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1,098 results for “Pinus”
Tree measurements and summaries of the field plots used to develop Rojo and Montero (1996) yield tables for Pinus sylvestris L. in central Spain
<p>Tree measurements for principal trees and trees marked for thinning and summaries of the Pinus silvestris L. plots measured for the construction of Rojo and Montero (1996) Pinus sylvestris L. yield tables for central Spain. PRM_Functions.R contains R functions implementing parameter recovery methods to transform Rojo and Montero (1996) Pinus sylvestris L. yield tables into a diameter distribution model.</p> <p><strong>Trees.csv: </strong>Comma separated file with headers in the first row. Each record represents a measured tree. Fields:</p> <ul> <li>"PlotID": Identifier of the plot where the tree was measured</li> <li>"Type": Code indicating if the tree was marked for thinning.</li> <li>"ID_tree" Tree_Identifier</li> <li>"DBH1": First Diameter at breast height measurement for the tree.(mm)</li> <li> "DBH2" Second diameter at breast height measurement for the tree. The second measurement was taken in the direction perpendicular to the first measurement. (mm)</li> <li>"DBHmean": Mean of DBH 1 and DBH 2 <strong>and converted to cm</strong> (cm)</li> </ul> <p><strong>Plot_summaries.csv: </strong>Comma separated file with headers in the first row. Data digitized from Annex II of Rojo and Montero (1996). Each record contains different forest attributes of the plot. Fields:</p> <ul> <li>"PlotID": Identifier of the plot where the tree was measured</li> <li>"Age": Age of the plot determined from tree cores (Years)</li> <li>"Ho" Assman Dominant height for the plot (meters)</li> <li>"SiteIndex": Site index for the plot in meters. Site index is defined as the dominant height in meters measured or expected for the plot for an Age of 100 years.</li> <li>"MeanH" Mean tree height (m)</li> <li>"Dg" Quadratic mean diameter (cm)</li> <li>"Do" Dominant diameter. Mean diameter of the 100 largest trees of a hectare (cm)</li> <li>"N" Stand density (trees per hectare)</li> <li>"G" Plot basal area (m<sup>2</sup>/ha)</li> <li>"V" Total plot volume per unit area (m<sup>3</sup>/ha)</li> <li>"DeltaV" Periodic increment of merchantable volume (m<sup>3</sup>/ha)</li> <li>"Bark" Average percentage of total volume that is Bark. (%)</li> </ul> <p><strong>PRM_Functions.R: </strong>R functions to solve parameter recovery systems of equations based on mean and quadratic mean diameter and dominant diameter, quadratic mean diameter and stand density. Details provided as comments.</p> <p><strong>References</strong></p> <p>Rojo Alberto, Montero G (1996) El pino silvestre en la Sierra de Guadarrama: historia y selvicultura de los Pinares de Cercedilla, Navacerrada y Valsain. Ministerio de Agricultura, Pesca y Alimentación, Secretaria General Tecnica, Centro de Publicaciones, Madrid</p>
Soil profile, climatic, physiographic, overstory and understory data in mixed and monospecific plots of Pinus sylvestris and Pinus pinaster in Spain
<p>This dataset provides valuable environmental information about a triplets’ essay of Scots pine and Maritime pine in Spain. The data characterizes the soil profile (physicochemical parameters of organic and mineral horizons), climate, physiography, understory and overstory.</p> <p>The essay, located in North-Central Spain, consists of eighteen forest plots divided in six triplets. Each triplet includes three circular plots of 15 m-radius located less than 1 km from each other: two monospecific plots dominated by <em>P. sylvestris</em> or <em>P. pinaster</em>, and one mixed plot of both species. In each plot, one pit up to 50 cm depth, one 15 m-radius overstory features inventory and ten understory 1x1 m inventories were carried out. Additionally, physiographic and climatic variables were collected per plot.</p> <p>The file contains information about the 218 environmental variables studied in the eighteen forest plots.</p> <p>Triplet: Triplet to which the plot belongs(1: Triplet 1; 2: Triplet 2; 3: Triplet 3; 4: Triplet 4; 5: Triplet 5; 6: Triplet 6).</p> <p>Stand_type: Type of stand (PS: monospecific stand of <em>Pinus sylvestris</em> L.; PP: monospecific stand of <em>Pinus pinaster</em> Ait.; MM: mixed stand of <em>Pinus sylvestris</em> L.and <em>Pinus pinaster</em> Ait.).</p> <p>Plot: Plot identification (PS01: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 1; PS02: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 2; PS03: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 3; PS04: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 4; PS05: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 5; PS06: monospecific stand of <em>Pinus sylvestris</em> L. of triplet 6; MM01: mixed stand of <em>Pinus sylvestris</em> L.and <em>Pinus pinaster</em> Ait. of triplet 1; MM02: mixed stand of <em>Pinus sylvestris </em>L.and <em>Pinus pinaster</em> Ait. of triplet 2; MM03: mixed stand of <em>Pinus sylvestris</em> L.and <em>Pinus pinaster</em> Ait. of triplet 3; MM04: mixed stand of <em>Pinus sylvestris</em> L.and <em>Pinus pinaster</em> Ait. of triplet 4; MM05: mixed stand of <em>Pinus sylvestris </em>L.and <em>Pinus pinaster</em> Ait. of triplet 5; MM06: mixed stand of <em>Pinus sylvestris </em>L.and <em>Pinus pinaster Ait</em>. of triplet 6; PP01: monospecific stand of <em>Pinus pinaster</em> Ait. of triplet 1; PP02: monospecific stand of <em>Pinus pinaster</em> Ait. of triplet 2; PP03: monospecific stand of <em>Pinus pinaster </em>Ait. of triplet 3; PP04: monospecific stand of <em>Pinus pinaster</em> Ait. of triplet 4; PP05: monospecific stand of <em>Pinus pinaster</em> Ait. of triplet 5; PP06: monospecific stand of<em> Pinus pinaster </em>Ait. of triplet 6).</p> <p>Lat: Plot latitude in degrees.</p> <p>Long: Plot longitude in degrees.</p> <p>Province: Province to which the plot belongs (B: Province of Burgos; Sp: Province of Soria).</p> <p>Municipality: Municipality to which the plot belongs (M: Town of Mamolar; HP: Town of Hontoria del Pinar; N: Town of Navaleno; St: Town of Soria; CP: Town of Cabrejas del Pinar).</p> <p>Forest: Name of the forest where is located the plot (MB: Mata Blanca; MR: Mata Robledo; FP: Fuente del Pardo; PM: Pajar de la molinera; MP: Mojon Pardo; CM: Cueva de Matarubias).</p> <p>Alti: Plot elevation above sea level in m a.s.l.</p> <p>Slope: Slope (gradient) of the plot in percentage.</p> <p>Ori: Plot orientation in degrees.