Find research datasets worth reusing

opencc-by-4.0Jan 2018View details →

Figure 1 in Description of the skeleton of the fossil beaked whale Messapicetus gregarius: searching potential proxies for deep-diving abilities

Figure 1. Set of linear measurements taken for the study exemplified in Hyperoodon ampullatus. (a) Skull in ventral view; (b) skull in lateral view; (c) scapula in lateral view; (d) humerus in lateral view; (e) radius in lateral view.

opencc-by-4.0Jan 2018View details →

Figure 2 in Description of the skeleton of the fossil beaked whale Messapicetus gregarius: searching potential proxies for deep-diving abilities

Figure 2. Cervical vertebrae of the specimen MUSM 2548, Messapicetus gregarius. Axis in anterior (a), posterior (b), and ventral view (c); C5–C6 in anterior (d), posterior (e), and dorsal view (f); C7 in anterior (g), posterior (h), and dorsal view (i).

opencc-by-4.0Apr 2024View details →

Plant silicon content as a proxy for understanding plant community properties and ecosystem structure

Main dataset from the paper entitled "Plant silicon content as a proxy for understanding plant community properties and ecosystem structure".

opencc-by-4.0May 2021View details →

FIGURE 3 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia

FIGURE 3. Modern vegetation types/formations delivered as proxies by Drudges 1 and 2 for the test set of fossil assemblages. Shown are the five best fitted results for the Taxonomic Similarity (TS) and the overall scores (synthesis of all similarity approaches), i.e., 25 proxies for every plant assemblage. Pastel colours represent East Asian vegetation types, bright colours European vegetation formations. For more detailed information see Appendix 4 which provides interactive colour signature (moving the cursor over the columns provides the designation of the proxies and their relevance for every fossil assemblage).

opencc-by-4.0May 2021View details →

FIGURE 2 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia

FIGURE 2. Modern vegetation types/formations delivered as proxies by Drudges 1 and 2 for the test set of fossil assemblages. Shown are the five best fitted results for the IPR Similarities based on Drudge 1 and Drudge 2 and for the Results Mix based on Drudge 1 and Drudge 2. Pastel colours represent East Asian vegetation types, bright colours European vegetation formations. For more detailed information see Appendix 4 which provides interactive colour signature (moving the cursor over the columns provides the designation of the proxies and their relevance for every fossil assemblage).

opencc-by-4.0May 2021View details →

FIGURE 7 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia

FIGURE 7 (previous page). Representation of modern European vegetation formations for the test set of fossil assemblages as delivered by Drudges 1 and 2 in more detail (see also Appendix 9). Formation H: H001, Colchic lowland to submontane mixed oak forests, in black; H002, Hyrcanian lowland-colline mixed broadleaved forests, in dark grey; H003, Hyrcanian colline to montane oak forests, in light grey. Formation G: G.1 - Subcontinental thermophilous (mixed) pedunculate oak and sessile oak forests, in black; G.2 - Sub-Mediterranean-subcontinental thermophilous bitter oak and Balkan oak and mixed forests, in dark grey; G.3 - Sub-Mediterranean and meso-supra-Mediterranean downy oak and mixed forests, in light grey; G.4 - Iberian supra- and meso-Mediterranean oak forests, in white. Formation F: F.1 - Species-poor acidophilous oak and mixed oak forests, in black; F.2 - Mixed oak-ash forests, in dark grey; F.3 - Mixed oak-hornbeam forests, in light grey; F.4 Lime-pedunculate oak forests, in white; F.5 - Beech and mixed beech forests, hatched lower left to upper right; F.6 - Oriental beech forests and hornbeam-oriental beech forests, hatched upper left to lower right; F.7 - Caucasian mixed hornbeam-oak forests, hatched vertically. Formation F, F.5 - Beech and mixed beech forests: F.5.1.1 - Species-poor oligotrophic to mesotrophic beech and mixed beech forests, lowland(-colline) types, in black; F.5.1.2 - Species-poor oligotrophic to mesotrophic beech and mixed beech forests, colline-submontane types, in dark grey; F.5.1.3 - Species-poor oligotrophic to mesotrophic beech and mixed beech forests, montane-altimontane types, in light grey; F.5.2.1 - Species-rich eutrophic and eu-mesotrophic beech and mixed beech forests, colline-submontane types, in white; F.5.2.2 - Species-rich eutrophic and eu-mesotrophic beech and mixed beech forests, colline-submontane types, hatched lower left to upper right; F.5.2.3 and 4 - Species-rich eutrophic and eu-mesotrophic beech and mixed beech forests, montane-altimontane types, hatched upper left to lower right. Formation D: D.1 - Western boreal spruce forests, in black; D.2 - Eastern boreal pine-spruce and fir-spruce forests, in dark grey; D.3 - Hemiboreal spruce and fir-spruce forests with broad-leaved trees, in light grey; D.4 - Montane to altimontane, partly submontane fir and spruce forests in the nemoral zone, in white; D.5 - Boreal and hemiboreal pine forests, hatched lower left to upper right; D.6 - Montane to altimontane (subalpine) pine forests in the nemoral zone; hatched upper left to lower right.

