Find research datasets worth reusing

opencc-by-4.0Jan 2022View details →

Model outputs from the study "A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce region"

Model outputs from the study "A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce region". The study is published as open access and can be found at the following link: <a href="https://www.sciencedirect.com/science/article/pii/S2950289625000326">https://www.sciencedirect.com/science/article/pii/S2950289625000326</a>   The file "SWAT_USERSOIL.csv" was included to facilitate the assimilation of the soil mapping data into the Soil & Water Assessment Tool (SWAT, https://swat.tamu.edu/) for hydrological modeling.    Regarding the raster files, please note: a) All values in these datasets have been multiplied by 10,000 to optimize file sizes. b) Files are named using the variable acronym, followed by the corresponding soil layer. For outputs derived from pedotransfer functions (PTFs), the PTF reference is appended after the variable acronym. c) Available data decrease with increasing soil layer number. This occurs because not all locations (grid cells) have the same soil depth or number of soil layers.   If you have any questions about the dataset or its use, please don't hesitate to contact us.

opencc-by-sa-4.0Feb 2018View details →

Appendix S3 from: Droissart V, Dauby G, Hardy OJ, Deblauwe V, Harris DJ, Janssens S, Mackinder BA, Blach-Overgaard A, Sonké B, Sosef MSM, Stévart T, Svenning J-C, Wieringa JJ, Couvreur TLP (2018) Beyond trees: biogeographical regionalization of tropical Africa. Journal of Biogeography. DOI:10.1111/jbi.13190

This dataset corresponds to GIS file that were generated in the study published by Droissart, Dauby et al. in Journal of Biogeography: Droissart V, Dauby G, Hardy OJ, Deblauwe V, Harris DJ, Janssens S, Mackinder BA, Blach-Overgaard A, Sonké B, Sosef MSM, Stévart T, Svenning J-C, Wieringa JJ, Couvreur TLP (2018) Beyond trees: biogeographical regionalization of tropical Africa. Journal of Biogeography. DOI:10.1111/jbi.13190 Please cite the aforementioned article and the dataset herein, when using of any of these files in this dataset.   The GIS file is referred in the paper as Appendix S3 and correspond to the map presented in Figure 1. Each polygons of the shapefile correspond to the main floristic bioregions and transition zones of tropical Africa delimited using bipartite network clustering analysis of 24,719 plant species. The coordinate system of the ESRI shapefile is GCS_WGS_1984. Field descriptions for the associate table are: <ul> <li>bionames: name of the bioregions as given in Table S1.1.</li> <li>bioreg_ID: identifier of the bioregions as given in Table S1.1 and Fig. 1. T= Transition zones</li> <li>cluster_ID: identifier of clusters delimited using bipartite network clustering on the 24,719 plant species of the RAINBIO database, as given in Table S1.1 and Fig. S2.1.</li> </ul>

opencc-by-4.0Jan 2019View details →

Tri-hourly dataset of wind and wave anomalies of the GFS and WAVEWATCH III models in the entire tropic region (TROPWA).

This dataset contains the anomalies of the total height and peak period of the waves and of the zonal and meridional components of the wind at 10 m above the sea surface obtained from the outputs of the GFS and WAVEWATCH III coupled global models in the entire tropical region (180°W to 178.75°E longitude/30°S to 30°N latitude), with a spatial resolution of 1.25°x1°. It is made up of two files in NetCDF format, where the data for wind anomalies (speed module and its zonal and meridional components) and wave anomalies (total wave height and peak period) are contained separately. This dataset was created in order to study all types of tropical storms, cold fronts and other physical processes that influence significant changes in wave parameters, as well as for the design and construction of coastal engineering works. The authors thanks to Tropical Atlantic Interdisciplinary Laboratory on physical, biogeochemical, ecological and human dynamics (IJL TAPIOCA).

