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177 results for “evergreen forest”
CONUS NG-IDF 2.0: Evergreen Forest
<p>The <strong>NG-IDF: Evergreen Forest</strong> datasets cover more than 200,000 sites at approximately 6 km resolution in the years 1951–2013 across the CONUS for the <strong>Evergreen Forest</strong> land use land cover (LULC).</p> <p>These [<strong>daily time series</strong> + <strong>annual maximum</strong>] datasets provide information on extreme hydrological events and their associated hydrometeorological drivers at a continental scale.</p> <p>They include 1) daily time series of precipitation (P), throughfall (TF), water available for runoff (W), and snow water equivalent (SWE); 2) annual maximum time series of P, W, TF, snowmelt, rain-on-snow (ROS); and 3) NG-IDF curves and their uncertainties.</p> <p>See the "Evergreen_readmefirst.txt" file for details.</p>
Data_Schönauer et al. (2023)_Root and branch hydraulic functioning and trait coordination across organs in drought-deciduous and evergreen tree species of a subtropical highland forest
<p>Data used in</p> <p>Schönauer, M., Hietz, P., Schuldt, B., and Rewald, B. (2023). Root and branch hydraulic functioning and trait coordination across organs in drought-deciduous and evergreen tree species of a subtropical highland forest. Frontiers in plant science 14, 1127292. doi: 10.3389/fpls.2023.1127292</p>
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).
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest.
Fig. 4. A in Testate Amoebae Communities in the Rhizosphere of Rhododendron ponticum (Ericaceae) in an Evergreen Broadleaf Forest in Southern Spain
Fig. 4. A combined SEM/light microscopy picture of Trigonopyxis arcula. This testate amoeba species was proposed as a possible indicator of Rhododendron-affected soils by Sutton and Wilkinson (2007). Some hyphae of soil fungi are attached to the shell of the SEM-depicted specimen. Bars: 50 µm.
Fig. 1. A in Testate Amoebae Communities in the Rhizosphere of Rhododendron ponticum (Ericaceae) in an Evergreen Broadleaf Forest in Southern Spain
Fig. 1. A photo showing characteristics of the first sampling site in Puerto de Galiz, Spain (Valley 1; for details see Materials and Methods).
Fig. 2. A in Testate Amoebae Communities in the Rhizosphere of Rhododendron ponticum (Ericaceae) in an Evergreen Broadleaf Forest in Southern Spain
Fig. 2. A photo showing characteristics of the second sampling site in Puerto de Galiz, Spain (Valley 2; for details see Materials and Methods). The Rhododendron cover at the UK sites was much denser than at these Spanish sites.
Fig. 1 in Home range size and microhabitat selection by a tropical partridge species in moist evergreen forest
Fig. 1. Analysis of green-legged partridges' group range based on radio locations using the characteristic hull polygon (CHP) and minimum convex polygon (MCP; 100%, 95% and 50%) methods. F refers to females, and M refers to males.
Fig. 1. A in Predation behaviour of the bridle snake (Lycodon cf. davisonii) on Asian tropical evergreen forest bird nests
Fig. 1. A photograph of a bridle snake (Lycodon cf. davisonii) having just consumed a bird's egg on 27 June 2019 at 2358 at the Sakaerat Environmental Research Station, north-eastern Thailand (photo by J. Goodyear).
Fig. 2 in Predation behaviour of the bridle snake (Lycodon cf. davisonii) on Asian tropical evergreen forest bird nests
Fig. 2. Percentage of depredated nests for eight focal species caused by the top five nest predators at the Sakaerat Environmental Research Station, Thailand during the 2013–2019 breeding seasons. ABBA = Abbott's babbler, BNMO = black-naped monarch, IBFL = Indochinese blue-flycatcher, PTBA = puff-throated babbler, PTBU = puff-throated bulbul, SCBA = scaly-crowned babbler, STBU = stripe-throated bulbul, WRSH = white-rumped shama. N represents number of observed predation events for each nesting species.
Fig. 31. Tropical lowland evergreen rain forest along the Sungai Sadaunta, 700 m in A Systematic Review Of Sulawesi Bunomys (Muridae, Murinae) With The Description Of Two New Species
Fig. 31. Tropical lowland evergreen rain forest along the Sungai Sadaunta, 700 m (in 1976). Examples of Bunomys chrysocomus were caught amid the rocks in the foreground, deep within the forest, and occasionally on trunks and branches spanning the stream. Similar dense streamside forest and bridging trunks and limbs from old treefalls characterize much of the habitat along the Sungai Sadaunta where B. chrysocomus as well as B. karokophilus, n. sp., were encountered (see Natural History in account of B. chrysocomus).
Fig. 29. Tropical lowland evergreen rain forest along the Sungai Sadaunta, 750–850 m in A Systematic Review Of Sulawesi Bunomys (Muridae, Murinae) With The Description Of Two New Species
Fig. 29. Tropical lowland evergreen rain forest along the Sungai Sadaunta, 750–850 m (in 1974). Typical forest composition on terraces just above the stream: dense undergrowth of shrubs and tree saplings, woody vines looping through the understory. Beneath the dense cover, the ground is wet, the air cool. Most Bunomys chrysocomus were trapped in this kind of stream terrace habitat, either on the ground beneath the shrubs, alongside decomposing, moss-covered trunks and limbs lying on the terrace, or among moss-covered rocks. Bunomys karokophilus, n. sp., was encountered in similar habitat.