Figure 5 in Flow cytometric measurements as a proxy for sporulation intensity in the cultured macroalga Ulva (Chlorophyta)

Figure 5: Sporulation index (SPI) for gametophytes of Ulva rigida after induction of gametogenesis at different temperatures (A) and irradiances (B). Experiment performed according workflow shown in Figure 1. Significant differences among means are indicated by different letters. Error bars represent mean ± standard deviation (n = 3).

Figure 4 in Flow cytometric measurements as a proxy for sporulation intensity in the cultured macroalga Ulva (Chlorophyta)

Figure 4: Gamete counts of Ulva mutabilis collected from well-mixed culture medium. (A) Fluorescence of a dilution series of gametes measured using a plate reader. (B) For method validation, number of gametes measured by light-scattering flow cytometry (FCM) was compared with number of gametes determined by the Neubauer improved chamber. Error bars represent the mean ± standard deviation (n = 3).

Figure 1 in Flow cytometric measurements as a proxy for sporulation intensity in the cultured macroalga Ulva (Chlorophyta)

Figure 1: Workflow to determine sporulation index (SPI). (i): (a) Ulva rigida thalli collected from cultivation tank. (b) Thalli dried at 20 °C for 1 h. (c) Three pieces of 1 cm2 U. rigida cut from each specimen used in experiment and left to dry together with thallus. (d) Each specimen photographed using light microscope (and area of known number of cells measured. (ii): (e) Fresh thalli chopped to induce sporulation. (f) Fragments weighed, washed with seawater, and inoculated into incubation flask. (g) After differentiation of thallus cells into gametangia, release of gametes induced through change of culture medium (Vtotal), defined volume (VFCM) of well-mixed culture medium (Vtotal) fixed with 2% glutaraldehyde before measuring number of gametes using flow cytometer and calculating total numbers in Vtotal. (iii): (h) Using cells per unit area and weight of 1 cm2, number of thallus cells in each flask calculated. SPI combined number of gametes discharged with number of thallus cells in incubation flask.

Figure 3 in Flow cytometric measurements as a proxy for sporulation intensity in the cultured macroalga Ulva (Chlorophyta)

Figure 3: Flow cytometric measurements used to compare active with inactivate gametes of Ulva mutabilis. (i, ii) Mobile gametes were collected at the brightest spot and prepared for flow cytometric measurements. (iii, iv) Gametes were collected at the brightest spot as well. After chlorophyll removal, they were prepared for flow cytometric measurements. Plots present populations of gametes separated by their expected size (i, iii, % of the total counting events is given) and by the measured chlorophyll autofluorescence (ii, iv). The fluorescence measurements correspond to the gametes framed by the red gates in (i, iii). FSC-H, forward scatter height; SSC-H, side scatter height; FL, fluorescence (Fluo).

Figure 2 in Flow cytometric measurements as a proxy for sporulation intensity in the cultured macroalga Ulva (Chlorophyta)

Figure 2: Distinction of gametes of Ulva mutabilis according to their autofluorescence using flow cytometric measurements. (A: i, ii) Gametes released by U. mutabilis were collected from the green layer in the spotlight (i.e., phototactically active gametes). (A: iii, iv) Gametes were collected after the culture medium was well-mixed. Plots present populations of gametes separated by their expected size (i, iii, % of the total counting events is given) and by the measured chlorophyll autofluorescence (ii, iv). The autofluorescence measurements correspond to the gametes framed by the red gates in (i, iii). (B) Percentages of high-level autofluorescence. Error bars represent mean ± standard deviation (n = 3); FSC-H, forward scatter height; SSC-H, side scatter height; FL, fluorescence (Fluo).

TABLE 5 in Paleoclimate estimates for the Paleogene-Neogene in southern South America using fossil leaves as proxies

