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468 results for “decidualization”
Figure 1 in Small mammal diversity in Semi-deciduous Seasonal Forest of the southernmost Brazilian Pampa: the importance of owl pellets for rapid inventories in human-changing ecosystems
Figure 1. Location of the sampling sites of the Tyto furcata pellets at the Municipality of São Lourenço do Sul, State of Rio Grande do Sul, Southern Brazil. BV1 = Boa Vista I; BV2 = Boa Vista II; BOQ = Boqueirão; CGA = Canta Galo; EV1 = Evaristo I; EV2 = Evaristo II; ANT = Picada das Antas; PF1 = Picada Feliz I; PF2 = Picada Feliz II; QV1 = Quevedos I; QV2 = Quevedos II; RES = Reserva. Map modified from MMA (1992).
Figure 3 in Small mammal diversity in Semi-deciduous Seasonal Forest of the southernmost Brazilian Pampa: the importance of owl pellets for rapid inventories in human-changing ecosystems
Figure 3. Chiroptera specimens from the Tyto furcata pellets from the Municipality of São Lourenço do Sul, State of Rio Grande do Sul, Brazil. (A) = ventral view of the skull of Sturnira lilium, UFSC-CF 40-5-1. (B) = labial view of the right dentary of Tadarida brasiliensis, UFSC-CF 42-11-1. Scale bars: 1 mm.
Figure 2 in Small mammal diversity in Semi-deciduous Seasonal Forest of the southernmost Brazilian Pampa: the importance of owl pellets for rapid inventories in human-changing ecosystems
Figure 2. Didelphimorphia specimens from the Tyto furcata pellets from the Municipality of São Lourenço do Sul, State of Rio Grande do Sul, Brazil. (A) = labial view of the left dentary of Cryptonanus guahybae, UFSC-CF 32-2-1. (B) = labial view of the right dentary of Gracilinanus microtarsus, UFSC-CF 42-10-1. Scale bars: 1 mm.
Figure 6 in Small mammal diversity in Semi-deciduous Seasonal Forest of the southernmost Brazilian Pampa: the importance of owl pellets for rapid inventories in human-changing ecosystems
Figure 6. Occlusal view of the upper and lower molars of the Muridae and Caviidae rodents fromTytofurcata pellets from the Municipality of São Lourenço do Sul, State of Rio Grande do Sul, Brazil.(A) = Mus musculus, UFSC-CF 32-15-1, left M1-M3. (B) = Rattus rattus, UFSC-CF 35-9-1, right M1-M3. (C) = Cavia aperea, UFSC-CF 43-8-1, left P4-M3. (D) = M. musculus, UFSC-CF 32-15-2, right m1-m3. (E) = R. rattus, UFSC-CF 35-9-2, right m1-m3. (F) = C. aperea, UFSC-CF 43-8-2, right p4-m3. Scale bars A, D = 200 µm; B, C, E, F = 1 mm.
