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1,271 results for “tropical forest”
FIGURE 5 in A new species of Scinax Wagler (Hylidae: Scinaxini) from the tropical forests of Northeastern Brazil
FIGURE 5. Holotype of Scinax tropicalia sp. nov. (MZUESC 20440). Individual in life (A) during daylight period and (B) hidden in a tree bark hole in calling activity at night. Freshly euthanized individual showing the color pattern in (C) dorsolateral, (D) ventral, and (E) ventrolateral views.
FIGURE 14 in A new species of Scinax Wagler (Hylidae: Scinaxini) from the tropical forests of Northeastern Brazil
FIGURE 14. Geographic distribution of Scinax tropicalia sp. nov. The numbers in the map indicate the localities where S. tropicalia occurs in the Brazilian states of Bahia and Ceará. (1) Serra do Baurité, Pacoti, Ceará (-4.244791°, -38.921142°). (2) Parque das Trilhas, Guaramiranga, Ceará. (3) Serra da Jibóia, Elisio Medrado, Bahia. (4) Estaç"o Ecológica Wenceslau Guimar"es, Wenceslau Guimar"es, Bahia. (5) Estrada para Piraí do Norte, Gandu, Bahia. (6) Reserva Ecológica Michelin, Igrapiúna, Bahia (-13.83°, -39.17°; Mira-Mendes et al. 2018). (7) Morro do Mara, Jitaúna, Bahia. (8) Ilha Grande, Camamu, Bahia. (9) Ilha Pequena, Camamu, Bahia. (10) Piracanga, Maraú, Bahia. (11) RPPN Fazenda Capit"o, Itacaré, Bahia. (12) Parque Estadual Serra do Conduru, Uruçuca, Bahia. (13) Fazenda Bonfim, Uruçuca, Bahia. (14) Fazenda Provis"o, Ilhéus, Bahia. (15) Fazenda Bom Pastor, Ilheús, Bahia. (16, red star, type locality) Campus Universidade Estadual de Santa Cruz (UESC), Ilheús, Bahia. (17) Fazenda Santo Antônio, Ibicaraí, Bahia. (18) Parque Nacional Serra das Lontras, Arataca, Bahia. (19) RPPN Serra Bonita, Camacan, Bahia. See the Species account section for coordinates.
FIGURE 9 in A new species of Scinax Wagler (Hylidae: Scinaxini) from the tropical forests of Northeastern Brazil
FIGURE 9. Advertisement call of Scinax tropicalia sp. nov. (holotype, MZUESC 20440, FonoZoo 12706). (A) Waveform of a series of five notes. (B) Waveform (upper), spectrogram (central), and power spectrum (lower) of one note with 14 pulses. Notice the presence of five pulse sub-units of discrete amplitude peaks in each pulse, except for the first one. The light green orthogonal line in the power spectrum (lower) indicates the dominant frequency of the note.
FIGURE 15 in A new species of Scinax Wagler (Hylidae: Scinaxini) from the tropical forests of Northeastern Brazil
FIGURE 15. Habitats of Scinax tropicalia sp. nov. (A, B) Serra de Baturité, municipality of Guaramiranga, state of Ceará, Brazil. (C) Campus of the Universidade Estadual de Santa Cruz (UESC), municipality of Ilhéus, state of Bahia, Brazil. (D) Ilha Pequena, municipality of Camamu, state of Bahia, Brazil.
FIGURE 17 in A new species of Scinax Wagler (Hylidae: Scinaxini) from the tropical forests of Northeastern Brazil
FIGURE 17. Comparisons between advertisement calls of Scinax hayii, S. perereca, and S. tropicalia sp. nov. (A) Scinax hayii (unvouchered specimen), municipality of Teresópolis, state of Rio de Janeiro, Brazil. Waveform, spectrogram, and power spectrum of one note of the advertisement call (FonoZoo 12930). In detail, waveform showing a series of six pulses. (B) Scinax perereca (CCLZU 3227), type locality in the municipality of Ribeir"o Branco, state of S"o Paulo, Brazil. Waveform, spectrogram, and power spectrum of one note of the advertisement call (FNJV 11885). In detail, waveform showing of a series of six pulses. (C) S. tropicalia sp. nov. (MZUESC 20413, male paratype). Waveform, spectrogram, and power spectrum of one note of the advertisement call (FonoZoo 12700). In detail, waveform showing a series of five pulses. The light green orthogonal line in the power spectrums (lower) indicates the dominant frequencies of the notes. Photo and recording call in A courtesy of Leandro Drummond. Photo B courtesy of Paulo H. Silva.
