampling locations were selected to capture the major hydrologic inputs to the main stream channel (glacial melt, tributaries, wetlands, and groundwater springs) and constrain the in-stream spatiotemporal variation in DOM quality and other hydrochemical characteristics. Two sets of high-resolution time series were collected on Oct 13, 2022 and Jun 14, 2024. Time series samples were collected at various upper catchment locations and the outlet simultaneously. DOM quality was characterized via fluorescence spectroscopy and processed using parallel factor analysis (PARAFAC). The DOM quality data are expressed as %FMax values obtained through a 4-component PARAFAC model, where %Fmax 1– 4 are interpreted as terrestrial humic-like, tyrosine-like, tryptophan-like, and microbial humic-like fluorescent components, respectively. DOC concentrations were quantified using high-temperature catalytic combustion, stable water isotopes were analyzed using laser-based spectroscopy, and EC was measured in situ with handheld multiparameter water quality probes.
Leslie Holdridge arboretum tree census, La Selva Research Station, Organization for Tropical Studies, Sarapiquí, Heredia, Costa Rica, 1972-2017.
This database is a collection of dendrometric and structural measurements for all the trees in the arboretum, it was compiled through the assessment of 10 census from 1972 to 2017 by O. Vargas and E. Castro for the Organization for Tropical Studies. The 3.5-hectare Holdridge Arboretum is located at La Selva Research Station. Leslie R. Holdridge, the original owner of the property, created the arboretum in 1968. Initially, it was a small cacao grove with an exceptionally rich overstory of native shade trees. To facilitate research in the arboretum, staff later removed the cacao. In 1970, Gary Hartshorn continued to plant seedlings of many native tree species. OTS continues to plant, tag, and measure trees. OTS maintains the arboretum by regular mowing and pruning to facilitate safe access. Courses, natural history visitors, students, and researchers use the arboretum for a wide range of observational studies, manipulations, dendrological practices, and taxonomy classes.
Tree Census Data of Tropical Dry Forest Succession in Permanent Plots at Palo Verde National Park, Costa Rica (1999–2004), Organization for Tropical Studies (OTS)
This data package contains tree census data from eight permanent forest plots established in 1999 across four successional sites within the tropical dry forest of Palo Verde National Park, Guanacaste, Costa Rica (10°21’N, 85°21’W). The plots were established to study forest structure, composition, and successional dynamics under different disturbance histories in the lowland dry forest ecosystem of northwestern Costa Rica. Each site represents a distinct successional stage, ranging from an early grass-dominated field (Jaragua) to an older partially disturbed remnant forest stand (Varillal). Two permanent 50×50 m plots were established at each site and subdivided into 10×10 m subplots. All woody stems with diameter at breast height (DBH) ≥ 10 cm were tagged, identified to species, and spatially referenced using X–Y coordinates within each plot. For multi-stemmed individuals, all stems meeting the diameter threshold were measured separately. Tree diameter, condition, and taxonomic identification were recorded during four measurement campaigns in 1999, 2001, 2002, and 2004. The dataset includes species identity, DBH, measurement year, individual condition, and subplot coordinates for each stem. These data provide a baseline for understanding forest regeneration, mortality, recruitment, and species composition changes in tropical dry forest succession under varying land-use histories. The dataset represents the historical component of an ongoing long-term monitoring program of forest succession conducted by the Organization for Tropical Studies at Palo Verde National Park, led, developed and supported by Eugenio González since its establishment in 1999.
Steady state carbon, nitrogen, phosphorus, and water budgets for twelve mature ecosystems ranging from prairie to forest and from the arctic to the tropics
We use the Multiple Element Limitation (MEL) model to examine the responses of twelve ecosystems - from the arctic to the tropics and from grasslands to forests - to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in annual precipitation. The ecosystems we simulated include moist acidic tundra, shrub tundra, and wet sedge tundra near Toolik Lake, Alaska, alpine dry meadow tundra near Niwot Ridge, Colorado, restored tallgrass prairie near Kellogg Biological Station, Michigan, native tallgrass prairie at the Konza Prairie, Kansas, upland and lowland boreal forest near Bonanza Creek, Alaska, temperate coniferous forest in HJ Andrews Experimental Forest, Oregon, a northern hardwood forest in Hubbard Brook Experimental Forest, New Hampshire, a transition oak-maple forest in Harvard Forest, Massachusetts, and lowland tropical rainforest near Caxiuanã National Forest, Pará, Brazil. For each of the twelve sites, we run six 100-year simulations beginning from the calibrated steady state (72 simulations total). The six simulations are: (1) increasing CO2 from 400 to 800 μmol mol-1, (2) warming from current temperatures to current plus 3.5oC, (3) decreasing precipitation from 100% to 80% of the current annual rate, (4) increasing precipitation from 100% to 120% of the current annual rate, (5) doubling of CO2, 3.5oC warming, and 20% decrease in precipitation, and (6) doubling of CO2, 3.5oC warming, and 20% increase in precipitation. The carbon, nitrogen, phosphorus, and water budgets presented here are used to calibrate the MEL model prior to running the climate change simulations. Citations and calculations for the data presented here are described in the individual site html files included in this dataset.
