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558 results for “dry forest”
Figure 1 in Three new Mexican species of the endemic Athysanini leafhopper genus Devolana DeLong (Hemiptera: Cicadellidae) from the tropical dry forest
Figure 1. Devolana tuxcacuensis Pinedo-Escatel and Aguilar-Pérez, sp. nov., holotype male: (a) habitus, dorsal; (b) habitus, face; (c) right forewing, dorsal; (d) habitus, lateral.
Figure 2 in Lichen diversity in colombian caribbean dry forest remnants
Figure 2. Examples of lichens found at the two localities and characteristic of DTF. a. Arthonia redingeri (42900a); b. Cresponea melanocheiloides (42970); c. Dirinaria confusa (42917); d. Helminthocarpon leprevostii (42976); e. Lecanora helva (42933a). f. Ocellularia bahiana (42980); g. Pyrenula ochraceoflavens (42945); h. Strigula smaragdula (42990). Thalli with ascomata, in h also with pycnidia. Scale = 1 mm.
Fig. 3 in The Cerambycid Fauna Of The Tropical Dry Forest Of ''El Aguacero,'' Chiapas, México (Coleoptera: Cerambycidae)
Fig. 3. Rankabundance pattern of the cerambycid species recorded in ''El Aguacero,'' Chiapas, México. The values used were only the data obtained during the year of regular sampling.
FIGURES 8–11 in A new species of Copa (Araneae: Corinnidae: Castianeirinae) from dry forests in the north west of Madagascar
FIGURES 8–11. Copa sakalava sp. nov. genitalia illustrations. 8-9 male pedipalp, 10-11 female epigyne. 8, 10 ventral, 9 retrolateral, 11 dorsal. CO = copulatory openings, CD = copulatory duct, Em = embolus, FD = fertilisation duct, H = epigynal hood, LC = Lateral chamber of copulatory duct, PcS = paracymbial spine, SD = sperm duct, St = subtegulum, ST I & ST II = spermathecae I and II. Scale bar for 10 & 11 = 0.25mm.
Distribution. NE & E Brazil, from Rio Grande do Norte S to N Minas Gerais, apparently mostly restricted to Caatinga Tropical Dry Forest ecoregion. in Phyllostomidae
Distribution. NE & E Brazil, from Rio Grande do Norte S to N Minas Gerais, apparently mostly restricted to Caatinga Tropical Dry Forest ecoregion.
FIGURE 2 in Stink bugs (Hemiptera, Heteroptera, Pentatomidae) of the Catimbau National Park, a protected area in Brazil's largest dry forest
FIGURE 2. Landscapes of Catimbau National Park, Pernambuco, during rainy (A–D) and dry (E–I) seasons.
FIGURE 2. Aphelandra almanegra. A. Flowering branch B. Terminal inflorescences showing spikes with fruits and flowers C. Terminal inflorescence with multiple spikes D in Aphelandra almanegra (Acanthaceae), a new species from the dry forests of the Cauca River canyon in Antioquia department, Colombia
FIGURE 2. Aphelandra almanegra. A. Flowering branch B. Terminal inflorescences showing spikes with fruits and flowers C. Terminal inflorescence with multiple spikes D. Close-up view of a flower spike, showing the position of the corolla lobes E. Cross section of an apical branch showing black heartwood F. Much-branched shrubby habit. (Photos of fresh specimens of P. Gallego et al. 1284-HUA).
Fig. 2 in Diversity of Beetles (Coleoptera) in an Inter-Andean Dry Tropical Forest in Ecuador
Fig. 2. Photographs of Bosque Protector Jerusalém (BPJ) and of the beetle sampling methods used during the survey. A) Typical forest structure and plant composition of BPJ during the wet season (photo taken during this survey), B) For comparison, typical forest structure and plant composition of BPJ during the dry season (photo taken September 2019), C) Sampling team showing the beating sheets and insect nets used to collect specimens from low vegetation, D) Night sampling at Site 6 using a white light trap. Photo credits: GNDM and GMRC.