opencc-by-4.0Jul 2023View details →

Recent Increases in Tropical Cyclone Rapid Intensification Events in Global Offshore Regions

The data and scripts in this repository can be used to support the main conclusion in the manuscript "Recent Increases in Tropical Cyclone Rapid Intensification Events in Global Offshore Regions" by Li et al., submitted to Nature Communications. The global distribution and annual variability of rapid intensification (RI) events of tropical cyclones (TCs) dervied from the open-source International Best Track Archive for Climate Stewardship (IBTrACS, https://www.ncei.noaa.gov/products/international-best-track-archive) are provided. The enviromental variables, including mid-level (600 hPa), vertical wind shear (200-850 hPa), and maximum potential intensity (MPI), were also calculated using the fifth generation of ECMWF reanalysis (ERA5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) forced in different scenarios. The python script (coastal_RI_submit ipynb) can be used to reproduce figures in the article.

opencc-zeroDec 2004View details →

FIGURES 1 – 4 in tropical Region

FIGURES 1 – 4. Neuzina diminuta. Male. (1) Habitus; (2). Head, frontodorsal view; (3) Head, frontal view; (4) Wing.

opencc-by-4.0Aug 2022View details →

Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)

Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest.

opencc-by-4.0Apr 2024View details →

Triple oxygen isotope variability of precipitation in a tropical mountainous region

Triple oxygen isotope data associated with monthly integrated precipitation and discrete (subsurface-sourced) tap water samples collected over one year from sites across Panama.

Fig 1 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions

Fig 1: Relative gene expression in tambaqui juveniles farmed in two Brazilian regions: Northern (Balbina; BA) and Southeast (Brumado; BRU). Different letters represent statistical differences between populations. The graphs show expression of A) hif-1α (p = 0.137), B) hsp-70 (p = 0.465), C) mstn (p = 0.907), D) ube3a (p = 0.205), E) ras (p = 0.041), F) cry-1 (p = 0.001), G) per-1 (p = 0.001), H) ogt (p = 0.001) and I) acly (p = 0.025).

Fig 3 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions

Fig 3: IBR analyses of relative gene expression in Balbina (BA) and Brumado (BRU) populations. The IBR values are 42.7 (Balbina) and 6.79 (Brumado).

Fig 2 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions

Fig 2: Heatmap of relative expression in Balbina (BA) and Brumado (BRU) populations. The colour scale ranges from blue (low transcript levels) to red (high transcript levels).

opencc-by-4.0Sep 2016View details →

Fig.1 in Parasites of two coexisting invasive sailfin catfishes (Siluriformes: Loricariidae) in a tropical region of Mexico

Fig.1. Intra-annual variations in: a) prevalence, b) intensity of infection and c) abundance of parasites in the invasive loricarids Pterygoplichthys disjunctivus and P. pardalis from a freshwater ecosystem of southeastern Mexico.

opencc-by-4.0Feb 2023View details →

Airglow Imaging Observations of Plasma Blobs: Merging and Bifurcation During Solar Minimum over Tropical Region

This is the dataset for the plasma blobs merging (26/10/2019) and bifurcations (01/03/2019) used in our research. It is the OI 630nm wavelength.

opencc-zeroAug 2020View details →

Species Abundance Distributions (SADs) for local tree communities in 1-ha forest plots on 20 tropical islands in the Indo-Pacific region

Species abundance distributions (SADs) characterise the distribution of individuals among species. This dataset was used to investigate the relative importance of disturbance regime (tropical cyclone regime) and island geography (the area and isolation of islands) on the shape of SADs.

opencc-by-4.0Feb 2021View details →

Code and Data Availability of the manuscript: Mapping tree diversity in the tropical forest region of Chocó-Colombia

