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171 results for “Deciduous forest”
Figure 15 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 15. Showing the monthly fluctuations of total population and temperature in Rows I, II and III.
Figure 14 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 14. Showing the monthly fluctuations of male, female and nymph population, temperature and humidity in Row III.
Figure 11 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 11. Showing the month wise vertical distribution of total Ctenolepisma (C.) udumalpetense population.
Figure 10 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 10. Showing the month wise vertical distribution of Ctenolepisma (C.) udumalpetense nymphal population.
Figure 8 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 8. Showing the vertical distribution of total population of Ctenolepisma (C.) udumalpetense in each row.
Figure 6 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 6. Showing the mean of total male, female and nymph of Ctenolepisma (C.) udumalpetense in three rows with standard error.
Figure 5 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 5. Showing the relative density of Ctenolepisma (C.) udumalpetense (Male, Female, Nymph) in each month in the forest floor of Trimurti Dam roadside, Tamil Nadu.
Figure 4 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 4. Showing the relative density of Ctenolepisma (C.) udumalpetense (Male, Female, Nymph) in each row in the forest floor of Trimurti Dam roadside, Tamil Nadu.
Figure 7 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 7. Showing the month wise mean density of male, female and nymph population of Ctenolepisma (C.) udumalpetense.
Figure 9 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 9. Showing the month wise vertical distribution of adult Ctenolepisma (C.) udumalpetense population.
Figure 16 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 16. Showing the monthly fluctuations of total population and humidity(%) in Rows I, II and III.
Figure 12 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 12. Showing the monthly fluctuations of male, female and nymph population, temperature and humidity in Row I.
Figure 32 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 32. Monthly changes in biomass (mg dry wt./m2) of male, female and nymph population of C. udumalpetense.
Comparative physiology of canopy tree leaves in evergreen and deciduous forests in lowland Thailand
<p><span>Three major forest types in lowland Thailand and its adjacent parts in Southeast Asia are mixed deciduous forest (MDF), dry dipterocarp forest (DDF) and dry evergreen forest (DEF). We report the leaf physiology of canopy trees in these forests. The leaf mass-based photosynthetic rates (<em>A</em><sub>max</sub>), stomatal conductance (<em>G</em><sub>max</sub>) and photosynthetic nitrogen use efficiency were significantly different between the deciduous forests (MDF and DDF) and the evergreen forest (DEF). The canopy trees of MDF with thick, eutrophic soils had the highest intrinsic water use efficiency (<em>A</em><sub>max</sub>/<em>G</em><sub>max</sub>) among the forest types. Forest-to-forest variations in leaf mass area were related to different nutrient use strategies (less vs. more conservative) associated with different soil nutrients rather than with leaf phenology/longevity. In the interspecific variations within each forest, <em>A</em><sub>max</sub> in MDF and DEF was limited by foliar phosphate, whereas that in DDF was limited by foliar nitrogen. The close association between leaf physiology and soil properties suggests that climate change and increasing human impacts will disrupt this association, leading to forest degradation and dysfunction.</span></p>
Comparative physiology of canopy tree leaves in evergreen and deciduous forests in lowland Thailand
<p>The major forest types in lowland Thailand and its adjacent parts in Southeast Asia with the distinct dry season are the mixed deciduous forest (MDF), dry dipterocarp forest (DDF) and dry evergreen forest (DEF). We report the first comprehensive data set in leaf physiology of canopy trees in these three forest types and clarify adaptive functional differences of woody plants among three forests. Unlike temperate forests, the forest variations in leaf mass per area (LMA) were related to nutrient use strategies (less vs. more conservative) associated with soil nutrients rather than with leaf phenology (evergreen vs. deciduous). In the interspecific variations within each forest, <em>A<sub>max</sub></em> in MDF and DEF was limited by foliar phosphate, whereas that in DDF was limited by foliar nitrogen. The close association between leaf physiology and soil properties suggests that climate change and increasing human impacts will disrupt this association, leading to forest degradation and dysfunction.</p>
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021). in Floristic, Vegetation And Climate Assessment Of The Early/Middle Miocene Parschlug Flora Indicates A Distinctly Seasonal Climate
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021).
Figure 13 in Population ecology of Ctenolepisma (C.) udumalpetense (Insecta: Zygentoma: Lepismatidae) in a deciduous forest floor of the Trimurti Dam, Tamil Nadu, India
Figure 13. Showing the monthly fluctuations of male, female and nymph population, temperature and humidity in Row II.
Phosphorous fertilization and soil pH affect the growth of deciduous trees in a temperate hardwood forest
<p>To better understand how a forest’s response to P limitation and acidic deposition can change over time, we added P, limestone to raise pH, and a cross-treatment where both P and limestone were added to 3 different northeastern Ohio forest stands over a 12-year period. Internally, we call this experiment APEX, which stands for Acid Precipitation EXperiment. We tracked diameter at breast height (DBH) of the trees annually, conducted foliar nutrient analyses, and collected tree roots to assess treatment impacts on mycorrhizal colonization. We analyzed our dataset in three sections: the first 6 years after manipulation, the latter 6 years, and the entire 12-year period. These sections allowed us to compare differences between early responses to manipulation and later responses. The R code included here shows how these sections of data were analyzed using linear mixed effect models and Tukey post hoc tests (with the R packages lme4 and multcomp, respectively) and graphed (with the package ggplot2). The three R code files include analyses of 1) litter biomass and chemistry (APEX_leaf_litter_R_code.R), 2) ectomycorrhizal (EM) and arbscular mycorrhizal (AM) fungal colonization and root biomass estimates from trees associated with these mycorrhizal types (APEX_mycorrhizal_roots_R_code.R), and 3) relative basal area increment that was calculated for different tree species and mycorrhizal association types using DBH measurements (APEX_RBAI_R_code.R). All input csv files are included here.</p>
Figure 3 in Influence of tree thinning on abundance and survival probability of small rodents in a natural deciduous forest
Figure 3. Survival probability (mean ± SE) of small rodents in the prethinning and postthinning periods in a natural deciduous forest, Mt. Maehwa, Hongcheon, South Korea. *: P <0.05, **: P <0.001 according to a Mann–Whitney U test.
Figure 1 in Influence of tree thinning on abundance and survival probability of small rodents in a natural deciduous forest
Figure 1. Mean numbers of small rodents captured per month (individuals/ha; mean ± SE) in the prethinning and postthinning periods in a natural deciduous forest, Mt. Maehwa, Hongcheon, South Korea. Asterisk indicates a significant difference (P <0.05) according to a Mann–Whitney U test.
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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)
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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
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