FIGURE 2 in A new species of Akodon Meyen, 1833 (Rodentia: Cricetidae) from dry forests of the Amazonia-Cerrado transition
FIGURE 2. Results of multivariate analyses performed on log-transformed 20 craniometrical variables of adult males (triangles, n=29 and n=67) and females (dots, n=39 and n=58) of Akodon n. sp. (blue) and A. cursor (black). Top: distribution of the factorial scores in the first (PC1) and second (PC2) principal component analysis (PCA). Bottom: distribution of the factorial scores in the first (DF1) and second (DF2) canonical variates of the discriminant function analysis (DFA).
Figure 1 in Use of remote cameras to evaluate ocelot (Leopardus pardalis) population parameters in seasonal tropical dry forests of central-western Mexico
Figure 1: Potential distribution of ocelot and location of study site a in western Mexico. (A) Location of study sites (star) within the potential ocelot distribution (gray area) and of studies using camera-traps and capture-recapture models included in our review (see, Table 2). (B) Location of the two sampling sites in the central-western of Mexico (black star = El Naranjal and white star = Playa del Venado). Urban areas are shown in black and protected natural areas are indicated with a black grid.
F I G U R E 2 in The nutritional importance of invertebrates to female Cebus capucinus imitator in a highly seasonal tropical dry forest
F I G U R E 2 The seasonality of energy intake by capuchins from the four most important invertebrate orders: (a) Lepidoptera, (b) Orthoptera, (c) Hemiptera, and (d) Hymenoptera. Axes represent the number of focal follows for that month (from 120 randomly selected focal follows) during which consumption of that order was observed. The arrow represents the mean vector of the analysis, which is the midpoint of the seasonal effect. Consumption of invertebrates in all four orders was significantly seasonal (p <.001). Although data are presented to reflect a calendar year, data were collected in three separate periods between 2009 and 2011
Fig. 1 in Longhorned Beetles (Coleoptera: Cerambycidae and Disteniidae) Collected in the Canopy of a Dry Tropical Lowland Forest in Panama
Fig. 1. Number of Cerambycidae and Disteniidae individuals collected with Malaise traps in nine tree species in Metropolitan Natural Park. The species column with less than or eQual to 10 individuals corresponds to data from 25 species. No specimens were collected in the trap placed in Cecropia peltata; this tree species is therefore not represented in the graph.
Figure 2 in Three new Mexican species of the endemic Athysanini leafhopper genus Devolana DeLong (Hemiptera: Cicadellidae) from the tropical dry forest
Figure 2. Devolana tuxcacuensis Pinedo-Escatel and Aguilar-Pérez, sp. nov.
Figure 1 in Lichen diversity in colombian caribbean dry forest remnants
Figure 1. Geographic location of the study areas.
Ecological and morphological traits determine community-wide responses of birds to climate change in a tropical dry forest
<p><strong><span>Description</span></strong></p> <p><span>Raw data and species distribution maps for conducting work on bird community changes caused by climate change in the largest block of tropical dry forests in South America. In addition to the R scripts for the climate modeling analyses and the subsequent analyses in the work. </span></p> <p> </p> <p><strong><span>File contents</span></strong></p> <p><span>Centoids.zip: Data with the centroids of the distributions of each species in the current scenario and the six future climate scenarios.</span></p> <p><span>Climate_valeus_scenrios.zip: Climate variable values for all the cells in the Caatinga grid. </span></p> <p><span>Correlation_species_variable.zip: Correlation for selecting the climate variables used for each species. </span></p> <p><span>Dataset_traits.csv: Species traits used in the analyses. </span></p> <p><span>Maps_species_distributions.zip: Distribution maps for all species, in the current climate scenario and the six future scenarios.</span></p> <p><span>occurrence_birds.zip: Occurrence data used to build the models for each species<br><br>PGLS.html: PGLS analysis correlating the percentage of change in the distribution area of </span><span>each species and species traits.</span></p> <p><span>Species_tree.zip: Species phylogeny built from BirdTree and used as input in PGLS</span></p> <p><span>Species_variables.csv: Individual variables used to build climate models for each species</span></p>