TABLE 5 — Fossil locations from Southern South America and age in million of years.Studied geological formations and sites used for comparison in the discussion.<table><thead><tr><th>Fossil Site</th><th>Geological Formation</th><th>Age (Ma)</th><th>Source</th></tr></thead><tbody><tr><th>Pico Quemado</th><td>Ñirihuau</td><td>middle Miocene?</td><td>Caviglia 2018</td></tr><tr><th>Cancha Carreras, Estancia Tres Marías</th><td>Río Guillermo</td><td>≤21.7 ± 0.3 to ≤23.5 ± 0.3</td><td>Fosdick et al. 2011; 2015a, b</td></tr><tr><th>Aluminé Basin</th><td>Rancahué</td><td>25.0 ± 1.4 to 26.0 ± 1.5</td><td>Brea et al. 2015; Franzese et al. 2011</td></tr><tr><th>Sierra Baguales</th><td>Río Leona</td><td>33.0 ± 2.8</td><td>Gutiérrez et al. 2017, 2019</td></tr><tr><th>Upper Río Turbio</th><td>Río Turbio</td><td>≤26.6 ± 0.2 to ≤33.4 to</td><td>Fosdick et al. 2015a</td></tr><tr><th>Lower Río Turbio</th><td>Río Turbio</td><td>≤46.3 ± 1.3 to ≤47.1 ± 2.7</td><td>Fosdick et al. 2015a</td></tr><tr><th>Río Pichileufú</th><td>Ventana</td><td>47.46 ± 0.05</td><td>Wilf et al. 2005</td></tr><tr><th>Laguna del Hunco</th><td>La Huitrera</td><td>51.91 ± 0.22</td><td>Wilf et al. 2005</td></tr><tr><th>Ligorio Márquez</th><td>Ligorio Márquez</td><td><57</td><td>Suárez et al. 2000; Hinojosa 2005</td></tr><tr><th>Palacio de los Loros</th><td>Salamanca</td><td>61.7</td><td>Iglesias et al. 2007</td></tr></tbody></table>

TABLE 2 in Paleoclimate estimates for the Paleogene-Neogene in southern South America using fossil leaves as proxies

TABLE 2 — Estimated values of temperature and precipitation for the upper Río Turbio Formation member. *Scarce fossil material.<table><thead><tr><th>Upper RTF</th><th></th><th>Equation</th><th>Dataset</th><th>R 2</th><th>Error</th><th>Source</th></tr></thead><tbody><tr><th>Temperature (°C)</th><td>15.3</td><td colspan="2">MAT = 3.25 + 0.24*% non-tooth CLAMP 3B SA</td><td>0.9</td><td>2.1°C</td><td>Hinojosa 2005; Hinojosa & Villagrán 2005</td></tr><tr><th>Temperature (°C)</th><td>14.3</td><td>MAT = 26.03pE + 1.31</td><td>SA</td><td>0.82</td><td>2.8°C</td><td>Hinojosa et al. 2011</td></tr><tr><th>Temperature (°C)</th><td>14.8</td><td>MAT = 0.204*E + 4.6</td><td>LMA</td><td>0.58</td><td>4.8°C</td><td>Peppe et al. 2011</td></tr><tr><th>Precipitation (mm)</th><td>*</td><td>Ln(MAP) = 1.63 + 0.49*MLnA</td><td>CLAMP 3B SA</td><td>0.6</td><td>Ln(0.5) cm</td><td>Hinojosa 2005; Hinojosa & Villagrán 2005</td></tr><tr><th>Precipitation (mm)</th><td>*</td><td>lnMAP = 0.283(MlnA) + 2.92</td><td>LAA</td><td>0.23</td><td>0.61</td><td>Peppe et al. 2011</td></tr></tbody></table>

TABLE 3 in Paleoclimate estimates for the Paleogene-Neogene in southern South America using fossil leaves as proxies

TABLE 3 — Estimated values of temperature and precipitation for the Río Guillermo Formation.<table><thead><tr><th>RGF</th><th></th><th>Equation</th><th>Dataset</th><th>R 2</th><th>Error</th><th>Source</th></tr></thead><tbody><tr><th>Temperature (°C)</th><td>5.3</td><td>MAT = 3.25 + 0.24*% non-tooth</td><td>CLAMP 3B SA</td><td>0.9</td><td>2.1°C</td><td>Hinojosa 2005; Hinojosa & Villagrán 2005</td></tr><tr><th>Temperature (°C)</th><td>3.5</td><td>MAT = 26.03pE + 1.31</td><td>SA</td><td>0.82</td><td>2.8°C</td><td>Hinojosa et al. 2011</td></tr><tr><th>Temperature (°C)</th><td>6.3</td><td>MAT = 0.204E + 4.6</td><td>LMA</td><td>0.58</td><td>4.8°C</td><td>Peppe et al. 2011</td></tr><tr><th>Precipitation (mm)</th><td>682</td><td>Ln(MAP) = 1.63 + 0.49*MLnA</td><td>CLAMP 3B SA</td><td>0.6</td><td>Ln(0.5) cm</td><td>Hinojosa 2005; Hinojosa & Villagrán 2005</td></tr><tr><th>Precipitation (mm)</th><td>829</td><td>lnMAP = 0.283(MlnA) + 2.92</td><td>LAA</td><td>0.23</td><td>0.61</td><td>Peppe et al. 2011</td></tr></tbody></table>