FIGURE 16 in A new species of Scinax Wagler (Hylidae: Scinaxini) from the tropical forests of Northeastern Brazil
FIGURE 16. Microhabitats and behaviors of Scinax tropicalia sp. nov.. (A–H) Calling males perched on vegetation. (I, J) Adult specimens on the ground, over the forest litter. (K) A specimen over a terrestrial bromeliad leaf. (L–P) Couples in amplexus. (Q, R) Juveniles. (S, T) Adult male specimen (CFBH 44691) performing the passive defensive behavior of contracting.
FIGURE 19 in Composition and organization of highly speciose Empidoidea (Diptera) communities in tropical montane forests of northern Thailand
FIGURE 19. Species abundance distributions of Empidoidea (Empididae, Hybotidae, Dolichopodidae & Brachystomatidae combined) in dry lowland forest (DL), mid elevation evergreen forest (EM) and moist hill evergreen (MHE) communities as delimited by cluster analysis in Figure 17. Main figure. Rank / abundance plots of log10 of abundance as a percentage of the most abundant species plotted against species rank (from highest to lowest). Inset. k-dominance plots of relative cumulative abundance plotted against log10 species rank (from highest to lowest).
FIGURES 17–18 in Composition and organization of highly speciose Empidoidea (Diptera) communities in tropical montane forests of northern Thailand
FIGURES 17–18. Cluster analysis of Empidoidea (Empididae, Hybotidae, Dolichopodidae & Brachystomatidae combined) using unweighted pair-group average and Sorensen similarity. Bootstrapping was performed with 1000 resamples; the percentage of replicates where each of the major clusters is still supported is shown at nodes. 17, Clustering of data for a full year for each individual trap on Doi Inthanon during 2014. Individual traps are identified at termini and clusters designated as A, B, C and D are indicated. Communities defined by the major clusters A, C and D are assigned names broadly consistent with the forest biotopes and elevations they occupy; MHE, moist hill evergreen; EM, evergreen mid-elevation; DL, dry lowland; 18, Clustering of data for each trap during a four month period during the early-monsoon (April–July) and late-monsoon (September–December). Individual traps are identified at the termini with a suffix "early" of "late". E and F indicate major clusters.
FIGURE 16 in Composition and organization of highly speciose Empidoidea (Diptera) communities in tropical montane forests of northern Thailand
FIGURE 16. Variation in taxonomic distinctness (J*) of Empidoidea (Empididae, Hybotidae, Dolichopodidae & Brachystomatidae combined) with elevation on Doi Inthanon (solid line). Values of J* were calculated for Empidoidea sampled throughout 12 months in all traps operated at each 500 m elevation zone (<500, 500–1000, 1000–1500, 1500–2000, 2000–2500, &>2500 m) and were plotted against the mean elevation of all traps in each zone. Data were fitted to a linear regression model in PAST (solid line; r2 = 0.8804, p = 0.0056) and 95% confidence intervals (dashed line) were computed from 1000 random replicates taken from the pooled data set.
FIGURE 15 in Composition and organization of highly speciose Empidoidea (Diptera) communities in tropical montane forests of northern Thailand
FIGURE 15. Spatiotemporal variation in Mean Local Turnover (βwL) of species of Empidoidea through 12 months sampling over six 500m elevation zones at Doi Inthanon in 2014. Values of βwL are plotted on a grid of elevation zone (vertical axis) and months (horizontal axis) using the multiquadric gridding algorithm in PAST. Values of βwL (indicated by colour scale bar) vary between 0 (complete identity) and 1.0 (complete non-identity). Data are not available for January and February at <500 m and 500–1000 m.