</p> <p>Clim: Climate classification according to Köppen classification (1936) (Cfb: Temperate without a dry season and temperate summer climate; Csb: Temperate with dry summer climate).</p> <p>XR: Accumulated rainfall in one year according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>JR: January rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ mm</p> <p>FR: February rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>MR: March rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>AR: April rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>MyR: May rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>JnR: June rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>JlR: July rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>AgR: August rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>SR: September rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>OR: October rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>NR: November rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>DR: December rainfall according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in mm.</p> <p>XT: Anual mean temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>JT: January temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>FT: February temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>MT: March temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>AT: April temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>MyT: May temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>JnT: June temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>JlT: July temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>AgT: August temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>ST: September temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>OT: October temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>NT: November temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>DT: December temperature according to ‘Atlas Agroclimático de Castilla y León-ITACYL-AEMET’ in ºC.</p> <p>Par_mat: Soil parental material according to Spanish Geological Map on a 1M scale. (IGME , 2015) (SM: Sandstones and Marls).</p> <p>Geo_age: Geological age of plot according to Spanish Geological Map on a 1M scale. (IGME, 2015) (Mz: Mesozoic age).</p> <p>Soil: Soil type according to Soil-Survey-Staff (2014) (TpDx: Typic Dystroxerept; TpHx:: Typic Humixerept; AqHx:: Aquic humixerept)</p> <p>Litter_B: Total Leaf Litter Biomass in Mg/ha.</p> <p>FF_Th: Forest floor Thickness in cm.</p> <p>Fs: Percentage of Fresh to Total Leaf Litter in %.</p> <p>Fr: Percentage of Fragmented to Total Leaf Litter in %.</p> <p>Hm: Percentage of Humified to Total Leaf Litter in %.</p> <p>GH1: Fist genetic soil horizon according to Soil Survey-Staff (2014) (Ah: Mineral horizon with accumulation of organic matter. This horizon is formed at the soil surface or below an O horizon).</p> <p>GH2: Second genetic soil horizon according to Soil Survey-Staff (2014) (AB: Transition horizon between A and B. A is a mineral horizon formed at the surface or below an O horizon, B is a subsurface horizon in which the structure of the rock is obliterated; AC: Transition horizon between A and C. A is a mineral horizon formed at the surface or below an O horizon; C is a mineral horizon, excluding hard bedrock, that is little affected by pedogenetic processes; Bw: Mineral B horizon where the development of color or structure are its more important diagnostic characteristics).</p> <p>GH3: Third genetic soil horizon according to Soil Survey-Staff (2014) (Bw: Mineral B horizon where the development of color or structure are its more important diagnostic characteristics; C: Mineral horizon, excluding hard bedrock, that is little affected by pedogenetic processes; Cg: Mineral horizon in which a distinct pattern of mottling occurs that reflects alternating conditions of oxidation and reduction of sesquioxides, caused by seasonal surface waterlogging).</p> <p>Th_H1: Thickness of the first soil horizon in cm.</p> <p>Th_H2: Thickness of the second soil horizon in cm.</p> <p>Th_H3: Thickness of the third soil horizon in cm.</p> <p>moistCol_H1: Wet matrix color (Hue Value/Chroma) of the first soil horizon according to Munsell soil color chards (10YR2/1: black; 10YR2/2: very dark brown; 10YR3/1: very dark grey; 10YR3/2: very dark greyish brown; 10YR4/1: dark grey; 10YR6/3: pale brown).</p> <p>moistCol_H2: Wet matrix colour (Hue Value/Chroma) of the second soil horizon according to Munsell soil color chards (5YR5/8: yellowish red; 7.5YR4/6: strong brown; 10YR3/2: very dark greyish brown; 10YR4/1: dark grey; 10YR4/2: dark greyish brown; 10YR4/4: dark yellowish brown with chroma 4; 10YR4/6: dark yellowish brown with chroma 6; 10YR5/3: brown; 10YR5/4: yellowish brown with chroma 4; 10YR5/6: yellowish brown with chroma 6; 10YR5/8: yellowish brown with chroma 8; 10YR6/4: light yellowish brown; 10YR6/6: brownish yellow).</p> <p>moistCol_H3: Wet matrix colour (Hue Value/Chroma) of the third soil horizon according to Munsell soil color chards (5YR4/6: yellowish red; 10YR4/4: dark yellowish brown with chroma 4; 10YR4/6: dark yellowish brown with chroma 6; 10YR5/8: yellowish brown; 10YR6/1: grey).</p> <p>dryCol_H1:Dry matrix color (Hue Value/Chroma) of the first soil horizon according to Munsell soil color chards (10YR4/1: dark grey; 10YR4/2: dark greyish brown; 10YR5/1: grey with value 5; 10YR5/2: greyish brown; 10YR5/3: brown; 10YR6/1: grey with value 6; 10YR6/2: light yellowish brown; 10YR7/2: light grey).</p> <p>dryCol_H2: Dry matrix color (Hue Value/Chroma) of the second soil horizon according to Munsell soil color chards (7.5YR6/6: redish brown; 10YR4/1: dark grey; 10YR6/1: grey with value 6; 10YR6/2: light yellowish brown with chroma 2; 10YR6/3: pale brown; 10YR6/4: light yellowish brown with chroma 4; 10YR6/6: brownish yellow; 10YR7/3: very pale brown with value 7 and choma 3; 10YR7/4: very pale brown withvalue 7 and choma 4; 10YR8/4: very pale brown with value 8 and choma 4).</p> <p>dryCol_H3: Dry matrix color (Hue Value/Chroma) of the third soil horizon according to Munsell soil color chards (5YR5/6: yellowish red; 7.5YR5/6: strong brown; 10YR6/4: light yellowish brown; 10YR6/6: brownish yellow; 10YR7/4: very pale brown; 10YR8/1: white).