Modelled past autumn leaf phenology of deciduous trees
<p> </p> <p><span>Autumn leaf phenology (i.e. leaf colouring or leaf senescence) marks the end of the growing season, during which trees assimilate atmospheric CO<sub>2</sub>. Since autumn leaf phenology responds to climatic conditions, climate change affects the length of the growing season. Thus, autumn phenology is often modelled to assess possible climate change effects on future CO<sub>2</sub> mitigating capacities and species compositions of forests.</span></p> <p><span>Here, we give access to the entire dataset of modelled autumn phenology analyzed in Meier and Bigler (2023). The data was derived from >2.3 million model calibration runs according to 21 such models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate model chains from two representative concentration pathways. Calibration and validation were based on >45 000 observations for common beech (Fagus sylvatica L.), pedunculate oak (Quercus robur L.), and European larch (Larix decidua Mill.) from 500 Central European sites each.</span></p> <p><span>Cite as </span><span>Meier, M., & Bigler, C. (2023). Process-oriented models of autumn leaf phenology: Ways to sound calibration and implications of uncertain projections. <em>Geoscientific Model Development</em>, <em>16</em>(23), 7171–7201. https://doi.org/10.5194/gmd-16-7171-2023</span></p>
Projected future autumn leaf phenology of deciduous trees
<p><span>Autumn leaf phenology (i.e. leaf colouring or leaf senescence) marks the end of the growing season, during which trees assimilate atmospheric CO<sub>2</sub>. Since autumn leaf phenology responds to climatic conditions, climate change affects the length of the growing season. Thus, autumn phenology is often modelled to assess possible climate change effects on future CO<sub>2</sub> mitigating capacities and species compositions of forests.</span></p> <p><span>Here, we give access to the entire dataset of projected autumn phenology analyzed in Meier and Bigler (2023). The data was derived from different combinations of 21 process-oriented phenology models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate model chains from two representative concentration pathways. The dataset contains the average autumn phenology per site and for the years 2080-2099 according to each combination that led to a successful calibration. Calibration and validation were based on >45 000 observations for common beech (<em>Fagus sylvatica L.</em>), pedunculate oak (<em>Quercus robur L.</em>), and European larch (<em>Larix decidua Mill</em>.) from 500 Central European sites each.</span></p> <p><span>Cite as Meier, M., & Bigler, C. (2023). Process-oriented models of autumn leaf phenology: Ways to sound calibration and implications of uncertain projections. <em>Geoscientific Model Development</em>, 16(23), 7171–7201. https://doi.org/10.5194/gmd-16-7171-2023</span></p>
Data from: Contrasting heat tolerance of evergreen and deciduous urban woody species during heat waves
<p>The increasing frequency and intensity of heat waves caused significant damages to urban woody species, and the different leaf structures between evergreen and deciduous species may be closely related to leaf heat tolerance. However, whether the different leaf structural traits of evergreen and deciduous plants contribute to their different responses under heat waves is still unclear.</p> <p>During the record-breaking and long-lasting 2022 summer heat waves in China, we investigated the relationships between leaf thermal indices and leaf structural traits of 36 urban woody species in four cities along the Yangtze River.</p> <p>We found that all the four thermal indices were significantly but weakly related with leaf damage status. The critical temperature that causes the initial 15% damage to photosystem II (Tcrit) may serve as a sensitive measure of heat tolerance. Evergreen species suffered less leaf damage during the heat waves and exhibited higher leaf heat tolerance, thicker leaves than deciduous species. Tcrit was significantly correlated with leaf mass per area, leaf thickness and thickness of spongy tissue.</p> <p><em>Synthesis</em>:<em> </em>Urban woody species with high Tcrit, leaf mass per area, and leaf thickness tend to be more tolerant to heat stress. This study provides insights for predicting leaf heat tolerance of urban woody plants in subtropical China and their physiological and ecological responses to severe heat waves.</p>
Figure 27 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 27. Regression of pH on C. (C.) udumalpetense population in third row.
Figure 25 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 25. Regression of Humidity on C. (C.) udumalpetense population in second row.
Figure 19 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 19. Regression of pH on C. (C.) udumalpetense population in first row.
Figure 18 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 18. Monthly fluctuations of total population and N(%) in Rows I, II and III.
Figure 30 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 30. Regression of N (%) on C. (C.) udumalpetense population in third row.
Figure 3 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 3. Showing the layout of the Sampling sites) Each sample unit (NWSE) represents 1m2).
Figure 24 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 24. Regression of temperature on C. (C.) udumalpetense population in second row.
Figure 1 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 1. Showing forest floor of the Trimurti Dam, Tamil Nadu.
Figure 17 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 17. Showing the monthly fluctuations of total population and pH in Rows I, II and III.
Figure 2 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 2. Showing location of the study area (forest floor of the Trimurti Dam, Tamil Nadu).
Figure 23 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 23. Regression of pH on C. (C.) udumalpetense population in second row.
Figure 26 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 26. Regression of N (%) on C. (C.) udumalpetense population in second row.
Figure 29 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 29. Regression of humidity on C. (C.) udumalpetense population in third row.
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.