ACER project members; Sanchez Goñi, Maria Fernanda; Desprat, Stéphanie; Daniau, Anne-Laure; Burbridge, Rachel E; Mayle, Francis E; Killeen, Timothy J (2017): CLAM age model and microcharcoal of sediment core Laguna_Bella_Vista [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.872567
Always quote citation above when using data! You can download the citation in several formats below.
Related to:
Burbridge, Rachel E; Mayle, Francis E; Killeen, Timothy J (2004): Fifty-thousand-year vegetation and climate history of Noel Kempff Mercado National Park, Bolivian Amazon. Quaternary Research, 61(2), 215-230, https://doi.org/10.1016/j.yqres.2003.12.004
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
Project(s):
Coverage:
Latitude: -13.616670 * Longitude: -61.550000
Minimum DEPTH, sediment/rock: 0.020 m * Maximum DEPTH, sediment/rock: 2.970 m
Parameter(s):
| # | Name | Short Name | Unit | Principal Investigator | Method/Device | Comment |
|---|---|---|---|---|---|---|
| 1 | DEPTH, sediment/rock | Depth sed | m | Geocode | ||
| 2 | Calendar age, minimum/young | Cal age min | ka BP | Sanchez Goñi, Maria Fernanda | Classical age-modeling approach, CLAM (Blaauw, 2010) | CLAM_min95 |
| 3 | Calendar age, maximum/old | Cal age max | ka BP | Sanchez Goñi, Maria Fernanda | Classical age-modeling approach, CLAM (Blaauw, 2010) | CLAM_max95 |
| 4 | Calendar age | Cal age | ka BP | Sanchez Goñi, Maria Fernanda | Classical age-modeling approach, CLAM (Blaauw, 2010) | CLAM_best |
| 5 | Accumulation model | Accu model | a/cm | Sanchez Goñi, Maria Fernanda | Classical age-modeling approach, CLAM (Blaauw, 2010) | |
| 6 | Type of age model | Age model type | Sanchez Goñi, Maria Fernanda | |||
| 7 | Charcoal | Charcoal | arbitrary units | Mayle, Francis E | ||
| 8 | Unit | Unit | Mayle, Francis E | |||
| 9 | Sample ID | Sample ID | Sanchez Goñi, Maria Fernanda | ACER sample ID |
License:
Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC-BY-NC-ND-3.0)
Size:
92 data points
Data
| 1 Depth sed [m] | 2 Cal age min [ka BP] | 3 Cal age max [ka BP] | 4 Cal age [ka BP] | 5 Accu model [a/cm] | 6 Age model type | 7 Charcoal [arbitrary units] | 8 Unit | 9 Sample ID |
|---|---|---|---|---|---|---|---|---|
| 0.020 | 36909.3143 | # fragments / ml (micro particles) | 18675 | |||||
| 0.100 | 164265.3611 | # fragments / ml (micro particles) | 18676 | |||||
| 0.180 | 310769.1946 | # fragments / ml (micro particles) | 18677 | |||||
| 0.260 | 966824.6087 | # fragments / ml (micro particles) | 18678 | |||||
| 0.400 | 1313998.4640 | # fragments / ml (micro particles) | 18680 | |||||
| 0.600 | 1678058.4200 | # fragments / ml (micro particles) | 18683 | |||||
| 0.840 | 3135071.2140 | # fragments / ml (micro particles) | 18686 | |||||
| 1.000 | 1752441.0650 | # fragments / ml (micro particles) | 18688 | |||||
| 1.200 | 626577.4167 | # fragments / ml (micro particles) | 18690 | |||||
| 1.280 | 11.5935 | 11.8788 | 11.7037 | 227.603 | Linear regression | 995834.8000 | # fragments / ml (micro particles) | 18691 |
| 1.390 | 42.1004 | 45.0313 | 43.1635 | 45.080 | Linear regression | 1978774.0670 | # fragments / ml (micro particles) | 18693 |
| 1.630 | 43.3880 | 45.5724 | 44.2455 | 45.080 | Linear regression | 804070.5827 | # fragments / ml (micro particles) | 18696 |
| 1.770 | 43.9813 | 46.0074 | 44.8766 | 45.080 | Linear regression | 1534744.7370 | # fragments / ml (micro particles) | 18698 |
| 1.970 | 44.7211 | 46.9459 | 45.7782 | 45.080 | Linear regression | 626337.1592 | # fragments / ml (micro particles) | 18701 |
| 2.270 | 45.6546 | 48.8480 | 47.1306 | 45.080 | Linear regression | 341005.9728 | # fragments / ml (micro particles) | 18704 |
| 2.470 | 46.1946 | 50.1848 | 48.0322 | 45.080 | Linear regression | 843232.1881 | # fragments / ml (micro particles) | 18706 |
| 2.650 | 911176.3000 | # fragments / ml (micro particles) | 18708 | |||||
| 2.810 | 31252.1967 | # fragments / ml (micro particles) | 18710 | |||||
| 2.970 | 23184.6893 | # fragments / ml (micro particles) | 18712 |