</p> <p>Sand_H1: Percentage of sand of the first soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Sand_H2: Percentage of sand of the second soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Sand_H3: Percentage of sand of the third soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Silt_H1: Percentage of silt of the first soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Silt_H2: Percentage of silt of the second soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Silt_H3: Percentage of silt of the third soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Clay_H1: Percentage of clay of the first soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Clay_H2: Percentage of clay of the second soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Clay_H3: Percentage of clay of the third soil horizon determined by the pipette method (Van-Reeuwijk 2002) according to Soil Survey Staff (2014) in % weight/weight.</p> <p>Tex_H1: Textural class of the first soil horizon according to Soil Survey Staff (2014) (SL: Sandy Loam; L: Loam).</p> <p>Tex_H2: Textural class of the second soil horizon according to Soil Survey Staff (2014) (SL: Sandy Loam; L: Loam).</p> <p>Tex_H3: Textural class of the third soil horizon according to Soil Survey Staff (2014) (SL: Sandy Loam; L: Loam; CL: Clay loam; C: Clay).</p> <p>Stones_H1: Coarse soil material (> 2 mm) of the first soil horizon in % weight/weight.</p> <p>Stones_H2: Coarse soil material (> 2 mm) of the second soil horizon in % weight/weight.</p> <p>Stones_H3: Coarse soil material (> 2 mm) of the third soil horizon in % weight/weight.</p> <p>bD_H1: Bulk density of the first soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>bD_H2: Bulk density of the second soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>bD_H3: Bulk density of the third soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>pD_H1: Particle density of the first soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>pD_H2: Particle density of the second soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>pD_H3: Particle density of the third soil horizon according to (Van-Reeuwijk 2002) in g/cm<sup>3</sup>.</p> <p>Poro_H1: Porosity of the first soil horizon according to (Van-Reeuwijk 2002) in % vol/vol.</p> <p>Poro_H2: Porosity of the second soil horizon according to (Van-Reeuwijk 2002) in % vol/vol.</p> <p>Poro_H3: Porosity of the third soil horizon according to (Van-Reeuwijk 2002) in % vol/vol.</p> <p>pH_H1: pH (1:2.5 H2O) of the first soil horizon according to (Van-Reeuwijk 2002)</p> <p>pH_H2: pH (1:2.5 H2O) of the second soil horizon according to (Van-Reeuwijk 2002)</p> <p>pH_H3: pH (1:2.5 H2O) of the third soil horizon according to (Van-Reeuwijk 2002)</p> <p>EC_H1: Electrical conductivity of the first soil horizon according to (Van-Reeuwijk 2002) in dS/m.</p> <p>EC_H2: Electrical conductivity of the second soil horizon according to (Van-Reeuwijk 2002) in dS/m.</p> <p>EC_H3: Electrical conductivity of the third soil horizon according to (Van-Reeuwijk 2002) in dS/m.</p> <p>avP_H1: Available phosphorus of the first soil horizon according to Olsen and Sommers (1982) in mg/kg.</p> <p>avP_H2: Available phosphorus of the second soil horizon according to Olsen and Sommers (1982) in mg/kg.</p> <p>avP_H3: Available phosphorus of the third soil horizon according to Olsen and Sommers (1982) in mg/kg.</p> <p>avPstock_H1: Available phosphorus stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>avPstock_H2: Available phosphorus stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>avPstock_H3: Available phosphorus stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>avPstock_50: Available phosphorus stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TN_Fs: Total nitrogen of the fresh forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TN_Fg: Total nitrogen of the fragmented forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TN_Hm: Total nitrogen of the humified forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TN_H1: Total nitrogen of the first soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TN_H2: Total nitrogen of the second soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TN_H3: Total nitrogen of the third soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TNstock_H1: Total nitrogen stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TNstock_H2: Total nitrogen stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TNstock_H3: Total nitrogen stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TNstock_50: Total nitrogen stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TOC_Fs: Total organic carbon of the fresh forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TOC_Fg: Total organic carbon of the fragmented forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TOC_Hm: Total organic carbon of the humified forest floor analyzed with a LECO-CHN 2000 elemental analyzer in g/kg</p> <p>TOC_H1: Total organic carbon of the first soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TOC_H2: Total organic carbon of the second soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TOC_H3: Total organic carbon of the third soil horizon analyzed with a LECO-CHN 2000 elemental analyzer in g/kg.</p> <p>TOCstock_H1:Total organic carbon stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TOCstock_H2: Total organic carbon stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TOCstock_H3: Total organic carbon stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>TOCstock_50: Total organic carbon stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>C/N_Fs: Ratio of total organic carbon to total nitrogen of the fresh forest floor </p> <p>C/N_Fg: Ratio of total organic carbon to total nitrogen of the fragmented forest floor </p> <p>C/N_Hm: Ratio of total organic carbon to total nitrogen of the humified forest floor </p> <p>C/N_H1: Ratio of total organic carbon to total nitrogen of the first soil horizon</p> <p>C/N_H2: Ratio of total organic carbon to total nitrogen of the second soil horizon</p> <p>C/N_H3: Ratio of total organic carbon to total nitrogen of the third soil horizon</p> <p>OxC_H1: Easily oxidizable carbon of the first soil horizon according to Walkley (1947) in mg/kg.</p> <p>OxC_H2: Easily oxidizable carbon of the second soil horizon according to Walkley (1947) in mg/kg.</p> <p>OxC_H3: Easily oxidizable carbon of the third soil horizon according to Walkley (1947) in mg/kg.</p> <p>OxCstock_H1: Easily oxidizable carbon stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>OxCstock_H2: Easily oxidizable carbon stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>OxCstock_H3: Easily oxidizable carbon stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>OxCstock_50: Easily oxidizable carbon stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>CEC_H1: Cation exchange capacity of the first soil horizon according to Mehlich (1953) in cmol<sub>+</sub>/kg.