Ecosystem responses to changes in climate and carbon dioxide in twelve mature ecosystems ranging from prairie to forest and from the arctic to the tropics
We use the Multiple Element Limitation (MEL) model to examine the responses of twelve ecosystems - from the arctic to the tropics and from grasslands to forests - to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in annual precipitation. The ecosystems we simulated include moist acidic tundra, shrub tundra, and wet sedge tundra near Toolik Lake, Alaska, alpine dry meadow tundra near Niwot Ridge, Colorado, restored tallgrass prairie near Kellogg Biological Station, Michigan, native tallgrass prairie at the Konza Prairie, Kansas, upland and lowland boreal forest near Bonanza Creek, Alaska, temperate coniferous forest in HJ Andrews Experimental Forest, Oregon, a northern hardwood forest in Hubbard Brook Experimental Forest, New Hampshire, a transition oak-maple forest in Harvard Forest, Massachusetts, and lowland tropical rainforest near Caxiuanã National Forest, Pará, Brazil. For each of the twelve sites, we run six 100-year simulations beginning from the calibrated steady state (72 simulations total). The six simulations are: (1) increasing CO2 from 400 to 800 μmol mol-1, (2) warming from current temperatures to current plus 3.5oC, (3) decreasing precipitation from 100% to 80% of the current annual rate, (4) increasing precipitation from 100% to 120% of the current annual rate, (5) doubling of CO2, 3.5oC warming, and 20% decrease in precipitation, and (6) doubling of CO2, 3.5oC warming, and 20% increase in precipitation. This dataset consists of the MEL model Windows executable, the driver and parameter file for each site, and the output files for each of the six simulations listed above.
Derived Data supporting "On the Seasonal Cycles of Tropical Cyclone Potential Intensity" (Gilford et al. 2017, JoC)
<p>Derived monthly mean tropical cyclone potential intensities (and associated variables) using the Bister and Emanuel 2002 PI algorithm, ftp://texmex.mit.edu/pub/emanuel/TCMAX; from MERRA2 (averaged over 1980-2016) and ERA-I data (averaged over 1980-2013), on 2.5x2.5 degree grids and with the ERA-I land-sea mask already applied. This data supported the publication of Gilford et al. (2017, JoC). When using this data, please include the citation:</p> <p>Daniel M. Gilford, Susan Solomon, and Kerry Emanuel, 2017: On the Seasonal Cycles of Tropical Cyclone Potential Intensity. <em>J. Climate, </em><strong>30</strong>, 6085–6096. doi: <a href="http://journals.ametsoc.org/doi/10.1175/JCLI-D-16-0827.1">10.1175/JCLI-D-16-0827.1</a>.</p> <p> </p>
Historical Tropical Cyclone Along-track Potential Intensity (and Derived Quantities) for Six Ocean Basins from Reanalyses
<p>Supporting derived data for Shields et al. (2020, GRL).</p> <p>Derived tropical cyclone potential intensities and associated variables across the North Atlantic (NA), Eastern North Pacific (EP), North Indian (NI), South Indian (SI), South Pacific (SP), and Western North Pacific (WP) ocean basins, from MERRA2, ERA-I, and MERRA2 with SSTs replaced by HadISSTs. NA/WP basins also have potential and observed intensities calculated with NCEP/NCAR and ERA-20C reanalyses over 1950-2016 and 1950-2010, respectively.</p> <p>All files are netcdf format, organized by basin, with suffixes on data variables to indicate reanalysis:</p> <ul> <li>"_m": MERRA2 (Gelaro et al. 2017)</li> <li>"_h": MERRA2-HadISSTs (Rayner et al. 2003)</li> <li>"_e": ERA-I (Dee et al. 2011)</li> <li>"_n": NCEP/NCAR (Kalnay et al. 2016)</li> <li>"_c": ERA-20C (Stickler et al. 2014)</li> </ul> <p>When using this data, please include the citation:</p> <blockquote> <p><strong>Shannon Shields, Allison Wing, and Daniel M. Gilford, 2020: A Global Analysis of Interannual Variability of Potential and Actual Tropical Cyclone Intensities. Geophys. Res. Lett.</strong></p> </blockquote> <p>Potential intensities calculated with the Bister and Emanuel (2002) algorithm (<strong>pcmin.m</strong>) by Kerry Emanuel (revised by Daniel Gilford, Gilford et al. 2019), available freely at: ftp://texmex.mit.edu/pub/emanuel/TCMAX</p> <p>MERRA2, ERA-I, and MERRA2 with SSTs replaced by HadISSTs calculations were performed by Daniel Gilford; NCEP/NCAR and ERA-20C calculations were performed by Dr. Suzana Camargo (many thanks!).</p> <p>Please direct any questions or comments to daniel[dot]gilford[at]rutgers[dot]edu.</p>
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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.