FIGURES 9–12 in Composition and organization of highly speciose Empidoidea (Diptera) communities in tropical montane forests of northern Thailand
FIGURES 9–12. Spatiotemporal variation in relative abundance (A* = number of individuals. trap-1. month-1). 9, Empididae; 10, Hybotidae; 11, Dolichopodidae; 12, Empidoidea (Empididae, Hybotidae, Dolichopodidae & Brachystomatidae combined). Values of A* from 12 months sampling over six 500 m elevation zones at Doi Inthanon in 2014 were log2 transformed [as log2(1+A*)] and plotted on a grid of elevation zone (vertical axis) and months (horizontal axis) using the multiquadric gridding algorithm in PAST. Values of A* are indicated by the colour scale bars (note logarithmic scale). Data were not available for January and February at <500 m and 500–1000 m.
FIGURES 5–8 in Composition and organization of highly speciose Empidoidea (Diptera) communities in tropical montane forests of northern Thailand
FIGURES 5–8. Spatiotemporal variation in observed species richness (Sobs). 5, Empididae; 6, Hybotidae; 7, Dolichopodidae; 8, Estimated species richness (Chao1) of Empidoidea (Empididae, Hybotidae, Dolichopodidae & Brachystomatidae combined). Data from 12 months sampling over six 500 m elevation zones at Doi Inthanon in 2014 are plotted on a grid of elevation zone (vertical axis) and months (horizontal axis) using the multiquadric gridding algorithm in PAST. Values of Sobs and Chao1 are indicated by the colour scale bars. Data were not available for January and February at <500 m and 500–1000 m.
FIGURE 4 in Composition and organization of highly speciose Empidoidea (Diptera) communities in tropical montane forests of northern Thailand
FIGURE 4. Monthly variation in relative abundance, A* (number of individuals. trap-1. month-1) of Empididae, Hybotidae and Dolichopodidae at all elevations on Doi Inthanon during 2014.
FIGURES 13–14. Spatiotemporal variation. 13 in Composition and organization of highly speciose Empidoidea (Diptera) communities in tropical montane forests of northern Thailand
FIGURES 13–14. Spatiotemporal variation. 13, Berger-Parker Dominance (DBP); 14, Equitability (J) of Empidoidea (Empididae, Hybotidae, Dolichopodidae & Brachystomatidae combined) through 12 months sampling over six 500 m elevation zones at Doi Inthanon in 2014. Values of DBP and J were calculated in PAST for each month in each elevation zone and smoothed using the adjacent elevation and month algorithm (see Material & methods) before plotting on a grid of elevation zone (vertical axis) and months (horizontal axis) using the multiquadric gridding algorithm in PAST. Values of DBP and J are indicated by the colour scale bars. Data were not available for January and February at <500 m and 500–1000 m.
FIGURE 3 in Composition and organization of highly speciose Empidoidea (Diptera) communities in tropical montane forests of northern Thailand
FIGURE 3. Monthly variation in species richness (Sobs) of Empididae, Hybotidae, Brachystomatidae and Dolichopodidae at all elevations on Doi Inthanon during 2014.
FIGURE 2 in Composition and organization of highly speciose Empidoidea (Diptera) communities in tropical montane forests of northern Thailand
FIGURE 2. Variation in relative abundance, A* (number of individuals. trap-1. month-1) of Empididae, Hybotidae and Dolichopodidae across different elevation zones on Doi Inthanon. Note that A* is log10 scale. Error bars indicate standard error.
FIGURE 1 in Composition and organization of highly speciose Empidoidea (Diptera) communities in tropical montane forests of northern Thailand
FIGURE 1. Species richness in each elevation zone as percentage of total richness for each family (%Sobs) at all elevations of Empididae (open squares), Brachystomatidae (closed squares), Dolichopodidae (shaded box) and Hybotidae (plain box) on Doi Inthanon.