</p> <p>CEC_H2: Cation exchange capacity of the second soil horizon according to Mehlich (1953) in cmol<sub>+</sub>/kg.</p> <p>CEC_H3: Cation exchange capacity of the third soil horizon according to Mehlich (1953) in cmol<sub>+</sub>/kg.</p> <p>Na<sup>+</sup>_H1: Exchangeable sodium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Na<sup>+</sup>_H2: Exchangeable sodium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Na<sup>+</sup>_H3: Exchangeable sodium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Na<sup>+</sup>stock_H1: Exchangeable sodium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Na<sup>+</sup>stock_H2: Exchangeable sodium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Na<sup>+</sup>stock_H3: Exchangeable sodium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Na<sup>+</sup>stock_50: Exchangeable sodium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>K<sup>+</sup>_H1: Exchangeable potassium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>K<sup>+</sup>_H2: Exchangeable potassium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>K<sup>+</sup>_H3: Exchangeable potassium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>K<sup>+</sup>stock_H1: Exchangeable potassium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>K<sup>+</sup>stock_H2: Exchangeable potassium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>K<sup>+</sup>stock_H3: Exchangeable potassium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>K<sup>+</sup>stock_50: Exchangeable potassium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Ca<sup>+2</sup>_H1: Exchangeable calcium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Ca<sup>+2</sup>_H2: Exchangeable calcium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Ca<sup>+2</sup>_H3: Exchangeable calcium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Ca<sup>+2</sup>stock_H1: Exchangeable calcium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Ca<sup>+2</sup>stock_H2: Exchangeable calcium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Ca<sup>+2</sup>stock_H3: Exchangeable calcium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Ca<sup>+2</sup>stock_50: Exchangeable calcium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Mg<sup>+2</sup>_H1: Exchangeable magnesium of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Mg<sup>+2</sup>_H2: Exchangeable magnesium of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Mg<sup>+2</sup>_H3: Exchangeable magnesium of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>Mg<sup>+2</sup>stock_H1: Exchangeable magnesium stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Mg<sup>+2</sup>stock_H2: Exchangeable magnesium stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Mg<sup>+2</sup>stock_H3: Exchangeable magnesium stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>Mg<sup>+2</sup>stock_50: Exchangeable magnesium stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>SB_H1: Sum of bases of the first soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>SB_H2: Sum of bases of the second soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>SB_H3: Sum of bases of the third soil horizon by means of extracting with 1N ammonium acetate (pH=7) (Schollenberger and Simon 1945) in cmol<sub>+</sub>/kg.</p> <p>SBstock_H1: Sum of bases stock of the first soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>SBstock_H2: Sum of bases stock of the second soil horizon according to López-Marcos et al. (2019) in Mg/ha.</p> <p>SBstock_H3: Sum of bases stock of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>SBstock_50: Sum of bases stock of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in Mg/ha.</p> <p>FC_H1: Field capacity of the first soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>FC_H2: Field capacity of the second soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>FC_H3: Field capacity of the third soil horizon according to Van-Reeuwijk (2002)) in %.</p> <p>PWP_H1: Permanent wilting point of the first soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>PWP_H2: Permanent wilting point of the second soil horizon according to Van-Reeuwijk (2002) in %. </p> <p>PWP_H3: Permanent wilting point of the third soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>AW_H1: Available water of the first soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>AW_H2: Available water of the second soil horizon according to MAPA (1994) in %.</p> <p>AW_H3: Available water of the third soil horizon according to Van-Reeuwijk (2002) in %.</p> <p>WHC_H1: Water holding capacity of the first soil horizon according to López-Marcos et al. (2019) in g/cm<sup>2</sup>.</p> <p>WHC_H2: Water holding capacity of the second soil horizon according to López-Marcos et al. (2019) in g/cm<sup>2</sup>.</p> <p>WHC_H3: Water holding capacity of the third soil horizon up to 50 cm depth according to López-Marcos et al. (2019) in g/cm<sup>2</sup>.</p> <p>WHC_50: Water holding capacity of whole soil profile up to 50 cm depth according to López-Marcos et al. (2019) in g/cm<sup>2</sup>.</p> <p>Pot_veg: Potential vegetation according to Rivas-Martínez (1987) (LfQp: <em>Luzulo forsteri-Querceto pyrenaicae </em>S.; FhQp: <em>Festuco heterophyllae-Querceto pyrenaicae</em> S.; Jht: <em>Junipereto hemisphaerico-thuriferae</em> S.).</p> <p>Cur_veg: Current vegetation according to WMS service of MAPAMA(<a href="http://wms.mapama.es/sig/Biodiversidad">http://wms.mapama.es/sig/Biodiversidad</a>) (ps: <em>Pinus sylvestris</em> L.; pp: <em>Pinus pinaster</em> Ait.; pi: <em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.).</p> <p>NT: Stems per hectare of both <em>Pinus </em>species (<em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.) in trees/ha.</p> <p>NPs: Stems per hectare of <em>Pinus sylvestris</em> L. in trees/ha.</p> <p>NPp: Stems per hectare of <em>Pinus pinaster</em> Ait. in trees/ha.</p> <p>GT: Basal area per hectare of both <em>Pinus</em> species (<em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.) in m<sup>2</sup>/ha.</p> <p>GPs: Basal area per hectare of <em>Pinus sylvestris</em> L. in m<sup>2</sup>/ha.</p> <p>GPp: Basal area per hectare of <em>Pinus pinaster</em> Ait. in m<sup>2</sup>/ha.