Code and Data Availability of the manuscript: Mapping tree diversity in the tropical forest region of Chocó-Colombia In revision at Environmental Research letters.   J. Camilo Fagua1*, Patrick Jantz1, Patrick Burns1, Richard Massey1, Jeiner Y. Buitrago2, Sassan Saatchi3 and Scott J. Goetz1   1 Global Earth Observation & Dynamics of Ecosystems Lab (GEODE), School of Informatics, Computing, and Cyber Systems (SICCS), Northern Arizona University, Flagstaff, Arizona, US.   2 Department of Agronomy, Faculty of Agricultural Sciences, Universidad Nacional de Colombia. Bogota (Colombia).   3 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA   *Corresponding author: J. Camilo Fagua, School of Informatics, Computing, and Cyber Systems (Building #90) 1295 S. Knoles Dr. Flagstaff, AZ 86011. Email: <a href="mailto:Camilo.Fagua@nau.edu">Camilo.Fagua@nau.edu</a>   1) The files “0_Forest_Structure_maps_in_tif” and “1_Forest_Structure_maps_in_tif” contain seven maps of forest structural metrics for the Chocó region of Colombia in the raster format .tif (a) Canopy height (CH) map: CHOCO_MAP_CH (b) 50th percentile of heights (z_50) map: CHOCO_MAP_z_50 (c) Vertical Canopy Heterogeneity (VCH) map:  CHOCO_MAP_VCH (d) Standard deviation of normalized heights (z_SD) map: CHOCO_MAP_z_SD (e) Horizontal standard deviation of canopy height (CH_ SD):  CHOCO_MAP_CH_SD (f) Horizontal heterogeneity of canopy height (CH_H): CHOCO_MAP_CH_H (g) Rumple Index of canopy height (R_INDEX): CHOCO_MAP_R_INDEX   2) The file “2_Diversity_maps_in_tif” contains five maps of forest structural metrics for the Chocó region of Colombia in the raster format .tif (a) Species richness (Richness): MAPS_MEAN_Richness (b) Shannon’s entropy index (ENS-Shannon): MAPS_MEAN_ENS_shannon (c) Simpson’s concentration index (ENS-Simpson): MAPS_MEAN_ENS_Simpson (d) Shannon diversity index (H): MAPS_MEAN_Shannon (e) Simpson diversity index (D): MAPS_MEAN_Simpson   3) The file “3_LiDAR_processing” contents data and R codes to estimate forest structural metrics from ALS-LiDAR data (.las) in an Amazon moist forest.

opencc-zeroDec 2017View details →

Data from: Divergent rates of change between tree cover types in a tropical pastoral region

Context: Forest cover change analyses have revealed net forest gain in many tropical regions. While most analyses have focused solely on forest cover, trees outside forests are vital components of landscape integrity. Quantifying regional-scale patterns of tree cover change, including non-forest trees, could benefit forest and landscape restoration (FLR) efforts. Objectives: We analyzed tree cover change in Southwestern Panama to quantify: 1) patterns of change from 1998-2014, 2) differences in rates of change between forest and non-forest classes, and 3) the relative importance of social-ecological predictors of tree cover change between classes. Methods: We digitized tree cover classes, including dispersed trees, live fences, riparian forest, and forest, in very high resolution images from 1998-2014. We then applied hurdle models to relate social-ecological predictors to the probability and amount of tree cover gain. Results: All tree cover classes increased in extent, but gains were highly variable between classes. Non-forest tree cover accounted for 21% of tree cover gains, while riparian trees constituted 31% of forest cover gains. Drivers of tree cover change varied widely between classes, with opposite impacts of some social-ecological predictors on non-forest and forest cover. Conclusions: We demonstrate that key drivers of forest cover change, including topography, road distance and historical forest cover, do not explain rates of non-forest tree cover change. Consequently, predictions from medium-resolution forest cover change analyses may not apply to finer-scale patterns of tree cover. We highlight the opportunity for FLR projects to target tree cover classes adapted to local social and ecological conditions.

opencc-zeroDec 2021View details →

Data from: Forest loss and treeless matrices cause the functional impoverishment of sapling communities in old-growth forest patches across tropical regions

