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370 results for “seasonal forest”
Fig. 3 in Seasonality of dung beetles (Coleoptera: Scarabaeinae) in Atlantic Forest sites with different levels of disturbance in southern Brazil
Fig. 3. Linear regression between the climatic variables (precipitation and temperature) and dung beetle abundance and richness sampled in Atlantic Forest sites of Rio Grande do Sul state (Turvo State Park, Moreno Fortes Biological Reserve, Morro do Cerrito, Val Feltrina) between MaY 2016 and JulY 2017.
Fig. 2 in Seasonality of dung beetles (Coleoptera: Scarabaeinae) in Atlantic Forest sites with different levels of disturbance in southern Brazil
Fig. 2. Rarefaction and extrapolation curves of the assemblages of Scarabaeinae sampled in four Atlantic Forest sites in Rio Grande do Sul state, Brazil, during MaY 2016 to JulY 2017.
Fig. 1 in Seasonality of dung beetles (Coleoptera: Scarabaeinae) in Atlantic Forest sites with different levels of disturbance in southern Brazil
Fig. 1. Location of the four Atlantic Forest sites sampled in Rio Grande do Sul state, Brazil, map and satellite images: A, Turvo State Park, Derrubadas; B, Moreno Fortes Biological Reserve, Dois IrmÃos das Missões; C, Morro do Cerrito, Santa Maria; D, Val Feltrina, Silveira Martins. Source: ArcGIS software map; satellite images Google Earth Explorer.
Figure 1 in Seasonality and bait type driving the diversity of dung beetle (Scarabaeidae: Scarabaeinae) communities in urban remnants of the Atlantic Forest
Figure 1. Partial map of the state of Pernambuco, with emphasis on the remnants of the Atlantic Forest (Green) and the urban area (Pink) located on the outskirts of FURB Jaguarana in the municipality of Paulista and APA Aldeia-Beberibe in the municipality of Camaragibe, PE, Brazil.
Figure 3 in Seasonality and bait type driving the diversity of dung beetle (Scarabaeidae: Scarabaeinae) communities in urban remnants of the Atlantic Forest
Figure 3. Canonical Correspondence Analysis (CCA) with group formations related to separation and types of baits used in the collection of dung beetles at FURB Jaguarana (A) and APA Aldeia-Beberibe (B).
Figure 2 in Seasonality and bait type driving the diversity of dung beetle (Scarabaeidae: Scarabaeinae) communities in urban remnants of the Atlantic Forest
Figure 2. Differences between diversity index values (q0, q1, q2) for different types of baits (feces, carrion, millipedes) in the rainy and dry season at FURB Jaguarana (A) and APA Aldeia-Beberibe (B). Meaningfulness: <0.001'***'; <0.01'**'; <0.05'*'.
Linked collectors and determiners for: Croton sertanejus, a new species from Seasonally Dry Tropical Forest in Brazil, and redescription of C. echioides (Euphorbiaceae).
Natural history specimen data linked to collectors and determiners held within, "Croton sertanejus, a new species from Seasonally Dry Tropical Forest in Brazil, and redescription of C. echioides (Euphorbiaceae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/95293339-8775-4667-aa17-7639808b7a7d">https://bionomia.net/dataset/95293339-8775-4667-aa17-7639808b7a7d</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/95293339-8775-4667-aa17-7639808b7a7d">https://gbif.org/dataset/95293339-8775-4667-aa17-7639808b7a7d</a>. Formatted as a Frictionless Data package.
Seasonal rainfall in subtropical montane cloud forests drives demographic fluctuations in a Green-backed Tit population
<p>Montane birds are vulnerable to climate change. However, the mechanisms by which weather drives demographic processes in montane birds have seldom been investigated. We conducted a long-term study (2009–2019) on the Green-backed Tit (<em>Parus monticolus)</em>, an insectivorous passerine, in the montane cloud forest of subtropical Taiwan. We explored the effects of weather variability on the productivity and survival of adult Green-backed Tits. Nest survival was negatively associated with seasonal rainfall during the breeding season (April–July) and was lower in early clutches than in late clutches. Higher typhoon-induced precipitation during the postbreeding period (July–September) was related to reduced adult survival, but neither summer temperature nor winter weather conditions were found to be related to adult bird survival. We developed a stochastic simulation model for Green-backed Tit population dynamics based on empirical data. We compared the simulated time-series and observed population growth rates (λ) and found that 80% (8/10 yr) of the observed λ fell within the 5th and 95th percentiles of the simulated data over the 10-yr period. Moreover, the simulated average (± standard deviation) of the geometric mean of λ over 10 yr (1.05 ± 0.07) was close to that observed from 2009–2019 (0.99), which provided confidence that the model effectively simulated the population growth rate of the Green-backed Tit. We conducted a sensitivity analysis for λ, and found that juvenile and adult survival influenced by typhoon-induced rainfall were the greatest contributors to the variance in the growth rate of the Green-backed Tit population. With the onset of intensified seasonal precipitation associated with global warming, the population growth and density of Green-backed Tits will decline substantially. Our results suggest that under scenarios of high emissions of greenhouse gas, this local population of Green-backed Tits will not persist in the near future.</p>
Fig. 6 in Areas of endemism in Thailand: has historical partitioning between seasonally dry lowland and aseasonal moist mountain forests shaped biodiversity in Southeast Asia?