TABLE 1 in Paleoclimate estimates for the Paleogene-Neogene in southern South America using fossil leaves as proxies

TABLE 1 — Estimated values of temperature and precipitation for the lower Río Turbio Formation member.<table><thead><tr><th>Lower RTF</th><th></th><th>Equation</th><th>Dataset</th><th>R 2</th><th>Error</th><th>Source</th></tr></thead><tbody><tr><th>Temperature (°C)</th><td>16.5</td><td>MAT = 3.25 + 0.24*% non-tooth</td><td>CLAMP 3B SA</td><td>0.9</td><td>2.1°C</td><td>Hinojosa 2005; Hinojosa & Villagrán 2005</td></tr><tr><th>Temperature (°C)</th><td>15.7</td><td>MAT = 26.03pE + 1.31</td><td>SA</td><td>0.82</td><td>2.8°C</td><td>Hinojosa et al. 2011</td></tr><tr><th>Temperature (°C)</th><td>15.6</td><td>MAT = 0.204E + 4.6</td><td>LMA</td><td>0.58</td><td>4.8°C</td><td>Peppe et al. 2011</td></tr><tr><th>Temperature (°C)</th><td>16.9</td><td>See manuscript (1)</td><td>DiLP</td><td>0.7</td><td>4°C</td><td>Peppe et al. 2011</td></tr><tr><th>Precipitation (mm)</th><td>1764</td><td>Ln(MAP) = 1.63 + 0.49*MLnA</td><td>CLAMP 3B SA</td><td>0.6</td><td>Ln(0.5) cm</td><td>Hinojosa 2005; Hinojosa & Villagrán 2005</td></tr><tr><th>Precipitation (mm)</th><td>1435</td><td>lnMAP = 0.283(MlnA) + 2.92</td><td>LAA</td><td>0.23</td><td>0.61 cm</td><td>Peppe et al. 2011</td></tr><tr><th>Precipitation (mm)</th><td>1303</td><td>See manuscript (2)</td><td>DiLP</td><td>0.27</td><td>0.6 cm</td><td>Peppe et al. 2011</td></tr></tbody></table>

opencc-by-4.0Dec 2023View details →

Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with calibrated AVHRR surface reflectance (LCSPP-AVHRR), 1982-2000

Usage Notes: This is the updated LCSPP dataset (v3.2), reconstructed using the AVHRR record from 1982–2023. Due to Zenodo’s size constraints, LCSPP-AVHRR is divided into two separate repositories. Previously referred to as "LCSIF," the dataset was renamed to emphasize its role as a SIF-informed long-term photosynthesis proxy derived from surface reflectance and to avoid confusion with directly measured SIF signals. Key updates in version 3.2 include: <ul> <li>Improved Calibration: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks. We also </li> <li>Quality Flags: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li>Extension to include observations from the year of 2023.</li> </ul> Other LCSPP repositories can be accessed via the following links: <ul> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11658088" target="_blank" rel="noopener">10.5281/zenodo.11658088</a></li> </ul> The user can choose between LCSPP-AVHRR and LCSPP-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCSPP-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCSPP-AVHRR or use a blend dataset of LCSPP-AVHRR and LCSPP-MODIS as a sensitivity test.  In addition, the updated long-term continuous reflectance datasets (LCREF), used for the production of LCSPP, can be accessed using the following links: <ul> <li>LCREF-AVHRR v3.1 (1982-2023): <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> <li>LCREF-MODIS v3.1 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, while detailed the uses and limitations of the dataset. In particular, we note that LCSPP is a reconstruction of SIF-informed photosynthesis proxy and should not be treated as SIF measurements. Although LCSPP has demonstrated skill in tracking the dynamics of GPP and PAR absorbed by canopy chlorophyll (APARchl), it is not suitable for estimating fluorescence quantum yield. All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1st day to the 15th day of a month, and the second file “b” representative of the 16th day to the last day of a month. Abstract: Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.

opencc-by-4.0May 2024View details →

Supporting information for: An assessment of monazite fission-track thermochronology as a proxy for low-magnitude cooling, Catalina-Rincon Metamorphic Core Complex, AZ, U.S.A.

The following supporting information contains: The detailed location and age data for the geochronological, isotopic, and geochemical data used in this study, and their associated publications. Detailed thermochronometric data and associated thermal history modelling information for all thermochronology and modelling presented in the study.

opencc-by-4.0Apr 2022View details →

Data supporting the study "The impact of molecular self-organisation on the atmospheric fate of a cooking aerosol proxy" by Milsom et al.

Model and experimental data from the study "The impact of molecular self-organisation on the atmospheric fate of a cooking aerosol proxy" to be published in Atmospheric Chemistry and Physics.

opencc-by-4.0Aug 2022View details →

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).