</p> <p>%PS: Percentage of basal area of <em>Pinus sylvestris</em> L. from total basal area</p> <p>%PP: Percentage of basal area of <em>Pinus pinaster</em> Ait. from total basal area</p> <p>dgT: Quadratic mean diameter of both <em>Pinus </em>species (<em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.) in cm.</p> <p>dgPs: Quadratic mean diameter of <em>Pinus sylvestris</em> L. in cm.</p> <p>dgPp: Quadratic mean diameter of <em>Pinus pinaster</em> Ait. in cm.</p> <p>HoT: Dominant height of both <em>Pinus</em> species (<em>Pinus sylvestris</em> L. and <em>Pinus pinaster</em> Ait.) in cm.</p> <p>HoPs: Dominant height of <em>Pinus sylvestris</em> L. in m.</p> <p>HoPp: Dominant height of <em>Pinus pinaster</em> Ait. in m.</p> <p>AgePs: Normal age of <em>Pinus sylvestris</em> L. in years.</p> <p>AgePp: Normal age of <em>Pinus pinaster</em> Ait. In years.</p> <p>SIPs: Site index of <em>Pinus sylvestris</em> L. related at age 100 for total plot according to Rojo and Montero (1999)</p> <p>SIPp: Site index of <em>Pinus pinaster</em> Ait. related at age 100 for total plot according to Bravo-Oviedo et al. (2007)</p> <p>Litter_cov: Cover of leaf litter in %.</p> <p>Vasc: Cover of understory vascular plants in %.</p> <p>Bryo: Cover of understory bryophytes in %.</p> <p>Under_sp: More abundant specie of understory vegetation (Aica: <em>Aira caryophyllea</em> L.; Aruv: <em>Arctostaphylos uva-ursi</em> (L.) Spreng.; Cavu: <em>Calluna vulgaris</em> (L.) Hull; Erar: <em>Erica arborea </em>L.; Erau: <em>Erica australis</em> L.; Pipi: <em>Pinus pinaster</em> Aiton. (seedlings/saplings); Pisy: <em>Pinus sylvestris</em> L. (seedlings/saplings; Ptaq: <em>Pteridium aquilinum</em> (L.) Kuhn)</p> <p>Aqu: Understory cover of family Aquifoliaceae in %.</p> <p>Aste: Understory cover of family Asteraceae in %.</p> <p>Cari: Understory cover of family Cariophyllaceae in %.</p> <p>Cist: Understory cover of family Cistaceae in %.</p> <p>Cupr: Understory cover of family Cupresaceae in %.</p> <p>Eric: Understory cover of family Ericaceae in %.</p> <p>Faba: Understory cover of family Fabaceae in %.</p> <p>Faga: Understory cover of family Fagaceae in %.</p> <p>Junc: Understory cover of family Juncaceae in %.</p> <p>Lili: Understory cover of family Liliaceae in %.</p> <p>Pina: Understory cover of family Pinaceae in %.</p> <p>Poac: Understory cover of family Poaceae in %.</p> <p>Poli: Understory cover of family Poligalaceae in %.</p> <p>Rosa: Understory cover of family Rosaceae in %.</p> <p>Rubi: Understory cover of family Rubiaceae in %.</p> <p>Scro: Understory cover of family Scrofulariaceae in %.</p> <p>Viol: Understory cover of family Violaceae in %.</p> <p>Xant: Understory cover of family Xanthorrhoeaceae in %.</p>
Genetic Relationships Between Terminal Shoot Length, Number of Flushes and Height in a Four-Year-old Progeny Test of Pinus brutia Ten.
<p><strong>Description of the data</strong></p> <p>A total of 188 plus trees were selected from eight natural seed stands of <em>Pinus brutia</em> in the Aegean region of Turkey. The number of trees selected per seed stand (provenance) varied between 7 to 53 trees. Open-pollinated seeds were collected from plus trees in 1998 and in 1999. In addition, six checklots consisting of bulk seeds from natural seed stands were included in the study to estimate genetic gain and link the progeny tests across different breeding zones in the Aegean region.</p> <p>Open-pollinated progeny tests were established at three locations in the Aegean region of Turkey (Hisaronu, Izmir, and Kinik) in March 2000. One-year-old bare-root seedlings were used in the study. Randomized complete block design with four-tree row plots was used in all sites. For each plus tree (female parent), about 72 half-sib progenies were planted across three test sites. The Hisaronu site had four blocks, while two other sites had seven blocks each. Each parent tree was represented by 16 half-sib progenies at the Hisaronu site and 28 progenies in the other two sites when the trials were planted. The spacing among seedlings was 2 x 3 m at each site. Each block was split into four sets (sets in replications) to accommodate a large number of trees, with checklots included in every set. In total, about 166 half-sib progenies and checklots were planted in each block.</p> <p>At the end of the first growing year after planting, survival was assessed. It was about 52% at the Hisaronu site. Dead seedlings at the Hisaronu site were replaced with 1083 two-year-old seedlings of the same families, which were grown in a nursery near Marmaris in the Aegean region. The other two sites had 91% (İzmir) and 94% (Kinik) survival. At age four after planting (2004), tree height (cm), terminal shoot length (cm), and the number of flushes were measured. In total, approximately 12100 trees were assessed across the three locations. </p>
Supplementary dataset Pinus Taeda for Cappa et al. (2015)
<p>Trial information and family numbers of the <em>Pinus Taeda L</em>. trial.</p> <p>Diameter at breast height of the <em>Pinus taeda </em>L. data set will be available upon request.</p>
A DETAILED TIME SERIES OF HOURLY CIRCUMFERENCE VARIATIONS IN PINUS PINEA L. IN CHILE
<ul> <li>The dataset provides digital dendrometer measurements on stem circumference of irrigated and non-irrigated<em> Pinus pinea</em> trees. Data were obtained in a xeric non-native habitat of central Chile. Forest mensuration were hourly collected from six adult trees during a growth year.</li> </ul>
Data from: Radial stem growth of the clonal shrub Alnus alnobetula at treeline is constrained by summer temperature and winter desiccation and differs in carbon allocation strategy compared to co-occurring Pinus cembra
