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Data Publisher for Earth & Environmental Science

ACER project members; Sanchez Goñi, Maria Fernanda; Desprat, Stéphanie; Daniau, Anne-Laure; Allen, Judy R M; Anderson, R Scott; Behling, Hermann; Bonnefille, Raymonde; Cheddadi, Rachid; Combourieu-Nebout, Nathalie; Dupont, Lydie M; Fletcher, William J; González, Catalina; Grigg, Laurie D; Grimm, Eric C; Hayashi, Ryoma; Helmens, Karin F; Hessler, Ines; Heusser, Linda E; Hooghiemstra, Henry; Huntley, Brian; Igarashi, Yaeko; Irino, Tomohisa; Jacobs, Bonnie Fine; Jiménez-Moreno, Gonzalo; Kawai, Sayuri; Kumon, Fujio; Lawson, Ian T; Lebamba, Judicael; Ledru, Marie-Pierre; Lézine, Anne-Marie; Liew, Ping-Mei; Londeix, Laurent; López-Martinez, Constancia; Magri, Donatella; Maley, Jean; Margari, Vasiliki; Marret, Fabienne; Müller, Ulrich C; Naughton, Filipa; Novenko, Elena Y; Oba, Tadamichi; Roucoux, Katherine H; Takahara, Hikaru; Tzedakis, Polychronis C; Vincens, Annie; Whitlock, Cathy L; Willard, Debra A; Yamamoto, Masanobu (2017): CLAM age model and biomes of sediment core Fuquene [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.872787

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Related to:
Fletcher, William J; Sanchez Goñi, Maria Fernanda; Allen, Judy R M; Cheddadi, Rachid; Combourieu-Nebout, Nathalie; Huntley, Brian; Lawson, Ian T; Londeix, Laurent; Magri, Donatella; Margari, Vasiliki; Müller, Ulrich C; Naughton, Filipa; Novenko, Elena Y; Roucoux, Katherine H; Tzedakis, Polychronis C (2010): Millennial-scale variability during the last glacial in vegetation records from Europe. Quaternary Science Reviews, 29(21-22), 2839-2864, https://doi.org/10.1016/j.quascirev.2009.11.015
Hessler, Ines; Dupont, Lydie M; Bonnefille, Raymonde; Behling, Hermann; González, Catalina; Helmens, Karin F; Hooghiemstra, Henry; Lebamba, Judicael; Ledru, Marie-Pierre; Lézine, Anne-Marie; Maley, Jean; Marret, Fabienne; Vincens, Annie (2010): Millennial-scale changes in vegetation records from tropical Africa and South America during the last glacial. Quaternary Science Reviews, 29(21-22), 2882-2899, https://doi.org/10.1016/j.quascirev.2009.11.029
Jiménez-Moreno, Gonzalo; Anderson, R Scott; Desprat, Stéphanie; Grigg, Laurie D; Grimm, Eric C; Heusser, Linda E; Jacobs, Bonnie Fine; López-Martinez, Constancia; Whitlock, Cathy L; Willard, Debra A (2010): Millennial-scale variability during the last glacial in vegetation records from North America. Quaternary Science Reviews, 29(21-22), 2865-2881, https://doi.org/10.1016/j.quascirev.2009.12.013