Data from "Lithological substrates influence tropical dry forest structure, diversity, and composition, but not its dynamics"
<p>Datasets and script of the manuscript “Lithological substrates influence tropical dry forest structure, diversity, and composition, but not its dynamics” authored by R. Muñoz*, M. Enríquez, F. Bongers, R.D. López-Mendoza, C. Miguel-Talonia & J.A. Meave*, published in Frontiers in Forests and Global Change (2023).</p> <p>* Correspondence: R. Muñoz (rod.munozaviles@gmail.com) & J.A. Meave (jorge.meave@ciencias.unam.mx)</p> <p>The original publication can be found in https://doi.org/10.3389/ffgc.2023.1082207</p> <p> </p> <p><strong>TERMS OF USE FOR THE CURRENT DATASETS AND SCRIPTS</strong></p> <p>All data and scripts associated with the current publication are intended ONLY for the reproduction and validation of the analyses conducted in the manuscript cited above. Use of this data for other purposes (for example, other publications or meta-analyses) is strictly forbidden without prior consent from the corresponding authors (R. Muñoz and/or J.A. Meave, contact details above).</p> <p> </p> <p><strong>FOLDER STRUCTURE</strong></p> <p>The ZIP folder is structured in the following manner:</p> <p>– Munoz et al 2023 Frontiers.zip</p> <p> – READ ME.txt</p> <p> – Script Munoz et al 2023 Frontiers.R</p> <p> – Data source</p> <p> – Dataset Munoz et al 2023 Frontiers stand data.csv</p> <p> – Dataset Munoz et al 2023 Frontiers species matrix.csv</p> <p> – Dataset Munoz et al 2023 Frontiers ONI.csv</p> <p> – Dataset Munoz et al 2023 Frontiers ENSO events.csv</p> <p> </p> <p><strong>DESCRIPTION OF SCRIPT</strong></p> <p>The script provided in the root of the ZIP folder (Script Munoz et al 2023 Frontiers.R) allows to reproduce the analyses, figures and tables supporting the original publication in Frontiers. When executed in full, the script generates a new folder named “Figures” where all figures are stored in their raw, unedited version. The figures for publication were later edited in Adobe Illustrator to enhance their visual appearance.</p> <p> </p> <p><strong>DESCRIPTION OF DATASETS</strong></p> <p>Four datasets are provided in this ZIP file (“Data source” folder):</p> <p>1. Dataset Munoz et al 2023 Frontiers stand data.csv (<em>Stand data</em>)</p> <p>2. Dataset Munoz et al 2023 Frontiers species matrix.csv (<em>Species matrix</em>)</p> <p>3. Dataset Munoz et al 2023 Frontiers ONI.csv (<em>ONI</em>)</p> <p>4. Dataset Munoz et al 2023 Frontiers ENSO events.csv (<em>ENSO events</em>)</p> <p> </p> <p><em>STAND DATA </em>contains information about the seven forest attributes included in the study, per substrate and year. It contains the following variables:</p> <ol> <li>Year: Year of measurement</li> <li>Plot: Plot code</li> <li>Set: Can only be “MatCan” (Mature Canopy)</li> <li>Subset: Either “Lim” (limestone) or “Phy" (phyllite)</li> <li>Dynamics: Whether there is a previous measurement allowing the estimation of dynamic rates (e.g., net change; FALSE/TRUE) </li> <li>Basal: Basal area expressed in m2/ha</li> <li>DeltaBasal: Annual net change in basal area</li> <li>R.basal: Annual change in basal area due to recruitment</li> <li>G.basal: Annual change in basal area due to growth</li> <li>M.basal: Annual change in basal area due to mortality</li> <li>AGB: Aboveground biomass expressed in Mg/ha, estimated from the allometric equation of Chave et al. 2014 (including DBH, height and WD)</li> <li>DeltaAGB: Annual net change in AGB</li> <li>R.agb: Annual change in AGB due to recruitment</li> <li>G.agb: Annual change in AGB due to growth</li> <li>M.agb: Annual change in AGB due to mortality</li> <li>Dens: Tree density expressed in individuals/ha</li> <li>DeltaDens: Annual net change in tree density</li> <li>R.dens: Annual change in tree density due to recruitment</li> <li>G.dens: Annual change in tree density due to “growth”. Here, “growth” is a term introduced to account for small differences in tree densities between years due to changes in the extrapolation factor of a tree. Due to the nested sampling design of the vegetation survey, sometimes trees change their extrapolation factor as they grow larger. Thus, is a tree changes extrapolation factor, those differences (that are neither recruitment or mortality) are added up here.