<p><strong>Data are documented in the following article:</strong></p> <p>Oberhuber W., G Wieser, F. Bernich, A. Gruber (2022) Radial stem growth of the clonal shrub <em>Alnus alnobetula</em> at treeline is constrained by summer temperature and winter desiccation and differs in carbon allocation strategy compared to co-occurring <em>Pinus cembra</em>. Forests 2022, 13, 440. doi: 10.3390/f13030440.</p> <p> </p> <p><strong>Summary:</strong></p> <p>Global change is affecting species areal distribution in many regions. A better understanding of how land-use change and climate warming affects shrub growth is essential for improved predictions of forest dynamics at the alpine treeline. Evaluation of radial stem growth of the clonal shrub <em>Alnus alnobetula</em> (= <em>Alnus viridis</em>) and the co-occurring tree species Swiss stone pine (<em>Pinus cembra</em>) within an alpine treeline ecotone revealed that mean ring width of nitrogen fixing <em>A. alnobetula</em> was about four times lower compared to <em>P. cembra</em>. Our findings are based on ring width data from <em>A. alnobetula</em> and <em>P. cembra</em> stems sampled at the alpine treeline ecotone on Mt. Patscherkofel (47°12’N, 11°27’E, Central European Alps, Austria, elevation range 2050 to 2190 m asl). Ring width time series include 86 radii from 51 stems of <em>A. alnobetula</em> (stems had mean age of 18±7 yrs) and 24 radii from 16 stems of <em>P. cembra </em>(18±4 yrs). We explain our findings by different carbon allocation strategies, i.e., preference of “vertical” stem growth in late successional <em>P. cembra</em> vs. favoring “horizontal” spread in the pioneer shrub<em> A. alnobetula.</em> By favouring clonal propagation over individual stem growth <em>A. alnobetula</em> is able to quickly spread at the alpine treeline ecotone.</p>
Potential and realized distribution at 30m for Aleppo pine (Pinus halepensis) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the Aleppo pine (<em>Pinus halepensis, Mill.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_pinus.halepensis_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>pinus.halepensis</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_pinus.halepensis_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_pinus.halepensis_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_pinus.halepensis_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_pinus.halepensis_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access</strong> the repository with the training dataset (<a href="http://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read</strong> the tutorial with executable code on our <a href="http://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="http://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Potential and realized distribution at 30m for Stone pine (Pinus pinea) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the stone pine (<em>Pinus pinea</em><em>, L.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_pinus.pinea_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>pinus.pinea</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_pinus.pinea_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_pinus.pinea_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_pinus.pinea_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_pinus.pinea_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Potential and realized distribution at 30m for Austrian pine (Pinus nigra) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the Austrian pine (<em>Pinus nigra</em> J. F. Arnold) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_pinus.nigra_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>pinus.nigra</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_pinus.nigra_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_pinus.nigra_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_pinus.nigra_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_pinus.nigra_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p> </p> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Potential and realized distribution at 30m for Scots pine (Pinus sylvestris) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the Scots pine (<em>Pinus sylvestris, L.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_pinus.sylvestris_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>pinus.sylvestris</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_pinus.sylvestris_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_pinus.sylvestris_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_pinus.sylvestris_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_pinus.sylvestris_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Soil, climatic, physiographic and stand data in Pinus sylvestris and Pinus halepensis plantations in Spain
<p>This dataset contains information about soil physical, chemical and biochemical, climatic, physiographic and stand parameters of 32 plots belonging to the Spanish National Forest Inventory (SNFI) located in <em>Pinus halepensis</em> Mill. plantations and 35 plots belonging to the Sustainable Forest Management Research Institute (iuFOR; University of Valladolid and INIA) located in <em>Pinus sylvestris </em>L. plantations in Spain.</p> <p>Parameters included in the dataset: <br> Plot: plot identification in the SNFI and iuFOR networks.<br> Species: species present in each plot (1: Pinus sylvestris; 2: Pinus halepensis)<br> Slope: gradient in the plot in percentage.<br> Altitude: elevation of the plot in meters above the sea level<br> Latitude and Longitude: geographical coordinates of the plots in degrees<br> Density: number of trees per hectare in the plot<br> Dg: quadratic mean diameter in centimetersç<br> Hm: mean height in meters of the trees in the plot<br> H0; dominant height in meters of the trees in the plot<br> BA: basal area of the plot in square meters per hectare<br> SI: site index; dominant height of the trees in the plot at the reference age (80 years for Pinus halepensis and 50 years for Pinus sylvestris stands) <br> SQ: the site quality class<br> Age: average age in years of the trees in the plot<br> AW: soil available water in percentage<br> CO: soil coarse particles in percentage<br> Porosity: soil porosity in percentage<br> CLAY: clay content in soil in percentage<br> SILTUS: silt content in soil following the USDA criteria in percentage<br> SILTIS: silt content in soil following the International criteria, in percentage<br> SANDUS: sand content in soil following the USDA criteria, in percentage<br> SANDIS: sand content in soil following the International criteria, in percentage<br> OHT: organic horizon thickness in the plot in centimeters<br> ([C/N]L): the total carbon to total nitrogen ratio in the litter fraction of the organic horizon<br> ([C/N]FH): the total carbon to total nitrogen ratio in the fragmented plus humified fractions of the organic horizon <br> L: amount of litter fraction in the organic horizon in tons per hectare<br> FH: amount of fragmented plus humified fraction in the organic horizon in tons per hectare. <br> pH: soil pH value <br> CEC: cation exchange capacity in soil in centimoles of charge per kilogram of soil (Bascomb, 1964)<br> EOC: amount of easily oxidizable C in soil in percentage (Walkley and Black, 1934)<br> AP: amount of available