Sanchez Goñi, Maria Fernanda; Desprat, Stéphanie; Daniau, Anne-Laure; Bassinot, Franck C; Polanco-Martínez, Josué M; Harrison, Sandy P; Allen, Judy R M; Anderson, R Scott; Behling, Hermann; Bonnefille, Raymonde; Burjachs, Francesc; Carrión, José S; Cheddadi, Rachid; Clark, James S; Combourieu-Nebout, Nathalie; Courtney-Mustaphi, Colin J; DeBusk, Georg H; Dupont, Lydie M; Finch, Jemma M; Fletcher, William J; Giardini, Marco; González, Catalina; Gosling, William D; Grigg, Laurie D; Grimm, Eric C; Hayashi, Ryoma; Helmens, Karin F; Heusser, Linda E; Hill, Trevor R; Hope, Geoffrey; Huntley, Brian; Igarashi, Yaeko; Irino, Tomohisa; Jacobs, Bonnie Fine; Jiménez-Moreno, Gonzalo; Kawai, Sayuri; Kershaw, A Peter; Kumon, Fujio; Lawson, Ian T; Ledru, Marie-Pierre; Lézine, Anne-Marie; Liew, Ping-Mei; Magri, Donatella; Marchant, Robert; Margari, Vasiliki; Mayle, Francis E; McKenzie, G Merna; Moss, Patrick T; Müller, Stefanie; Müller, Ulrich C; Naughton, Filipa; Newnham, Rewi M; Oba, Tadamichi; Pérez-Obiol, Ramon P; Pini, Roberta; Ravazzi, Cesare; Roucoux, Katherine H; Rucina, Stephen M; Scott, Louis; Takahara, Hikaru; Tzedakis, Polychronis C; Urrego, Dunia H; van Geel, Bas; Valencia, Bryan G; Vandergoes, Marcus J; Vincens, Annie; Whitlock, Cathy L; Willard, Debra A; Yamamoto, Masanobu (2017): The ACER pollen and charcoal database: a global resource to document vegetation and fire response to abrupt climate changes during the last glacial period. Earth System Science Data, 9(2), 679-695, https://doi.org/10.5194/essd-9-679-2017
Takahara, Hikaru; Igarashi, Yaeko; Hayashi, Ryoma; Kumon, Fujio; Liew, Ping-Mei; Yamamoto, Masanobu; Kawai, Sayuri; Oba, Tadamichi; Irino, Tomohisa (2010): Millennial-scale variability in vegetation records from the East Asian Islands: Taiwan, Japan and Sakhalin. Quaternary Science Reviews, 29(21-22), 2900-2917, https://doi.org/10.1016/j.quascirev.2009.11.026
Project(s):
Abrupt Climate Changes and Environmental Responses (ACER)
Coverage:
Latitude: 5.450000 * Longitude: -73.460000
Minimum DEPTH, sediment/rock: 0.063 m * Maximum DEPTH, sediment/rock: 11.938 m
Event(s):
Fuquene * Latitude: 5.450000 * Longitude: -73.460000 * Elevation: 2540.0 m
Parameter(s):
#NameShort NameUnitPrincipal InvestigatorMethod/DeviceComment
1 DEPTH, sediment/rockDepth sedmGeocode
2 AGEAgeka BPSanchez Goñi, Maria FernandaGeocode – original age
3 Calendar age, minimum/youngCal age minka BPSanchez Goñi, Maria FernandaClassical age-modeling approach, CLAM (Blaauw, 2010)CLAM_min95
4 Calendar age, maximum/oldCal age maxka BPSanchez Goñi, Maria FernandaClassical age-modeling approach, CLAM (Blaauw, 2010)CLAM_max95
5 Calendar ageCal ageka BPSanchez Goñi, Maria FernandaClassical age-modeling approach, CLAM (Blaauw, 2010)CLAM_best
6 Accumulation modelAccu modela/cmSanchez Goñi, Maria FernandaClassical age-modeling approach, CLAM (Blaauw, 2010)
7 Type of age modelAge model typeSanchez Goñi, Maria Fernanda
8 Sample IDSample IDSanchez Goñi, Maria FernandaACER sample ID
9 Pollen, warm-temperate forestPollen wte forest%Sanchez Goñi, Maria Fernanda
10 Pollen, grasslandPollen grassland%Sanchez Goñi, Maria Fernanda
License:
Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC-BY-NC-ND-3.0)
Size:
626 data points