</li> <li>M.dens: Annual change in tree density due to mortality</li> <li>Species: Species richness expressed in spp/plot. Redundant with “q0” column.</li> <li>DeltaSpecies: Annual net change in species richness</li> <li>R.species: Annual change in species richness due to recruitment</li> <li>M.species: Annual change in species richness due to mortality</li> <li>Height: Average plot canopy height expressed in m</li> <li>q0: Hill number of order 0 expressed in species effective number (species richness)</li> <li>q1: Hill number of order 1 expressed in species effective number (typical species)</li> <li>q2: Hill number of order 2 expressed in species effective number (dominant species)</li> </ol> <p> </p> <p><em>SPECIES MATRIX</em> contains an abundance matrix per species, plot and year. It contains the following variables:</p> <ol> <li>PlotYear: This column actually does not have a name to it in the file, but is the first column in the dataset, It contains the three-character identifier for the plot and the four numbers of the year of measurement. For instance, “BER2008” would represent the observations made for the plot BER in 2008.</li> <li>treat: This indicates whether the plot is located on limestone (1) or phyllite (2) substrate</li> <li>sp001-sp127: indicates the abundance (in number of individuals per plot) of a given species. Species numbers were assigned randomly, thus they do not match the order of the table provided in Supplementary Material 3 of the publication in Frontiers.</li> </ol> <p> </p> <p><em>ONI</em> contains the Oceanic El Niño Index values per month and year. It is a “year by month” contingency matrix, where years are presented in the rows name, and months are presented in the columns name. ONI values are given in Celsius degrees, and they represent the 3-month rolling average of the temperature anomaly in the Nino3.4 region. The data source and details of this dataset can be found at the NOAA webpage (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php).</p> <p> </p> <p><em>ENSO EVENTS</em> contains the occurrence of events of El Niño (warm and dry episodes) and La Niña (cold and wet episodes). It contains the following variables:</p> <ol> <li>Year: Year</li> <li>Month: Month</li> <li>ONI: Oceanic El Niño Index (see ONI dataset description above)</li> <li>Year.cont: Time as a continuous variable (instead of having years and months separately, for plotting)</li> <li>Nino: El Niño (warm and dry) episode occurrence (“1” indicates occurrence)</li> <li>Nina: La Niña (cold and wet) episode occurrence (“1” indicates occurrence)</li> </ol>
Fig. 2 in Longhorned Beetles (Coleoptera: Cerambycidae and Disteniidae) Collected in the Canopy of a Dry Tropical Lowland Forest in Panama
Fig. 2. Number of Cerambycidae and Disteniidae specimens collected over 60 weeks in the MNP canopy.
Dataset used for "Recruitment credit cannot compensate for extinction debt in a degraded dry Afromontane forest, northern Ethiopia"
<p>The data is part of a large dataset collected by WeForest Ethiopia, a nonprofit organisation engaged in restoring degraded forests in different parts of Ethiopia. This is particularly data from Desa'a forest, a dry Afromontane forest in Tigray. This data presents the identity and number of mature woody plant species (individuals with >1.5 height), their DBH (diameter at breast height, 1.3 m) or DSH and height (diameter at stump height, 0.3 m) measured at 400 m<sup>2</sup> and, the identity and number of regeneration of woody plants, height < 1.5 m, measured in 9m<sup>2</sup> nested within the 400 m<sup>2</sup> plot.</p> <p>These data were used in a manuscript entitled "Recruitment credit cannot compensate for extinction debt in a degraded dry Afromontane forest, northern Ethiopia", submitted to the Journal of Vegetation Sciences and accepted for publication.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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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.