phosphorus in soil in miligrams per kilogram of soil extracted with anion exchange membranes and determined with colorimetry (Murphy and Riley, 1962)<br> TN: total N in soil in percentage<br> TOC/TN: total organic C to total N ratio in soil<br> Ca, Mg, Na, K: exchangeable calcium, magnesium, sodium and potassium in soil in centimoles of charge per kilogram of soil (Schollenberger and Simon, 1945)<br> WSP: water soluble phenols in soil in micrograms of TAE per gram of soil (Box, 1983)<br> Carbonates: amount of carbonates in soil in percentage (Bundy and Bremner, 1972)<br> React_carb: amount of reactive carbonates in soil in percentage (Bashour and Sayegh, 2007)<br> Gypsum: amount of gypsum in soil in centimoles of charge per kilogram of soil (Richards, 1954)<br> Cu, Fe, Mn, Zn: amount of copper, iron, manganese and zinc in miligrams per kilogram of soil (Lindsay and Norvell, 1978)<br> EA: soil exchangeable acidity in centimoles of charge per kilogram of soil (Bascomb, 1964)<br> Sat: base saturation of soil in percentage <br> AlA, FeA, MnA: amorphous aluminum, iron and manganese (AlA, FeA, MnA) in soil in centimoles of charge per kilogram of soil (Bascomb, 1968)<br> AlM, FeM, MnM: organically bound aluminum, iron and manganese in soil in centimoles of charge per kilogram of soil (Blakemore et al. 1987) <br> AlE: exchangeable aluminum in soil in centimoles of charge per kilogram of soil (Bertsch & Bloom, 1996)<br> AlI: inorganic aluminum in soil in centimoles of charge per kilogram of soil (Mc-Keague et al., 1971)<br> Cmic, Nmic, Pmic: amount of microbial biomass carbon, nitrogen and phosphorus in soil in milligrams per kilogram of soil (Vance et al. 1987)<br> Cmin: amount of mineralizable carbon in soil in milligrams per kilogram of soil (Isermeyer, 1952)<br> Cmin/TOC: mineralizable carbon to total organic carbon ratio <br> Cmic/TOC: microbial biomass carbon to total organic carbon ratio<br> qCO2: microbial metabolic quotient (Cmin/Cmic) in soil in grams per week and gram of soil<br> FDA: fluorescein diacetate hydrolysis reaction (Alef and Nannipieri, 1995) in milliunits per gram of dry soil (nanomoles of fluorescein diacetate produced per gram of soil and minute)<br> DHA: dehydrogenase activity (Casida et al., 1964) in milliunits per gram of dry soil (nanomoles of triphenyl formazan produced per gram of soil and minute)<br> AcPhos, AlkPhos: acid and alkaline phosphatase activity (Tabatabai and Bremner, 1969) in milliunits per gram of dry soil (nanomoles of p-nitrophenol produced per gram of soil and minute)<br> Urease: urease activity in soil (Hofmann, 1963) in milliunits per gram of dry soil (nanomoles of N per gram of soil and minute)<br> Catalase: catalase activity (Tabatabai and Beck, 1971) in milliunits per gram of dry soil (nanomoles of O<sub>2</sub> produced per gram of soil and minute)<br> MAT: mean annual temperature in degrees centigrade (Ninyerola et al., 2005)<br> MMWM: mean maximum temperature of the warmest month in degrees centigrade (Ninyerola et al., 2005)<br> MMCM: mean maximum temperature of the coldest month in degrees centigrade (Ninyerola et al., 2005)<br> MTWM: mean temperature of the warmest month in degrees centigrade (Ninyerola et al., 2005)<br> MTCM: mean temperature of the coldest month in degrees centigrade (Ninyerola et al., 2005)<br> TP: total precipitation in millimeters (Ninyerola et al., 2005)<br> PW, PSP, PSU, PA: winter, spring, summer and autumn precipitation in millimeters (Ninyerola et al., 2005)<br> PET, RET: potential and real evapotranspiration in millimetres (Thornthwaite, 1949 and Thorntwaite and Mather, 1955) <br> Deficit: mean annual hydric deficit in millimeters (Thornthwaite, 1949 and Thorntwaite and Mather, 1955) <br> Surplus: mean annual hydric surplus in millimetres (Thornthwaite, 1949 and Thorntwaite and Mather, 1955) <br> AHI: Annual Hydric Index (Thornthwaite, 1949)<br> Martonne: Martonne index (De-Martonne, 1926)<br> Lang: Lang index (Lang, 1919)</p> <p>Code -999.99 indicates missing values.</p>
Herbarium specimen image of Pinus armandii var. mastersiana (Hayata) Hayata, part of the collection of Royal Botanic Garden Edinburgh
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
Geographic variation of tree height of Pinus pinea L. gathered from common gardens in Europe
<p>This dataset collects individual georeferenced tree height data from <em>Pinus pinea </em>L. planted in common gardens in France and Spain, between years 1993 and 1997. The experimental design varies depending on the common garden, from a randomized complete to incomplete block design, RCB or RIB, respectively. The final dimensions of this database is 56,624 individual tree height measurements <em> </em>with 9 common gardens and 55 different provenances. The data can be used to assess genetic variation and phenotypic plasticity with further applications in biogeography and forest management. </p>
Geographic variation of tree height of Pinus nigra Arn. gathered from common gardens in Europe
<p>This dataset collects individual georeferenced tree height data from <em>Pinus nigra</em> Arn. planted in common gardens in France, Germany and Spain, between years 1968 and 2009. The experimental design varies depending on the common garden, from a randomized complete to incomplete block design, RCB or RIB, respectively. The final dimension of the dataset is 194,642 individual tree height data measurements <em> </em>with 15 common gardens and 78 different provenances. The data can be used to assess genetic variation and phenotypic plasticity with further applications in biogeography and forest management. </p> <p> </p>
Geographic variation of tree height of Pinus pinaster Aiton gathered from common gardens in Europe and North-Africa
<p>This dataset collects individual georeferenced tree height data from <em>Pinus pinaster</em> Aiton planted in common gardens in France, Morocco and Spain, between years 1966 and 1992. The experimental design varies depending on the common garden, from a randomized complete to incomplete block design, RCB or RIB, respectively. The final dimension of the dataset is 123,801 individual tree height data measurements <em> </em>with 14 common gardens and 182 different genetic units. The data can be used to assess genetic variation and phenotypic plasticity with further applications in biogeography and forest management. </p>
Data for "Age effect on tree structure and biomass allocation in Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies [L.] Karst.)"