Data

Download dataset as tab-delimited text — use the following character encoding:

1 
Depth sed [m]		2 
Age [ka BP]		3 
Cal age min [ka BP]		4 
Cal age max [ka BP]		5 
Cal age [ka BP]		6 
Accu model [a/cm]		7 
Age model type		8 
Sample ID		9 
Pollen wte forest [%]		10 
Pollen grassland [%]
0.0630.0000074735.21970.355
0.1880.26990747414.96138.386
0.3130.53980747522.34613.222
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0.5631.07970747723.39818.033
0.6881.34960747832.15924.449
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0.9381.88940748035.21434.241
1.0632.15940748132.46429.858
1.1882.42930748232.79133.333
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4.56310.63420750922.90136.260
4.68811.1850011.0098012.2332011.6776040.956Polynomial regression-order 4751013.74045.038
4.81312.1350011.9553012.4213012.2045043.401Polynomial regression-order 4751119.59841.709
4.93812.9563012.5141013.0214012.7598045.475Polynomial regression-order 4751212.67658.803
5.00013.2167012.7130013.4658013.0469046.489Polynomial regression-order 4751328.38337.624
5.06313.4771012.9111013.9005013.3398047.361Polynomial regression-order 4751429.29029.882
5.12513.9086013.1183014.3277013.6381048.171Polynomial regression-order 4751542.04514.773
5.18814.4067013.3352014.7433013.9414048.921Polynomial regression-order 4751637.74826.821
5.25014.9048013.5708015.1480014.2493049.666Polynomial regression-order 4751742.42420.455
5.31315.4030013.8175015.5439014.5614050.295Polynomial regression-order 4751828.71042.903
5.37515.9011014.0785015.9290014.8774050.869Polynomial regression-order 4751922.64832.404
5.43816.3992014.3480016.3061015.1969051.387Polynomial regression-order 4752022.08255.205
5.56317.3954014.9239017.0301015.8449052.298Polynomial regression-order 4752111.02776.806
5.68818.3916015.5468017.7156016.5029052.968Polynomial regression-order 475229.96278.161
5.81018.9811016.1944018.3572017.1548053.459Polynomial regression-order 475236.84983.219
5.93819.2928016.8955018.9924017.8384053.756Polynomial regression-order 475244.41279.412
6.06019.5923017.5906019.5778018.4978053.883Polynomial regression-order 4752510.00075.500
6.18819.9040018.3403020.1633019.1848053.847Polynomial regression-order 475267.06373.978
6.31020.2034019.0624020.7244019.8435053.657Polynomial regression-order 475278.81464.407
6.43820.5151019.8121021.3105020.5258053.333Polynomial regression-order 475283.58474.910
6.56020.8146020.5276021.8957021.1765052.873Polynomial regression-order 475297.60568.441
6.68821.1263021.2502022.5422021.8474052.308Polynomial regression-order 475305.93286.017
6.81021.5127021.9152023.1871022.4843051.626Polynomial regression-order 4753115.92961.947
6.93822.0398022.5693023.8823023.1380050.866Polynomial regression-order 4753214.40769.068
7.06022.5463023.1532024.5648023.7562050.010Polynomial regression-order 4753316.78663.929
7.18823.0734023.7127025.2529024.3884049.102Polynomial regression-order 4753419.67258.607
7.31023.5798024.2082025.9179024.9842048.118Polynomial regression-order 4753516.07754.984
7.43824.1498024.7122026.5994025.5916047.108Polynomial regression-order 4753613.87165.484
7.56324.7151025.1836027.2384026.1741046.033Polynomial regression-order 4753714.40361.317
7.68825.2804025.6418027.8703026.7432044.978Polynomial regression-order 475389.12472.628
7.81325.8457026.0738028.4968027.2987043.870Polynomial regression-order 475398.10875.290
7.93826.4109026.5257029.1065027.8407042.806Polynomial regression-order 475408.43477.108
8.06326.9762026.9804029.6751028.3692041.713Polynomial regression-order 475416.73473.401
8.18827.5415027.4347030.2221028.8845040.687Polynomial regression-order 475428.01458.537
8.31328.1068027.8645030.7476029.3868039.656Polynomial regression-order 475439.77254.072
8.43828.6720028.2869031.2672029.8768038.713Polynomial regression-order 4754410.03159.248
8.56329.2373028.7264031.7426030.3551037.793Polynomial regression-order 475452.28882.353
8.68829.8026029.1841032.2081030.8226036.979Polynomial regression-order 475464.57576.471
8.81330.3679029.5940032.6580031.2801036.216Polynomial regression-order 475478.22465.789
8.93830.9332030.0307033.0936031.7289035.577Polynomial regression-order 475489.73256.040
9.06331.4984030.4402033.5162032.1702035.021Polynomial regression-order 4754912.44043.062
9.18832.0637030.8649033.9102032.6053034.603Polynomial regression-order 4755014.74857.914
9.31332.6290031.2588034.2858033.0359034.301Polynomial regression-order 4755121.66744.000
9.43833.1943031.6484034.6530033.4636034.150Polynomial regression-order 4755220.56744.681
9.56333.7595032.0050035.0374033.8903034.149Polynomial regression-order 4755320.22154.779
9.68834.3248032.3562035.4212034.3179034.311Polynomial regression-order 4755422.10548.421
9.81334.8901032.7455035.7917034.7487034.660Polynomial regression-order 4755519.73244.147
9.87535.1727032.9435035.9794034.9660034.897Polynomial regression-order 4755616.96137.102
10.00035.7380033.3389036.3898035.4058035.542Polynomial regression-order 475579.12232.432
10.06336.0207033.5572036.6111035.6289035.927Polynomial regression-order 4755810.34544.540
10.18836.6066033.9467037.0662036.0833036.852Polynomial regression-order 475598.22429.276
10.31337.1926034.3814037.5819036.5509038.044Polynomial regression-order 4756010.06536.364
10.43837.7786034.8565038.1440037.0344039.419Polynomial regression-order 475618.64233.951
10.56338.3646035.3828038.7282037.5370041.104Polynomial regression-order 475628.70911.712
10.68838.9506035.9301039.3285038.0618042.976Polynomial regression-order 475637.53015.663
10.81339.5365036.5274039.9490038.6121045.202Polynomial regression-order 475648.03123.834
10.93840.1225037.1639040.5835039.1913047.617Polynomial regression-order 4756516.18016.446
11.06340.7085037.8810041.2503039.8030050.431Polynomial regression-order 475667.53217.662
11.18841.2945038.6463041.9487040.4511053.434Polynomial regression-order 475673.44828.161
11.31341.8805039.4986042.6652041.1394056.885Polynomial regression-order 475686.46230.769
11.43842.4665040.3606043.4277041.8720060.523Polynomial regression-order 4756912.07432.198
11.50042.7594040.7750043.8166042.2561062.626Polynomial regression-order 475705.64334.483
11.62543.3454041.6926044.7016043.0629066.773Polynomial regression-order 475717.97329.900
11.68843.6384042.1470045.2082043.4867068.976Polynomial regression-order 475728.11732.468
11.81344.2244042.9581046.2847044.3777073.843Polynomial regression-order 4757311.64837.500
11.93844.69240757410.40537.861