Landscape-level disturbances, such as forest loss, can profoundly alter the functional composition and diversity of biotic assemblages. In fact, the landscape-moderated functional trait selection (LMFTS) hypothesis states that landscape-level disturbances may act as environmental filters that select a set of species with disturbance-adapted attributes while causing the loss of species with disturbance-sensitive attributes, ultimately compromising ecosystem functioning. However, the impact of landscape patterns on the functional composition and diversity of tropical regenerating trees (saplings) is unknown. Using a multiscale approach to identify the best spatial scale (i.e. the scale of effect), we tested the effect of forest cover, matrix openness and forest patch density (fragmentation) on functional composition and functional diversity of tree saplings in old-growth forest patches (n = 59) in three Mexican rainforest regions with different degree of deforestation. For 368 species and ~23,000 individuals, we compiled information from global and national databases on six functional traits related to seed dispersal and plant establishment and calculated their community abundance-weighted mean (CWMs) and three complementary functional diversity indices. Forest loss and matrix openness reduced functional richness and evenness, but only in the two most deforested regions. Overall, fragmentation had contrasting effects on functional diversity and composition, but correlated negatively with some functional traits in the most deforested region. Importantly, in the regions with high-to-intermediate degree of deforestation, functional composition experienced major changes: maximum height, seed mass, fruit size and wood density decreased, and SLA increased, in forest patches surrounded by open matrices in highly deforested and fragmented landscapes. This caused a shift of community traits towards more disturbed-adapted attributes. Synthesis and applications. In agreement with the LMFTS hypothesis, our results confirm that landscape modifications in regions undergoing high and long-lasting deforestation greatly impoverish the functional composition and diversity of sapling communities. The shift from communities composed mainly by conservative attributes towards communities with a higher prevalence of disturbance-adapted attributes disrupts the future community structure and jeopardizes critical ecosystem functions. Management practices focused on preventing deforestation, increasing forest cover, and promoting treed matrices are necessary to preserve the functionality of these species-rich but increasingly threatened rainforests.

opencc-by-nd-4.0Apr 2018View details →

Figure 2 in Athamas whitmeei (Araneae: Salticidae: Euophryini) from islands of the tropical Western Pacific Region

Figure 2. Adult male Athamas whitmeei from Tanna Island, Tafea Province, Vanuatu (19.53°S, 169.26°E), 25 FEB 2011. Note the high carapace (3) and position of the large ALE above the AME (5, detail from 4). Photographs © Felix Fleck, subject to a Creative Commons Attribution Noncommercial 4.0 International license (CC BY-NC 4.0).

opencc-by-nd-4.0Apr 2018View details →

Figure 1 in Athamas whitmeei (Araneae: Salticidae: Euophryini) from islands of the tropical Western Pacific Region

Figure 1. Distribution of Athamas species in the tropical western Pacific. References for each locality shown here are listed in Appendix 1. A. univittata was synonymized with A. whitmeei by Benton & Lehtinen (1995). The relief map in the background is adapted from the NOAA ETOPO1 1-arc minute global relief model.

opencc-zeroMay 2024View details →

Raw data: Temperature and water availability drive insect seasonality across a temperate and a tropical region

The more insects there are, the more food there is for insectivores and the higher the likelihood of insect-associated ecosystem services. Yet, we lack insights into the drivers of insect biomass over space and seasons, both for tropical and temperate zones. We used 245 Malaise traps, managed by 191 volunteers and park guards, to characterise year-round flying insect biomass in a temperate (Sweden) and a tropical (Madagascar) country. Surprisingly, we found that local insect biomass was similar across zones. In Sweden, local insect biomass increased with accumulated heat and varied across habitats, while biomass in Madagascar was unrelated to the environmental predictors measured. Drivers behind seasonality partly converged: In both countries, the seasonality of insect biomass differed between warmer and colder sites and wetter and drier sites. In Sweden, short-term deviations from expected season-specific biomass were explained by week-to-week fluctuations in accumulated heat, rainfall, and soil moisture, whereas in Madagascar, weeks with higher soil moisture had higher insect biomass. Overall, our study identifies key drivers of the seasonal distribution of flying insect biomass in a temperate and tropical climate. This knowledge is key to understanding the spatial and seasonal availability of insects — as well as predicting future scenarios of insect biomass change.