Fig. 6. Relationship between elevation and geographical range of Hybos spp. in Thailand. The number of 1° grids in which a species was recorded is plotted against the median elevation of all records. Line fitted by linear regression in PAST (r2=0.1026).
Fig. 5 in Areas of endemism in Thailand: has historical partitioning between seasonally dry lowland and aseasonal moist mountain forests shaped biodiversity in Southeast Asia?
Fig. 5. EZPAE down-weighted against homoplasy, using altitudinally zoned mountain ranges as OGU, 'characters' made additive. Strict consensus tree of two equally parsimonious trees (CI = 0.716, RI = 0.534) produced by maximum parsimony analysis with weighted 'characters' and TBR branch swapping in TNT. Symmetrical resampling support is given under the nodes. Alphabetic codes of termini correspond with mountain ranges as abbreviated in Fig. 3; the suffixes 'low' & 'high' refer to low (<1,250m) and high (>1,250m) elevation sample data.
Fig. 4 in Areas of endemism in Thailand: has historical partitioning between seasonally dry lowland and aseasonal moist mountain forests shaped biodiversity in Southeast Asia?
Fig. 4. EZPAE down-weighted against homoplasy, using altitudinally zoned mountain ranges as OGU, 'characters' made non-additive. Strict consensus tree of four equally parsimonious trees (CI = 0.674, 0.580) produced by maximum parsimony analysis with weighted 'characters' and TBR branch swapping in TNT. Symmetrical resampling support is given under the nodes. Alphabetic codes of termini correspond with mountain ranges as abbreviated in Fig. 3; the suffixes 'low' & 'high' refer to low (<1,250m) and high (>1,250m) sample data.
Fig. 2. PAE using 1 in Areas of endemism in Thailand: has historical partitioning between seasonally dry lowland and aseasonal moist mountain forests shaped biodiversity in Southeast Asia?
Fig. 2. PAE using 1° grids as OGU. Strict consensus tree of 760 equally parsimonious trees (CI = 0.501, RI = 0.557) produced by maximum parsimony analysis with unweighted 'characters' and TBR branch swapping in TNT. Symmetrical resampling support is given under the nodes (see Fig 1A for explanation of alphabetic codes).
Fig. 3 in Areas of endemism in Thailand: has historical partitioning between seasonally dry lowland and aseasonal moist mountain forests shaped biodiversity in Southeast Asia?
Fig. 3. PAE using mountain ranges as OGU. Strict consensus tree of nine equally parsimonious trees (CI = 0.745, RI = 0.722) produced by maximum parsimony analysis with weighted 'characters' and implicit enumeration in TNT. Symmetrical resampling support is given under the nodes. Abbreviations. – CM, Cardamom Mountains; DK, Dong Paya Yen – Khao Yai Forest Complex; DL, Daen Lao Range; LP, Luang Prabang Range; NST, Nakhon Si Thammarat Range; PM, Petchabun Mountains; PR, Phuket Range; PPR, Phu Pan Range; TH, Tenasserim Hills; TT, Thanon Thongchai Range. Grid-B and Grid-L refer to 1° grids (B and L in Fig. 1A) that were not assigned to any mountain range.
Fig. 1 in Areas of endemism in Thailand: has historical partitioning between seasonally dry lowland and aseasonal moist mountain forests shaped biodiversity in Southeast Asia?