Data from: On the scaling of activity in tropical forest mammals
<p>Activity range – the amount of time spent active per day – is a fundamental aspect contributing to the optimization process by which animals achieve energetic balance. Based on their size and the nature of their diet, theoretical expectations are that larger carnivores need more time active to fulfil their energetic needs than do smaller ones and also more time active than similar-sized non-carnivores. Despite the relationship between daily activity, individual range and energy acquisition, large-scale relationships between activity range and body mass among wild mammals have never been properly addressed. This study aimed to understand the scaling of activity range with body mass, while controlling for phylogeny and diet. We built simple empirical predictions for the scaling of activity range with body mass for mammals of different trophic guilds and used a phylogenetically controlled mixed model to test these predictions using activity records of 249 mammal populations (128 species) in 19 tropical forests (in 15 countries) obtained using camera traps. Our scaling model predicted a steeper scaling of activity range in carnivores (0.21) with higher levels of activity (higher intercept), and near-zero scaling in herbivores (0.04). Empirical data showed that activity ranges scaled positively with body mass for carnivores (0.061), which also had higher intercept value, but not for herbivores, omnivores and insectivores, in general, corresponding with the predictions. Despite the many factors that shape animal activity at local scales, we found a general pattern showing that large carnivores need more time active in a day to meet their energetic demands.</p>
Data from: Landscape genetics of leaf-toed geckos in the tropical dry forest of northern Mexico
Habitat fragmentation due to both natural and anthropogenic forces continues to threaten the evolution and maintenance of biological diversity. This is of particular concern in tropical regions that are experiencing elevated rates of habitat loss. Although less well-studied than tropical rain forests, tropical dry forests (TDF) contain an enormous diversity of species and continue to be threatened by anthropogenic activities including grazing and agriculture. However, little is known about the processes that shape genetic connectivity in species inhabiting TDF ecosystems. We adopt a landscape genetic approach to understanding functional connectivity for leaf-toed geckos (Phyllodactylus tuberculosus) at multiple sites near the northernmost limit of this ecosystem at Alamos, Sonora, Mexico. Traditional analyses of population genetics are combined with multivariate GIS-based landscape analyses to test hypotheses on the potential drivers of spatial genetic variation. Moderate levels of within-population diversity and substantial levels of population differentiation are revealed by FST and Dest. Analyses using STRUCTURE suggest the occurrence of from 2 to 9 genetic clusters depending on the model used. Landscape genetic analysis suggests that forest cover, stream connectivity, undisturbed habitat, slope, and minimum temperature of the coldest period explain more genetic variation than do simple Euclidean distances. Additional landscape genetic studies throughout TDF habitat are required to understand species-specific responses to landscape and climate change and to identify common drivers. We urge researchers interested in using multivariate distance methods to test for, and report, significant correlations among predictor matrices that can impact results, particularly when adopting least-cost path approaches. Further investigation into the use of information theoretic approaches for model selection is also warranted.
Data from: Closing a gap in tropical forest biomass estimation: taking crown mass variation into account in pantropical allometries
Accurately monitoring tropical forest carbon stocks is an outstanding challenge. Allometric models that consider tree diameter, height and wood density as predictors are currently used in most tropical forest carbon studies. In particular, a pantropical biomass model has been widely used for approximately a decade, and its most recent version will certainly constitute a reference in the coming years. However, this reference model shows a systematic bias for the largest trees. Because large trees are key drivers of forest carbon stocks and dynamics, understanding the origin and the consequences of this bias is of utmost concern. In this study, we compiled a unique tree mass dataset on 673 trees measured in five tropical countries (101 trees > 100 cm in diameter) and an original dataset of 130 forest plots (1 ha) from central Africa to quantify the error of biomass allometric models at the individual and plot levels when explicitly accounting or not accounting for crown mass variations. We first showed that the proportion of crown to total tree aboveground biomass is highly variable among trees, ranging from 3 to 88 %. This proportion was constant on average for trees < 10 Mg (mean of 34 %) but, above this threshold, increased sharply with tree mass and exceeded 50 % on average for trees ≥ 45 Mg. This increase coincided with a progressive deviation between the pantropical biomass model estimations and actual tree mass. Accounting for a crown mass proxy in a newly developed model consistently removed the bias observed for large trees (> 1 Mg) and reduced the range of plot-level error from −23–16 to 0–10 %. The disproportionally higher allocation of large trees to crown mass may thus explain the bias observed recently in the reference pantropical model. This bias leads to far-from-negligible, but often overlooked, systematic errors at the plot level and may be easily corrected by accounting for a crown mass proxy for the largest trees in a stand, thus suggesting that the accuracy of forest carbon estimates can be significantly improved at a minimal cost.
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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.