Table 1 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions

Table 1: Details of target genes (hif-1α, hsp70, ras, mstn, acly, per-1, cry-1, ube3a and ogt) and reference genes (β- tubulin and β- actin) primers.<table><tbody><tr><th>Gene</th><th>Length (bp)</th><th>R 2</th><th>Efficiency (%)</th><th>Primers sequence (5ʹ-3ʹ) forward/reverse</th></tr></tbody><tbody><tr><th>tubulin -F</th><td>20</td><td>0.99</td><td>109.5</td><td>GACGTGGTGCCCAAAGATGT</td></tr><tr><th>tubulin -R</th><td>18</td><td>TGGATGGTGCGCTTGGT</td></tr><tr><th>β- actin -F</th><td>21</td><td>0.99</td><td>100.5</td><td>GCTGTTTTCCCCTCCATTGTT</td></tr><tr><th>β- actin -R</th><td>19</td><td>TCCCATGCCAACCATCACT</td></tr><tr><th>hif-1α -F</th><td>20</td><td>0.99</td><td>105.2</td><td>CTTCTGAGCTCTGATGAGGC</td></tr><tr><th>hif-1α -R</th><td>20</td><td>GAAAGCACCATCAGGAAGCC</td></tr><tr><th>hsp-70 -F</th><td>20</td><td>0.99</td><td>100.9</td><td>GCAAGGAGAACAAGATCACC</td></tr><tr><th>hsp-70 -R</th><td>19</td><td>CACTCCGTTGCACTTGTCC</td></tr><tr><th>mstn -F</th><td>20</td><td>0.98</td><td>100.5</td><td>AATCCAAGCGAGGGAAAAGC</td></tr><tr><th>mstn -R</th><td>22</td><td>CCTCCATCACCTGAAAGGTCTT</td></tr><tr><th>ras -F</th><td>20</td><td>0.97</td><td>99.31</td><td>CCAGTACATGAGGACAGGAG</td></tr><tr><th>ras -R</th><td>20</td><td>CAAGCACCATTGGCACATCG</td></tr><tr><th>acly -F</th><td>19</td><td>0.99</td><td>100.7</td><td>ATCATCTCCCGCACTACAG</td></tr><tr><th>acly -R</th><td>19</td><td>TACCTCCAATCTCTCCCAG</td></tr><tr><th>ube3a -F</th><td>21</td><td>0.98</td><td>103.3</td><td>GCCATAAGCAAGCAGCACAAC</td></tr><tr><th>ube3a -R</th><td>19</td><td>CCAGTCAGTCCGCACATCG</td></tr><tr><th>per-1 -F</th><td>20</td><td>0.98</td><td>104.1</td><td>TGTTGAAGTTTGTGCCCCAG</td></tr><tr><th>per-1 -R</th><td>18</td><td>CAGTCCAGATGCTCCTCC</td></tr><tr><th>cry-1 -F</th><td>19</td><td>0.99</td><td>103.6</td><td>GTCCAACAGCCCTCAAACT</td></tr><tr><th>cry-1 -R</th><td>18</td><td>TACGCCAAGCACTCCAGA</td></tr><tr><th>ogt -F</th><td>19</td><td>0.99</td><td>104.1</td><td>CCTCCCTTTGCTGTGTTCC</td></tr><tr><th>ogt -R</th><td>20</td><td>TGTCTGCTTTCCGCTTTCGC</td></tr></tbody></table>