<p>VAPU dataset for tree biomass was collected from southern Finland in 1988-1990 by the Finnish Forest Research Institute (Metla, now Natural Resources Institute Finland, Luke) (VAPU data set).</p> <p>Those sample trees (162 Scots pine and 163 Norway spruce) are originated from the whole VAPU data set. The sheet 'Pine' and 'Spruce' data have been matched between 'sample branch measurements' and the 'biomass' information (by cluster X, Y, and plot, tree number).</p> <p>Biomass estimation for foliage and branches has been described here: https://doi.org/10.1016/j.ecolmodel.2004.04.024 and https://doi.org/10.1093/treephys/25.7.803<br> </p>
Data from: Association genetics of growth and adaptive traits in loblolly pine (Pinus taeda L.) using whole-exome-discovered polymorphisms
In the United States, forest genetics research began over 100 years ago and loblolly pine breeding programs were established in the 1950s. However, the genetics underlying complex traits of loblolly pine remains to be discovered. To address this, adaptive and growth traits were measured and analyzed in a clonally tested loblolly pine (Pinus taeda L.) population. Over 2.8 million single nucleotide polymorphism (SNP) markers detected from exome sequencing were used to test for single locus associations, SNP-SNP interactions and correlation of individual heterozygosity with phenotypic traits. A total of 36 SNP-trait associations were found for specific leaf area (5 SNPs), branch angle (2), crown width (3), stem diameter (4), total height (9), carbon isotope discrimination (4), nitrogen concentration (2), and pitch canker resistance traits (7). Eleven SNP-SNP interactions were found to be associated with branch angle (1 SNP-SNP interaction), crown width (2), total height (2), carbon isotope discrimination (2), nitrogen concentration (1), and pitch canker resistance (3). Non-additive effects imposed by dominance and epistasis account for a large fraction of the genetic variance for the quantitative traits. Genes that contain the identified SNPs have a wide spectrum of functions. Individual heterozygosity positively correlated with water use efficiency and nitrogen concentration. In conclusion, multiple effects identified in this study influence the performance of loblolly pines, provide resources for understanding the genetic control of complex traits, and have potential value for assessing with breeding through marker assisted selection and genomic selection.
Food and social cues modulate reproductive development but not migratory behavior in a nomadic songbird, the Pine Siskin (Pinus spinus)
<p>Many animals rely on photoperiodic and non-photoperiodic environmental cues to gather information and appropriately time life history stages across the annual cycle, such as reproduction, molt, and migration. Here, we experimentally demonstrate that the reproductive physiology, but not migratory behavior, of captive Pine Siskins responds to both food and social cues during the spring migratory-breeding period. Pine Siskins are a nomadic finch with a highly flexible breeding schedule and, in the spring, free-living Pine Siskins can wander large geographic areas and opportunistically breed. To understand the importance of non-photoperiodic cues to the migratory-breeding transition, we maintained individually housed birds on either a standard or enriched diet in the presence of group-housed heterospecifics or conspecifics experiencing either the standard or enriched diet type. We measured body condition and reproductive development of all Pine Siskins and, among individually housed Pine Siskins, quantified nocturnal migratory restlessness. In group-housed birds, the enriched diet caused increases in body condition and, among females, promoted reproductive development. Among individually housed birds, female reproductive development differed between treatment groups whereas male reproductive development did not. Specifically, individually housed females showed greater reproductive development when presented with conspecifics compared to heterospecifics. The highest rate of female reproductive development, however, was observed amongst individually housed females provided the enriched diet and maintained with group-housed conspecifics on an enriched diet. Changes in nocturnal migratory restlessness did not vary by treatment group or sex. By manipulating both the physical and social environment, this study demonstrates how multiple environmental cues can affect the timing of transitions between life history stages with differential responses between sexes and between migratory and reproductive systems.</p>
Text-fig. 3. Pinaceae, Taxaceae. a: Pinus needle fascicle with 3 needles, UAPC-ALTA S 25088A. b: Pinus needle fascicle with at least 4 needles, UAPC-ALTA S 59496. c: Articulate Pinus seed (section Diploxylon) showing seed body partly detached from wing, BBM-PAL-P000007. d: Winged pinaceous seed with elongate, flattened wing and narrow seed body, BBM-PAL-P000048. e: Another winged pinaceous seed with very narrow seed body, BBM-PAL-P000008. f: Amentotaxus leaf, UAPC-ALTA S S25086A. g: Higher magnification counterpart of (f) showing abaxial (lower) leaf surface with two parallel stomatal bands and tapered leaf tip S 25086B. h: Higher magnification of specimen in (f) showing adaxial (upper) leaf surface with detail of single midvein. Scale bars: a–e, g, h = 1 cm, f = 2 cm. in The Early Eocene Flora Of Horsefly, British Columbia, Canada And Its Phytogeographic Significance
Text-fig. 3. Pinaceae, Taxaceae. a: Pinus needle fascicle with 3 needles, UAPC-ALTA S 25088A. b: Pinus needle fascicle with at least 4 needles, UAPC-ALTA S 59496. c: Articulate Pinus seed (section Diploxylon) showing seed body partly detached from wing, BBM-PAL-P000007. d: Winged pinaceous seed with elongate, flattened wing and narrow seed body, BBM-PAL-P000048. e: Another winged pinaceous seed with very narrow seed body, BBM-PAL-P000008. f: Amentotaxus leaf, UAPC-ALTA S S25086A. g: Higher magnification counterpart of (f) showing abaxial (lower) leaf surface with two parallel stomatal bands and tapered leaf tip S 25086B. h: Higher magnification of specimen in (f) showing adaxial (upper) leaf surface with detail of single midvein. Scale bars: a–e, g, h = 1 cm, f = 2 cm.
Pinus strobus (Pinaceae) - cone - female - receptive
Image of Pinus strobus (Pinaceae) - cone - female - receptive
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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.