Comparative physiology of canopy tree leaves in evergreen and deciduous forests in lowland Thailand
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Comparative physiology of canopy tree leaves in evergreen and deciduous forests in lowland Thailand
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Data from: Gross primary productivity from leaf-age-dependent light use efficiency (LA-LUE) model over pantropical evergreen broadleaved forests
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Data from: Addition of nitrogen to canopy versus understory has different effects on leaf traits of understory plants in a subtropical evergreen broad–leaved forest
<p>Atmospheric nitrogen (N) deposition has substantial effects on forest ecosystems. The effects of N deposition on understory plants have been simulated by spraying N on the forest floor. Such understory addition of N (UAN) might simulate atmospheric N deposition in a biased manner, because it bypasses the canopy.</p> <p>We compared the effects of UAN and canopy addition of N (CAN) at 0, 25, and 50 kg N ha<sup>–1</sup> year<sup>–1</sup> on specific leaf area (SLA), leaf construction costs (CC), concentrations of leaf carbon ([C]), nitrogen ([N]), phosphorus ([P]), minerals ([Mineral]), nitrate ([NO<sub>3</sub><sup>-</sup>]), lignin ([Lignin]), lipids ([Lipid]), organic acids ([OA]), soluble phenolics ([SP]), total non-structural carbohydrates ([TNC]), and total structural carbohydrates ([TSC]) in six dominant understory species in a subtropical evergreen forest after five years of N treatments.</p> <p>We found that leaf CC, [C], [Lignin], [OA], [TNC] and [TSC] were significantly affected by N-addition approach and rate, but leaf [P] and [Lipid] were affected by N-addition approach and N-addition rate, respectively; leaf CC, [C], [P], [OA], and [TNC] were significantly lower under UAN than under CAN, but leaf [TSC] and [Lignin] were significantly higher and lower, respectively, under UAN than under CAN at 50 kg N ha<sup>–1</sup> year<sup>–1</sup>; the decline of leaf [C] and [Lignin] contributed to the significantly lower leaf CC under UAN than under CAN.</p> <p><em>Synthesis</em>. We show that canopy and understory N addition exerted significantly different effects on leaf traits of understory plants. The results indicate that understory plants in subtropical forest respond differently to understory addition of N from those to atmospheric deposition of N. Further studies are warranted to evaluate the unbiased ecological processes and functions of forest ecosystem responding to atmospheric N deposition via both canopy and understory N addition experiments over a longer term.</p>
Varying impacts of logging frequency on tree communities and carbon storage across evergreen and deciduous tropical forests in the Andaman Islands, India
<p>The majority of Earth's tropical forests have been selectively logged; some on repeated occasions. Selective logging is known to affect forest structure, composition and function in various ways, but how such effects vary with logging frequency and across forest types remains unclear. In the Andaman Archipelago in India, we examined adult and pole-sized trees in baseline (unlogged since 1990s), once-logged (logged between 2007 and 2014) and twice-logged (logged in early 1990s and between 2007 and 2014) evergreen and deciduous forests, and tested whether higher logging frequency was associated with lower canopy cover, tree density, tree diversity, aboveground carbon stocks and divergence in species composition, including increased relative abundances of deciduous species in these forests. While once-logged evergreen and deciduous forests were similar to their respective baselines for most attributes assessed, and twice-logged evergreen forests had 22–24% lower adult tree density and diversity, twice-logged deciduous forests had 17–50% lower canopy cover, pole density, adult species diversity and above-ground carbon stocks, and 12% higher deciduous fractions compared to the baselines. Collectively our results reveal lasting impacts of repeated selective logging, even at relative low intensity, on tree communities and carbon storage. These impacts can potentially be mitigated by reducing logging frequency and retaining unlogged patches in logged landscapes, but management must also incorporate heterogeneity in responses and recovery across different forest types. In the Andaman Islands, all forests may require more than 15–25 years between logging events, with deciduous forests potentially requiring more stringent extraction limits compared to evergreen forests.</p>
Data from: Age‐dependent leaf physiology and consequences for crown‐scale carbon uptake during the dry season in an Amazon evergreen forest
* Satellite and tower-based metrics of forest-scale photosynthesis generally increase with dry season progression across central Amazônia, but the underlying mechanisms lack consensus. * We conducted demographic surveys of leaf age composition, and measured age-dependence of leaf physiology in broadleaf canopy trees of abundant species at a central eastern Amazon site. Using a novel leaf-to-branch scaling approach, we used this data to independently test the much-debated hypothesis—arising from satellite and tower-based observations—that leaf phenology could explain the forest-scale pattern of dry season photosynthesis. * Stomatal conductance and biochemical parameters of photosynthesis were higher for recently mature leaves than for old leaves. Most branches had multiple leaf age categories simultaneously present, and the number of recently mature leaves increased as the dry season progressed because old leaves were exchanged for new leaves. * These findings provide the first direct field evidence that branch-scale photosynthetic capacity increases during the dry season, with a magnitude consistent with increases in ecosystem-scale photosynthetic capacity derived from flux towers. Interaction between leaf age-dependent physiology and shifting leaf age-demographic composition are sufficient to explain the dry season photosynthetic capacity pattern at this site, and should be considered in vegetation models of tropical evergreen forests.
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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.