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Figure 3 in Breeding season of the hermit crab Dardanus deformis H. Milne Edwards, 1836 (Anomura, Diogenidae) in Maputo Bay, southern Mozambique
Figure 3. Dardanus deformis (H. Milne Edwards, 1836). Regression lines for the relationships between percentage of ovigerous females and (A) temperature (Y5246.71194+4.10545X, r250.94922, P,0.0001) and (B) rainfall (Y528.28968+0.11643X, r250.96076, P,0.001).
Figure 1 in Breeding season of the hermit crab Dardanus deformis H. Milne Edwards, 1836 (Anomura, Diogenidae) in Maputo Bay, southern Mozambique
Figure 1. Dardanus deformis (H. Milne Edwards, 1836). Frequency of crabs collected during the study period at Costa do Sol, Maputo Bay, southern Mozambique. Values above and below columns correspond to the total number of non-ovigerous and ovigerous females sampled during the study period.
Figure 3 in Population structure and breeding season of the hermit crab Diogenes brevirostris Stimpson, 1858 (Decapoda, Anomura, Diogenidae) from southern Mozambique
Figure 3. Diogenes brevirostris (Stimpson, 1858). Percentage of ovigerous females collected from January to December 2003 at Costa do Sol, Maputo Bay, southern Mozambique. Error bars represent standard deviation. Bars sharing the same letter do not differ statistically (Scheffé's test, P.0.05).
Figure 1 in Population structure and breeding season of the hermit crab Diogenes brevirostris Stimpson, 1858 (Decapoda, Anomura, Diogenidae) from southern Mozambique
Figure 1. Diogenes brevirostris (Stimpson, 1858). Overall size frequency distribution for the total sample collected from January to December 2003 at Costa do Sol, Maputo Bay, southern Mozambique.
Figure 3 in Do male tree frogs feed during the breeding season? Stomach flushing of five syntopic hylid species in Rio Grande do Sul, Brazil
Figure 3. Relation between length of the reproductive period and food intake by 50 males per species.
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).
Fig. 9 in Spatiotemporal dynamics of insect diversity in tropical seasonal forests is linked to season and elevation, a case from northern Thailand
Fig. 9. Spatiotemporal variation in species turnover measured as Mean Local Turnover βwL of Diptera (A) and Auchenorrhyncha (B) trapped during 12 months of sampling over six 500 m elevation zones at Doi Inthanon in 2014. Data were plotted on a grid of elevation zone (vertical axis) and months (horizontal axis) and mapped using the multiquadric gridding algorithm in the gridding module of 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 500–1,000 m.
Fig. 7 in Spatiotemporal dynamics of insect diversity in tropical seasonal forests is linked to season and elevation, a case from northern Thailand
Fig. 7. Monthly variation in Equitability (J) of Diptera assemblages during 12 months of sampling over six 500 m elevation zones at Doi Inthanon in 2014. Only points linking data from elevation zones 2,000–2,500 m and>2,500 m are connected by lines. Equitability declines profoundly at higher elevations between September and November indicating a decline in evenness of Diptera assemblages with corresponding prevalence of a number of relatively abundant species compared with other times of year and other elevations.
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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.