Fig. 1. Maps of Thailand showing: A, Grid of 1° of latitude and longitude denoted by single-letters A–W. Mountain ranges are indicated by two- or three letter codes (CD, DK, DL, LP, NST, PM, PPR, PR, TH & TT) and the grids that comprise each range are colour-coded. Grids B and L were not assigned to any mountain range; B, Species richness (number of species) of Hybos present in 1° grids; C, reciprocal weighted endemicity of Hybos spp. calculated for 1° grids.
Fig. 6 in Spatiotemporal dynamics of insect diversity in tropical seasonal forests is linked to season and elevation, a case from northern Thailand
Fig. 6. Variation in Equitability (J) and Berger-Parker dominance (DBP) of Diptera (A) and Auchenorrhyncha (B) during 12 months of sampling over six 500 m elevation zones at Doi Inthanon in 2014. Values of J (bars) and DBP (lines) were computed in PAST and 95% confidence intervals obtained by bootstrapping using 9999 random samples. In Kruskal-Wallis H-tests there was a significant difference between the medians for Berger-Parker dominance in Diptera (H = 26.7, p <0.01) and Auchenorrhyncha (H = 14.9, p <0.01). Equitability was significantly different for Diptera (H = 36.5, p <0.01) but not for Auchenorrhyncha (H = 10.7, p = 0.0582).
Fig. 10 in Spatiotemporal dynamics of insect diversity in tropical seasonal forests is linked to season and elevation, a case from northern Thailand
Fig. 10. Variation in Mean Monthly Turnover (βwM) of Diptera (A) and Auchenorrhyncha (B) during 12 months of sampling over six 500 m elevation zones at Doi Inthanon in 2014. The mean value of βwM in each elevation zone ± standard error is indicated. Note that the vertical axis does not extend to zero. In Kruskal-Wallis H-tests there was a significant difference between the medians for Diptera (H = 29.0, p <0.01) and Auchenorrhyncha (H = 22.1, p <0.01).
Fig. 2 in Spatiotemporal dynamics of insect diversity in tropical seasonal forests is linked to season and elevation, a case from northern Thailand
Fig. 2. Observed species richness (Sobs) of Diptera and Auchenorrhyncha trapped in six elevation zones over 12 months sampling at Doi Inthanon in 2014. Diptera, open circles; Auchenorrhyncha, closed circles.). In Kruskal-Wallis H-tests there was a significant difference between the medians for Diptera (H = 22.1, p <0.01) and Auchenorrhyncha (H = 14.3, p <0.05).
Fig. 1. Relative abundance, A in Spatiotemporal dynamics of insect diversity in tropical seasonal forests is linked to season and elevation, a case from northern Thailand
Fig. 1. Relative abundance, A* (number of individuals caught. trap-1. month-1) of Diptera and Auchenorrhyncha trapped in six elevation zones over 12 months sampling at Doi Inthanon in 2014. Standard errors indicated. Note log10 scale. Data were fitted to a linear regression model in PAST; Diptera, open circles (r2 = 0.8567, p = 0.0081); Auchenorrhyncha, closed circles (r2 = 0.3182, p = 0.2434). In Kruskal-Wallis H-tests of untransformed data there was a significant difference between the medians for Diptera (H = 29.3, p <0.01) but not for Auchenorrhyncha (H = 3.3, p = 0.657).
Fig. 8 in Spatiotemporal dynamics of insect diversity in tropical seasonal forests is linked to season and elevation, a case from northern Thailand
Fig. 8. Variation in species turnover measured as βw of Diptera (a) and Auchenorrhyncha (b) during 12 months of sampling over six 500 m elevation zones at Doi Inthanon in 2014. Pairwise calculations of βw between each quadrat of a grid of elevation and month with the quadrat with maximum species richness (April/1,500–2,000 m quadrat for Diptera and June/500–1,000 m quadrat for Auchenorrhyncha) were mapped using the multiquadric gridding algorithm in the gridding module of PAST. Values of βw (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 500–1,000 m.
Fig. 3. Relative abundance, A in Spatiotemporal dynamics of insect diversity in tropical seasonal forests is linked to season and elevation, a case from northern Thailand
Fig. 3. Relative abundance, A* (number of individuals caught. trap-1. month-1) of Diptera and Auchenorrhyncha over 12 months sampling at Doi Inthanon in 2014. Standard errors indicated. Note log10 scale. In Kruskal-Wallis H-tests of untransformed data there was a significant difference between the medians for Diptera (H = 24.5, p <0.05) and Auchenorrhyncha (H = 34.3, p <0.01).
ScienceDex guides
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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)
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.